Identification of non-synonymous SNPs Impacting Structure and Function of MLH1 and NBN Proteins: A computational approach.

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Vaishnavi Gund, Siddharth Sharma, Swet chandan, Sidhartha singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5621917/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 May, 2025 Read the published version in Journal of Applied Genetics → Version 1 posted 5 You are reading this latest preprint version Abstract The genes NBN and MLH1 are critical for DNA repair, and this study aimed to detect and predict the effects of pathogenic single nucleotide polymorphisms (SNPs) in their mRNA and protein sequences. An in silico analysis assessed the impact of SNPs on the physicochemical properties, structure, stability, and function of MLH1 and NBN proteins. Results revealed that some SNPs significantly alter protein stability, structure, and binding interactions, potentially impairing DNA repair. Molecular docking studies further indicated disruptions in protein-protein interactions due to specific SNPs. These findings underscore the importance of using in silico methods to predict the functional effects of genetic variations, providing insights that could guide personalized treatments and improve cancer detection. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The MLH1 gene is situated at 3p22.2 on the short (p) arm of chromosome 3, precisely at position 22.2. It encompasses 19 exons spanning a region of 57,360 base pairs (bp) and encodes a protein with a molecular weight of 84.6 kilo Daltons (kDa), consisting of 756 amino acids. This protein plays a crucial role in DNA repair, specifically addressing errors occurring during DNA replication in preparation for cell division. Notably, MLH1 lacks known enzymatic activity. The MLH1 protein contains an ATPase domain and two interaction domains: one binds MutS homologs (MSH2, MSH3, MSH6), and the other interacts with PMS2, MLH3, and PMS1 (Domingo et al., 2005). MLH1 forms the MutLα heterodimer primarily with PMS2 but can also pair with PMS1 or MLH3. This complex binds MutSα (MSH2-MSH6) or MutSβ (MSH2-MSH3), which recognize DNA lesions, and facilitates the recruitment of proteins required for excision and repair synthesis (Kadyrov et al., 2006) (comment1). In examining the genetic factors contributing to lung cancer, it has been observed that specific polymorphisms in the hMLH1 gene play a significant role. In 2008, An et al. reported a borderline significance between the hMLH1 rs1799977 I219V variant and lung cancer risk, noting that this association was more pronounced in younger individuals. Following this, Lo et al. (2011) identified a significant correlation between the hMLH1−93G>A polymorphism (rs1800734) in the promoter region of MLH1 and increased lung cancer susceptibility among never-smoking individuals in Taiwan. Nibrin, also known as NBN or NBS1, is a protein coding gene located on chromosome 8 at position q21 which is composed of 754 amino acids (Nithya et. al. 2019). Also it is an integral part of the MRE11/RAD50/NBN complex, crucial for detecting and repairing DNA double-strand breaks early on. Mutations in the NBN gene lead to Nijmegen breakage syndrome (NBS), a condition affecting this repair process (Berardinelli et. al. 2013). The MRE11/RAD50/NBN (MRN) complex plays a key role in activating cell cycle checkpoints during DNA repair. The NBN protein can be divided into three main parts: The N-terminus includes a forkhead-associated (FHA) domain, while the central region contains two BRCA1 C-terminal tandem domains known as BRCT1 and BRCT2. The C-terminus has two motifs that bind to MRE11, another component of the complex, as well as a motif that binds to ATM (Otahalova et al., 2023). In a separate study, Nithya and her team showed that SNPs in the **NBN** gene are associated with structural and functional changes in the protein. The NBN gene SNP evaluation revealed the presence of multiple polymorphic sites, which alter the functionality of the receptor and have been linked to phenotypic traits and increased risk of infection. Four SNPs were predicted to be severely damaged in coding regions which cause a change in the functionality of the receptor and were found to be associated with phenotypic traits and higher chances of infection (Nithya et. al. 2019). Recent advancements in bioinformatics have produced refined computational algorithms for processing raw data, analysing protein and gene expression, modelling protein, DNA, and RNA structures, and mining literature for useful information (de Almeida et al., 2022). This study identifies and predicts pathogenic SNPs in the MLH1 and NBN genes, examining their association with disease and their impact on protein structure, stability, activity, and function. We retrieved the complete list of nsSNPs from dbSNP and analysed them using SIFT, Poly-Phen, PANTHER, PROVEAN, PhD-SNP, SNP-GO, I-Mutant, and Mu-Pro to identify the most deleterious nsSNPs. The objective is to pinpoint non-synonymous SNPs in MLH1 and NBN genes and apply computational tools to identify the most harmful ones. The study of non-synonymous SNPs in MLH1 and NBN gene holds significant potential for advancing personalized medicine, particularly in cancer treatment. Both MLH1, a key mismatch repair protein (Domingo et. Al., 2005), and NBN, crucial for DNA double stranded break repair (Williams et al., 2012), play vital roles in maintaining genomic stability. Identifying harmful SNPs in these genes enhances precision in risk assessment by uncovering genetic variants linked to cancer susceptibility, enabling early detection and preventive strategies (Hampel et al., 2008). These SNPs can also serve as biomarkers to stratify patients based on genetic risk and molecular tumor profiles, guiding personalized therapeutic decisions (Berger et al., 2016). Structural and functional analyses of SNP-induced alterations provide insights into how these mutations impact cellular pathways, which can influence drug sensitivity or resistance. For example, MLH1 mutations can alter responses to platinum based therapies (Santos et al., 2018), allowing for the development of tailored treatments. Furthermore, this research sheds light on tumor heterogeneity, providing a deeper understanding of mechanisms underlying cancer evolution and treatment resistance (Gerlinger et al., 2012). Structural biology and docking studies also pave the way for designing targeted therapies, such as small molecule inhibitors or peptide therapeutics, to counteract the effects of deleterious SNPs (Karakoc et. Al., 2019). By contributing to population specific genomics, the study can identify ethnic-specific predispositions to cancer, supporting regionally tailored precision medicine initiatives (Green et al., 2020). Ultimately, this work bridges the gap between genomics and clinical applications aligning with the goals of precision oncology to provide patient centric, personalized solutions for cancer prevention, diagnosis and treatment (Hortobagyi, 2020) (Comment 1). 2. Material and method 2.1 Data Retrieval: We gathered a comprehensive catalogue of MLH1 and NBN gene-related Single Nucleotide Polymorphisms (SNPs) by querying the National Center for Biotechnology Information (NCBI) dbSNP database, using MLH1 and NBN as the search keywords. Subsequently, the SNPs were filtered based on our criteria (https://www.ncbi.nlm.nih.gov ).The full FASTA sequence corresponding to the human MLH1 and NBN protein was acquired from the NCBI database. Figure 1 provides a schematic outline of the methodology adopted for this investigation. 2.2 Predicting deleterious nsSNPs: All missense non-synonymous single nucleotide polymorphisms (nsSNPs) associated with the MLH1 and NBN genes were assessed using various tools and servers. The selection of computational tools such as SIFT, Polyphen-2 and PANTHER grounded the ability to predict the functional impact of genetic variants, particularly nonsynonymous single nucleotide polymorphisms (nsSNPs), which are key to understand the molecular basis of disease. These tools leverage the sequence conservation, structural features and biological context to provide a comprehensive assessment of the pathogenic potential of variants. The SIFT (Sorting Intolerant From Tolerant) tool (http://sift.bii.a-star.edu.sg) uses sequence homology, the physicochemical properties of amino acids, and evolutionary conservation to identify intolerant amino acid substitutions from those that are tolerated. It operates under the premise that functionally important amino acids are highly conserved across species, and substitutions at these positions are more likely to affect the protein function (Ng and Henikoff, 2001). A score less than 0.05 (0.00–0.05) in SIFT categorizes a SNP as intolerant, while a score greater than 0.05 indicates tolerance (Ng et al., 2003). Its efficiency in processing a large number of variants makes it a popular choice in genome wide studies. Polyphen (Polymorphism Phenotyping v2) tool assesses the potential impact of amino acid substitutions on human protein structure and function using various algorithms such as THMM, Colis2 program, SignalP program, etc. (George et al., 2008). PolyPhen-2 evaluates the substitution site, maps SNPs to known 3D protein structures, retrieves sequence annotations and structural features, and predicts whether missense mutations are likely to be damaging, probably damaging, or benign. Its dual-layered prediction model enhances accuracy, making it a reliable resource for interpretation of nsSNPs associated with complex diseases. The PANTHER (Protein Analysis through Evolutionary Relationships) tool (http://pantherdb.org/tools/cSNPscoreForm.jsp?) measures the evolutionary preservation duration of a given amino acid among different species, predicting functional and structural effects of amino acid substitutions also predict the impact of coding and non-coding variants (Mi et al., 2021). It provides insights into the evolutionary pressure acting on the protein coding regions, highlighting mutations likely to disrupt protein function. Additionally, pathway level annotations of this tool enables researchers to contextalize the functional consequences of variants within biological systems. PhD-SNP (Predictor of human Deleterious Single Nucleotide Polymorphisms) is a classifier based on support vector machines (SVM) and evolutionary information from sequences (http://SNPs.biofold.org/phd-snp/phd-snp.html). It builds a classification model using a large dataset of known disease-associated and neutral mutations, providing predictions based upon the specific sequence context of the SNP. Robust performance and ability to generalize across various datasets make it a reliable tool for high-throughput variant analysis (comment2). (Capriotti et al., 2017). SNP&GO employs gene ontology (GO) annotation data to predict whether a mutation is likely to be associated with a disease or not (Capriotti et al., 2013). By combining sequence features with functional information, SNPs&GO provides more detailed assessment of the impact of mutations on protein function. This tool uses GO terms associated with the protein to contextualize the biological significance of the variant, offering enhanced accuracy in distinguishing between disease-causing and neutral mutations. (Calbrese et al., 2009) SIFT and Polyphen-2 are often chosen over tools like MutPred and SNAP2 for initial analysis of nsSNPs due to their simplicity, speed and suitability for high-throughput screening. These tools are validated, efficient and user friendly, making them ideal for large scale studies, such as genome wide association studies (GWAS). While MutPred and SNAP2 offers advanced insights, such as prediction of molecular mechanism and functional impacts, they are more computationally intensive and complex to interpret. It integrates machine learning with functional annotations to predict molecular consequences like altered binding affinity (Li et al., 2009). SNAP2 powered by neural networks, excels at identifying effects in diverse and poorly conserved regions but provides more granular outputs that require additional expertise for interpretation (Hecht et al., 2015). In contrast SIFT and Polyphen are faster and require few computational resources making them more accessible for researchers with limited infrastructure. Thus, they are well suited for initial screening and prioritization of variants, while MutPred and SNAP2 are better utilized in targeted in depth functional studies where detailed mechanistic insights are needed. (comment3) For further analysis, nsSNPs predicted as deleterious or damaging by all six servers unanimously were shortlisted. So as to avoid the discrepancies and the differences caused by the output results of the all the softwares. (comment4) 2.3 Modeling the native MLH1 and NBN protein using MODELLER v9.22 and v10.2: The complete structure of MLH1 and NBN protein was modeled using MODELLER v9.22. It is software which utilizes comparative homology modeling techniques for building protein structures. Both MODELLER versions was obtained from Andrej Sali website (https://salilab.org/). MODELLER can be used with and without installing python. If python is not installed then python scripts can be executed by command “mod9.22 SCRIPT_NAME.py”. For protein modelling using python the modeller server itself provides the scripts of the sequence alignment and actual structure generation and are included in the supplementary material (comment 5). Comparative homology modeling using MODELLER include the following steps: template selection using BLAST checking the shortlisted template with the query sequence, generating the model, and finally it’s verified by Ramachandran’s plot. The shortlisted model was saved as MLH1 _WILD and NBN_WILD respectively . 2.4 Generation of mutant protein structures: The best validated model was selected and is further used as template to incorporate mutation in protein structures and subsequent evaluation via MODELLER software (version 9.22). The process utilizes a combination of comparative modeling techniques and optimization algorithms to introduce mutations or modifications to the protein sequence. These mutations could be substitutions, insertions, or deletions of amino acids. During the modeling process, MODELLER aligns the target protein sequence with the template structure, incorporating information about the template's spatial arrangement of atoms. MODELLER then optimizes the model by adjusting dihedral angles, bond lengths, and other structural parameters to generate a three-dimensional representation of the mutant protein. 2.5 Model Validation: All the selected mutant models were validated by Ramachandran plot. A Ramachandran plot is a graphical representation used in structural biology to assess the stereochemical quality of protein structures. Named after its creator G. N. Ramachandran, this plot depicts the dihedral angles of a protein's amino acid residues. Specifically, it illustrates the phi (ϕ) and psi (ψ) angles, which represent the rotations around the Cα-C and C-N bonds, respectively. In a Ramachandran plot, each point corresponds to a specific combination of phi and psi angles for a given residue in the protein structure. The plot is divided into regions that represent allowed and disallowed conformations based on steric hindrance and clash considerations. The allowed regions indicate favorable and energetically feasible conformations for protein backbone torsion angles, while the disallowed regions suggest sterically hindered or unlikely conformations. Further all the proteins generated were energy minimized by utilizing Chimera tool which using steepest descent method for energy minimization. 2.6 RMSD value calculation: The RMSD value of the both proteins was computed by superimposing the two structures in PyMOL using the "align" feature. RMSD value is an indication of structural and functional deviation from native protein, higher the value more is the deviation. 2.7 Determining SNP’s impact on the MLH1 and NBN protein stability: In order to ascertain the stability or denaturation of the MLH1 and NBN protein resulting from amino acid substitutions, we employed two distinct in silico algorithms (I-Mutant and MuPro). Both of them are based on sequence analysis. 2.8 Molecular Docking for Protein Interaction Identification: Mutant’s model of MLH1 protein and MLH1_WILD was docked with MLH3 protein. MLH1_WILD and MLH3 structures were absolute protein structures, the Cluspro server performed protein/protein docking with all parameters set to native values. Further, Molecular docking between wild type NBN protein and its mutant with different interacting proteins (MRE11 and RAD50) was also performed using the CLUSPRO server (https://cluspro.org/help.php) with default settings. The cluspro server is widely used tool for protein protein docking and was selected for this study due to its high reliability, accuracy and user friendly interface. It employs a systematic approach that includes rigid body docking, energy based scoring and clustering of low energy conformations to identify the most probable binding poses for protein protein complexes (kozakov et al., 2017). Additionally the most frequently occurring conformations, which are likely to present native binding modes are prioritized. The server also allows users to specify restraints or bias the docking process towards certain residues, enabling more accurate modeling of biologically relevant interactions (Kozakov et. al., 2013) (comment 5) ClusPro forecasts ten distinct docked poses for every experiment, using model scores that indicate the docked molecule's binding energy. 3. Results The identification of harmful SNPs holds paramount importance in unraveling the underlying genetic factors contributing to different cancer. These genetic variations often play a pivotal role in predisposing individuals to these types of cancers. By harnessing advanced computational tools and databases, researchers can delve deep into the molecular mechanisms associated with cancer, gaining valuable insights into potential therapeutic targets or preventive strategies. The diagram in Fig. 1 meticulously illustrates the workflow and database servers specifically designed for identifying harmful Single Nucleotide Polymorphisms (SNPs) within the human genes MLH1 and NBN. These servers are essential components of the analysis pipeline, contributing significantly to the precise pinpointing and characterization of detrimental genetic variations. Furthermore, the workflow and database servers delineated in Fig. 1 facilitate seamless data integration, robust analysis, and insightful interpretation. The 57360 base pairs that make up the human MLH1 gene and the 756 amino acids that make up the MLH1 protein. As of May 2023, the NCBI-dbSNP database ( https://www.ncbi.nlm.nih.gov/snp/ ) has 15721 SNPs related to the human MLH1 gene. Of them, 1420 were deemed to be clinically significant and were subjected to analysis. Also included 21,274 total SNP for gene NBN. Out of 21,274 SNPs were discovered in the output of the dbSNP database with NBN hits, of which 17,478 were located in the intronic region, 1254 were nsSNPs (missense), and 523 were coding synonymous. 3.1 Finding harmful and detrimental nsSNPs using a sequence-based homology methodology: The 1420 SNP IDs were analyzed using the SIFT and Panther tools, which use a sequence-based homology method to predict the deleterious and harmful nsSNPs in the MLH1 gene. The IDs are checked in SIFT and out of these 1420 SNPs, 1238 could not be found on the SIFT server. Further, SIFT was showing eight protein IDs for the same “rsID” hence six protein IDs which are covering the entire length of protein (756 amino acid) are taken for further analysis. The two protein IDs (Ensemble IDs) that are rejected are ENSP00000416687 and ENSP00000398392. After removing the protein IDs, out of 182 nsSNPs, 165 were remaining and are used for all further analysis. Out of 165 SIFT predicted, 122 and 43 as deleterious and tolerated respectively. PANTHER server demonstrated 59 nsSNPs as damaging and 3 as probably benign, rest are predicted as invalid substitution. Intriguingly, 51 nsSNPs were predicted to be deleterious by these two computational tools/ servers as shown in Table 1 . For the NBN gene, out of 21,274 SNPs filtered by SIFT, only 233 SNPs were located in the CDS region. SIFT predicted 61 of these as deleterious and 166 as tolerated. PANTHER conducted a more comprehensive analysis of every nsSNP for each gene to predict potentially detrimental or damaging nsSNPs. Of the 233 CDS entries in NBN, PANTHER predicted 47 to have detrimental effects (possibly or probably damaging) and 45 to be benign. 24 nsSNPs are commonly predicted to be deleterious by SIFT and PANTHER webservers. After selecting single protein ID ENSP00000265433, both webserver unanimously reported 16 nsSNPs to be deleterious (Table 1 ) 3.2 Sequence- and structure-based homology-based Polyphen server predicted the following functionally harmful nsSNPs: PolyPhen-2 (Polymorphism Phenotyping v2) servers are advanced computational tools designed to predict the functional consequences of genetic variations, particularly amino acid substitutions, on protein structure and function. Developed by the Bork Group, these servers integrate both sequence-based and structure-based information to assess the potential pathogenicity of genetic variants in human proteins. It forecasts the potential effects of amino acid substitutions on protein function and structure by analyzing factors like phylogenetic data, structural details, and the protein sequence. Out of 51 nsSNPs selected by sequence based homology approach, Polyphen-2 reported 48 nsSNPs to be deleterious by both HumDiv and HumVar subsets for MLH1 gene. Table 1 represents the nsSNPs that are commonly predicted by all the three tools, underlined entries are not selected by PolyPhen-2 webserver for both MLH1 and NBN gene. 3.3 The disease prediction of nsSNP’s by the PhD-SNP and SNP & GO web tools: PhD-SNP is a tool designed to anticipate the phenotypic consequences of non-synonymous substitutions, offering insights into the potential impact of genetic variations. Beyond that, it extends its predictive capabilities to determine the association of such substitutions with diseases. Out of the 48 nsSNPs PhD SNP server has predicted 39 nsSNPs to be functionally associated with disease, rest are not associated with any disease or are showing neutral association whereas SNP&GO reported 30 nsSNPs to be associated with disease, unanimously, both webservers predicted 30 nsSNPs to be associated with the occurrence of disease (Table 2 ). Out of the 14 nsSNPs for NBN gene 2 were predicted to be associated with disease and 12 were predicted to have no association with the development of any disease by PhD -SNP and SNP&GO disease association prediction computational servers. The two shortlisted nsSNPs are rs199845467 and rs371480039 associated with G224E and L312S (Table 2 ). 3.4 Modelling of the complete MLH1 and NBN proteins using MODELLER (Comparative Homology Modeling): For subsequent analysis, these 30 nsSNPs were considered. Since the full structures of the MLH1 and NBN proteins were not present in PDB database, we used a homology modeling approach to create the MLH1 protein structure via MODELLER v9.22. The mutation models for MLH1 and NBN proteins were also generated using the same software to predict their effects on the stability and functionality. A template structure for model generation of MLH1 protein was obtained using psiBLAST, selecting PDB as source database to find the structure templates. The sequences which are showing at least > 30% identity and > 40% query coverage were selected for model generation. PDB ID 4P7A was identified as having 100% similarity with the query sequence and 3RBN showed 98.12% similarity, 5AKB and 1BKN showed > 36% similarity with the query and hence were selected as the template for model generation of MLH1 protein. The structures of 4P7A, 3RBN and 5AKB were further utilized as the template for modeling MLH1 protein using comparative homology modeling, for NBN, since the complete structure was available in the PBD database so we have downloaded it and used for further analysis. MODELLER was instructed to generate 5 protein structures based on the templates provided. Several parameters are used to choose the best model from the five models generated by MODELLER. A frequently used parameter is DOPE score, the lower the DOPE score the better the modeled structure is considered hence we have selected the structure having the lowest DOPE score. Based on the lowest DOPE score we have selected model “MLH1.B99990001.pdb” and “NBN.B99990001.pdb” (Fig. 2a & b) as the best model for MLH1 and NBN protein. The selected model was further analyzed by Ramachandran Plot to check protein structure and folding properties (Supplementary table 1 ). Further, we have also generated structures of other proteins that are interacting with MLH1 and NBN proteins for their proper functioning. MLH1 interacts with MLH3 via its MLH3 interacting domain and NBN interacts with MRE11 and RAD50 for their proper functioning via BRCT2 domain. Same homology modelling was used to generate these protein structures. Further energy minimized structures of all these proteins were generated via Chimera server using steepest descent and conjugate gradient algorithms. 3.5 RMSD value calculation of the modeled mutant protein : The RMSD values calculated using PYMOL for MLH1 and NBN mutants provide insights into the structural impact of specific mutations and their potential biological relevance. For MLH1 and NBN proteins, PYMOL was used to calculate RMSD values. For MLH1, 30 mutants were analyzed, RMSD value for mutant L550P was 1.5 Å indicating a significant structural deviation from the wild protein, as MLH1 plays critical role in DNA mismatch repair. In contrast G167E has the lowest RMSD of 0.147 Å, indicating minimal structural perturbation and potentially retaining near-native functionality. Rest of the 28 mutants have RMSD value ranging from 0.155–0.181 Å that also indicates some level of structural deviations, which may still compromise the protein stability but to a lesser extent. (Table 3 ). For NBN protein which is crucial for DNA damage response and double stranded break repair, two mutants i.e. L312S and G224E have values RMSD values of 0.39 and 1.32 respectively the higher deviation in G224E suggests significant structural disruption, which could impair the protein role in stabilizing and activating the MRN complex during DNA repair. In comparison L312S have moderate RMSD indicates structural change that may affect the protein function less drastically but still warrants investigation. These results emphasize the potential functional consequences of these mutations, highlighting their importance in the context of genomic stability and cancer predisposition. (Comment 6) (Table 3 ). 3.6 Analysing the stability changes of mutants using the I-Mutant and MuPro web tools: Among the 24 nsSNPs (30 amino acid change) submitted, I-mutant predicted 4 nsSNPs (5 amino acid changes) to have increased stability (T117R, T117M, P28L, A128P and S44F), rest 20 nsSNPs reported an decrease in stability. On the other hand MuPro reported 1 nsSNPs to have increase instability (P654L). Stability for one mutant was reported to increase by both servers. Further, both servers unanimously reported 19 nsSNPs (24 amino acid change) to have a decrease in stability (Table 3 ). Similarly for NBN protein (both SNPs), a decline in stability was observed by both webservers (Table 3 ). 3.7 MLH1 protein domain mutation mapping: MLH1 comprises 3 parts: the ATPase, the MutS homolog interaction, and the MLH3 interaction. According to literature, these 19 nsSNPs (24 AA alterations) are dispersed throughout all three parts (Fig. 3a). 10 SNPs (12 AA change) were located in ATPase domain, 4 SNPs (6 AA change) were present in MutS homologs interaction part, 4 SNPs (5 AA change) were located in MLH3 interaction domain and 1 nsSNP is present between ATPase domain and MutS homolog domain. In NBN gene, there are 4 domains i.e. FHA (1-110 amino acid), BRCT1 (11–184), BRCT2 (217–325) and NBS-1 (640–691) domain. The two nsSNPs that are predicted to impact protein stability and are deleterious are G224E and L312S which are present in BRCT2 domain as shown in Fig. 3b. 3.8 Protein -protein docking analysis: DNA mismatch repair protein MLH3 is synthesised in human by MLH3 gene. It is a member of Mut-L homolog family. MLH gene family is extensively reported to maintain genomic integrity during DNA replication and after meiotic recombination. MLH3 interacts with MLH1 to form MutLγ and helps in the process of DNA replication in humans. Therefore, for docking, nsSNPs in the MLH3 interaction part were chosen. Docking analysis of MLH1 and MLH3 protein was done using Cluspro server (Table 4 , Fig. 4a to 4f). The binding energy for MLH1_Wild- MLH3 is -1473.5 Kcal/mol. All the 4 SNPs (5 amino acid changes) reported an increase in binding energy depicting an increase in the stability of the docked complex due to the corresponding polymorphisms. For NBN, as shown in Fig. 5, NBN have 4 domains and the two shortlisted nsSNPs (G224E and L312S) are present in BRCT-2 domain. NBN directly interacts with MRE11 and RAD50 proteins hence we docked them in order to understand the effect of these SNPs. Cluspro predicted that then when NBN-wild interacted with MRE11 the binding energy is -1737.7 Kcal/mol, however when the NBN mutant interacted with MRE11 the binding energy takes a sharp dip and is measured to be -1572.6 for G224E indicating that this polymorphisms effect the proper binding and hence reduced functionality of the complex. Similarly, NBN also directly interacts with RAD50 hence we docked these two as well. The results suggested a sharp decline in binding energy of NBN (G244E-1420) as compared to NBN (wild − 1592) (Table 5 , Fig. 5a to 5f). We also performed the docking of NBN with MRE11 and RAD50 protein simultaneously on the same model. The Docking energy of NBN(W)-MRE11-RAD50 is -1467.9 which is better than NBN(G224E)-MRE11-RAD50 (-1385.9) indicating a less stable complex and lower than NBN(L312S)-MRE11-RAD50 (-1562) indicating L312S structure having higher binding efficiency than NBN (W) (Supplementary table 2 , Supplementary Fig. 1–3) 4. Discussion In this investigation, we conducted a thorough in silico assessment to predict pathogenic Single Nucleotide Polymorphisms (SNPs) and their potential effects on the structure and functionality of the MLH1 and NBN protein by utilizing various computational approaches in combination. In order to improve the precision of predictions, we combined tools from various categories, such as homology-based, sequence-based, consensus-based, and structure-based approaches. This strategy is designed to enhance confidence in identifying potentially harmful missense Single Nucleotide Polymorphisms (SNPs) by minimizing biases in the results (Khalid et al., 2020). The analysis of 15,721 SNPs located in the MLH1 gene identified 1,420 clinically significant SNPs, for NBN gene we retrieved 21,274 total SNPs and 12,753 were further evaluated using various bioinformatics tools. Three tools (SIFT, PolyPhen, and PANTHER) were used to predict deleterious SNPs, two tools (SNP&GO and PhD-SNP) identified associations between SNPs and disease development, and two web servers (I-Mutant and MUPro) estimated protein stability. The use of multiple tools is beneficial because each employs different algorithms, providing a comprehensive analysis. The MLH1 protein contains three key parts: the ATPase, the MutS homolog interaction, and the PMS2/MLH3/PMS1 interaction part. All the SNPs identified by these tools are located within these domains, which are vital for the effective DNA repair. Any mutation in these three parts of MLH1 protein may influence the efficiency of DNA repair. Notably, the PMS2/MLH3/PMS1 interaction domain plays a crucial role, with our study reporting four nsSNPs (five AA changes) within this domain. Of these, rs63750193 is associated with Lynch syndrome, while rs63750610, rs63750693, and rs63750899 are linked to hereditary non-polyposis colorectal cancer (HNPCC) (Mahdouani et al., 2022, Müller et al., 2001, Godino et al., 2001, Raevaara et al., 2004). Protein stability, a critical factor influencing structure, function, evolution, and biological activity, is impacted by these nsSNPs, which may lead to aberrant protein accumulation, misfolding, or degradation. Given the location of these nsSNPs in the PMS2/MLH3/PMS1 interaction domain, they are expected to significantly affect MLH1 protein activity and function. NBN, part of the MRN complex (MRE11-RAD50-NBN), plays a critical role in detecting DSBs (double strand break repair) and initiating repair. The MRN complex binds to the broken DNA ends, processes them, and recruits ATM kinase, which activates various DNA damage response pathways. There were only 2 deleterious SNPs from the CDS region of the gene NBN that were predicted deleterious by all the webservers namely rs199845467 (G224E), and rs371480039 (L312S) that are present in the BRCT2 domain of NBN protein. At least four proteins (ATM, BRCA1, MRE11, RAD50, and P95) interact with NBN protein (directly or indirectly) and play a part in the DNA damage response. The FHA and BRCT domains bind to multiple phosphorylated proteins, which regulate interactions within the MRN complex. Through its FHA domain, NBN interacts with the C-terminal-binding protein interacting protein (CtIP, also known as retinoblastoma-binding protein 8 or RBBP8) (Otahalova et. al. 2023). Modern computational tools and techniques enable the analysis of genetic data, gene expression, evolutionary and SNP analysis, molecular dynamics simulations, and the derivation of models for the desired protein, all of which contribute to a better understanding of protein functionality. The compilation of data from multiple studies has illustrated how various missense Single Nucleotide Polymorphisms (SNPs) contribute to the development of a range of diseases (Karimi et al., 2022, Khan et al., 2023). Kumar and coworkers employed computational approach to investigate the impact of missense mutations that lead to d-2-hydroxyglutaric aciduria. Through these approaches, they elucidated the structural alterations induced by mutations, ultimately aiding in the identification of novel targets for the development of new drug therapies (Kumar et al., 2018). Similarly, another study by Sidhartha and Colleagues investigated SNPs in the MSH2 gene, a key player in DNA repair, using computational tools to identify those linked to cancer development. 27 SNPs, including 5 with two amino acid changes, were found to potentially cause structural and functional changes in the protein structure of MSH2. Further, it was demonstrated that 6 SNPs impacted the way MSH2 and MSH6 interacted, and 12 were linked to Lynch syndrome and hereditary nonpolyposis colorectal cancer. (Singh et al., 2022). Protein MutL was first discovered in bacteria, where it is essential for the control of genetic recombination and post-replicative DNA mismatch repair (MMR). Demonstrating their ubiquity, these proteins are integral to various DNA metabolism pathways. In eukaryotes, MutL proteins exist as heterodimers, and mammalian cells harbor three primary forms: MutLα (MLH1–PMS2 heterodimer), MutLβ (MLH1–PMS1), and MutLγ (MLH1–MLH3). MutLγ, an endonuclease, remains inadequately understood despite its extensive implication in triplet repeat expansion, a process fundamental to around 40 neurological disorders in humans. Recent reports suggest that human MutLγ acts as an endonuclease, cleaving DNA in a manner dependent on MutSβ and loops. The incision of DNA containing loops by MutLγ endonuclease initiates a cascade of events leading to DNA expansion (Kadyrova et al., 2020). As per NCCN, MLH1 exhibits a robust association with the onset of Colorectal, Endometrial, and Ovarian cancers, with percentages ranging from 46–61%, 34–54%, and 4–20%, respectively. MLH1 is also linked to Bladder, Gastric, Small bowel, Brain, Biliary tract, and pancreatic cancers. In addition to these mutations, MLH1 alterations are robustly linked to Lynch Syndrome (Hereditary Nonpolyposis Colorectal Cancer), thereby elevating the susceptibility to breast cancer (Harkness et al., 2015). In a study by Anderson et. al., assessed functional characterization of MLH1 missense variants associated with lynch syndrome, an inherited colorectal cancer syndrome caused by mutations in DNA mismatch repair genes. Researchers analyzed a panel of MLH1 missense mutations identified in lynch syndrome families to assess their pathogenicity through functional assays, cellular localization studies and protein-protein interaction test with PMS2 and exonuclease1 (EXO1). The study found several variants displayed defects in nuclear localization or interactions with MMR proteins, potentially contributing to cancer development. The findings correlate with in silico predictions and existing MMR activity data reinforcing the role of these mutations in lynch syndrome. Another study investigated the functional deficiency of NBN protein in breast cancer cell line carrying the p.P125W mutation. Researchers identified the mutation in the HCC1395 cells and examined its impact on DNA damage repair mechanisms. The study found that cells exhibited reduced NBN protein levels increased sensitivity to ionizing radiation and defective formation of DNA repair foci involving H2AX, MDC1 and 53BP1. Despite this ATM signaling remain unaffected. The study also highlighted that the HCC1395 cells which carry both NBN mutations and BRCA1 truncation were highly sensitive to PARP inhibition. These findings suggest that the mutations impairs NBN function. (Comment 10) Protein-to-protein communication may be impacted by modifications to the interaction domain. These interactions are better understood and important mutations that affect binding are found through docking studies. Using the ClusPro server, docking studies were conducted for MLH1 and NBN proteins. Reveals significant alterations in protein-protein interactions due to single nucleotide polymorphism potentially impacting cellular functions critical for genome stability. For MLH1_Wild-MLH3, the binding energy was − 1473.5 Kcal/mol, with all four SNPs (five amino acid changes) showing an increase in binding energy, indicating enhanced stability of the docked complex due to these polymorphisms. The increased binding energy suggest enhanced interaction stability, which may influence the efficiency of the mismatch repair pathway (MMR). MMR proteins, including MLH1 and altered binding affinity may modify repair efficiency, possibly leading to an imbalance in DNA repair process and contributing to carcinogenesis (Jiricny et. al., 2013). MLH3, play crucial roles in maintaining genomic integrity by correcting replication errors. ClusPro also predicted that when NBN-wild interacted with MRE11, the binding energy was − 1737.7 Kcal/mol. However, with the NBN mutant (G224E), the binding energy dropped sharply to -1572.6 Kcal/mol, suggesting that this polymorphism affects proper binding and reduces the complex's functionality. Similarly, docking of NBN with RAD50 showed a significant decline in binding energy for the NBN mutant (G244E, -1420 Kcal/mol) compared to NBN wild-type (-1592 Kcal/mol). Docking results suggest an impact on the MLH1/MLH3 and NBN/MRE11/RAD50 interaction It is suggesting weakened interactions, potentially impairing the formation and function of the MRN complex. The MRN complex is essential for DNA double strand break repair, signaling and maintaining chromosomal stability (Stacker and Petrini et. al., 2011). Disruptions in this complex, particularly linked to Nijemen Breakage syndrome, a disorder characterized by increased cancer susceptibility and genomic instability (Varon et. al., 1998). The observed decrease in binding affinity for NBN mutants could lead inefficient DNA damage response, making cells more prone to accumulations of mutations, thereby increasing the risk of malignancies such as breast, ovarian and colorectal cancers (Deriano & Roth, 2013). We have also added the hydrogen bonding details for these docked structure which shows contradictory results as compared to binding energy. More negative binding energy reflects more stable docked complex which should have more hydrogen bonds but we are reported an increase hydrogen bonds with a decline in binding energy which may be due to other intermolecular interactions such Van Der Waal etc. While these docking studies provide insights into potential structural and functional disruptions caused by SNPs, further molecular dynamics simulations and in vitro validations are necessary to confirm their precise mechanistic effects on DNA repair pathways and their contribution to tumorigenesis (Comment 8). There is evidence linking a few of these in silico screened SNPs to a higher risk of cancer. Validating the SNPs in an in vitro experimental system is crucial to determining its precise function. Our analysis of MLH1 and NBN genes identified several high risk SNPs that align with previously reported variants linked to cancer predisposition, while also, highlighting novel insights into their structural and functional impacts. Variant rs63750193 has been associated with lynch syndrome (Mahdouni et. al., 2022, Raeveera et. al., 2004), was predicted in our study to significantly reduce protein stability and alter the binding interactions in PMS2/MLH3/PMS1 interaction domain. This finding is consistent with prior reports emphasizing the critical role of this domain in DNA mistmatch repair (Jirincy, 2013). In a study conducted by Anderson et al., 2012, reported that MLH1 (L550P) renders the binding of MLH1 to PMS2 weakened, this defective MLH1-PMS2 dimerization would leads to compromised MMR leading to conclusion that this polymorphism is indeed pathogenic. Further they have also shown that defective MutLα dimerization due to L550P is linked with reduced MMR activity For the NBN gene, our identification of the deleterious variants rs199845467(G224E) and rs371480039(L312S) aligns with findings that mutations in the BRCT domain disrupt interactions with key proteins in MRN complex, compromising double stranded break repair (Otahalova et. al., 2023, Stacker and Pterini, 2011). These SNPs particularly, G224E showed significant reduction in binding energy indicating weakened complex formation, which is consistent with the structural instability observed in patients with Nijemen Breakage syndrome (Comment 9 and 12). While this study provides valuable insights into the pathogenicity of SNPs in MLH1 and NBN, further research is essential to validate these findings and expand their applications. Experimental validation through mutagenesis studies and functional assays would help confirm the predicted impacts of high-risk SNPs to protein function and stability. For example, site directed mutagenesis followed by protein expression and stability assays could determine how mutations such as rs63750193 (MLH1) and rs199845467 (NBN) influence protein-protein interactions and DNA repair efficiency (Kumar et. al., 2018). Functional assays, such as DNA damage response assays, could further elucidate the biological consequences of these mutations. (Wang et.al., 2020). Additionally, molecular dynamic simulations could offer more detailed insights into how these SNPs affect the structural dynamics of MLH1 and NBN proteins over time. This would complement the docking results and provide a deeper understanding of conformational changes caused by specific mutations (Zhang et. al., 2020). Such simulations have been successfully used in other studies to analyze protein ligand interactions and predict the long term impact of missense mutations on protein functionality (Hassan et.al., 2021). The computational approach used in this study can be adapted to investigate other cancer associated genes involved in critical pathways such as cell cycle regulation, apoptosis and DNA damage repair (Huang et. al., 2018). By integrating multi omics data including transcriptomics, proteomics and epigenomics – future studies could achieve a more comprehensive understanding of how genetic variants contribute to cancer development (Subramanian et. al., 2019). For instance, combing SNP analysis with gene expression data could help identify regulatory mutations that influence gene activity (Chen et. al., 2016). Furthermore, expanding the methodology to analyze somatic mutations in cancer genomes could offer insights into tumor specific alterations, which may have implications for personalized medicine and drug development (Dienstmann et. al., 2017). By pursuing these future directions, researchers can enhance the clinical relevance of computational findings, refine predictive models, and contribute to a broader understanding of cancer epigenetics and genomics (Comment 13). While the study offers valuable insights into the potential pathogenic effects of SNPs in MLH1 and NBN it is important to acknowledge the limitations of the computational methods employed. Computational tools, despite their power and efficiency, have inherent biases that can impact the accuracy of predictions. For example SIFT and Polyphen rely on evolutionary conservation and may misclassify SNPs in regions with limited conservation either underestimating or overestimating their functional impact (Kumar et. a., 2018). Similarly, stability prediction tools such as I-mutant and MuPro are based on static models that cannot fully capture the complex folding dynamics of protein in vivo, potentially leading to false- positives or negative predictions. Furthermore docking studies provide valuable information but they are limited by their static nature and do not account for the dynamic behavior of proteins in cellular environment (Eriano and Roth, 2013). This simplification may result in discrepancies when validating the results experimentally, as real time protein interactions are influenced by various cellular factors, including post translational modifications and presence of cofactors. These limitations underscore the need of integrating additional approaches as molecular dynamics simulations, to capture the dynamic behavior of proteins and improve the accuracy of interaction predictions. Addressing these constraints not only highlights the challenges inherent in computational genomics but also provides more balanced interpretation of the findings (Comment 11) 5. Conclusion This research highlights the relevance of in silico analyses in evaluating some single nucleotide polymorphisms vis a vis the structure and function of proteins, specifically targeting DNA repair genes such as MLH1 and NBN. The combination of several computer programs enabled an assessment that concluded certain SNPs, especially those within the interaction sites, can significantly alter protein stability and binding interactions. These structural changes are likely to impede the DNA repair activities, thereby predisposing to a variety of cancers such as lynch syndrome and hereditary nonpolyposis colorectal cancer. Molecular docking analyses indicate that certain SNPs may disrupt protein-protein interactions, potentially affecting structural stability and functional efficiency. Notably, SNPs such as rs6750193 in MLH1 have been previously associated with cancer, and our findings provide mechanistic insights into their biological impact. To further substantiate these computational predictions, in vitro experimental validation and advanced simulations are essential. Future research should incorporate site-directed mutagenesis, functional assays to assess DNA repair efficiency, and molecular dynamics simulations to explore the structural consequences of SNP-induced alterations (Comment 14). Alongside that, such studies can be applied to clinical practice, allowing for the SNPs that are associated with modulating protein stability and DNA repair efficiency to be genotyped in high-risk patients. By adding these variants to the standard genetic tests, the clinicians would be able to better evaluate the chances for developing cancer and implement advanced measures and tailor the risk control approaches. In addition, these SNPs could be added to the predictive models, which would enhance the estimation of cancer risk and consequently improve patient care. In a broader research context, this computational framework could be expanded to analyse SNPs in other DNA repair genes, potentially uncovering new biomarkers for hereditary cancers. Future studies could also integrate multi-omics approaches, such as transcriptomics and proteomics, to provide a more comprehensive understanding of how these genetic variations influence cellular pathways. Such interdisciplinary strategies would not only validate the computational predictions but also bridge the gap between in silico analyses and clinical applications, ultimately advancing precision oncology and targeted therapeutic development (Comment 15) References Domingo, E., Niessen, R. C., Oliveira, C., Alhopuro, P., Moutinho, C., Espín, E., ... & Hofstra, R. M. (2005). 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DNA Repair, 32, 32–40. https://doi.org/10.1016/j.dnarep.2013.04.011 Tables Table 1: Deleterious SNPs of MLH1 and NBN commonly predicted by Sequence and Sequence-structure homology based tools (Total four tools used: SIFT, PROVEAN, Polyphen-2 and PANTHER) MLH1 S. No rs IDs Amino acid change S. No rs IDs Amino acid change 1 rs11541859 E89Q 27 rs63750693 L622H 2 rs35001569 K618E 28 rs63750693 L622P 3 rs63750549 G638R 29 rs63750726 P654L 4 rs63749829 E71Q 30 rs63750781 T117R 5 rs63749859 C77R 31 rs63750781 T117M 6 rs63749939 G67E 32 rs63750792 P28L 7 rs63749950 A281V 33 rs63750796 E319K 8 rs63749990 F80V 34 rs63750809 C680R 9 rs63750005 T82I 35 rs63750809 C680G 10 rs63750098 V49E 36 rs63750866 A128P 11 rs63750144 S295N 37 rs63750899 P648S 12 rs63750144 S295T 38 rs63751012 E37K 13 rs63750193 L550P 39 rs63751109 S44F 14 rs63750206 G67R 40 rs63751194 R265S 15 rs63750206 G67W 41 rs63751194 R265C 16 rs63750211 R182G 42 rs63751275 R687W 17 rs63750216 A29G 43 rs63751283 L260R 18 rs63750217 A681T 44 rs63751428 Q62K 19 rs63750303 G244D 45 rs63751598 S295R 20 rs63750303 G244V 46 rs63751598 S295G 21 rs63750437 C77Y 47 rs63750314 D387H 22 rs63750507 I107R 48 rs63750360 A282G 23 rs63750580 N38H 49 rs63750395 K286Q 24 rs63750580 N38D 50 rs63750430 R385H 25 rs63750610 P648L 51 rs63750430 R385P 26 rs63750656 A29S NBN S. No rs IDs Amino acid change S. No rs IDs Amino acid change 1 rs13312858 K105N 9 rs193921030 K82E 2 rs61753720 D95N 10 rs199845467 G224E 3 rs61754966 I171V 11 rs201781110 R660T 4 rs78870221 I35M 12 rs201816949 M152I 5 rs141137543 T452P 13 rs371480039 L312S 6 rs151070415 A183S 14 rs377700348 D211E 7 rs182756889 R169C 15 rs377730553 Q39K 8 rs185493105 V101A 16 rs368703936 L650F *Underlined denotes it is not commonly predicted by both the approaches This table lists the single nucleotide polymorphisms (SNPs) identified in the MLH1 and NBN genes, along with the corresponding amino acid changes. Variants underlined are not commonly predicted by both computational approaches. Abbreviations: rs ID – Reference SNP Identifier. Table 2: Disease Prediction of nsSNPs in MLH1 and NBN Genes by PhD-SNP and SNP&GO Servers MLH1 S. No rs IDs Amino acid change S. No rs IDs Amino acid change 1 rs63749859 C77R 16 rs63750610 P648L 2 rs63749939 G67E 17 rs63750693 L622H 3 rs63749990 F80V 18 rs63750693 L622P 4 rs63750005 T82I 19 rs63750726 P654L 5 rs63750098 V49E 20 rs63750781 T117R 6 rs63750193 L550P 21 rs63750781 T117M 7 rs63750206 G67R 22 rs63750792 P28L 8 rs63750206 G67W 23 rs63750866 A128P 9 rs63750211 R182G 24 rs63750899 P648S 10 rs63750303 G244D 25 rs63751012 E37K 11 rs63750303 G244V 26 rs63751109 S44F 12 rs63750437 C77Y 27 rs63751194 R265S 13 rs63750507 I107R 28 rs63751194 R265C 14 rs63750580 N38H 29 rs63751283 L260R 15 rs63750580 N38D 30 rs63750430 R385P NBN S. No rs IDs Amino acid change S. No rs IDs Amino acid change 1 rs199845467 G224E 2 rs371480039 L312S This table lists the nonsynonymous single nucleotide polymorphisms (nsSNPs) identified in the MLH1 and NBN genes, along with their associated reference SNP IDs (rs IDs) and predicted amino acid changes. These variants were analysed using the PhD-SNP and SNP&GO servers for their potential disease association. Table 3: Stability Prediction and RMSD Values of Selected Deleterious SNPs in MLH1 and NBN MLH1 S. No rs IDs Amino acid change RMSD I-Mutant MuPro 1 rs63749859 C77R 0.181 decrease decrease 2 rs63749939 G67E 0.147 decrease decrease 3 rs63749990 F80V 0.158 decrease decrease 4 rs63750005 T82I 0.153 decrease decrease 5 rs63750098 V49E 0.159 decrease decrease 6 rs63750193 L550P 1.51 decrease decrease 7 rs63750206 G67R 0.173 decrease decrease 8 rs63750206 G67W 0.156 decrease decrease 9 rs63750211 R182G 0.16 decrease decrease 10 rs63750303 G244D 0.16 decrease decrease 11 rs63750303 G244V 0.163 decrease decrease 12 rs63750437 C77Y 0.169 decrease decrease 13 rs63750507 I107R 0.16 decrease decrease 14 rs63750580 N38H 0.169 decrease decrease 15 rs63750580 N38D 0.173 decrease decrease 16 rs63750610 P648L 0.172 decrease decrease 17 rs63750693 L622H 0.16 decrease decrease 18 rs63750693 L622P 0.177 decrease decrease 19 rs63750726 P654L 0.169 decrease Increase 20 rs63750781 T117R 0.159 Increase decrease 21 rs63750781 T117M 0.158 Increase decrease 22 rs63750792 P28L 0.155 Increase Increase 23 rs63750866 A128P 0.162 Increase decrease 24 rs63750899 P648S 0.155 decrease decrease 25 rs63751012 E37K 0.168 decrease decrease 26 rs63751109 S44F 0.173 Increase decrease 27 rs63751194 R265S 0.175 decrease decrease 28 rs63751194 R265C 0.167 decrease decrease 29 rs63751283 L260R 0.179 decrease decrease 30 rs63750430 R385P 0.173 decrease decrease NBN S. No rs IDs Amino acid change RMSD I-Mutant MuPro 1 rs199845467 G224E 1.32 decrease decrease 2 rs371480039 L312S 0.39 decrease decrease This table summarizes the stability predictions and root mean square deviation (RMSD) values for selected deleterious nonsynonymous single nucleotide polymorphisms (nsSNPs) in the MLH1 and NBN genes. Predictions were made using the I-Mutant and MuPro tools. RMSD values provide structural deviation data, and stability changes are indicated as "Increase" or "Decrease." Table 4: Docking results of MLH1-Wild and MLH1 mutations located in the MLH3 interaction with MLH3. rs IDs Amino Acid Change Binding energy Kcal/mol H bond MLH1_Wild_MLH3 -1473.5 26 rs63750193 L550P-MLH3 -1654.2 32 rs63750693 L622H-MLH3 -1508.9 43 L622P-MLH3 -1663.8 23 rs63750610 P648L-MLH3 -1595 32 rs63750899 P648S-MLH3 -1587.9 33 This table presents the molecular docking results for the interaction between the wild-type and selected mutant variants of the MLH1 protein and MLH3 . The binding energy (in kcal/mol) indicates the strength of the interaction, where more negative values represent stronger binding affinity. Mutants are named by their reference SNP ID (rs ID) and associated amino acid changes. Binding Energy: Lower (more negative) values indicate stronger interactions. Binding energy is measured in kilocalories per mole (kcal/mol). Table 5: Docking Results of NBN Mutatins with Their Interactions with MRE11 and RAD50 rsIDs SNP Binding energy Kcal/mol H bonds Binding energy Kcal/mol H bonds MRE11 RAD50 Wild -1737.7 18 -1592 32 rs199845467 G224E -1572.6 42 -1420 34 rs371480039 L312S -1590 20 -1590 39 This table presents the molecular docking results for the interaction between wild-type and selected mutant variants of the NBN protein with MRE11 and RAD50 . The binding energy (in kcal/mol) is used to evaluate the strength of the interaction, where more negative values indicate stronger binding affinity. MRE11 and RAD50: Proteins that interact with NBN in the context of DNA damage repair. The table shows how these interactions vary with different NBN mutations. Supplementary Files Supplementaryfigures.rar Supplementarytable.docx proteinmodellingscripts.rar Cite Share Download PDF Status: Published Journal Publication published 02 May, 2025 Read the published version in Journal of Applied Genetics → Version 1 posted Reviewers agreed at journal 21 Mar, 2025 Reviewers invited by journal 21 Mar, 2025 Editor assigned by journal 21 Mar, 2025 First submitted to journal 17 Mar, 2025 Editorial decision: Minor Revisions Needed 18 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Introduction","content":"\u003cp\u003eThe MLH1 gene is situated at 3p22.2 on the short (p) arm of chromosome 3, precisely at position 22.2. It encompasses 19 exons spanning a region of 57,360 base pairs (bp) and encodes a protein with a molecular weight of 84.6 kilo Daltons (kDa), consisting of 756 amino acids. This protein plays a crucial role in DNA repair, specifically addressing errors occurring during DNA replication in preparation for cell division. Notably, MLH1 lacks known enzymatic activity. The MLH1 protein contains an ATPase domain and two interaction domains: one binds MutS homologs (MSH2, MSH3, MSH6), and the other interacts with PMS2, MLH3, and PMS1 (Domingo et al., 2005). MLH1 forms the MutLα heterodimer primarily with PMS2 but can also pair with PMS1 or MLH3. This complex binds MutSα (MSH2-MSH6) or MutSβ (MSH2-MSH3), which recognize DNA lesions, and facilitates the recruitment of proteins required for excision and repair synthesis (Kadyrov et al., 2006) (comment1). In examining the genetic factors contributing to lung cancer, it has been observed that specific polymorphisms in the hMLH1 gene play a significant role. In 2008, An et al. reported a borderline significance between the hMLH1 rs1799977 I219V variant and lung cancer risk, noting that this association was more pronounced in younger individuals. Following this, Lo et al. (2011) identified a significant correlation between the hMLH1−93G\u0026gt;A polymorphism (rs1800734) in the promoter region of MLH1 and increased lung cancer susceptibility among never-smoking individuals in Taiwan.\u003c/p\u003e\n\u003cp\u003eNibrin, also known as NBN or NBS1, is a protein coding gene located on chromosome 8 at position q21 which is composed of 754 amino acids (Nithya et. al. 2019). Also it is\u0026nbsp;an integral part of the MRE11/RAD50/NBN complex, crucial for detecting and repairing DNA double-strand breaks early on. Mutations in the NBN gene lead to Nijmegen breakage syndrome (NBS), a condition affecting this repair process (Berardinelli et. al. 2013). The MRE11/RAD50/NBN (MRN) complex plays a key role in activating cell cycle checkpoints during DNA repair. The NBN protein can be divided into three main parts: The N-terminus includes a forkhead-associated (FHA) domain, while the central region contains two BRCA1 C-terminal tandem domains known as BRCT1 and BRCT2. The C-terminus has two motifs that bind to MRE11, another component of the complex, as well as a motif that binds to ATM (Otahalova et al., 2023).\u003c/p\u003e\n\u003cp\u003eIn a separate study, Nithya and her team showed that SNPs in the **NBN** gene are associated with structural and functional changes in the protein. The NBN gene SNP evaluation revealed the presence of multiple polymorphic sites, which alter the functionality of the receptor and have been linked to phenotypic traits and increased risk of infection. Four SNPs were predicted to be severely damaged in coding regions which cause a change in the functionality of the receptor and were found to be associated with phenotypic traits and higher chances of infection (Nithya et. al. 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecent advancements in bioinformatics have produced refined computational algorithms for processing raw data, analysing protein and gene expression, modelling protein, DNA, and RNA structures, and mining literature for useful information (de Almeida et al., 2022). This study identifies and predicts pathogenic SNPs in the MLH1 and NBN genes, examining their association with disease and their impact on protein structure, stability, activity, and function. We retrieved the complete list of nsSNPs from dbSNP and analysed them using SIFT, Poly-Phen, PANTHER, PROVEAN, PhD-SNP, SNP-GO, I-Mutant, and Mu-Pro to identify the most deleterious nsSNPs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe objective is to pinpoint non-synonymous SNPs in MLH1 and NBN genes and apply computational tools to identify the most harmful ones. The study of non-synonymous SNPs in MLH1 and NBN gene holds significant potential for advancing personalized medicine, particularly in cancer treatment. Both MLH1, a key mismatch repair protein (Domingo et. Al., 2005), and NBN, crucial for DNA double stranded break repair (Williams et al., 2012), play vital roles in maintaining genomic stability. Identifying harmful SNPs in these genes enhances precision in risk assessment by uncovering genetic variants linked to cancer susceptibility, enabling early detection and preventive strategies (Hampel et al., 2008). These SNPs can also serve as biomarkers to stratify patients based on genetic risk and molecular tumor profiles, guiding personalized therapeutic decisions (Berger et al., 2016). Structural and functional analyses of SNP-induced alterations provide insights into how these mutations impact cellular pathways, which can influence drug sensitivity or resistance. For example, MLH1 mutations can alter responses to platinum based therapies (Santos et al., 2018), allowing for the development of tailored treatments. Furthermore, this research sheds light on tumor heterogeneity, providing a deeper understanding of mechanisms underlying cancer evolution and treatment resistance (Gerlinger et al., 2012). Structural biology and docking studies also pave the way for designing targeted therapies, such as small molecule inhibitors or peptide therapeutics, to counteract the effects of deleterious SNPs (Karakoc et. Al., 2019). By contributing to population specific genomics, the study can identify ethnic-specific predispositions to cancer, supporting regionally tailored precision medicine initiatives (Green et al., 2020). Ultimately, this work bridges the gap between genomics and clinical applications aligning with the goals of precision oncology to provide patient centric, personalized solutions for cancer prevention, diagnosis and treatment (Hortobagyi, 2020) (Comment 1).\u003c/p\u003e"},{"header":"2. Material and method","content":"\u003cp\u003e\u003cstrong\u003e2.1 Data Retrieval:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gathered a comprehensive catalogue of MLH1 and NBN gene-related Single Nucleotide Polymorphisms (SNPs) by querying the National Center for Biotechnology Information (NCBI) dbSNP database, using MLH1 and NBN as the search keywords. Subsequently, the SNPs were filtered based on our criteria (https://www.ncbi.nlm.nih.gov ).The full FASTA sequence corresponding to the human MLH1 and NBN protein was acquired from the NCBI database. Figure 1 provides a schematic outline of the methodology adopted for this investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Predicting deleterious nsSNPs:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll missense non-synonymous single nucleotide polymorphisms (nsSNPs) associated with the MLH1 and NBN genes were assessed using various tools and servers. The selection of computational tools such as SIFT, Polyphen-2 and PANTHER grounded the ability to predict the functional impact of genetic variants, particularly nonsynonymous single nucleotide polymorphisms (nsSNPs), which are key to understand the molecular basis of disease. These tools leverage the sequence conservation, structural features and biological context to provide a comprehensive assessment of the pathogenic potential of variants. The SIFT (Sorting Intolerant From Tolerant) tool (http://sift.bii.a-star.edu.sg) uses sequence homology, the physicochemical properties of amino acids, and evolutionary conservation to identify intolerant amino acid substitutions from those that are tolerated. It operates under the premise that functionally important amino acids are highly conserved across species, and substitutions at these positions are more likely to affect the protein function (Ng and Henikoff, 2001). A score less than 0.05 (0.00\u0026ndash;0.05) in SIFT categorizes a SNP as intolerant, while a score greater than 0.05 indicates tolerance (Ng et al., 2003). Its efficiency in processing a large number of variants makes it a popular choice in genome wide studies. \u0026nbsp;Polyphen (Polymorphism Phenotyping v2) tool assesses the potential impact of amino acid substitutions on human protein structure and function using various algorithms such as THMM, Colis2 program, SignalP program, etc. (George et al., 2008). PolyPhen-2 evaluates the substitution site, maps SNPs to known 3D protein structures, retrieves sequence annotations and structural features, and predicts whether missense mutations are likely to be damaging, probably damaging, or benign. Its dual-layered prediction model enhances accuracy, making it a reliable resource for interpretation of nsSNPs associated with complex diseases. The PANTHER (Protein Analysis through Evolutionary Relationships) tool (http://pantherdb.org/tools/cSNPscoreForm.jsp?) measures the evolutionary preservation duration of a given amino acid among different species, predicting functional and structural effects of amino acid substitutions also predict the impact of coding and non-coding variants (Mi et al., 2021). It provides insights into the evolutionary pressure acting on the protein coding regions, highlighting mutations likely to disrupt protein function. Additionally, pathway level annotations of this tool enables researchers to contextalize the functional consequences of variants within biological systems. PhD-SNP (Predictor of human Deleterious Single Nucleotide Polymorphisms) is a classifier based on support vector machines (SVM) and evolutionary information from sequences (http://SNPs.biofold.org/phd-snp/phd-snp.html). It builds a classification model using a large dataset of known disease-associated and neutral mutations, providing predictions based upon the specific sequence context of the SNP. Robust performance and ability to generalize across various datasets make it a reliable tool for high-throughput variant analysis (comment2). (Capriotti et al., 2017). SNP\u0026amp;GO employs gene ontology (GO) annotation data to predict whether a mutation is likely to be associated with a disease or not (Capriotti et al., 2013). By combining sequence features with functional information, SNPs\u0026amp;GO provides more detailed assessment of the impact of mutations on protein function. This tool uses GO terms associated with the protein to contextualize the biological significance of the variant, offering enhanced accuracy in distinguishing between disease-causing and neutral mutations. (Calbrese et al., 2009)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSIFT and Polyphen-2 are often chosen over tools like MutPred and SNAP2 for initial analysis of nsSNPs due to their simplicity, speed and suitability for high-throughput screening. These tools are validated, efficient and user friendly, making them ideal for large scale studies, such as genome wide association studies (GWAS). While MutPred and SNAP2 offers advanced insights, such as prediction of molecular mechanism and functional impacts, they are more computationally intensive and complex to interpret. It integrates machine learning with functional annotations to predict molecular consequences like altered binding affinity (Li et al., 2009). SNAP2 powered by neural networks, excels at identifying effects in diverse and poorly conserved regions but provides more granular outputs that require additional expertise for interpretation (Hecht et al., 2015). In contrast SIFT and Polyphen are faster and require few computational resources making them more accessible for researchers with limited infrastructure. Thus, they are well suited for initial screening and prioritization of variants, while MutPred and SNAP2 are better utilized in targeted in depth functional studies where detailed mechanistic insights are needed.\u0026nbsp;(comment3)\u003c/p\u003e\n\u003cp\u003eFor further analysis, nsSNPs predicted as deleterious or damaging by all six servers unanimously were shortlisted. So as to avoid the discrepancies and the differences caused by the output results of the all the softwares.\u0026nbsp; (comment4)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Modeling the native MLH1 and NBN protein using MODELLER v9.22 and v10.2:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe complete structure of MLH1 and NBN protein was modeled using MODELLER v9.22. It is software which utilizes comparative homology modeling techniques for building protein structures. Both MODELLER versions was obtained from Andrej Sali website (https://salilab.org/). MODELLER can be used with and without installing python. If python is not installed then python scripts can be executed by command \u0026ldquo;mod9.22 SCRIPT_NAME.py\u0026rdquo;. For protein modelling using python the modeller server itself provides the scripts of the sequence alignment and actual structure generation and are included in the supplementary material (comment 5). Comparative homology modeling using MODELLER include the following steps: template selection using BLAST checking the shortlisted template with the query sequence, generating the model, and finally it\u0026rsquo;s verified by Ramachandran\u0026rsquo;s plot. The shortlisted model was saved as MLH1 _WILD and NBN_WILD respectively\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Generation of mutant protein structures:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe best validated model was selected and is further used as template to incorporate mutation in protein structures and subsequent evaluation via MODELLER software (version 9.22). The process utilizes a combination of comparative modeling techniques and optimization algorithms to introduce mutations or modifications to the protein sequence. These mutations could be substitutions, insertions, or deletions of amino acids. During the modeling process, MODELLER aligns the target protein sequence with the template structure, incorporating information about the template\u0026apos;s spatial arrangement of atoms. MODELLER then optimizes the model by adjusting dihedral angles, bond lengths, and other structural parameters to generate a three-dimensional representation of the mutant protein.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Model Validation:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the selected mutant models were validated by Ramachandran plot. A Ramachandran plot is a graphical representation used in structural biology to assess the stereochemical quality of protein structures. Named after its creator G. N. Ramachandran, this plot depicts the dihedral angles of a protein\u0026apos;s amino acid residues. Specifically, it illustrates the phi (ϕ) and psi (\u0026psi;) angles, which represent the rotations around the C\u0026alpha;-C and C-N bonds, respectively. In a Ramachandran plot, each point corresponds to a specific combination of phi and psi angles for a given residue in the protein structure. The plot is divided into regions that represent allowed and disallowed conformations based on steric hindrance and clash considerations. The allowed regions indicate favorable and energetically feasible conformations for protein backbone torsion angles, while the disallowed regions suggest sterically hindered or unlikely conformations. Further all the proteins generated were energy minimized by utilizing Chimera tool which using steepest descent method for energy minimization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 RMSD value calculation:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RMSD value of the both proteins was computed by superimposing the two structures in PyMOL using the \u0026quot;align\u0026quot; feature. RMSD value is an indication of structural and functional deviation from native protein, higher the value more is the deviation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Determining SNP\u0026rsquo;s impact on the MLH1 and NBN protein stability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to ascertain the stability or denaturation of the MLH1 and NBN protein resulting from amino acid substitutions, we employed two distinct in silico algorithms (I-Mutant and MuPro). Both of them are based on sequence analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Molecular Docking for Protein Interaction Identification:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMutant\u0026rsquo;s model of MLH1 protein and MLH1_WILD was docked with MLH3 protein. MLH1_WILD and MLH3 structures were absolute protein structures, the Cluspro server performed protein/protein docking with all parameters set to native values. Further, Molecular docking between wild type NBN protein and its mutant with different interacting proteins (MRE11 and RAD50) was also performed using the CLUSPRO server (https://cluspro.org/help.php) with default settings. The cluspro server is widely used tool for protein protein docking and was selected for this study due to its high reliability, accuracy and user friendly interface. It employs a systematic approach that includes rigid body docking, energy based scoring and clustering of low energy conformations to identify the most probable binding poses for protein protein complexes (kozakov et al., 2017). Additionally the most frequently occurring conformations, which are likely to present native binding modes are prioritized. The server also allows users to specify restraints or bias the docking process towards certain residues, enabling more accurate modeling of biologically relevant interactions (Kozakov et. al., 2013) (comment 5) ClusPro forecasts ten distinct docked poses for every experiment, using model scores that indicate the docked molecule\u0026apos;s binding energy.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe identification of harmful SNPs holds paramount importance in unraveling the underlying genetic factors contributing to different cancer. These genetic variations often play a pivotal role in predisposing individuals to these types of cancers. By harnessing advanced computational tools and databases, researchers can delve deep into the molecular mechanisms associated with cancer, gaining valuable insights into potential therapeutic targets or preventive strategies. The diagram in Fig.\u0026nbsp;1 meticulously illustrates the workflow and database servers specifically designed for identifying harmful Single Nucleotide Polymorphisms (SNPs) within the human genes MLH1 and NBN. These servers are essential components of the analysis pipeline, contributing significantly to the precise pinpointing and characterization of detrimental genetic variations. Furthermore, the workflow and database servers delineated in Fig.\u0026nbsp;1 facilitate seamless data integration, robust analysis, and insightful interpretation.\u003c/p\u003e\n\u003cp\u003eThe 57360 base pairs that make up the human MLH1 gene and the 756 amino acids that make up the MLH1 protein. As of May 2023, the NCBI-dbSNP database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/snp/\u003c/span\u003e\u003c/span\u003e) has 15721 SNPs related to the human MLH1 gene. Of them, 1420 were deemed to be clinically significant and were subjected to analysis. Also included 21,274 total SNP for gene NBN. Out of 21,274 SNPs were discovered in the output of the dbSNP database with NBN hits, of which 17,478 were located in the intronic region, 1254 were nsSNPs (missense), and 523 were coding synonymous.\u003c/p\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Finding harmful and detrimental nsSNPs using a sequence-based homology methodology:\u003c/h2\u003e\n \u003cp\u003eThe 1420 SNP IDs were analyzed using the SIFT and Panther tools, which use a sequence-based homology method to predict the deleterious and harmful nsSNPs in the MLH1 gene. The IDs are checked in SIFT and out of these 1420 SNPs, 1238 could not be found on the SIFT server. Further, SIFT was showing eight protein IDs for the same \u0026ldquo;rsID\u0026rdquo; hence six protein IDs which are covering the entire length of protein (756 amino acid) are taken for further analysis. The two protein IDs (Ensemble IDs) that are rejected are ENSP00000416687 and ENSP00000398392. After removing the protein IDs, out of 182 nsSNPs, 165 were remaining and are used for all further analysis. Out of 165 SIFT predicted, 122 and 43 as deleterious and tolerated respectively. PANTHER server demonstrated 59 nsSNPs as damaging and 3 as probably benign, rest are predicted as invalid substitution. Intriguingly, 51 nsSNPs were predicted to be deleterious by these two computational tools/ servers as shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eFor the NBN gene, out of 21,274 SNPs filtered by SIFT, only 233 SNPs were located in the CDS region. SIFT predicted 61 of these as deleterious and 166 as tolerated. PANTHER conducted a more comprehensive analysis of every nsSNP for each gene to predict potentially detrimental or damaging nsSNPs. Of the 233 CDS entries in NBN, PANTHER predicted 47 to have detrimental effects (possibly or probably damaging) and 45 to be benign. 24 nsSNPs are commonly predicted to be deleterious by SIFT and PANTHER webservers. After selecting single protein ID ENSP00000265433, both webserver unanimously reported 16 nsSNPs to be deleterious (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Sequence- and structure-based homology-based Polyphen server predicted the following functionally harmful nsSNPs:\u003c/h2\u003e\n \u003cp\u003ePolyPhen-2 (Polymorphism Phenotyping v2) servers are advanced computational tools designed to predict the functional consequences of genetic variations, particularly amino acid substitutions, on protein structure and function. Developed by the Bork Group, these servers integrate both sequence-based and structure-based information to assess the potential pathogenicity of genetic variants in human proteins. It forecasts the potential effects of amino acid substitutions on protein function and structure by analyzing factors like phylogenetic data, structural details, and the protein sequence. Out of 51 nsSNPs selected by sequence based homology approach, Polyphen-2 reported 48 nsSNPs to be deleterious by both HumDiv and HumVar subsets for MLH1 gene. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e represents the nsSNPs that are commonly predicted by all the three tools, underlined entries are not selected by PolyPhen-2 webserver for both MLH1 and NBN gene.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 The disease prediction of nsSNP\u0026rsquo;s by the PhD-SNP and SNP \u0026amp; GO web tools:\u003c/h2\u003e\n \u003cp\u003ePhD-SNP is a tool designed to anticipate the phenotypic consequences of non-synonymous substitutions, offering insights into the potential impact of genetic variations. Beyond that, it extends its predictive capabilities to determine the association of such substitutions with diseases. Out of the 48 nsSNPs PhD SNP server has predicted 39 nsSNPs to be functionally associated with disease, rest are not associated with any disease or are showing neutral association whereas SNP\u0026amp;GO reported 30 nsSNPs to be associated with disease, unanimously, both webservers predicted 30 nsSNPs to be associated with the occurrence of disease (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eOut of the 14 nsSNPs for NBN gene 2 were predicted to be associated with disease and 12 were predicted to have no association with the development of any disease by PhD -SNP and SNP\u0026amp;GO disease association prediction computational servers. The two shortlisted nsSNPs are rs199845467 and rs371480039 associated with G224E and L312S (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Modelling of the complete MLH1 and NBN proteins using MODELLER (Comparative Homology Modeling):\u003c/h2\u003e\n \u003cp\u003eFor subsequent analysis, these 30 nsSNPs were considered. Since the full structures of the MLH1 and NBN proteins were not present in PDB database, we used a homology modeling approach to create the MLH1 protein structure via MODELLER v9.22. The mutation models for MLH1 and NBN proteins were also generated using the same software to predict their effects on the stability and functionality.\u003c/p\u003e\n \u003cp\u003eA template structure for model generation of MLH1 protein was obtained using psiBLAST, selecting PDB as source database to find the structure templates. The sequences which are showing at least\u0026thinsp;\u0026gt;\u0026thinsp;30% identity and \u0026gt;\u0026thinsp;40% query coverage were selected for model generation. PDB ID 4P7A was identified as having 100% similarity with the query sequence and 3RBN showed 98.12% similarity, 5AKB and 1BKN showed\u0026thinsp;\u0026gt;\u0026thinsp;36% similarity with the query and hence were selected as the template for model generation of MLH1 protein. The structures of 4P7A, 3RBN and 5AKB were further utilized as the template for modeling MLH1 protein using comparative homology modeling, for NBN, since the complete structure was available in the PBD database so we have downloaded it and used for further analysis. MODELLER was instructed to generate 5 protein structures based on the templates provided. Several parameters are used to choose the best model from the five models generated by MODELLER. A frequently used parameter is DOPE score, the lower the DOPE score the better the modeled structure is considered hence we have selected the structure having the lowest DOPE score. Based on the lowest DOPE score we have selected model \u0026ldquo;MLH1.B99990001.pdb\u0026rdquo; and \u0026ldquo;NBN.B99990001.pdb\u0026rdquo; (Fig. 2a \u0026amp; b) as the best model for MLH1 and NBN protein. The selected model was further analyzed by Ramachandran Plot to check protein structure and folding properties (Supplementary table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFurther, we have also generated structures of other proteins that are interacting with MLH1 and NBN proteins for their proper functioning. MLH1 interacts with MLH3 via its MLH3 interacting domain and NBN interacts with MRE11 and RAD50 for their proper functioning via BRCT2 domain. Same homology modelling was used to generate these protein structures. Further energy minimized structures of all these proteins were generated via Chimera server using steepest descent and conjugate gradient algorithms.\u003c/p\u003e\n \u003cp\u003e3.5 \u003cstrong\u003eRMSD value calculation of the modeled mutant protein\u003c/strong\u003e:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eThe\u003c/strong\u003e RMSD values calculated using PYMOL for MLH1 and NBN mutants provide insights into the structural impact of specific mutations and their potential biological relevance. For MLH1 and NBN proteins, PYMOL was used to calculate RMSD values. For MLH1, 30 mutants were analyzed, RMSD value for mutant L550P was 1.5 \u0026Aring; indicating a significant structural deviation from the wild protein, as MLH1 plays critical role in DNA mismatch repair. In contrast G167E has the lowest RMSD of 0.147 \u0026Aring;, indicating minimal structural perturbation and potentially retaining near-native functionality. Rest of the 28 mutants have RMSD value ranging from 0.155\u0026ndash;0.181 \u0026Aring; that also indicates some level of structural deviations, which may still compromise the protein stability but to a lesser extent. (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). For NBN protein which is crucial for DNA damage response and double stranded break repair, two mutants i.e. L312S and G224E have values RMSD values of 0.39 and 1.32 respectively the higher deviation in G224E suggests significant structural disruption, which could impair the protein role in stabilizing and activating the MRN complex during DNA repair. In comparison L312S have moderate RMSD indicates structural change that may affect the protein function less drastically but still warrants investigation. These results emphasize the potential functional consequences of these mutations, highlighting their importance in the context of genomic stability and cancer predisposition. (Comment 6) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Analysing the stability changes of mutants using the I-Mutant and MuPro web tools:\u003c/h2\u003e\n \u003cp\u003eAmong the 24 nsSNPs (30 amino acid change) submitted, I-mutant predicted 4 nsSNPs (5 amino acid changes) to have increased stability (T117R, T117M, P28L, A128P and S44F), rest 20 nsSNPs reported an decrease in stability. On the other hand MuPro reported 1 nsSNPs to have increase instability (P654L). Stability for one mutant was reported to increase by both servers. Further, both servers unanimously reported 19 nsSNPs (24 amino acid change) to have a decrease in stability (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Similarly for NBN protein (both SNPs), a decline in stability was observed by both webservers (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e3.7 MLH1 protein domain mutation mapping:\u003c/p\u003e\n \u003cp\u003eMLH1 comprises 3 parts: the ATPase, the MutS homolog interaction, and the MLH3 interaction. According to literature, these 19 nsSNPs (24 AA alterations) are dispersed throughout all three parts (Fig.\u0026nbsp;3a). 10 SNPs (12 AA change) were located in ATPase domain, 4 SNPs (6 AA change) were present in MutS homologs interaction part, 4 SNPs (5 AA change) were located in MLH3 interaction domain and 1 nsSNP is present between ATPase domain and MutS homolog domain.\u003c/p\u003e\n \u003cp\u003eIn NBN gene, there are 4 domains i.e. FHA (1-110 amino acid), BRCT1 (11\u0026ndash;184), BRCT2 (217\u0026ndash;325) and NBS-1 (640\u0026ndash;691) domain. The two nsSNPs that are predicted to impact protein stability and are deleterious are G224E and L312S which are present in BRCT2 domain as shown in Fig.\u0026nbsp;3b.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.8 Protein -protein docking analysis:\u003c/h2\u003e\n \u003cp\u003eDNA mismatch repair protein MLH3 is synthesised in human by MLH3 gene. It is a member of Mut-L homolog family. MLH gene family is extensively reported to maintain genomic integrity during DNA replication and after meiotic recombination. MLH3 interacts with MLH1 to form MutL\u0026gamma; and helps in the process of DNA replication in humans. Therefore, for docking, nsSNPs in the MLH3 interaction part were chosen. Docking analysis of MLH1 and MLH3 protein was done using Cluspro server (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;4a to 4f). The binding energy for MLH1_Wild- MLH3 is -1473.5 Kcal/mol. All the 4 SNPs (5 amino acid changes) reported an increase in binding energy depicting an increase in the stability of the docked complex due to the corresponding polymorphisms.\u003c/p\u003e\n \u003cp\u003eFor NBN, as shown in Fig. 5, NBN have 4 domains and the two shortlisted nsSNPs (G224E and L312S) are present in BRCT-2 domain. NBN directly interacts with MRE11 and RAD50 proteins hence we docked them in order to understand the effect of these SNPs. Cluspro predicted that then when NBN-wild interacted with MRE11 the binding energy is -1737.7 Kcal/mol, however when the NBN mutant interacted with MRE11 the binding energy takes a sharp dip and is measured to be -1572.6 for G224E indicating that this polymorphisms effect the proper binding and hence reduced functionality of the complex. Similarly, NBN also directly interacts with RAD50 hence we docked these two as well. The results suggested a sharp decline in binding energy of NBN (G244E-1420) as compared to NBN (wild \u0026minus;\u0026thinsp;1592) (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;5a to 5f).\u003c/p\u003e\n \u003cp\u003eWe also performed the docking of NBN with MRE11 and RAD50 protein simultaneously on the same model. The Docking energy of NBN(W)-MRE11-RAD50 is -1467.9 which is better than NBN(G224E)-MRE11-RAD50 (-1385.9) indicating a less stable complex and lower than NBN(L312S)-MRE11-RAD50 (-1562) indicating L312S structure having higher binding efficiency than NBN (W) (Supplementary table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplementary Fig.\u0026nbsp;1\u0026ndash;3)\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this investigation, we conducted a thorough in silico assessment to predict pathogenic Single Nucleotide Polymorphisms (SNPs) and their potential effects on the structure and functionality of the MLH1 and NBN protein by utilizing various computational approaches in combination. In order to improve the precision of predictions, we combined tools from various categories, such as homology-based, sequence-based, consensus-based, and structure-based approaches. This strategy is designed to enhance confidence in identifying potentially harmful missense Single Nucleotide Polymorphisms (SNPs) by minimizing biases in the results (Khalid et al., 2020).\u003c/p\u003e\u003cp\u003eThe analysis of 15,721 SNPs located in the MLH1 gene identified 1,420 clinically significant SNPs, for NBN gene we retrieved 21,274 total SNPs and 12,753 were further evaluated using various bioinformatics tools. Three tools (SIFT, PolyPhen, and PANTHER) were used to predict deleterious SNPs, two tools (SNP\u0026amp;GO and PhD-SNP) identified associations between SNPs and disease development, and two web servers (I-Mutant and MUPro) estimated protein stability. The use of multiple tools is beneficial because each employs different algorithms, providing a comprehensive analysis. The MLH1 protein contains three key parts: the ATPase, the MutS homolog interaction, and the PMS2/MLH3/PMS1 interaction part. All the SNPs identified by these tools are located within these domains, which are vital for the effective DNA repair. Any mutation in these three parts of MLH1 protein may influence the efficiency of DNA repair. Notably, the PMS2/MLH3/PMS1 interaction domain plays a crucial role, with our study reporting four nsSNPs (five AA changes) within this domain. Of these, rs63750193 is associated with Lynch syndrome, while rs63750610, rs63750693, and rs63750899 are linked to hereditary non-polyposis colorectal cancer (HNPCC) (Mahdouani et al., 2022, Müller et al., 2001, Godino et al., 2001, Raevaara et al., 2004). Protein stability, a critical factor influencing structure, function, evolution, and biological activity, is impacted by these nsSNPs, which may lead to aberrant protein accumulation, misfolding, or degradation. Given the location of these nsSNPs in the PMS2/MLH3/PMS1 interaction domain, they are expected to significantly affect MLH1 protein activity and function.\u003c/p\u003e\u003cp\u003eNBN, part of the MRN complex (MRE11-RAD50-NBN), plays a critical role in detecting DSBs (double strand break repair) and initiating repair. The MRN complex binds to the broken DNA ends, processes them, and recruits ATM kinase, which activates various DNA damage response pathways. There were only 2 deleterious SNPs from the CDS region of the gene NBN that were predicted deleterious by all the webservers namely rs199845467 (G224E), and rs371480039 (L312S) that are present in the BRCT2 domain of NBN protein. At least four proteins (ATM, BRCA1, MRE11, RAD50, and P95) interact with NBN protein (directly or indirectly) and play a part in the DNA damage response. The FHA and BRCT domains bind to multiple phosphorylated proteins, which regulate interactions within the MRN complex. Through its FHA domain, NBN interacts with the C-terminal-binding protein interacting protein (CtIP, also known as retinoblastoma-binding protein 8 or RBBP8) (Otahalova et. al. 2023).\u003c/p\u003e\u003cp\u003eModern computational tools and techniques enable the analysis of genetic data, gene expression, evolutionary and SNP analysis, molecular dynamics simulations, and the derivation of models for the desired protein, all of which contribute to a better understanding of protein functionality. The compilation of data from multiple studies has illustrated how various missense Single Nucleotide Polymorphisms (SNPs) contribute to the development of a range of diseases (Karimi et al., 2022, Khan et al., 2023). Kumar and coworkers employed computational approach to investigate the impact of missense mutations that lead to d-2-hydroxyglutaric aciduria. Through these approaches, they elucidated the structural alterations induced by mutations, ultimately aiding in the identification of novel targets for the development of new drug therapies (Kumar et al., 2018). Similarly, another study by Sidhartha and Colleagues investigated SNPs in the MSH2 gene, a key player in DNA repair, using computational tools to identify those linked to cancer development. 27 SNPs, including 5 with two amino acid changes, were found to potentially cause structural and functional changes in the protein structure of MSH2. Further, it was demonstrated that 6 SNPs impacted the way MSH2 and MSH6 interacted, and 12 were linked to Lynch syndrome and hereditary nonpolyposis colorectal cancer. (Singh et al., 2022).\u003c/p\u003e\u003cp\u003eProtein MutL was first discovered in bacteria, where it is essential for the control of genetic recombination and post-replicative DNA mismatch repair (MMR). Demonstrating their ubiquity, these proteins are integral to various DNA metabolism pathways. In eukaryotes, MutL proteins exist as heterodimers, and mammalian cells harbor three primary forms: MutLα (MLH1–PMS2 heterodimer), MutLβ (MLH1–PMS1), and MutLγ (MLH1–MLH3). MutLγ, an endonuclease, remains inadequately understood despite its extensive implication in triplet repeat expansion, a process fundamental to around 40 neurological disorders in humans. Recent reports suggest that human MutLγ acts as an endonuclease, cleaving DNA in a manner dependent on MutSβ and loops. The incision of DNA containing loops by MutLγ endonuclease initiates a cascade of events leading to DNA expansion (Kadyrova et al., 2020). As per NCCN, MLH1 exhibits a robust association with the onset of Colorectal, Endometrial, and Ovarian cancers, with percentages ranging from 46–61%, 34–54%, and 4–20%, respectively. MLH1 is also linked to Bladder, Gastric, Small bowel, Brain, Biliary tract, and pancreatic cancers. In addition to these mutations, MLH1 alterations are robustly linked to Lynch Syndrome (Hereditary Nonpolyposis Colorectal Cancer), thereby elevating the susceptibility to breast cancer (Harkness et al., 2015).\u003c/p\u003e\u003cp\u003eIn a study by Anderson et. al., assessed functional characterization of MLH1 missense variants associated with lynch syndrome, an inherited colorectal cancer syndrome caused by mutations in DNA mismatch repair genes. Researchers analyzed a panel of MLH1 missense mutations identified in lynch syndrome families to assess their pathogenicity through functional assays, cellular localization studies and protein-protein interaction test with PMS2 and exonuclease1 (EXO1). The study found several variants displayed defects in nuclear localization or interactions with MMR proteins, potentially contributing to cancer development. The findings correlate with in silico predictions and existing MMR activity data reinforcing the role of these mutations in lynch syndrome. Another study investigated the functional deficiency of NBN protein in breast cancer cell line carrying the p.P125W mutation. Researchers identified the mutation in the HCC1395 cells and examined its impact on DNA damage repair mechanisms. The study found that cells exhibited reduced NBN protein levels increased sensitivity to ionizing radiation and defective formation of DNA repair foci involving H2AX, MDC1 and 53BP1. Despite this ATM signaling remain unaffected. The study also highlighted that the HCC1395 cells which carry both NBN mutations and BRCA1 truncation were highly sensitive to PARP inhibition. These findings suggest that the mutations impairs NBN function. (Comment 10)\u003c/p\u003e\u003cp\u003eProtein-to-protein communication may be impacted by modifications to the interaction domain. These interactions are better understood and important mutations that affect binding are found through docking studies. Using the ClusPro server, docking studies were conducted for MLH1 and NBN proteins. Reveals significant alterations in protein-protein interactions due to single nucleotide polymorphism potentially impacting cellular functions critical for genome stability. For MLH1_Wild-MLH3, the binding energy was − 1473.5 Kcal/mol, with all four SNPs (five amino acid changes) showing an increase in binding energy, indicating enhanced stability of the docked complex due to these polymorphisms. The increased binding energy suggest enhanced interaction stability, which may influence the efficiency of the mismatch repair pathway (MMR). MMR proteins, including MLH1 and altered binding affinity may modify repair efficiency, possibly leading to an imbalance in DNA repair process and contributing to carcinogenesis (Jiricny et. al., 2013). MLH3, play crucial roles in maintaining genomic integrity by correcting replication errors. ClusPro also predicted that when NBN-wild interacted with MRE11, the binding energy was − 1737.7 Kcal/mol. However, with the NBN mutant (G224E), the binding energy dropped sharply to -1572.6 Kcal/mol, suggesting that this polymorphism affects proper binding and reduces the complex's functionality. Similarly, docking of NBN with RAD50 showed a significant decline in binding energy for the NBN mutant (G244E, -1420 Kcal/mol) compared to NBN wild-type (-1592 Kcal/mol). Docking results suggest an impact on the MLH1/MLH3 and NBN/MRE11/RAD50 interaction It is suggesting weakened interactions, potentially impairing the formation and function of the MRN complex. The MRN complex is essential for DNA double strand break repair, signaling and maintaining chromosomal stability (Stacker and Petrini et. al., 2011). Disruptions in this complex, particularly linked to Nijemen Breakage syndrome, a disorder characterized by increased cancer susceptibility and genomic instability (Varon et. al., 1998). The observed decrease in binding affinity for NBN mutants could lead inefficient DNA damage response, making cells more prone to accumulations of mutations, thereby increasing the risk of malignancies such as breast, ovarian and colorectal cancers (Deriano \u0026amp; Roth, 2013).\u003c/p\u003e\u003cp\u003eWe have also added the hydrogen bonding details for these docked structure which shows contradictory results as compared to binding energy. More negative binding energy reflects more stable docked complex which should have more hydrogen bonds but we are reported an increase hydrogen bonds with a decline in binding energy which may be due to other intermolecular interactions such Van Der Waal etc. While these docking studies provide insights into potential structural and functional disruptions caused by SNPs, further molecular dynamics simulations and in vitro validations are necessary to confirm their precise mechanistic effects on DNA repair pathways and their contribution to tumorigenesis (Comment 8). There is evidence linking a few of these in silico screened SNPs to a higher risk of cancer. Validating the SNPs in an in vitro experimental system is crucial to determining its precise function. Our analysis of MLH1 and NBN genes identified several high risk SNPs that align with previously reported variants linked to cancer predisposition, while also, highlighting novel insights into their structural and functional impacts. Variant rs63750193 has been associated with lynch syndrome (Mahdouni et. al., 2022, Raeveera et. al., 2004), was predicted in our study to significantly reduce protein stability and alter the binding interactions in PMS2/MLH3/PMS1 interaction domain. This finding is consistent with prior reports emphasizing the critical role of this domain in DNA mistmatch repair (Jirincy, 2013). In a study conducted by Anderson et al., 2012, reported that MLH1 (L550P) renders the binding of MLH1 to PMS2 weakened, this defective MLH1-PMS2 dimerization would leads to compromised MMR leading to conclusion that this polymorphism is indeed pathogenic. Further they have also shown that defective MutLα dimerization due to L550P is linked with reduced MMR activity\u003c/p\u003e\u003cp\u003eFor the NBN gene, our identification of the deleterious variants rs199845467(G224E) and rs371480039(L312S) aligns with findings that mutations in the BRCT domain disrupt interactions with key proteins in MRN complex, compromising double stranded break repair (Otahalova et. al., 2023, Stacker and Pterini, 2011). These SNPs particularly, G224E showed significant reduction in binding energy indicating weakened complex formation, which is consistent with the structural instability observed in patients with Nijemen Breakage syndrome (Comment 9 and 12).\u003c/p\u003e\u003cp\u003eWhile this study provides valuable insights into the pathogenicity of SNPs in MLH1 and NBN, further research is essential to validate these findings and expand their applications. Experimental validation through mutagenesis studies and functional assays would help confirm the predicted impacts of high-risk SNPs to protein function and stability. For example, site directed mutagenesis followed by protein expression and stability assays could determine how mutations such as rs63750193 (MLH1) and rs199845467 (NBN) influence protein-protein interactions and DNA repair efficiency (Kumar et. al., 2018). Functional assays, such as DNA damage response assays, could further elucidate the biological consequences of these mutations. (Wang et.al., 2020).\u003c/p\u003e\u003cp\u003eAdditionally, molecular dynamic simulations could offer more detailed insights into how these SNPs affect the structural dynamics of MLH1 and NBN proteins over time. This would complement the docking results and provide a deeper understanding of conformational changes caused by specific mutations (Zhang et. al., 2020). Such simulations have been successfully used in other studies to analyze protein ligand interactions and predict the long term impact of missense mutations on protein functionality (Hassan et.al., 2021).\u003c/p\u003e\u003cp\u003eThe computational approach used in this study can be adapted to investigate other cancer associated genes involved in critical pathways such as cell cycle regulation, apoptosis and DNA damage repair (Huang et. al., 2018). By integrating multi omics data including transcriptomics, proteomics and epigenomics – future studies could achieve a more comprehensive understanding of how genetic variants contribute to cancer development (Subramanian et. al., 2019). For instance, combing SNP analysis with gene expression data could help identify regulatory mutations that influence gene activity (Chen et. al., 2016). Furthermore, expanding the methodology to analyze somatic mutations in cancer genomes could offer insights into tumor specific alterations, which may have implications for personalized medicine and drug development (Dienstmann et. al., 2017).\u003c/p\u003e\u003cp\u003eBy pursuing these future directions, researchers can enhance the clinical relevance of computational findings, refine predictive models, and contribute to a broader understanding of cancer epigenetics and genomics (Comment 13).\u003c/p\u003e\u003cp\u003eWhile the study offers valuable insights into the potential pathogenic effects of SNPs in MLH1 and NBN it is important to acknowledge the limitations of the computational methods employed. Computational tools, despite their power and efficiency, have inherent biases that can impact the accuracy of predictions. For example SIFT and Polyphen rely on evolutionary conservation and may misclassify SNPs in regions with limited conservation either underestimating or overestimating their functional impact (Kumar et. a., 2018). Similarly, stability prediction tools such as I-mutant and MuPro are based on static models that cannot fully capture the complex folding dynamics of protein in vivo, potentially leading to false- positives or negative predictions.\u003c/p\u003e\u003cp\u003eFurthermore docking studies provide valuable information but they are limited by their static nature and do not account for the dynamic behavior of proteins in cellular environment (Eriano and Roth, 2013). This simplification may result in discrepancies when validating the results experimentally, as real time protein interactions are influenced by various cellular factors, including post translational modifications and presence of cofactors.\u003c/p\u003e\u003cp\u003eThese limitations underscore the need of integrating additional approaches as molecular dynamics simulations, to capture the dynamic behavior of proteins and improve the accuracy of interaction predictions. Addressing these constraints not only highlights the challenges inherent in computational genomics but also provides more balanced interpretation of the findings (Comment 11)\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis research highlights the relevance of in silico analyses in evaluating some single nucleotide polymorphisms vis a vis the structure and function of proteins, specifically targeting DNA repair genes such as MLH1 and NBN. The combination of several computer programs enabled an assessment that concluded certain SNPs, especially those within the interaction sites, can significantly alter protein stability and binding interactions. These structural changes are likely to impede the DNA repair activities, thereby predisposing to a variety of cancers such as lynch syndrome and hereditary nonpolyposis colorectal cancer.\u003c/p\u003e\u003cp\u003eMolecular docking analyses indicate that certain SNPs may disrupt protein-protein interactions, potentially affecting structural stability and functional efficiency. Notably, SNPs such as rs6750193 in MLH1 have been previously associated with cancer, and our findings provide mechanistic insights into their biological impact. To further substantiate these computational predictions, in vitro experimental validation and advanced simulations are essential. Future research should incorporate site-directed mutagenesis, functional assays to assess DNA repair efficiency, and molecular dynamics simulations to explore the structural consequences of SNP-induced alterations (Comment 14). Alongside that, such studies can be applied to clinical practice, allowing for the SNPs that are associated with modulating protein stability and DNA repair efficiency to be genotyped in high-risk patients. By adding these variants to the standard genetic tests, the clinicians would be able to better evaluate the chances for developing cancer and implement advanced measures and tailor the risk control approaches. In addition, these SNPs could be added to the predictive models, which would enhance the estimation of cancer risk and consequently improve patient care. In a broader research context, this computational framework could be expanded to analyse SNPs in other DNA repair genes, potentially uncovering new biomarkers for hereditary cancers. Future studies could also integrate multi-omics approaches, such as transcriptomics and proteomics, to provide a more comprehensive understanding of how these genetic variations influence cellular pathways. Such interdisciplinary strategies would not only validate the computational predictions but also bridge the gap between in silico analyses and clinical applications, ultimately advancing precision oncology and targeted therapeutic development (Comment 15)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDomingo, E., Niessen, R. C., Oliveira, C., Alhopuro, P., Moutinho, C., Esp\u0026iacute;n, E., ... \u0026amp; Hofstra, R. M. (2005). BRAF-V600E is not involved in the colorectal tumorigenesis of HNPCC in patients with functional MLH1 and MSH2 genes. \u003cem\u003eOncogene\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(24), 3995-3998.\u003c/li\u003e\n \u003cli\u003eKadyrov, F. A., Dzantiev, L., Constantin, N., \u0026amp; Modrich, P. (2006). 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Modernizing the nonhomologous end-joining repertoire: Alternative and classical NHEJ. \u003cem\u003eDNA Repair, 32,\u003c/em\u003e 32\u0026ndash;40. https://doi.org/10.1016/j.dnarep.2013.04.011\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ch2\u003e\u003cstrong\u003eTable 1: Deleterious SNPs of MLH1 and NBN commonly predicted by Sequence and Sequence-structure homology based tools (Total four tools used: SIFT, PROVEAN, Polyphen-2 and PANTHER)\u003c/strong\u003e\u003c/h2\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 593px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMLH1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS. No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ers IDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmino acid change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS. No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ers IDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmino acid change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers11541859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eE89Q\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eL622H\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers35001569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eK618E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eL622P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eG638R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eP654L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63749829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eE71Q\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eT117R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63749859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eC77R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eT117M\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63749939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eG67E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eP28L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cu\u003ers63749950\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cu\u003eA281V\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eE319K\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63749990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eF80V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eC680R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eT82I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eC680G\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eV49E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eA128P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eS295N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eP648S\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eS295T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63751012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eE37K\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eL550P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63751109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eS44F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eG67R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63751194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eR265S\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eG67W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63751194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eR265C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eR182G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63751275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eR687W\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eA29G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63751283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eL260R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eA681T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63751428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eQ62K\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eG244D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63751598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eS295R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eG244V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63751598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eS295G\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eC77Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cu\u003ers63750314\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cu\u003eD387H\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eI107R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cu\u003ers63750360\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cu\u003eA282G\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eN38H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eK286Q\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eN38D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eR385H\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eP648L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eR385P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eA29S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 593px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNBN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS. No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ers IDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmino acid change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS. No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ers IDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmino acid change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers13312858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eK105N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers193921030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eK82E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers61753720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eD95N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers199845467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eG224E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers61754966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eI171V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cu\u003ers201781110\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cu\u003eR660T\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers78870221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eI35M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers201816949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eM152I\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers141137543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eT452P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers371480039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eL312S\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers151070415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eA183S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers377700348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eD211E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cu\u003ers182756889\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cu\u003eR169C\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers377730553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eQ39K\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers185493105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eV101A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers368703936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eL650F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 593px;\"\u003e\n \u003cp\u003e*Underlined denotes it is not commonly predicted by both the approaches\u003c/p\u003e\n \u003cp\u003eThis table lists the single nucleotide polymorphisms (SNPs) identified in the \u003cem\u003eMLH1\u003c/em\u003e and \u003cem\u003eNBN\u003c/em\u003e genes, along with the corresponding amino acid changes. Variants underlined are not commonly predicted by both computational approaches.\u003c/p\u003e\n \u003cp\u003eAbbreviations: rs ID \u0026ndash; Reference SNP Identifier.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cstrong\u003eTable 2:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDisease Prediction of nsSNPs in MLH1 and NBN Genes by PhD-SNP and SNP\u0026amp;GO Servers\u003c/strong\u003e\u003c/h2\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 593px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMLH1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS. No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ers IDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmino acid change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS. No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ers IDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmino acid change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63749859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eC77R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eP648L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63749939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eG67E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eL622H\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63749990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eF80V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eL622P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eT82I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eP654L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eV49E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eT117R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eL550P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eT117M\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eG67R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eP28L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eG67W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eA128P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eR182G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eP648S\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eG244D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63751012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eE37K\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eG244V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63751109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eS44F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eC77Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63751194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eR265S\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eI107R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63751194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eR265C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eN38H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63751283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eL260R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eN38D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers63750430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eR385P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 593px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNBN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS. No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ers IDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmino acid change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS. No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ers IDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmino acid change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers199845467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eG224E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ers371480039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eL312S\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 593px;\"\u003e\n \u003cp\u003eThis table lists the nonsynonymous single nucleotide polymorphisms (nsSNPs) identified in the \u003cem\u003eMLH1\u003c/em\u003e and \u003cem\u003eNBN\u003c/em\u003e genes, along with their associated reference SNP IDs (rs IDs) and predicted amino acid changes. These variants were analysed using the PhD-SNP and SNP\u0026amp;GO servers for their potential disease association.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cstrong\u003eTable 3:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStability Prediction and RMSD Values of Selected Deleterious SNPs in MLH1 and NBN\u003c/strong\u003e\u003c/h2\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMLH1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS. No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ers IDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmino acid change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI-Mutant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMuPro\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63749859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eC77R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63749939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eG67E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63749990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eF80V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eT82I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eV49E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eL550P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eG67R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eG67W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eR182G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eG244D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eG244V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eC77Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eI107R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eN38H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eN38D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eP648L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eL622H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eL622P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eP654L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eIncrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eT117R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eIncrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eT117M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eIncrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eP28L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eIncrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eIncrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eA128P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eIncrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eP648S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63751012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eE37K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63751109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eS44F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eIncrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63751194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eR265S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63751194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eR265C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63751283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eL260R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eR385P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNBN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS. No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ers IDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmino acid change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI-Mutant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMuPro\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers199845467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eG224E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ers371480039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eL312S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003edecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 0px;\"\u003e\n \u003cp\u003eThis table summarizes the stability predictions and root mean square deviation (RMSD) values for selected deleterious nonsynonymous single nucleotide polymorphisms (nsSNPs) in the \u003cem\u003eMLH1\u003c/em\u003e and \u003cem\u003eNBN\u003c/em\u003e genes. Predictions were made using the I-Mutant and MuPro tools. RMSD values provide structural deviation data, and stability changes are indicated as \u0026quot;Increase\u0026quot; or \u0026quot;Decrease.\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cstrong\u003eTable 4: Docking results of MLH1-Wild and MLH1 mutations located in the MLH3 interaction with MLH3.\u003c/strong\u003e\u003c/h2\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ers IDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmino Acid Change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBinding energy\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Kcal/mol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH bond\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMLH1_Wild_MLH3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1473.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eL550P-MLH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-1654.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eL622H-MLH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-1508.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eL622P-MLH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-1663.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eP648L-MLH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-1595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003ers63750899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eP648S-MLH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-1587.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eThis table presents the molecular docking results for the interaction between the wild-type and selected mutant variants of the \u003cem\u003eMLH1\u003c/em\u003e protein and \u003cem\u003eMLH3\u003c/em\u003e. The binding energy (in kcal/mol) indicates the strength of the interaction, where more negative values represent stronger binding affinity. Mutants are named by their reference SNP ID (rs ID) and associated amino acid changes.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBinding Energy:\u003c/strong\u003e Lower (more negative) values indicate stronger interactions. Binding energy is measured in kilocalories per mole (kcal/mol).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cstrong\u003eTable 5: Docking Results of NBN Mutatins with Their Interactions with MRE11 and RAD50\u003c/strong\u003e\u003c/h2\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ersIDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBinding energy\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eKcal/mol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eH bonds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBinding energy\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eKcal/mol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eH bonds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRE11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAD50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWild\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1737.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1592\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003ers199845467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eG224E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-1572.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-1420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003ers371480039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eL312S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-1590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-1590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 624px;\"\u003e\n \u003cp\u003eThis table presents the molecular docking results for the interaction between wild-type and selected mutant variants of the \u003cem\u003eNBN\u003c/em\u003e protein with \u003cem\u003eMRE11\u003c/em\u003e and \u003cem\u003eRAD50\u003c/em\u003e. The binding energy (in kcal/mol) is used to evaluate the strength of the interaction, where more negative values indicate stronger binding affinity.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMRE11 and RAD50:\u003c/strong\u003e Proteins that interact with \u003cem\u003eNBN\u003c/em\u003e in the context of DNA damage repair. The table shows how these interactions vary with different \u003cem\u003eNBN\u003c/em\u003e mutations.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joag","sideBox":"Learn more about [Journal of Applied Genetics](https://www.springer.com/journal/13353)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/joag/default.aspx","title":"Journal of Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5621917/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5621917/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe genes NBN and MLH1 are critical for DNA repair, and this study aimed to detect and predict the effects of pathogenic single nucleotide polymorphisms (SNPs) in their mRNA and protein sequences. An in silico analysis assessed the impact of SNPs on the physicochemical properties, structure, stability, and function of MLH1 and NBN proteins. Results revealed that some SNPs significantly alter protein stability, structure, and binding interactions, potentially impairing DNA repair. Molecular docking studies further indicated disruptions in protein-protein interactions due to specific SNPs. These findings underscore the importance of using in silico methods to predict the functional effects of genetic variations, providing insights that could guide personalized treatments and improve cancer detection.\u003c/p\u003e","manuscriptTitle":"Identification of non-synonymous SNPs Impacting Structure and Function of MLH1 and NBN Proteins: A computational approach.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-27 13:56:09","doi":"10.21203/rs.3.rs-5621917/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-03-21T23:08:22+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-21T20:45:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-21T14:18:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Applied Genetics","date":"2025-03-17T12:35:37+00:00","index":"","fulltext":""},{"type":"decision","content":"Minor Revisions Needed","date":"2025-01-18T10:49:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joag","sideBox":"Learn more about [Journal of Applied Genetics](https://www.springer.com/journal/13353)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/joag/default.aspx","title":"Journal of Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"62c3ce73-e682-45dc-b63e-dd377a0e86b2","owner":[],"postedDate":"March 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-05T15:59:50+00:00","versionOfRecord":{"articleIdentity":"rs-5621917","link":"https://doi.org/10.1007/s13353-025-00968-2","journal":{"identity":"journal-of-applied-genetics","isVorOnly":false,"title":"Journal of Applied Genetics"},"publishedOn":"2025-05-02 15:57:16","publishedOnDateReadable":"May 2nd, 2025"},"versionCreatedAt":"2025-03-27 13:56:09","video":"","vorDoi":"10.1007/s13353-025-00968-2","vorDoiUrl":"https://doi.org/10.1007/s13353-025-00968-2","workflowStages":[]},"version":"v1","identity":"rs-5621917","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5621917","identity":"rs-5621917","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}