The Study on the Expression Level of PARP-1 in Glioma and Computational Screening of PARP-1 Inhibitors

The Study on the Expression Level of PARP-1 in Glioma and Computational Screening of PARP-1 Inhibitors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Study on the Expression Level of PARP-1 in Glioma and Computational Screening of PARP-1 Inhibitors Hui Li, Zhenhua Wang, Jianxin Xi, Han Lu, Zhishan Du, Sheng Zhong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1714523/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Gliomas are the most common primary intracranial malignancies. Current standard treatments include surgical resection, supplemented by radiotherapy and chemotherapy, but the prognosis is poor. PARP-1 (Poly ADP-ribose polymerase 1) inhibitors have become a hotspot in cancer treatment by affecting DNA damage repair pathways, such as metastatic breast cancer, advanced prostate cancer, ovarian cancer and pancreatic cancer. Recently, a study found that the expression level of PARP-1 in glioma cell lines was significantly increased; and PARP-1 inhibitor significantly suppressed the proliferation of glioma cells and aggravated the DNA damage effect of temozolomide( 1 ). Therefore, PARP-1 is expected to become a therapeutic target of molecular targeted therapy for glioma and a sensitizer for radiotherapy and chemotherapy. Moreover, the expression level of PARP-1 is expected to become an evaluation indicator for the prognosis of patients. Firstly, we downloaded RNA-seq data from 416 glioma samples from the GEO (Gene Expression Omnibus) database and divided them into PARP1_H and PARP1_L, according to the expression level of PARP-1. The overall prognosis of PARP1_L patients was better than that of PARP1_H. The effect of radiotherapy and chemotherapy in PARP1_L was better. Results also showed that the expression level of 278 genes of DNA damage repair in PARP1_H is higher than that in PARP1_L. Next, LASSO (Least Absolute Shrinkage and Selection Operator) Cox analysis was carried out for genes of DNA-repair proteins differentially expressed between PARP1_H and PARP1_L patients. According to the developed four-gene DPS (DNA-repair prognostic signature), glioma patients were divided into high-risk and low-risk groups. Then, we developed a new nomogram to assess overall survival in glioma patients. Furthermore, to search for more effective PARP-1 inhibitors with fewer side effects, we used a series of computer-aided techniques such as Discovery Studio 4.5, Schrodinger and PyMol for screening and evaluation. Finally, ZINC000014951634 and ZINC000053057130 proved to be favorable PARP-1 inhibitors by analyzing pharmacological and toxicological properties, ligand-protein complex affinity, and stability. In conclusion, this study investigated the effect of PARP-1 expression on prognosis and the sensitization effect of radiotherapy and chemotherapy in glioma and further screened PARP-1 targeted inhibitors to improve the prognosis of glioma patients. Glioma PARP-1 (Poly ADP-ribose polymerase 1) DNA damage repair Inhibitors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Gliomas are the most common primary intracranial malignancies, and current standard treatments include surgical resection, supplemented by radiotherapy and chemotherapy, but the prognosis is poor( 2 , 3 ). At present, most chemotherapeutic drugs and radiotherapy inhibit or kill tumor cells in different stages of cell growth by affecting the synthesis and function of macromolecules (DNA, RNA, protein) ( 4 , 5 ). Normal cells have multiple repair mechanisms for a certain amount of damage caused by external intervention, which plays a vital role in maintaining the stability of the genome and normal cell physiology ( 5 ). Similarly, in anti-tumor therapy, after radiation and chemical factors damage the DNA of tumor cells, the tumor cells, like normal cells, can activate their damage repair mechanism to repair, thereby increasing the insensitivity to radiotherapy and chemotherapy( 6 – 8 ). A lot of experience has been accumulated in tumor electric field therapy, immunotherapy, and targeted molecular therapy for glioma, but few results can truly change clinical practice( 9 – 12 ). Cellular DNA will be damaged under normal physiological processes and external factors such as ionization and radiation, and the frequency of damage is higher in pathological states such as tumors ( 13 ). There are many ways of DNA damage repair (DDR), including nucleotide excision repair (NER), non-homologous end joining (NHEJ), homologous recombination (H.R.), mismatch repair (MMR), base excision repair (BER) etc (Fig. 1 ). Single-strand defects (complete complementary strands) are mainly repaired through BER, NER and MMR pathways ( 14 ). Double-strand breaks (DSBs), which damage both DNA strands, are the most threatening form of DNA damage and are mainly repaired by H.R. Homologous recombination is the high-precision repair of homologous chromatids as repair templates. Double-strand breaks can also be repaired by NHEJ, which does not require homologous chromatids as repair templates but has a high error rate ( 15 ). PARP (Poly ADP-ribose polymerase) plays a pivotal part in the DNA repair pathway. The PARP family has seventeen members, and their catalytic regions share homology ( 16 ). It plays an essential role in maintaining genome integrity, cell senescence, apoptosis, inflammatory response, neuropathological changes and other physiological and pathological processes ( 17 , 18 ). Among them, PARP-1 plays a significant role. PARP-1 protein mainly includes three domains: ( 1 ) N-terminal DNA binding domain includes zinc finger structures (Zn1, Zn2) that recognize DNA damage gaps, nuclear localization sequence and a third Zinc-binding domain that mainly mediates communication between the domains( 19 ); ( 2 )The acidic domain mainly performs protein self-modification( 20 , 21 ); ( 3 ) The C-terminus is responsible for attaching to the DNA strand, amplifying the poly-ADP-ribose, and inducing the catalytic domain of the PAR chain forks. PARP-1 responds immediately when cellular DNA is damaged. It rapidly binds to DNA in various conformations such as single/double-strand break DNA, crossover, helix, etc., and catalyzes the decomposition of NAD + into nicotinamide and ADP. Then, ADP (adenosine diphosphate) is linked to the self-modified region of PARP-1 or other nuclear receptor proteins such as histones and undergoes a complex reaction of poly-ADP-ribose to form PAR (Poly ADP-ribose). When the PAR reaches a certain length, the PAR poly chain dissociates from the DNA and then guides DNA repair enzymes such as XRCC 1 (X-ray repair cross-complementary gene 1) and DNA ligase III to carry out BER to remove the wrong or damaged bases.( 22 , 23 ). Next, DNA polymerase uses the corresponding complementary strand at the damaged site as a template to synthesize a new single-stranded DNA fragment. Finally, ligase connects the newly synthesized single-stranded fragment and the original single-stranded through phosphodiester bonds to complete DNA damage repair( 19 ). At present, the research reports on PARP-1 mainly focus on its expression and function in tumor tissues lacking the BRCA1/2 gene, such as ovarian cancer, metastatic breast cancer, advanced prostate cancer and pancreatic cancer ( 24 – 27 ). Recently, a study found that the expression level of PARP-1 mRNA in glioma cell lines was significantly increased; and PARP-1 inhibitor significantly suppressed the proliferation of glioma cells and aggravated the DNA damage effect of temozolomide. According to the theory of combined lethality, the DNA damage of tumor cells is caused by radiotherapy and chemotherapy, and the inhibition of PARP-1 is combined to block its DNA repair, resulting in a more effective cytotoxic effect on tumor cells, which provides a new idea for the treatment of glioma( 28 ). Therefore, PARP-1 is expected to become a new target for anti-glioma therapy and an evaluation indicator for the prognosis of patients. In this study, we explored the effect of PARP-1 expression on the prognosis of glioma patients and then screened inhibitors targeting PARP-1 to provide new therapeutic options for the treatment of glioma. Firstly, we downloaded RNA-seq data from 416 glioma samples from the GEO (Gene Expression Omnibus) database and divided them into PARP1_H and PARP1_L according to the level of PARP-1 expression. Results showed that the expression level of 278 genes of DNA-repair proteins in PARP1_H is higher than that in PARP1_L. The overall prognosis of PARP1_L patients was better than that of PARP1_H, and the effect of radiotherapy and chemotherapy in PARP1_L was better. Next, LASSO (Least Absolute Shrinkage and Selection Operator) Cox analysis was carried out for genes of DNA-repair proteins that were differentially expressed between PARP1_H and PARP1_L. According to the developed four-gene DPS (DNA-repair prognostic signature), glioma patients were divided into high-risk and low-risk groups. Furthermore, we developed a new nomogram to assess overall survival in glioma patients. Additionally, to search for more effective PARP-1 inhibitors with fewer side effects, we used a series of computer-aided techniques such as Discovery Studio 4.5, Schrodinger and PyMol for screening and evaluation. Finally, ZINC000014951634 and ZINC000053057130 proved to be favorable PARP-1 inhibitors by analyzing pharmacological and toxicological properties, ligand-protein complex affinity, and stability. In conclusion, this study investigated the effect of PARP-1 expression on prognosis and the sensitization effect of radiotherapy and chemotherapy in glioma and further screened PARP-1 targeted inhibitors to improve the prognosis of glioma patients. Methods And Materials Gene expression datasets, data processing and Functional enrichment analysis We acquired RNA-seq data of 426 glioma samples from the GEO (Gene Expression Omnibus) database. The survival data were obtained for all patients. According to the median value of PARP-1 expression, the patients were divided into 213 PARP1_H patients and 213 PARP1_L patients. Additionally, the RNA transcriptome analysis was carried out by transformation of log2-based FPKM values. To analyze signalling pathway enrichment, 278 genes of DNA-repair proteins were uploaded to Metascape, an online tool for gene annotation, attributes and visualization (https://metascape.org/)(29). Enrichment levels of the 278 genes above in each glioma sample were calculated. Prognosis and Gene enrichment between PARP1_H and PARP1_L The OS (overall survival) of glioma patients were compared between PARP1_H and PARP1_L, PARP1_H and PARP1_L after chemotherapy, and PARP1_H and PARP1_L after radiation therapy. Kaplan-Meier curves were drawn to show differences in survival time. The log-rank test was carried out to evaluate the significance of differences in survival times with a threshold of P < 0.05. In addition, expression levels of the 278 genes of the DNA-repair proteins were compared between PARP1_H and PARP1_L and exhibited in the heatmap. Differential expression analysis Differential expression analysis was calculated and carried out on Rstudio by the Wilcoxon Rank Sum and Signed Rank Tests between PARP1_H and PARP1_L (30). Genes with log2 |fold change| ≥1 and FDR (False Discovery Rate) <0.05 were chosen as DEGs (differentially expressed genes). Then, the ImmPort database identifies differentially expressed genes for DNA-repair proteins (DEDGs) (https://www.immport.org/). Patients were divided into High and Low groups based on the median expression level of each DEDG, with 213 patients in each group. The OS of glioma patients was compared between the High and Low groups of each DEDG. Kaplan-Meier curves were drawn to show differences in survival time. The log-rank test was carried out to evaluate the significance of differences in survival times with a threshold of P < 0.05. Construction of the PARP-related DNA-repair prognostic Signature (DPS) To build the PARP-related DNA-repair prognostic signature (DPS), DEDGs were put in LASSO Cox regression and analyzed by the “glmnet” R package (31) (32). The DPS model was constructed from weighted Cox regression coefficients to estimate the risk score for each patient. Patients were classified as high or low risk according to the best cutoff values obtained by the "survminer" R package. We used the "survival ROC" R package to generate ROC (Receiver Operating Characteristic) curves (33). And the area under the curve values of the ROC curve was calculated to assess the specificity and sensitivity of DPS. Development of the nomogram We applied univariate and multivariate Cox analyses to assess the independent prognostic ability of DPS. And we performed the “rms” package to construct an innovative nomogram according to the Cox analysis results. To determine the accuracy, calibration plots of observed vs. predicted probabilities of 1-, 3-, and 5-year OS were developed. The C-Index (Concordance Index) was calculated to determine the discriminative power of the model. And the C- index was corrected using bootstraps. Docking software and database of ligands and protein DS 4.5 (Discovery Studio 4.5, Accelrys, Inc) is a suite of software for modelling large and small-molecule systems. Libdock, ADME (absorption, distribution, metabolism, excretion) and TOPKAT (Toxicity Prediction by Computer Assisted Technology) modules of DS 4.5 were used for virtual screening firstly. CDOCKER module was then applied for a precise docking study. Additionally, Schrodinger and PyMol software were also used to demonstrate ligand binding interactions with PARP-1 further. Moreover, we downloaded 3D molecular files of 17799 natural, named and purchasable molecules from the ZINC15 database for virtual screening of PARP-1 inhibitors ( https://zinc.docking.org/ ). And molecule structure of PARP-1 was downloaded from PDB (Protein Database) (https://www.rcsb.org ). Virtual screening using Libdock, ADME and TOPKAT Virtual screening was performed by Libdock, a rigid-based docking program of DS 4.5. Hotspots for PARP-1 were calculated by a grid put into the binding region as well as by apolar and polar probes. Next, the ligands formed favorable interactions based on the hotspots. The minimization was performed using the CHARMm force field and Smart Minimiser algorithm (Cambridge, MA, USA). Afterwards, poses of all the ligands were ranked according to their Libdock scores. The 3.22 Å crystal structure of PARP-1 in complex with inhibitor PJ34L was downloaded from PDB. NAD+ binds to PARP-1 and is catalyzed to ADP ribose. And this binding region is an important regulatory site of PARP-1. Common inhibitors for PARP-1 all have a portion that competes with NAD+ to combine with nicotinamide. Therefore, this pocket region was chosen as the docking region to identify new candidates for PARP-1 inhibitors. Lynparza is a selective inhibitor of PARP1/2, which is tenfold to fifteenfold more selective for PARP1 than for PARP2. Besides, because PARP1 plays a major role, compared with other members belonging to PARP family, in DNA repair. In this study, we focus on PARP1.Finally, Lynparza was chosen as a reference inhibitor of PARP-1 to assess the binding ability of ligands. And PARP-1 was prepared by removing crystal water and other hetero-atoms, followed by hydrogen addition, protonation, ionization and energy minimization(34). Only the top 20 molecules were chosen for the following analysis. Then, the ADME module of DS 4.5 was applied for calculating the pharmacological properties of selected compounds and Lynparza, including the absorption, distribution, metabolism, and excretion. TOPKAT module of DS 4.5 was also used to assess their toxicological properties. These pharmacological and toxicological properties were fully considered in selecting ideal candidates for PARP-1. Finally, two molecules were chosen as favorable candidates based on the above results. Precise molecular docking using CDOCKER Precise docking study between selected compounds, Lynparza and prepared PARP-1 by CDOCKER module of DS 4.5 based on CHARMm36 force field. The receptor is held rigid while the ligands are allowed to flex during the docking process. For each complex pose, the CDOCKER interaction energies indicating ligand binding affinity were calculated. The binding site sphere of PARP-1 was defined as the region within a radius of 13 Å from the geometric centroid of Lynparza. Ligands can bind to residues within the binding site sphere during the docking process. Different poses of each test molecule were generated and analyzed on the basis of the CDOCKER interaction energy, respectively. Schrodinger and PyMol software further showed the optimal pose binding of selected compounds, Lynparza and PARP-1. Pharmacological analysis and Molecular dynamics simulation Pharmacophores of selected compounds and Lynparza were analyzed by the 3D-QSAR module of DS 4.5. Only those with energies below 10 kcal/mol can be retained, and a maximum of 255 confirmations can be generated per molecule. In addition, to assess the stability and affinity of each compounds-PARP-1 complex in the natural environment, the best binding conformations were selected and prepared for molecular dynamics simulation. The ligand- PARP-1 complex was placed in an orthorhombic box and solvated using an explicit periodic boundary solvation water model. To simulate the physiological environment of the system, solidum chloride was added. Then, the CHARMm force field was applied, and the system was relaxed by energy minimization with the final RMS gradient of 0.289. The system temperature was driven slowly from 50 K to 300 K, and the simulation was carried out at this constant temperature. The production procedure was carried out for 100 ps and the time step was 2 fs. The long-range electrostatics was calculated by the Particle Mesh Ewald algorithm, and all bonds involving hydrogen were fixed by the adapted Linear Constraint Solver algorithm. Concerning the initial complex setup, the trajectory protocol of DS 4.5 was performed to determine the trajectory for potential energy and RMSD (root-mean-square deviation). Result Functional enrichment analysis The 278 genes of the DNA-repair proteins were uploaded to the Metascape website to identify GO. (Gene Ontology) Terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways. Terms were thought significant when the conditions of P < 0.01 and the number of enriched genes ≥ 3 were met and grouped separately according to their membership similarity. The term with the best p-value in every 20 clusters was selected. Also, the number of terms per cluster cannot exceed 20 for 250. The 278 genes of the DNA damage repair proteins were mainly enriched in GO:0006281: DNA repair, GO:0006302: double-strand break repair, R-HSA-73894: DNA Repair, R-HSA-5685942: HDR through Homologous Recombination (HRR), R-HSA-5696399: Global Genome Nucleotide Excision Repair (GG-NER), WP4946: DNA repair pathways, full network. Each node represents a collective term, colored first by cluster I.D. and its P-value, separately (Figure 2(A-C)). Prognosis and Gene enrichment between PARP1_H and PARP1_L 213 patients were included in the PARP1_H and PARP1_L groups, respectively. Survival analysis indicated that the clinical prognoses of PARP1_H and PARP1_L were different. The PARP1_L had a better survival prognosis than the PARP1_H (Log-Rank test, P=0.034) (Figure 2D). For patients treated with radiotherapy, PARP1_L had a better prognosis than PARP1_H (Log-Rank test, P(radiotherapy)=0.015) (Figure 2D). Similarly, for patients treated with chemotherapy, PARP1_L had a better prognosis than PARP1_H (Log-Rank test, P(chemotherapy)=0.039) (Figure 2D). Additionally, as shown in figure 2E, the expression levels of 278 DNA repair-related protein genes were significantly different between PARP1_H and PARP1_L, and the expression level in PARP1_H was higher than that in PARP1_L. Discrimination against differentially expressed genes of DNA-repair proteins Rstudio software (Version 1.2.5001) was applied to distinguish the differentially expressed genes of DNA-repair proteins (DEDGs) between PARP1_H and PARP1_L. Seven genes were confirmed according to the standard (log2 |fold change| ≥1 and FDR < 0.05), of which 3 genes were up-regulated, and 4 genes were down-regulated. Seven DEDGs were chosen using the ImmPort database for performing prognostic analysis. For each DEDG, the 416 patients were divided into two groups: High and Low, according to their median expression level. Then, the OS of glioma patients was compared between the High and Low groups of each DEDG. For each DEDG, the High group of each DEDG had a worse prognosis than the Low group (Log-Rank test, CCNA1: P<0.001, CLSPN: P<0.001, DTL: P<0.001, MGMT: P=0.03, POLN: P<0.001, SFN: P<0.001, XRCC2: P<0.001). Construction of the PARP1-related prognostic signature LASSO Cox regression analysis of DEDGs was conducted to construct a PARP1- related DNA-repair prognostic Signature (DPS) (Figure 3C, 3D). Risk scores were evaluated for each glioma patient (risk score= CLSPN*0.734+MGMT*0.28+POLN*0.3+ SFN*0.187). Patients were divided into low-risk and high-risk groups according to the optimal cutoff value (0.89005466) evaluated by the "survminer" R package. Kaplan-Meier analysis showed that patients with low-risk scores had better outcomes than those with high-risk scores (Figure 3F). The ROC curve analysis of the DPS suggested good prognostic ability for OS (Figure 3G). Gene expression patterns and the risk score distributions were shown in figure 3E. Establishment of a DPS-based nomogram model The DPS was indicated to be significantly associated with OS (Hazard ratio: 4.737, 95% confidence interval: 3.626-6.189, P < 0.001) by the univariate Cox analysis (Figure 4A). From the multivariate Cox analysis, DPS proved to be an independent prognostic factor (Hazard ratio: 2.739, 95% confidence interval: 1.923-3.902, P < 0.001) (Figure 4B). Finally, a DPS-based nomogram model was established. The C-index was 0.674, which revealed the specific discriminative ability of the nomogram model. Moreover, the observed vs. predicted probabilities of 1-, 3-, and 5-year OS showed good agreement in the calibration plot (Figure 4C). Virtual screening using Libdock Based on the above results, we found that PARP-1 is an essential target for glioma therapy and prognosis. Therefore, we took PARP-1 as the target for further drug screening. NAD+ binds to PARP-1 and is catalyzed to ADP ribose. And this binding region was selected as the docking site. From the ZINC15 database, 17799 purchasable, natural and named molecules were downloaded for virtual screening of PARP-1’s inhibitors. Lynparza was selected as a reference inhibitor of PARP-1 to assess the binding capacity of ligands. The 3D (three-dimensional) structures of PARP-1 and the Lynparza – PARP-1 complex were displayed in Figure 5(A, B). According to Libdock's results, 2996 compounds were identified that stably bind to PARP-1. Among them, 37 molecules had higher Libdock scores than Lynparza (ranking: 38, Libdock score: 153.31). Table 1 lists the top 20 compounds on the basis of Libdock scores. Pharmacological and toxicological prediction using ADME and TOPKAT of DS 4.5 Pharmacological properties of the top 20 compounds and Lynparza were evaluated by the ADME module of DS 4.5, including aqueous solubility, brain/blood barrier (BBB), plasma protein binding properties (PPB), cytochrome P450 2D6 (CYP2D6) binding, human intestinal absorption and hepatotoxicity (Table 2). All the molecules were shown to be soluble in water. For human intestinal absorption, two compounds and Lynparza had a good absorption level; one compound had a moderate absorption level; seventeen compounds had a poor or very poor absorption level. Almost all compounds had very high penetrant in BBB level except ZINC000002528486 and Lynparza. There are 15 compounds, and Lynparza were predicted to be a non-inhibitors of CYP2D6. The other compounds indicated as inhibitors of CYP2D6, including ZINC000028968107, ZINC000002528509, ZINC000002033589, ZINC000034944433 and ZINC000002528486. 7 compounds were predicted as toxic for hepatotoxicity, which was similar to Lynparza. In addition, safety was also thoroughly investigated in this study. To examine the safety of the top 20 compounds and Lynparza, different toxicity indicators, including Rodent carcinogenicity (based on the U.S. National Toxicology Program (NTP)dataset), AMES (Ames mutagenicity) and DTP (developmental toxicity potential) properties, were analyzed by TOPKAT module of DS 4.5. Table 3 shows that all 20 compounds and Lynparza are safe except for ZINC000049872065, which has DTP property. Considering all the above results, compound 1 (ZINC000014951634) and compound 2 (ZINC000053057130) were non-hepatotoxic, non-CYP2D6-inhibitory, and have lower Ames mutagenicity, rodent carcinogenicity compared to other compounds and developmental toxicity potential, which also strongly suggests their application in perspective in drug development. Furthermore, both compounds are structurally identical to Lynparza, with multiple benzene rings and carbonyl groups. The three-dimensional (3D) and two-dimensional (2D) chemical structure of compounds 1，2 and Lynparza were shown in Figure 5(C-E). In particular, carbanyl groups were connected with nitrogen-atoms which were similar to NAD+ to combine with nicotinamide. In summary, compounds 1 and 2 were identified as safe drug candidates and selected for follow-up studies. Ligand binding analysis The RMSD between the docked pose and the crystal structure of the complex was 0.6 Å, indicating the high reliability of the CDOCKER module applied in the study. Compounds 1，2 and Lynparza were precisely docked into the function pocket of PARP-1 by the CDOCKER module under the CHARMm36 force field (Figure 6, 7). Table 4 showed that the CDOCKER interaction energy of compound 1 and compound 2 is significantly lower than that of the reference ligand Lynparza (-54.2416kcal/mol), indicating that these two compounds have higher stability and affinity with PARP-1 than Lynparza. Structural analyses of the ligands-PARP-1 complex were also performed for the hydrogen bonds, Pi-Pi interaction, Pi-Alkyl interaction, Pi-Anion interaction and Alkyl interactions (Figure 6, 7 and Table 5, 6). Results showed that compound 1 formed four pairs of hydrogen bonds with PARP-1, by the O40 of compound and B: ARG878: HH21 of PARP-1, the H44 of compound and B: TYR896:O of PARP-1, the H43 of compound and B: TRP861:O of PARP-1, the 03 of compound and B: SER904: H.G. of PARP-1. Compound 1 also formed one pair of Pi-Pi staked interaction, three pairs of Pi-Alkyl interaction and one pair of Pi-Anion interaction. Compound 2 formed three pairs of hydrogen bonds by the H39 of the compound and B: GLY863: O of PARP-1, the H66 of the compound and B: ASP766: OD1 of PARP-1, the 036 of the compound and B: ARG878: HH21 of PARP-1. Compound 2 also formed one pair of Pi-Pi T-shaped interactions, one pair of Pi-Alkyl interactions, and one pair of Alkyl interactions. The reference compound Lynparza formed two hydrogen bonds with PARP-1(B: SER904: HG-Molecule： O27, B: GLN759: HE21-Molecule： O11, respectively). It also formed three pairs of Pi-Alkyl interactions and two pairs of Pi-Anion interactions with PARP-1(Table 6). Besides, Schrodinger and PyMol software were performed to analyze intermolecular interaction further between ligand and PARP-1 in the binding pocket (Figure 7). As results showed, compounds 1,2 and Lynparza interacted with PARP-1 by Amino acid residues 861-988, which suggested the active position of the binding pocket and provided deep learning of PARP-1’s structure and a guide for targeted drug research. Pharmacological analysis and Molecular dynamics simulation According to the evaluation of feature pharmacophores by the 3D-QSAR module of DS 4.5, ZINC000014951634 displayed eight hydrogen bond acceptors, ten hydrogen donors, four hydrophobic centres, and six aromatic rings and one ionizable positive, respectively (Figure 8A). ZINC000053057130 displayed seven hydrogen bond acceptors, nine hydrogen donors, four hydrophobic centres, four aromatic rings and one ionizable positive, respectively (Figure 8B). In addition, Lynparza formed sixteen feature pharmacophores, including seven hydrogen bond acceptors, one hydrogen donor, four hydrophobic centres and four rings aromatic, respectively (Figure 8C). Additionally, the molecular dynamics simulation module was carried out to assess the stabilities of the ligand-PARP-1 complexes in the natural environment. The potential energy and RMSD curves of each complex were shown in Figures 8(D,E). All the RMSD and potential energies of these complexes are stable over time. And the RMSD trajectory of each complex reached equilibrium after 70 ps. Molecular dynamics simulation results verify that these hydrogen bonds and Pi-related interactions formed by compounds with PARP-1 contribute to the stability of these complexes. And their complexes could exist in a natural environment stably and have inhibitory effects on PARP-1 as Lynparza. Discussion Gliomas are the most common primary intracranial malignancies, and current standard treatments include surgical resection, supplemented by radiotherapy and chemotherapy, but the prognosis is poor( 2 , 3 ). In anti-tumor therapy, radiation and chemical factors damage the DNA of tumor cells. However, like normal cells, tumour cells activate their damage repair mechanisms to repair DNA damage, thereby increasing the insensitivity to radiotherapy and chemotherapy( 6 – 8 ). In addition, a lot of experience has been accumulated in tumor electric field therapy, immunotherapy, and targeted molecular therapy for glioma. Still, few results can truly change clinical practice( 9 – 12 ). Cellular DNA will be damaged under normal physiological processes and external factors such as ionization and radiation, and the frequency of damage is higher in pathological states such as tumors ( 13 ). There are many ways of DNA damage repair (DDR), including nucleotide excision repair (NER), homologous recombination (H.R.), non-homologous end joining (NHEJ), base excision repair (BER), mismatch repair (MMR) etc. The PARP family plays a vital role in maintaining genome integrity, cell senescence, apoptosis, inflammatory response, neuropathological changes and other physiological and pathological processes ( 17 , 18 ). Among them, PARP-1 matters a lot in the DNA repair pathway. When cellular DNA is damaged, PARP-1 responds immediately. It quickly binds to the damage site through its zinc finger domain, uses NAD + as a substrate, changes its conformation, catalyzes ADP-ribose transfer, and finally completes single-strand repair( 35 ). At present, the research reports on PARP-1 mainly focus on its expression and function in tumor tissues lacking the BRCA1/2 gene, such as ovarian cancer, metastatic breast cancer, advanced prostate cancer and pancreatic cancer ( 24 – 27 ). Recently, a study found that the expression level of PARP-1 mRNA in glioma cell lines was significantly increased; and PARP-1 inhibitor significantly suppressed the proliferation of glioma cells and aggravated the DNA damage effect of temozolomide( 36 ). Therefore, it is significant to explore the effect of PARP-1’s expression level on glioma prognosis and seek new targets for glioma therapy. In this study, we first investigated the effect of PARP-1 expression on prognosis and the sensitization effect of radiotherapy and chemotherapy in glioma. Furthermore, a series of computer-aided techniques such as DS 4.5, Schrodinger and PyMol were used to search for more effective PARP-1 inhibitors with fewer side effects. First of all, according to the G.O. and KEGG analysis, 278 genes of the DNA damage repair proteins were mainly enriched in GO:0006281: DNA repair, GO:0006302: double-strand break repair, R-HSA-73894: DNA Repair, R-HSA-5685942: HDR through Homologous Recombination (HRR), R-HSA-5696399: Global Genome Nucleotide Excision Repair (GG-NER), WP4946: DNA repair pathways, full network. Then, we downloaded RNA-seq data of 416 glioma samples from the GEO database and divided them into PARP1_H and PARP1_L, according to the level of PARP-1 expression. The prognosis of patients, the effects of radiotherapy and chemotherapy, and the gene expression levels of DNA-repair proteins were compared between these two groups. As the results showed, the overall prognosis of PARP1_L patients was better than that of PARP1_H (Log-Rank test, P = 0.034), and the effect of radiotherapy and chemotherapy in PARP1_L was better (Log-Rank test, P(radiotherapy) = 0.015, P(chemotherapy) = 0.039). Moreover, the expression levels of 278 genes of DNA-repair proteins in PARP1_H were higher than those in PARP1_L. The expression level of PARP-1 was consistent with the gene expression levels of all 278 DNA-repair proteins. Therefore, we speculate that the expression level of PARP-1 is an evaluation index of cellular DNA damage repair function, and the level of PARP-1 expression is expected to be a prognostic indicator for glioma. In addition, PARP-1 may affect the expression of other genes of DNA-repair protein through some mechanism, which remains to be further studied. What’s more, on the basis of chemotherapy and radiotherapy, inhibiting the function of PARP-1 to block its DNA repair can have a more effective cytotoxic effect on tumor cells and reduce the drug resistance of chemotherapy drugs, which offers new ideas for the treatment of glioma. Next, we analyzed their DEDGs between PARP1_H and PARP1_L. There were seven DEDGs, including CCNA1, CLSPN, DTL, MGMT, POLN, SFN, and XRCC2. For each DEDG, the 416 patients were divided into two groups: High and Low, based on their median expression level. As the results showed, the High group of each DEDG had a worse OS than that of the Low group (Log-Rank test, CCNA1: P < 0.001, CLSPN: P < 0.001, DTL: P < 0.001, MGMT: P = 0.03, POLN: P < 0.001, SFN: P < 0.001, XRCC2: P < 0.001). Therefore, these genes can be potential prognostic indicators and targets for glioma. Subsequently, we developed a PARP1-related DPS, which was related to prognosis. CLSPN, MGMT, POLN and SFN were identified as hub genes in our DPS by LASSO Cox regression. The four-gene DPS was also an independent prognostic factor by univariate and multivariate Cox analyses. Moreover, a predicting nomogram was developed based on the DPS and patient clinical data to predict the survival of patients with glioma. For now, some predictive models of glioblastoma have been constructed( 37 – 39 ). Our current study found that PARP-1 expression was associated with prognosis and developed a novel PARP-1-related DPS with an AUC value of 0.765 for predicting 1, 3 and 5-year patient survival. Furthermore, Discovery Studio 4.5, Schrodinger and PyMol were applied to search for more favorable PARP-1 inhibitors. Lynparza was chosen as a reference inhibitor of PARP-1 to assess the binding ability of other compounds. 17799 purchasable, natural, named molecules were obtained from the ZINC15 database for virtual screening. Firstly, LIbdock was performed between ligands and PARP-1 for virtual screening. Libdock score represented the degree of energy optimization and stability of the conformation. Compounds with a high Libdock score indicate that it has a comparable energy-optimized and stable conformation compared to other compounds. Compared with Lynparza, 37 compounds can bind to PARP-1 with a more stable conformation and better energy optimization. On the basis of the Libdock score, the top 20 compounds were chosen for subsequent pharmacological and toxicological analysis. Finally, ZINC000014951634 and ZINC000053057130 were shown to be non-hepatotoxic, non-CYP2D6-inhibitory, and have lower Ames mutagenicity, rodent carcinogenicity compared to other compounds and developmental toxicity potential, which also strongly suggests their application in perspective in drug development. Additionally, to further evaluate ligand-protein complex affinity and stability, Molecular dynamics simulation and precise docking by CDOCKER were performed. Table 4 showed that the CDOCKER interaction energies of compounds 1 and 2 were significantly lower than that of the reference ligand Lynparza (-54.2416kcal/mol), which indicated that these two compounds had higher stability and affinity with PARP-1 compared to Lynparza. Moreover, compound 1 formed four pairs of hydrogen bonds, one pair of Pi-Pi staked interaction, three pairs of Pi-Alkyl interaction and one pair of Pi-Anion interaction. Compound 2 formed three pairs of hydrogen bonds, one pair of Pi-Pi T-shaped interaction, one pair of Pi-Alkyl interaction and one pair of Alkyl interaction. The chemical bonds above may contribute to the stability of compounds 1 and 2 and PARP-1 binding. Additionally, compounds 1, 2 and Lynparza interacted with PARP-1 by amino acid residues 861–988, which indicated the active position of the binding pocket and provided deep learning for PARP-1’s structure and a guide for PARP-1 targeted drug research. Moreover, according to the molecular dynamics simulation’s results, both potential energy and RMSD of these two complexes stabilized with time like Lynparza, which validated the stabilities of the ligand-PARP-1 complexes in the natural environment. Furthermore, compounds 1 and 2 were shown to have multiple pharmacophores, which again suggested the potential of compounds 1, and 2 as drugs. In conclusion, this study revealed the effect of PARP-1 expression on the prognosis of glioma and the sensitization effect of radiotherapy and chemotherapy and screened favorable PARP-1 inhibitors to improve the prognosis of glioma. Furthermore, we developed a novel nomogram to quantitatively predict patient survival based on PARP-1-related DPS. This study provided new insight into the treatment and prognosis of glioma. Although this study was well designed and accurately measured, we acknowledge that this study still has some limitations. More experiments are needed to validate our results, and more indicators of drug safety should also be evaluated in our future studies. Conclusion In this study, we first investigated the effect of PARP-1 expression on prognosis and the sensitization effect of radiotherapy and chemotherapy in glioma. Furthermore, a series of computer-aided techniques such as Discovery Studio 4.5, Schrodinger and PyMol were used to search for more effective PARP-1 inhibitors with fewer side effects. This study revealed the effect of PARP-1 expression on the prognosis of glioma and the sensitization effect of radiotherapy and chemotherapy and screened favorable PARP-1 inhibitors to improve the prognosis of glioma. Furthermore, we developed a novel nomogram to quantitatively predict patient survival based on PARP-1-related DPS. This study provided new insight into the treatment and prognosis of glioma. Although this study was well designed and accurately measured, we acknowledge that this study still has some limitations. More experiments are needed to validate our results, and more indicators of drug safety should also be evaluated in our future studies. Declarations Acknowledgements We thanked the foundation of the open innovation experiment of the College of Basic Medical Sciences, Jilin University (2018) Author Contribution statement This study was completed with teamwork. Every author has made substantial contributions to the study. Sheng Zhong has come up with the conception. Addtionally, Hui Li did the design of the work and was responsible for the creation of new software used in the work. Zhenhua Wang has drafted the work. Jianxin Xi completed the data collection part. Furthermore, an analysis of the data was done by Han Lu. As for the interpretation of the data, Zhishan Du has contributed a lot to this part. Sheng Zhong substantively revised it. Competing Interest Statement All authors declare no conflicts of interest related to this manuscript, and all authors have approved the publication of this work. References AA Z, M V. - Capitalizing on ATRX loss in glioma via PARP inhibition: Comment on "Loss of ATRX. D - 101472619. 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Number Compounds Libdock score Number Compounds Libdock score 1 ZINC000003995616 197.589 11 ZINC000021992902 169.035 2 ZINC000011616634 183.062 12 ZINC000012495612 165.482 3 ZINC000011616633 180.699 13 ZINC000031298217 162.833 4 ZINC000017654900 179.771 14 ZINC000044306670 162.746 5 ZINC000028968107 173.664 15 ZINC000003979028 162.196 6 ZINC000049872065 172.943 16 ZINC000002033589 161.044 7 ZINC000002528509 171.533 17 ZINC000044086691 160.416 8 ZINC000073280937 171.524 18 ZINC000034944433 159.795 9 ZINC000014951634 170.928 19 ZINC000038143594 159.372 10 ZINC000053057130 170.314 20 ZINC000002528486 158.692 Table 2. ADME (Adsorption, Distribution, Metabolism, Excretion) properties of compounds. Number Compounds Solubility Level a BBB level b CYP2D6 c Hepatotoxicity d Absorption Level e PPB Level f 1 ZINC000003995616 1 4 0 0 2 1 2 ZINC000011616634 2 4 0 0 3 0 3 ZINC000011616633 2 4 0 0 3 0 4 ZINC000017654900 2 4 0 1 2 0 5 ZINC000028968107 1 4 1 1 3 1 6 ZINC000049872065 3 4 0 0 2 0 7 ZINC000002528509 2 4 1 1 0 1 8 ZINC000073280937 2 4 0 1 2 1 9 ZINC000014951634 3 4 0 0 3 0 10 ZINC000053057130 3 4 0 0 3 0 11 ZINC000021992902 3 4 0 0 1 0 12 ZINC000012495612 3 4 0 1 3 0 13 ZINC000031298217 2 4 0 1 2 0 14 ZINC000044306670 2 4 0 0 3 1 15 ZINC000003979028 2 4 0 1 3 0 16 ZINC000002033589 2 4 1 0 3 0 17 ZINC000044086691 1 4 0 0 3 1 18 ZINC000034944433 2 4 1 0 2 0 19 ZINC000038143594 3 4 0 0 3 0 20 ZINC000002528486 2 2 1 1 0 1 21 Lynparza 3 3 0 1 0 1 a Aqueous-solubility level: 0 (extremely low); 1 (very low, but possible); 2 (low); 3 (good) b Blood Brain Barrier level: 0 (Very high penetrant); 1 (High); 2 (Medium); 3 (Low); 4 (Undefined) c Cytochrome P450 2D6 level: 0 (Non-inhibitor); 1 (Inhibitor) d Hepatotoxicity: 0 (Nontoxic); 1 (Toxic) e Human-intestinal absorption level: 0 (good); 1 (moderate); 2 (poor); 3 (very poor) f Plasma Protein Binding: 0 (Absorbent weak); 1 (Absorbent strong) Table 3. Toxicities of compounds. Number Compounds Mouse NTP a Rat NTP a AMES b DTP c Female Male Female Male 1 ZINC000003995616 0.235 0.002 0.245 0.300 0.004 0.252 2 ZINC000011616634 0.761 0.509 0.308 0.583 0.000 0.493 3 ZINC000011616633 0.761 0.509 0.308 0.583 0.000 0.493 4 ZINC000017654900 0.572 0.005 0.162 0.517 0.000 0.321 5 ZINC000028968107 0.110 0.321 0.336 0.045 0.115 0.629 6 ZINC000049872065 0.576 0.611 0.215 0.525 0.000 0.793 7 ZINC000002528509 0.299 0.439 0.422 0.483 0.000 0.618 8 ZINC000073280937 0.802 0.873 0.476 0.290 0.012 0.502 9 ZINC000014951634 0.136 0.016 0.228 0.482 0.001 0.462 10 ZINC000053057130 0.157 0.005 0.223 0.465 0.000 0.437 11 ZINC000021992902 0.578 0.614 0.220 0.492 0.001 0.764 12 ZINC000012495612 0.475 0.574 0.309 0.653 0.075 0.834 13 ZINC000031298217 0.218 0.552 0.523 0.566 0.593 0.677 14 ZINC000044306670 0.385 0.614 0.411 0.146 0.126 0.780 15 ZINC000003979028 0.479 0.482 0.494 0.748 0.511 0.660 16 ZINC000002033589 0.470 0.348 0.325 0.486 0.002 0.856 17 ZINC000044086691 0.562 0.829 0.193 0.281 0.031 0.823 18 ZINC000034944433 0.502 0.433 0.327 0.526 0.002 0.836 19 ZINC000038143594 0.384 0.405 0.265 0.300 0.178 0.614 20 ZINC000002528486 0.275 0.564 0.462 0.443 0.000 0.587 21 Lynparza 0.665 0.311 0.440 0.627 0.368 0.672 a 0.7 (Carcinogen) b 0.7 (Mutagen) c 0.7 (Toxic) Table 4. CDOCKER interaction energy of compounds with PARP-1. Compounds CDOCKER Interaction energy (Kcal/mol) ZINC000014951634 -72.8455 ZINC000053057130 -70.3196 Lynparza -54.2416 Table 5: Hydrogen bond interaction parameters for each compound and PARP-1 residues. Receptor Compound Donor atom Receptor Atom Distances (Å) PARP-1 ZINC000014951634 B:ARG878:HH21 ZINC000014951634:O40 2.3 B:TYR896:O ZINC000014951634:H44 2.1 B:TRP861:O ZINC000014951634:H43 2.9 B:SER904:HG ZINC000014951634:O3 1.9 ZINC000053057130 B:GLY863:O ZINC000053057130:H39 3.1 B:ASP766:OD1 ZINC000053057130:H66 2.1 B:ARG878:HH21 ZINC000053057130:O36 2.3 Lynparza B:SER904:HG Molecule：O27 2.1 B:GLN759:HE21 Molecule：O11 2.3 Table 6: Pi-Pi interaction, Pi-Alkyl interaction, Pi-Anion interaction and Alkyl interaction parameters for each compound and PARP-1 residues. Interaction parameters Receptor Compound Donor atom Receptor Atom Distances (Å) Pi-Pi staked interaction PARP-1 ZINC000014951634 B:TYR896 ZINC000014951634 4.62 Pi-Pi T-shaped interaction ZINC000053057130 B:TYR889 ZINC000053057130 5.08 Pi-Alkyl interaction ZINC000014951634 B:TYR889 ZINC000014951634 4.95 B:ARG878 ZINC000014951634 5.32 B:LEU877 ZINC000014951634 5.04 ZINC000053057130 B:TYR896 ZINC000053057130 5.49 B:TYR907 ZINC000053057130 4.97 B:HIS862 ZINC000053057130 5.26 B:ARG878 ZINC000053057130 4.93 Lynparza B:TYR907 Molecule 4.00 B:HIS862 Molecule 4.77 B:ALA762 Molecule 5.47 Pi-Anion interaction ZINC000014951634 B:GLU988:OE1 ZINC000014951634 4.18 Lynparza B:GLU763:OE2 Molecule 4.52 B:GLU763:OE2 Molecule 4.87 Alkyl interaction ZINC000053057130 B:ALA898 ZINC000053057130 5.17 Lynparza B:ALA898 Molecule 4.49 B:LYS903 Molecule 4.86 Cite Share Download PDF Status: Posted Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1714523","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":111905092,"identity":"7a3ad525-436a-4749-a9f7-9caa1e443255","order_by":0,"name":"Hui Li","email":"","orcid":"","institution":"The First Hospital of Jilin University: Bethune First Hospital of Jilin University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Li","suffix":""},{"id":111905093,"identity":"5a3e55e5-cca5-4862-a20d-0877d80e8b53","order_by":1,"name":"Zhenhua Wang","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhenhua","middleName":"","lastName":"Wang","suffix":""},{"id":111905094,"identity":"78c006f1-d182-45d9-9bef-040c097dabe0","order_by":2,"name":"Jianxin Xi","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jianxin","middleName":"","lastName":"Xi","suffix":""},{"id":111905095,"identity":"5facde49-64f6-4de2-8968-114c1ac57cbf","order_by":3,"name":"Han Lu","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Lu","suffix":""},{"id":111905096,"identity":"5ed91309-1daf-4783-854b-56e6b4fcdfe5","order_by":4,"name":"Zhishan Du","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhishan","middleName":"","lastName":"Du","suffix":""},{"id":111905097,"identity":"e1087cb3-c379-4914-b5a9-aa069f777d83","order_by":5,"name":"Sheng Zhong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYDACCTB5AIiZD5CshS2BZC08BsTpkJ/dfOzhlz935Mz513z+zPPnsDx/A/Oxj18Y7PJwaWGccyzdWLbtmbHljLfbpHnbDhvOOMCWPFuGIbkYlxZmiRwzacmGw4kbbpzdxszbcDjBgIHHmFmC4UBiAw4tbCAtEn8O12+4ceYxyGGEtfAAtUh+YAOqPN/DIM3DBtHC+AGPFgmJtDRpRqAXNtxgM5Oc25ZuOOMwWzIzg0EyTi3yM5KPSf4ABpTB+cOPP7z5Yy3P3958mPFHhR1OLeAg4AHbl8DABGYwg0QIxBHjDxDJfwDKgIuMglEwCkbBKIAAAAfFWI/M6t3tAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-2853-6347","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sheng","middleName":"","lastName":"Zhong","suffix":""}],"badges":[],"createdAt":"2022-06-01 06:52:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1714523/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1714523/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":22792793,"identity":"424892a5-96fe-4252-b3c2-62e5f18cebfc","added_by":"auto","created_at":"2022-06-17 20:22:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":724375,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the DNA damage repair pathway.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1714523/v1/ef4a84265d82780212ce4c43.png"},{"id":22792401,"identity":"087377b6-b8ed-41bd-a07f-e87cf8621263","added_by":"auto","created_at":"2022-06-17 20:17:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5083849,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of 278 genes of the DNA-repair proteins, and analyses between PARP1_L (n=213) and PARP1_L (n=213) from GEO database.\u003c/strong\u003e (A) Enriched terms are colored by cluster ID, where nodes that share the same cluster ID are typically close to each other in 278 genes of the DNA-repair proteins. (B) Enriched terms are colored by P-value, where terms containing more genes have a more significant P-value in in 278 genes of the DNA-repair proteins. (C) Heatmap of enriched terms across input gene lists, colored by P-values. (D) Comparison of survival prognosis between PARP1_H and PARP1_L, between PARP1_H and PARP1_L after chemotherapy, and between PARP1_H and PARP1_L after radiation therapy from GEO using the Log-Rank test. \u0026nbsp;(E) expression levels of the 278 genes of the DNA-repair proteins were compared between PARP1_H and PARP1_L and exhibited in heatmap.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1714523/v1/0e82fbd7bb52e7db1ee91b5e.png"},{"id":22792205,"identity":"09e1a83e-1d23-441a-ba95-4f0648950962","added_by":"auto","created_at":"2022-06-17 20:12:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1334860,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of PARP1-associated differentially expressed genes of DNA-repair proteins, and construction of the PARP1-associated DNA-repair prognostic signature (DPS). \u003c/strong\u003e(A) Volcano plot of 7 DNA-repair proteins differentially expressed between PARP1_L (n=213) and PARP1_L (n=213). (B) Heatmap of genes of DNA-repair proteins differentially expressed between PARP1_H (n=213) and PARP1_L (n=213). (C, D) The relationship between DEDG expression level and prognosis of glioma patients. The OS of glioma patients was compared between High and Low groups of each DEDG using the Log-Rank test. (E) LASSO Cox analysis identified four genes most correlated with overall survival. (F) Kaplan–Meier curves of overall survival based on the DPS (n=426). (G)ROC curve analysis of the DPS.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1714523/v1/9641dd9e412405286e6b0683.png"},{"id":22792212,"identity":"b303d058-7736-4057-8cc4-ad5467995bf6","added_by":"auto","created_at":"2022-06-17 20:12:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1015847,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of the nomogram model. (\u003c/strong\u003eA) Univariate and multivariate Cox analyses indicating that the DPS is significantly associated with OS. (B) Nomogram model for predicting the probability of 1-, 3-, and 5- year OS in Gliomas patients. (C) Calibration plots of the nomogram for predicting the probability of OS at 1, 3, and 5 years.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-1714523/v1/ef04163421b54a90edd1daf0.png"},{"id":22792403,"identity":"cf9b967f-1282-46db-b517-26d9e132a099","added_by":"auto","created_at":"2022-06-17 20:17:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2210100,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The molecular structure of PARP-1 and the complex structure of PARP-1 with Lynparza. Initial molecular structure was shown. (B) The molecular structure of PARP-1 and the complex structure of PARP-1 with Lynparza. The surface of the complex was added, green for Lynparza and gray for PARP. (C) 2D and 3D chemical structure of ZINC000014951634. (D) 2D and 3D chemical structure of ZINC000053057130. (E) 2D and 3D chemical structure of Lynparza.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-1714523/v1/e057e5edd04da5006337fdf1.png"},{"id":22792794,"identity":"f1532844-8f79-4866-b672-f2f9513cac69","added_by":"auto","created_at":"2022-06-17 20:22:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3538948,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe 3D and 2D schematic drawing of interactions between ligands and PARP by DS 4.5 and Schrodinger. The surface of the binding area was added; blue represented positive charge; red represented negative charge; ligands were shown in sticks; the structure around the ligand-receptor junction was shown in thinner sticks. In addition, the surface of the complex was added, purple for ZINC000014951634, orange for ZINC000053057130, green for Lynparza and gray for PARP-1. \u003c/strong\u003e(A) ZINC000014951634- PARP-1 complex; (B) ZINC000053057130- PARP-1 complex; (C) Lynparza -PARP-1 complex.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-1714523/v1/40fc349212332c3a84bb0463.png"},{"id":22792795,"identity":"7009b994-4c14-4579-adf2-c162e2fe1373","added_by":"auto","created_at":"2022-06-17 20:22:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2629552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe 3D and 2D intermolecular interaction in the binding pockets by Schrodinger and PyMol of ligand-PARP-1 complex. Green represents Hydrogen bond.\u003c/strong\u003e (A, D) ZINC000014951634- PARP-1 complex; (B, E) ZINC000053057130- PARP-1 complex; (C, F) Lynparza -PARP-1 complex.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-1714523/v1/736f67a138adfc5892f2917c.png"},{"id":22792208,"identity":"c456c4f8-ba88-4426-be02-3bbae755f6d4","added_by":"auto","created_at":"2022-06-17 20:12:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2129034,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePharmacophore predictions and molecular dynamics simulations of three complexes by DS 4.5. \u003c/strong\u003e(A) ZINC000014951634: green represents hydrogen acceptor; blue represents the hydrophobic center; purple represents hydrogen donor; yellow represents aromatic ring; red represents inozable positive. (B) ZINC000053057130: green represents hydrogen acceptor; blue represents the hydrophobic center; purple represents hydrogen donor; yellow represents aromatic ring; red represents inozable positive. (C) Lynparza: green represents hydrogen acceptor; blue represents the hydrophobic center; purple represents hydrogen donor; yellow represents aromatic ring. (D) Potential Energy by molecular dynamics simulations of ZINC000014951634 and ZINC000053057130; (E) Potential Energy by molecular dynamics simulations of Lynparza. (F) Average backbone RMSD of molecular dynamics simulations to ZINC000014951634 and ZINC000053057130. (G) Average backbone RMSD of molecular dynamics simulations to Lynparza.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-1714523/v1/d2f1a16603cbd8c8aaa8a895.png"},{"id":26446756,"identity":"a5c605ff-9741-488b-a062-793d939ee4fe","added_by":"auto","created_at":"2022-09-14 11:22:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3738423,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1714523/v1/6e206f2b-fdbc-4a37-801d-f614f16361e6.pdf"}],"financialInterests":"","formattedTitle":"The Study on the Expression Level of PARP-1 in Glioma and Computational Screening of PARP-1 Inhibitors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGliomas are the most common primary intracranial malignancies, and current standard treatments include surgical resection, supplemented by radiotherapy and chemotherapy, but the prognosis is poor(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). At present, most chemotherapeutic drugs and radiotherapy inhibit or kill tumor cells in different stages of cell growth by affecting the synthesis and function of macromolecules (DNA, RNA, protein) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Normal cells have multiple repair mechanisms for a certain amount of damage caused by external intervention, which plays a vital role in maintaining the stability of the genome and normal cell physiology (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Similarly, in anti-tumor therapy, after radiation and chemical factors damage the DNA of tumor cells, the tumor cells, like normal cells, can activate their damage repair mechanism to repair, thereby increasing the insensitivity to radiotherapy and chemotherapy(\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). A lot of experience has been accumulated in tumor electric field therapy, immunotherapy, and targeted molecular therapy for glioma, but few results can truly change clinical practice(\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCellular DNA will be damaged under normal physiological processes and external factors such as ionization and radiation, and the frequency of damage is higher in pathological states such as tumors (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). There are many ways of DNA damage repair (DDR), including nucleotide excision repair (NER), non-homologous end joining (NHEJ), homologous recombination (H.R.), mismatch repair (MMR), base excision repair (BER) etc (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Single-strand defects (complete complementary strands) are mainly repaired through BER, NER and MMR pathways (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Double-strand breaks (DSBs), which damage both DNA strands, are the most threatening form of DNA damage and are mainly repaired by H.R. Homologous recombination is the high-precision repair of homologous chromatids as repair templates. Double-strand breaks can also be repaired by NHEJ, which does not require homologous chromatids as repair templates but has a high error rate (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePARP (Poly ADP-ribose polymerase) plays a pivotal part in the DNA repair pathway. The PARP family has seventeen members, and their catalytic regions share homology (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). It plays an essential role in maintaining genome integrity, cell senescence, apoptosis, inflammatory response, neuropathological changes and other physiological and pathological processes (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Among them, PARP-1 plays a significant role. PARP-1 protein mainly includes three domains: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) N-terminal DNA binding domain includes zinc finger structures (Zn1, Zn2) that recognize DNA damage gaps, nuclear localization sequence and a third Zinc-binding domain that mainly mediates communication between the domains(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)The acidic domain mainly performs protein self-modification(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) The C-terminus is responsible for attaching to the DNA strand, amplifying the poly-ADP-ribose, and inducing the catalytic domain of the PAR chain forks.\u003c/p\u003e \u003cp\u003ePARP-1 responds immediately when cellular DNA is damaged. It rapidly binds to DNA in various conformations such as single/double-strand break DNA, crossover, helix, etc., and catalyzes the decomposition of NAD\u0026thinsp;+\u0026thinsp;into nicotinamide and ADP. Then, ADP (adenosine diphosphate) is linked to the self-modified region of PARP-1 or other nuclear receptor proteins such as histones and undergoes a complex reaction of poly-ADP-ribose to form PAR (Poly ADP-ribose). When the PAR reaches a certain length, the PAR poly chain dissociates from the DNA and then guides DNA repair enzymes such as XRCC 1 (X-ray repair cross-complementary gene 1) and DNA ligase III to carry out BER to remove the wrong or damaged bases.(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Next, DNA polymerase uses the corresponding complementary strand at the damaged site as a template to synthesize a new single-stranded DNA fragment. Finally, ligase connects the newly synthesized single-stranded fragment and the original single-stranded through phosphodiester bonds to complete DNA damage repair(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt present, the research reports on PARP-1 mainly focus on its expression and function in tumor tissues lacking the BRCA1/2 gene, such as ovarian cancer, metastatic breast cancer, advanced prostate cancer and pancreatic cancer (\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Recently, a study found that the expression level of PARP-1 mRNA in glioma cell lines was significantly increased; and PARP-1 inhibitor significantly suppressed the proliferation of glioma cells and aggravated the DNA damage effect of temozolomide. According to the theory of combined lethality, the DNA damage of tumor cells is caused by radiotherapy and chemotherapy, and the inhibition of PARP-1 is combined to block its DNA repair, resulting in a more effective cytotoxic effect on tumor cells, which provides a new idea for the treatment of glioma(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Therefore, PARP-1 is expected to become a new target for anti-glioma therapy and an evaluation indicator for the prognosis of patients.\u003c/p\u003e \u003cp\u003eIn this study, we explored the effect of PARP-1 expression on the prognosis of glioma patients and then screened inhibitors targeting PARP-1 to provide new therapeutic options for the treatment of glioma. Firstly, we downloaded RNA-seq data from 416 glioma samples from the GEO (Gene Expression Omnibus) database and divided them into PARP1_H and PARP1_L according to the level of PARP-1 expression. Results showed that the expression level of 278 genes of DNA-repair proteins in PARP1_H is higher than that in PARP1_L. The overall prognosis of PARP1_L patients was better than that of PARP1_H, and the effect of radiotherapy and chemotherapy in PARP1_L was better. Next, LASSO (Least Absolute Shrinkage and Selection Operator) Cox analysis was carried out for genes of DNA-repair proteins that were differentially expressed between PARP1_H and PARP1_L. According to the developed four-gene DPS (DNA-repair prognostic signature), glioma patients were divided into high-risk and low-risk groups. Furthermore, we developed a new nomogram to assess overall survival in glioma patients. Additionally, to search for more effective PARP-1 inhibitors with fewer side effects, we used a series of computer-aided techniques such as Discovery Studio 4.5, Schrodinger and PyMol for screening and evaluation. Finally, ZINC000014951634 and ZINC000053057130 proved to be favorable PARP-1 inhibitors by analyzing pharmacological and toxicological properties, ligand-protein complex affinity, and stability. In conclusion, this study investigated the effect of PARP-1 expression on prognosis and the sensitization effect of radiotherapy and chemotherapy in glioma and further screened PARP-1 targeted inhibitors to improve the prognosis of glioma patients.\u003c/p\u003e"},{"header":"Methods And Materials","content":"\u003ch2\u003eGene expression datasets, data processing and Functional enrichment analysis\u003c/h2\u003e\n\u003cp\u003eWe acquired RNA-seq data of 426 glioma samples from the GEO (Gene Expression Omnibus) database. The survival data were obtained for all patients. According to the median value of PARP-1 expression, the patients were divided into 213 PARP1_H patients and 213 PARP1_L patients. Additionally, the RNA transcriptome analysis was carried out by transformation of log2-based FPKM values.\u003c/p\u003e\n\u003cp\u003eTo analyze signalling pathway enrichment, 278 genes of DNA-repair proteins were uploaded to Metascape, an online tool for gene annotation, attributes and visualization (https://metascape.org/)(29). Enrichment levels of the 278 genes above in each glioma sample were calculated.\u003c/p\u003e\n\u003ch2\u003ePrognosis and Gene enrichment between PARP1_H and PARP1_L\u003c/h2\u003e\n\u003cp\u003eThe OS (overall survival) of glioma patients were compared between PARP1_H and PARP1_L, PARP1_H and PARP1_L after chemotherapy, and PARP1_H and PARP1_L after radiation therapy. Kaplan-Meier curves were drawn to show differences in survival time. The log-rank test was carried out to evaluate the significance of differences in survival times with a threshold of P \u0026lt; 0.05. In addition, expression levels of the 278 genes of the DNA-repair proteins were compared between PARP1_H and PARP1_L and exhibited in the heatmap.\u003c/p\u003e\n\u003ch2\u003eDifferential expression analysis\u003c/h2\u003e\n\u003cp\u003eDifferential expression analysis was calculated and carried out on Rstudio by the Wilcoxon Rank Sum and Signed Rank Tests between PARP1_H and PARP1_L (30). Genes with log2 |fold change| \u0026ge;1 and FDR (False Discovery Rate) \u0026lt;0.05 were chosen as DEGs (differentially expressed genes). Then, the ImmPort database identifies differentially expressed genes for DNA-repair proteins (DEDGs) (https://www.immport.org/). Patients were divided into High and Low groups based on the median expression level of each DEDG, with 213 patients in each group.\u003c/p\u003e\n\u003cp\u003eThe OS of glioma patients was compared between the High and Low groups of each DEDG. Kaplan-Meier curves were drawn to show differences in survival time. The log-rank test was carried out to evaluate the significance of differences in survival times with a threshold of P \u0026lt; 0.05.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConstruction of the PARP-related DNA-repair prognostic Signature (DPS)\u003c/h2\u003e\n\u003cp\u003eTo build the PARP-related DNA-repair prognostic signature (DPS), DEDGs were put in LASSO Cox regression\u0026nbsp;and analyzed\u0026nbsp;by the \u0026ldquo;glmnet\u0026rdquo; R package\u0026nbsp;(31)\u0026nbsp;(32). The DPS model was constructed from weighted Cox regression coefficients to estimate the risk score for each patient. Patients were classified as high or low risk according to the best cutoff values obtained by the \u0026quot;survminer\u0026quot; R package. We used the \u0026quot;survival ROC\u0026quot; R package to generate ROC (Receiver Operating Characteristic) curves\u0026nbsp;(33).\u0026nbsp;And the area under the curve values of the ROC curve was calculated to assess the specificity and sensitivity of DPS.\u003c/p\u003e\n\u003ch2\u003eDevelopment of the nomogram\u003c/h2\u003e\n\u003cp\u003eWe applied univariate and multivariate Cox analyses to assess the independent prognostic ability of DPS. And we performed the \u0026ldquo;rms\u0026rdquo; package to\u0026nbsp;construct\u0026nbsp;an innovative nomogram according to the\u0026nbsp;Cox analysis results. To determine the accuracy, calibration plots of observed vs. predicted probabilities of 1-, 3-, and 5-year OS were developed.\u0026nbsp;The C-Index (Concordance Index) was calculated to determine the discriminative power of the model. And\u0026nbsp;the C- index\u0026nbsp;was corrected\u0026nbsp;using bootstraps.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eDocking software and database of ligands and protein\u003c/h2\u003e\n\u003cp\u003eDS 4.5 (Discovery Studio 4.5, Accelrys, Inc) is a suite of software for modelling large and small-molecule systems. Libdock, ADME (absorption, distribution, metabolism, excretion) and TOPKAT (Toxicity Prediction by Computer Assisted Technology) modules of DS 4.5 were used for virtual screening firstly. CDOCKER module was then applied for a precise docking study. Additionally, Schrodinger and PyMol software were also used to demonstrate ligand binding interactions with PARP-1 further. Moreover, we downloaded 3D molecular files of 17799 natural, named and purchasable molecules from the ZINC15 database for virtual screening of PARP-1 inhibitors ( https://zinc.docking.org/ ). And molecule structure of PARP-1 was downloaded from PDB (Protein Database) (https://www.rcsb.org ).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eVirtual screening using Libdock, ADME and TOPKAT\u003c/h2\u003e\n\u003cp\u003eVirtual screening was performed by Libdock, a rigid-based docking program of DS 4.5. Hotspots for PARP-1 were calculated by a grid put into the binding region as well as by apolar and polar probes. Next, the ligands formed favorable interactions based on the hotspots. The minimization was performed using the CHARMm force field and Smart Minimiser algorithm (Cambridge, MA, USA). Afterwards, poses of all the ligands were ranked according to their Libdock scores. The 3.22 \u0026Aring; crystal structure of PARP-1 in complex with inhibitor PJ34L was downloaded from PDB. NAD+ binds to PARP-1 and is catalyzed to ADP ribose. And this binding region is an important regulatory site of PARP-1. Common inhibitors for PARP-1 all have a portion that competes with NAD+ to combine with nicotinamide. Therefore, this pocket region was chosen as the docking region to identify new candidates for PARP-1 inhibitors. Lynparza is a selective inhibitor of PARP1/2, which is tenfold to fifteenfold more selective for PARP1 than for PARP2. Besides, because PARP1 plays a major role, compared with other members belonging to PARP family, in DNA repair. In this study, we focus on PARP1.Finally, Lynparza was chosen as a reference inhibitor of PARP-1 to assess the binding ability of ligands. And PARP-1 was prepared by removing crystal water and other hetero-atoms, followed by hydrogen addition, protonation, ionization and energy minimization(34). Only the top 20 molecules were chosen for the following analysis.\u003c/p\u003e\n\u003cp\u003eThen, the ADME module of DS 4.5 was applied for calculating the pharmacological properties of selected compounds and Lynparza, including the absorption, distribution, metabolism, and excretion. TOPKAT module of DS 4.5 was also used to assess their toxicological properties. These pharmacological and toxicological properties were fully considered in selecting ideal candidates for PARP-1. Finally, two molecules were chosen as favorable candidates based on the above results.\u003c/p\u003e\n\u003ch2\u003ePrecise molecular docking using CDOCKER\u003c/h2\u003e\n\u003cp\u003ePrecise docking study between selected compounds, Lynparza and prepared PARP-1 by CDOCKER module of DS 4.5 based on CHARMm36 force field. The receptor is held rigid while the ligands are allowed to flex during the docking process. For each complex pose, the CDOCKER interaction energies indicating ligand binding affinity were calculated. The binding site sphere of PARP-1 was defined as the region within a radius of 13 \u0026Aring; from the geometric centroid of Lynparza. Ligands can bind to residues within the binding site sphere during the docking process. Different poses of each test molecule were generated and analyzed on the basis of the CDOCKER interaction energy, respectively. Schrodinger and PyMol software further showed the optimal pose binding of selected compounds, Lynparza and PARP-1.\u003c/p\u003e\n\u003ch2\u003ePharmacological analysis and Molecular dynamics simulation\u003c/h2\u003e\n\u003cp\u003ePharmacophores of selected compounds and Lynparza were analyzed by the 3D-QSAR module of DS 4.5. Only those with energies below 10 kcal/mol can be retained, and a maximum of 255 confirmations can be generated per molecule.\u003c/p\u003e\n\u003cp\u003eIn addition, to assess the stability and affinity of each compounds-PARP-1 complex in the natural environment, the best binding conformations were selected and prepared for molecular dynamics simulation. The ligand- PARP-1 complex was placed in an orthorhombic box and solvated using an explicit periodic boundary solvation water model. To simulate the physiological environment of the system, solidum chloride was added. Then, the CHARMm force field was applied, and the system was relaxed by energy minimization with the final RMS gradient of 0.289. The system temperature was driven slowly from 50 K to 300 K, and the simulation was carried out at this constant temperature. The production procedure was carried out for 100 ps and the time step was 2 fs. The long-range electrostatics was calculated by the Particle Mesh Ewald algorithm, and all bonds involving hydrogen were fixed by the adapted Linear Constraint Solver algorithm. Concerning the initial complex setup, the trajectory protocol of DS 4.5 was performed to determine the trajectory for potential energy and RMSD (root-mean-square deviation).\u003c/p\u003e"},{"header":"Result","content":"\u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e\n\u003cp\u003eThe 278 genes of the DNA-repair proteins were uploaded to the Metascape website to identify GO. (Gene Ontology) Terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways. Terms were thought significant when the conditions of P \u0026lt; 0.01 and the number of enriched genes \u0026ge; 3 were met and grouped separately according to their membership similarity. The term with the best p-value in every 20 clusters was selected. Also, the number of terms per cluster cannot exceed 20 for 250. The 278 genes of the DNA damage repair proteins were mainly enriched in GO:0006281: DNA repair, GO:0006302: double-strand break repair, R-HSA-73894: DNA Repair, R-HSA-5685942: HDR through Homologous Recombination (HRR), R-HSA-5696399: Global Genome Nucleotide Excision Repair (GG-NER), WP4946: DNA repair pathways, full network.\u0026nbsp;Each node represents a collective term, colored first by cluster I.D. and its P-value, separately (Figure 2(A-C)).\u003c/p\u003e\n\u003ch2\u003ePrognosis and Gene enrichment between PARP1_H and PARP1_L\u003c/h2\u003e\n\u003cp\u003e213 patients were included in the PARP1_H and PARP1_L groups, respectively. Survival analysis indicated that the clinical prognoses of PARP1_H and PARP1_L were different. The PARP1_L had a better survival prognosis than the PARP1_H (Log-Rank test, P=0.034) (Figure 2D). For patients treated with radiotherapy, PARP1_L had a better prognosis than PARP1_H (Log-Rank test, P(radiotherapy)=0.015) (Figure 2D). Similarly, for patients treated with chemotherapy, PARP1_L had a better prognosis than PARP1_H (Log-Rank test, P(chemotherapy)=0.039) (Figure 2D). Additionally, as shown in figure 2E, the expression levels of 278 DNA repair-related protein genes were significantly different between PARP1_H and PARP1_L, and the expression level in PARP1_H was higher than that in PARP1_L.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eDiscrimination against differentially expressed genes of DNA-repair proteins\u003c/h2\u003e\n\u003cp\u003eRstudio software (Version 1.2.5001) was applied to\u0026nbsp;distinguish\u0026nbsp;the differentially expressed genes of DNA-repair proteins (DEDGs) between PARP1_H and PARP1_L. Seven genes were confirmed\u0026nbsp;according to\u0026nbsp;the standard (log2 |fold change| \u0026ge;1 and FDR \u0026lt; 0.05), of which 3 genes were up-regulated, and 4 genes were down-regulated. Seven DEDGs were chosen using the ImmPort database for performing prognostic analysis. For each DEDG, the 416 patients were divided into two groups: High and Low, according to their median expression level. Then, the OS of glioma patients was compared between the High and Low groups of each DEDG. For each DEDG, the High group of each DEDG had a worse prognosis than the Low group (Log-Rank test, CCNA1: P\u0026lt;0.001, CLSPN: P\u0026lt;0.001, DTL: P\u0026lt;0.001, MGMT: P=0.03, POLN: P\u0026lt;0.001, SFN: P\u0026lt;0.001, XRCC2: P\u0026lt;0.001).\u003c/p\u003e\n\u003ch2\u003eConstruction of the PARP1-related prognostic signature\u003c/h2\u003e\n\u003cp\u003eLASSO Cox regression analysis of DEDGs was conducted to construct a PARP1- related DNA-repair prognostic Signature (DPS) (Figure 3C, 3D). Risk scores were evaluated for each glioma patient (risk score= CLSPN*0.734+MGMT*0.28+POLN*0.3+ SFN*0.187). Patients were divided into low-risk and high-risk groups according to the optimal cutoff value (0.89005466) evaluated by the \u0026quot;survminer\u0026quot; R package. Kaplan-Meier analysis showed that patients with low-risk scores had better outcomes than those with high-risk scores (Figure 3F). The ROC curve analysis of the DPS suggested good prognostic ability for OS (Figure 3G).\u0026nbsp;Gene expression patterns and the risk score distributions were shown in figure 3E.\u003c/p\u003e\n\u003ch2\u003eEstablishment of a DPS-based nomogram model\u003c/h2\u003e\n\u003cp\u003eThe DPS was indicated to be significantly associated with OS (Hazard ratio: 4.737, 95% confidence interval: 3.626-6.189, P \u0026lt; 0.001) by the univariate Cox analysis (Figure 4A). From the multivariate Cox analysis, DPS proved to be an independent prognostic factor (Hazard ratio: 2.739, 95% confidence interval: 1.923-3.902, P \u0026lt; 0.001) (Figure 4B). Finally, a DPS-based nomogram model was established. The C-index was 0.674, which revealed the specific\u0026nbsp;discriminative\u0026nbsp;ability of the nomogram model. Moreover, the observed vs. predicted probabilities of 1-, 3-, and 5-year OS\u0026nbsp;showed good agreement in the calibration plot\u0026nbsp;(Figure 4C).\u003c/p\u003e\n\u003ch2\u003eVirtual screening using Libdock \u003c/h2\u003e\n\u003cp\u003eBased on the above results, we found that PARP-1 is an essential target for glioma therapy and prognosis. Therefore, we took PARP-1 as the target for further drug screening. NAD+ binds to PARP-1 and is catalyzed to ADP ribose. And this binding region was selected as the docking site. From the ZINC15 database, 17799 purchasable, natural and named molecules were downloaded for virtual screening of PARP-1\u0026rsquo;s inhibitors. Lynparza was selected as a reference inhibitor of PARP-1 to assess the binding\u0026nbsp;capacity\u0026nbsp;of ligands. The 3D (three-dimensional) structures of PARP-1 and the Lynparza \u0026ndash; PARP-1 complex were displayed in Figure 5(A, B). According to Libdock\u0026apos;s results, 2996 compounds were identified that stably bind to PARP-1. Among them, 37 molecules had higher Libdock scores than Lynparza (ranking: 38, Libdock score: 153.31). Table 1 lists the top 20 compounds on the basis of Libdock scores.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003ePharmacological and toxicological prediction using ADME and TOPKAT of DS 4.5\u003c/h2\u003e\n\u003cp\u003ePharmacological\u0026nbsp;properties of the top 20 compounds and Lynparza were evaluated by the ADME module of DS 4.5, including aqueous solubility, brain/blood barrier (BBB), plasma protein binding properties (PPB), cytochrome P450 2D6 (CYP2D6) binding, human intestinal absorption and hepatotoxicity (Table 2). All the molecules were shown to be soluble in water. For human intestinal absorption, two compounds and Lynparza had a good absorption level; one compound had a moderate absorption level; seventeen compounds had a poor or very poor absorption level. Almost all compounds had very high penetrant in BBB level except ZINC000002528486 and Lynparza. There are 15 compounds, and Lynparza were predicted to be a non-inhibitors of CYP2D6. The other compounds indicated as inhibitors of CYP2D6, including ZINC000028968107, ZINC000002528509, ZINC000002033589, ZINC000034944433 and ZINC000002528486. 7 compounds were predicted as toxic for hepatotoxicity, which was similar to Lynparza.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, safety was also thoroughly investigated in this study. To examine the safety of the top 20 compounds and Lynparza, different toxicity indicators, including Rodent carcinogenicity (based on the U.S. National Toxicology Program (NTP)dataset), AMES (Ames mutagenicity) and DTP (developmental toxicity potential) properties, were analyzed by TOPKAT module of DS 4.5. Table 3 shows that all 20 compounds and Lynparza are safe except for ZINC000049872065, which has DTP property. Considering all the above results, compound 1 (ZINC000014951634) and compound 2 (ZINC000053057130) were non-hepatotoxic, non-CYP2D6-inhibitory, and have lower Ames mutagenicity, rodent carcinogenicity compared to other compounds and developmental toxicity potential, which also strongly suggests their application in perspective in drug development. Furthermore, both compounds are structurally identical to Lynparza, with multiple benzene rings and carbonyl groups.\u0026nbsp;The three-dimensional (3D) and two-dimensional (2D)\u0026nbsp;chemical structure\u0026nbsp;of compounds 1，2 and Lynparza were shown in Figure 5(C-E).\u0026nbsp;In particular,\u0026nbsp;carbanyl groups were connected with nitrogen-atoms which were similar to NAD+ to combine with nicotinamide.\u0026nbsp;In summary, compounds 1 and 2 were identified as safe drug candidates and selected for follow-up studies.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eLigand binding analysis\u003c/h2\u003e\n\u003cp\u003eThe RMSD between the docked pose and the crystal structure of the complex was 0.6 \u0026Aring;,\u0026nbsp;indicating the high reliability of the CDOCKER module applied in the study.\u0026nbsp;Compounds 1，2 and Lynparza were precisely docked into the function pocket of PARP-1 by the CDOCKER module under the CHARMm36 force field (Figure 6, 7). Table 4 showed that the CDOCKER interaction energy of compound 1 and compound 2 is significantly lower than that of the reference ligand Lynparza (-54.2416kcal/mol), indicating that these two compounds have higher stability and affinity with PARP-1 than Lynparza.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStructural analyses of the ligands-PARP-1 complex were also performed for the hydrogen bonds, Pi-Pi interaction, Pi-Alkyl interaction, Pi-Anion interaction and Alkyl interactions (Figure 6, 7 and Table 5, 6). Results showed that compound 1 formed four pairs of hydrogen bonds with PARP-1, by the O40 of compound and B: ARG878: HH21 of PARP-1, the H44 of compound and B: TYR896:O of PARP-1, the H43 of compound and B: TRP861:O of PARP-1, the 03 of compound and B: SER904: H.G. of PARP-1. Compound 1 also formed one pair of Pi-Pi staked interaction, three pairs of Pi-Alkyl interaction and one pair of Pi-Anion interaction. Compound 2 formed three pairs of hydrogen bonds by the H39 of the compound and B: GLY863: O of PARP-1, the H66 of the compound and B: ASP766: OD1 of PARP-1, the 036 of the compound and B: ARG878: HH21 of PARP-1. Compound 2 also formed one pair of Pi-Pi T-shaped interactions, one pair of Pi-Alkyl interactions, and one pair of Alkyl interactions. The reference compound Lynparza formed two hydrogen bonds with PARP-1(B: SER904: HG-Molecule： O27, B: GLN759: HE21-Molecule： O11, respectively). It also formed three pairs of Pi-Alkyl interactions and two pairs of\u0026nbsp;Pi-Anion\u0026nbsp;interactions with PARP-1(Table 6). Besides, Schrodinger and PyMol software were performed to analyze intermolecular interaction further between ligand and PARP-1 in the binding pocket (Figure 7). As results showed, compounds 1,2 and Lynparza interacted with PARP-1 by\u0026nbsp;Amino acid residues\u0026nbsp;861-988, which suggested the active position of the binding pocket and provided deep learning of PARP-1\u0026rsquo;s structure and a guide for targeted drug research.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003ePharmacological analysis and Molecular dynamics simulation\u003c/h2\u003e\n\u003cp\u003eAccording to the evaluation of feature pharmacophores by the 3D-QSAR module of DS 4.5, ZINC000014951634 displayed eight hydrogen bond acceptors, ten hydrogen donors, four hydrophobic centres, and six aromatic rings and one\u0026nbsp;ionizable positive, respectively (Figure 8A). ZINC000053057130 displayed seven hydrogen bond acceptors, nine hydrogen donors, four hydrophobic centres, four aromatic rings and one\u0026nbsp;ionizable positive, respectively (Figure 8B). In addition, Lynparza formed sixteen feature pharmacophores, including seven hydrogen bond acceptors, one hydrogen donor, four hydrophobic centres\u0026nbsp;and four rings aromatic, respectively (Figure 8C).\u003c/p\u003e\n\u003cp\u003eAdditionally, the molecular dynamics simulation module was carried out to assess the stabilities of the ligand-PARP-1 complexes in the natural environment. The potential energy and RMSD curves of each complex were shown in Figures 8(D,E). All the RMSD and potential energies of these complexes are stable over time. And the RMSD trajectory of each complex reached equilibrium after 70 ps. Molecular dynamics simulation results verify that these hydrogen bonds and Pi-related interactions formed by compounds with PARP-1 contribute to the stability of these complexes. And their complexes could exist in a natural environment stably and have inhibitory effects on PARP-1 as Lynparza.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGliomas are the most common primary intracranial malignancies, and current standard treatments include surgical resection, supplemented by radiotherapy and chemotherapy, but the prognosis is poor(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In anti-tumor therapy, radiation and chemical factors damage the DNA of tumor cells. However, like normal cells, tumour cells activate their damage repair mechanisms to repair DNA damage, thereby increasing the insensitivity to radiotherapy and chemotherapy(\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In addition, a lot of experience has been accumulated in tumor electric field therapy, immunotherapy, and targeted molecular therapy for glioma. Still, few results can truly change clinical practice(\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCellular DNA will be damaged under normal physiological processes and external factors such as ionization and radiation, and the frequency of damage is higher in pathological states such as tumors (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). There are many ways of DNA damage repair (DDR), including nucleotide excision repair (NER), homologous recombination (H.R.), non-homologous end joining (NHEJ), base excision repair (BER), mismatch repair (MMR) etc. The PARP family plays a vital role in maintaining genome integrity, cell senescence, apoptosis, inflammatory response, neuropathological changes and other physiological and pathological processes (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Among them, PARP-1 matters a lot in the DNA repair pathway. When cellular DNA is damaged, PARP-1 responds immediately. It quickly binds to the damage site through its zinc finger domain, uses NAD\u0026thinsp;+\u0026thinsp;as a substrate, changes its conformation, catalyzes ADP-ribose transfer, and finally completes single-strand repair(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt present, the research reports on PARP-1 mainly focus on its expression and function in tumor tissues lacking the BRCA1/2 gene, such as ovarian cancer, metastatic breast cancer, advanced prostate cancer and pancreatic cancer (\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Recently, a study found that the expression level of PARP-1 mRNA in glioma cell lines was significantly increased; and PARP-1 inhibitor significantly suppressed the proliferation of glioma cells and aggravated the DNA damage effect of temozolomide(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Therefore, it is significant to explore the effect of PARP-1\u0026rsquo;s expression level on glioma prognosis and seek new targets for glioma therapy.\u003c/p\u003e \u003cp\u003eIn this study, we first investigated the effect of PARP-1 expression on prognosis and the sensitization effect of radiotherapy and chemotherapy in glioma. Furthermore, a series of computer-aided techniques such as DS 4.5, Schrodinger and PyMol were used to search for more effective PARP-1 inhibitors with fewer side effects.\u003c/p\u003e \u003cp\u003eFirst of all, according to the G.O. and KEGG analysis, 278 genes of the DNA damage repair proteins were mainly enriched in GO:0006281: DNA repair, GO:0006302: double-strand break repair, R-HSA-73894: DNA Repair, R-HSA-5685942: HDR through Homologous Recombination (HRR), R-HSA-5696399: Global Genome Nucleotide Excision Repair (GG-NER), WP4946: DNA repair pathways, full network.\u003c/p\u003e \u003cp\u003eThen, we downloaded RNA-seq data of 416 glioma samples from the GEO database and divided them into PARP1_H and PARP1_L, according to the level of PARP-1 expression. The prognosis of patients, the effects of radiotherapy and chemotherapy, and the gene expression levels of DNA-repair proteins were compared between these two groups. As the results showed, the overall prognosis of PARP1_L patients was better than that of PARP1_H (Log-Rank test, P\u0026thinsp;=\u0026thinsp;0.034), and the effect of radiotherapy and chemotherapy in PARP1_L was better (Log-Rank test, P(radiotherapy)\u0026thinsp;=\u0026thinsp;0.015, P(chemotherapy)\u0026thinsp;=\u0026thinsp;0.039). Moreover, the expression levels of 278 genes of DNA-repair proteins in PARP1_H were higher than those in PARP1_L. The expression level of PARP-1 was consistent with the gene expression levels of all 278 DNA-repair proteins. Therefore, we speculate that the expression level of PARP-1 is an evaluation index of cellular DNA damage repair function, and the level of PARP-1 expression is expected to be a prognostic indicator for glioma. In addition, PARP-1 may affect the expression of other genes of DNA-repair protein through some mechanism, which remains to be further studied. What\u0026rsquo;s more, on the basis of chemotherapy and radiotherapy, inhibiting the function of PARP-1 to block its DNA repair can have a more effective cytotoxic effect on tumor cells and reduce the drug resistance of chemotherapy drugs, which offers new ideas for the treatment of glioma.\u003c/p\u003e \u003cp\u003eNext, we analyzed their DEDGs between PARP1_H and PARP1_L. There were seven DEDGs, including CCNA1, CLSPN, DTL, MGMT, POLN, SFN, and XRCC2. For each DEDG, the 416 patients were divided into two groups: High and Low, based on their median expression level. As the results showed, the High group of each DEDG had a worse OS than that of the Low group (Log-Rank test, CCNA1: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, CLSPN: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, DTL: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, MGMT: P\u0026thinsp;=\u0026thinsp;0.03, POLN: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, SFN: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, XRCC2: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Therefore, these genes can be potential prognostic indicators and targets for glioma.\u003c/p\u003e \u003cp\u003eSubsequently, we developed a PARP1-related DPS, which was related to prognosis. CLSPN, MGMT, POLN and SFN were identified as hub genes in our DPS by LASSO Cox regression. The four-gene DPS was also an independent prognostic factor by univariate and multivariate Cox analyses. Moreover, a predicting nomogram was developed based on the DPS and patient clinical data to predict the survival of patients with glioma. For now, some predictive models of glioblastoma have been constructed(\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Our current study found that PARP-1 expression was associated with prognosis and developed a novel PARP-1-related DPS with an AUC value of 0.765 for predicting 1, 3 and 5-year patient survival. Furthermore, Discovery Studio 4.5, Schrodinger and PyMol were applied to search for more favorable PARP-1 inhibitors. Lynparza was chosen as a reference inhibitor of PARP-1 to assess the binding ability of other compounds. 17799 purchasable, natural, named molecules were obtained from the ZINC15 database for virtual screening. Firstly, LIbdock was performed between ligands and PARP-1 for virtual screening. Libdock score represented the degree of energy optimization and stability of the conformation. Compounds with a high Libdock score indicate that it has a comparable energy-optimized and stable conformation compared to other compounds. Compared with Lynparza, 37 compounds can bind to PARP-1 with a more stable conformation and better energy optimization. On the basis of the Libdock score, the top 20 compounds were chosen for subsequent pharmacological and toxicological analysis. Finally, ZINC000014951634 and ZINC000053057130 were shown to be non-hepatotoxic, non-CYP2D6-inhibitory, and have lower Ames mutagenicity, rodent carcinogenicity compared to other compounds and developmental toxicity potential, which also strongly suggests their application in perspective in drug development.\u003c/p\u003e \u003cp\u003eAdditionally, to further evaluate ligand-protein complex affinity and stability, Molecular dynamics simulation and precise docking by CDOCKER were performed. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e showed that the CDOCKER interaction energies of compounds 1 and 2 were significantly lower than that of the reference ligand Lynparza (-54.2416kcal/mol), which indicated that these two compounds had higher stability and affinity with PARP-1 compared to Lynparza. Moreover, compound 1 formed four pairs of hydrogen bonds, one pair of Pi-Pi staked interaction, three pairs of Pi-Alkyl interaction and one pair of Pi-Anion interaction. Compound 2 formed three pairs of hydrogen bonds, one pair of Pi-Pi T-shaped interaction, one pair of Pi-Alkyl interaction and one pair of Alkyl interaction. The chemical bonds above may contribute to the stability of compounds 1 and 2 and PARP-1 binding. Additionally, compounds 1, 2 and Lynparza interacted with PARP-1 by amino acid residues 861\u0026ndash;988, which indicated the active position of the binding pocket and provided deep learning for PARP-1\u0026rsquo;s structure and a guide for PARP-1 targeted drug research. Moreover, according to the molecular dynamics simulation\u0026rsquo;s results, both potential energy and RMSD of these two complexes stabilized with time like Lynparza, which validated the stabilities of the ligand-PARP-1 complexes in the natural environment. Furthermore, compounds 1 and 2 were shown to have multiple pharmacophores, which again suggested the potential of compounds 1, and 2 as drugs.\u003c/p\u003e \u003cp\u003eIn conclusion, this study revealed the effect of PARP-1 expression on the prognosis of glioma and the sensitization effect of radiotherapy and chemotherapy and screened favorable PARP-1 inhibitors to improve the prognosis of glioma. Furthermore, we developed a novel nomogram to quantitatively predict patient survival based on PARP-1-related DPS. This study provided new insight into the treatment and prognosis of glioma. Although this study was well designed and accurately measured, we acknowledge that this study still has some limitations. More experiments are needed to validate our results, and more indicators of drug safety should also be evaluated in our future studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we first investigated the effect of PARP-1 expression on prognosis and the sensitization effect of radiotherapy and chemotherapy in glioma. Furthermore, a series of computer-aided techniques such as Discovery Studio 4.5, Schrodinger and PyMol were used to search for more effective PARP-1 inhibitors with fewer side effects. This study revealed the effect of PARP-1 expression on the prognosis of glioma and the sensitization effect of radiotherapy and chemotherapy and screened favorable PARP-1 inhibitors to improve the prognosis of glioma. Furthermore, we developed a novel nomogram to quantitatively predict patient survival based on PARP-1-related DPS. This study provided new insight into the treatment and prognosis of glioma. Although this study was well designed and accurately measured, we acknowledge that this study still has some limitations. More experiments are needed to validate our results, and more indicators of drug safety should also be evaluated in our future studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We thanked the foundation of the open innovation experiment of the College of Basic Medical Sciences, Jilin University (2018)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was completed with teamwork. Every author has made substantial contributions to the study. Sheng Zhong has come up with the conception. Addtionally, Hui Li did the design of the work and was responsible for the creation of new software used in the work. Zhenhua Wang has drafted the work. Jianxin Xi completed the data collection part. Furthermore, an analysis of the data was done by Han Lu. As for the interpretation of the data, Zhishan Du has contributed a lot to this part. Sheng Zhong substantively revised it.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflicts of interest related to this manuscript, and all authors have approved the publication of this work.\u003c/p\u003e"},{"header":"References","content":"\u003col class=\"decimal_type\"\u003e\n\u003cli\u003eAA Z, M V. - Capitalizing on ATRX loss in glioma via PARP inhibition: Comment on \u0026quot;Loss of ATRX. D - 101472619. (- 1936-5233 (Print)):- 101173.\u003c/li\u003e\n\u003cli\u003eProkhorova E, Zobel F, Smith R, Zentout S, Gibbs-Seymour I, Schutzenhofer K, et al. Serine-linked PARP1 auto-modification controls PARP inhibitor response. Nat Commun. 2021;12(1):4055.\u003c/li\u003e\n\u003cli\u003eAmes BN, Gold LS. Endogenous mutagens and the causes of aging and cancer. Mutat Res. 1991;250(1-2):3-16.\u003c/li\u003e\n\u003cli\u003eKrishnakumar R, Kraus WL. The PARP side of the nucleus: molecular actions, physiological outcomes, and clinical targets. Mol Cell. 2010;39(1):8-24.\u003c/li\u003e\n\u003cli\u003eLangelier MF, Servent KM, Rogers EE, Pascal JM. A third zinc-binding domain of human poly(ADP-ribose) polymerase-1 coordinates DNA-dependent enzyme activation. J Biol Chem. 2008;283(7):4105-14.\u003c/li\u003e\n\u003cli\u003eSatoh MS, Poirier GG, Lindahl T. Dual function for poly(ADP-ribose) synthesis in response to DNA strand breakage. Biochemistry. 1994;33(23):7099-106.\u003c/li\u003e\n\u003cli\u003eFauzee NJ, Pan J, Wang YL. PARP and PARG inhibitors--new therapeutic targets in cancer treatment. Pathol Oncol Res. 2010;16(4):469-78.\u003c/li\u003e\n\u003cli\u003eSalemi M, Galia A, Fraggetta F, La Corte C, Pepe P, La Vignera S, et al. Poly (ADP-ribose) polymerase 1 protein expression in normal and neoplastic prostatic tissue. Eur J Histochem. 2013;57(2):e13.\u003c/li\u003e\n\u003cli\u003eStupp R, Taillibert S, Kanner A, Read W, Steinberg D, Lhermitte B, et al. Effect of Tumor-Treating Fields Plus Maintenance Temozolomide vs Maintenance Temozolomide Alone on Survival in Patients With Glioblastoma: A Randomized Clinical Trial. JAMA. 2017;318(23):2306-16.\u003c/li\u003e\n\u003cli\u003eHargrave DR, Bouffet E, Tabori U, Broniscer A, Cohen KJ, Hansford JR, et al. Efficacy and Safety of Dabrafenib in Pediatric Patients with BRAF V600 Mutation-Positive Relapsed or Refractory Low-Grade Glioma: Results from a Phase I/IIa Study. Clin Cancer Res. 2019;25(24):7303-11.\u003c/li\u003e\n\u003cli\u003eFangusaro J, Onar-Thomas A, Young Poussaint T, Wu S, Ligon AH, Lindeman N, et al. Selumetinib in paediatric patients with BRAF-aberrant or neurofibromatosis type 1-associated recurrent, refractory, or progressive low-grade glioma: a multicentre, phase 2 trial. Lancet Oncol. 2019;20(7):1011-22.\u003c/li\u003e\n\u003cli\u003eReardon DA, Brandes AA, Omuro A, Mulholland P, Lim M, Wick A, et al. Effect of Nivolumab vs Bevacizumab in Patients With Recurrent Glioblastoma: The CheckMate 143 Phase 3 Randomized Clinical Trial. JAMA Oncol. 2020;6(7):1003-10.\u003c/li\u003e\n\u003cli\u003eHannigan K, Kulkarni SS, Bdzhola VG, Golub AG, Yarmoluk SM, Talele TT. Identification of novel PARP-1 inhibitors by structure-based virtual screening. Bioorg Med Chem Lett. 2013;23(21):5790-4.\u003c/li\u003e\n\u003cli\u003eAshworth A. A synthetic lethal therapeutic approach: poly(ADP) ribose polymerase inhibitors for the treatment of cancers deficient in DNA double-strand break repair. J Clin Oncol. 2008;26(22):3785-90.\u003c/li\u003e\n\u003cli\u003eOhmoto A, Yachida S. Current status of poly(ADP-ribose) polymerase inhibitors and future directions. Onco Targets Ther. 2017;10:5195-208.\u003c/li\u003e\n\u003cli\u003eKumar M, Jaiswal RK, Yadava PK, Singh RP. An assessment of poly (ADP-ribose) polymerase-1 role in normal and cancer cells. Biofactors. 2020;46(6):894-905.\u003c/li\u003e\n\u003cli\u003eBryant HE, Schultz N, Thomas HD, Parker KM, Flower D, Lopez E, et al. Specific killing of BRCA2-deficient tumours with inhibitors of poly(ADP-ribose) polymerase. Nature. 2005;434(7035):913-7.\u003c/li\u003e\n\u003cli\u003eBrown JS, O\u0026apos;Carrigan B, Jackson SP, Yap TA. Targeting DNA Repair in Cancer: Beyond PARP Inhibitors. Cancer Discov. 2017;7(1):20-37.\u003c/li\u003e\n\u003cli\u003eOssovskaya V, Koo IC, Kaldjian EP, Alvares C, Sherman BM. Upregulation of Poly (ADP-Ribose) Polymerase-1 (PARP1) in Triple-Negative Breast Cancer and Other Primary Human Tumor Types. Genes Cancer. 2010;1(8):812-21.\u003c/li\u003e\n\u003cli\u003eGoncalves A, Finetti P, Sabatier R, Gilabert M, Adelaide J, Borg JP, et al. Poly(ADP-ribose) polymerase-1 mRNA expression in human breast cancer: a meta-analysis. Breast Cancer Res Treat. 2011;127(1):273-81.\u003c/li\u003e\n\u003cli\u003eGalia A, Calogero AE, Condorelli R, Fraggetta F, La Corte A, Ridolfo F, et al. PARP-1 protein expression in glioblastoma multiforme. Eur J Histochem. 2012;56(1):e9.\u003c/li\u003e\n\u003cli\u003eSandhu SK, Schelman WR, Wilding G, Moreno V, Baird RD, Miranda S, et al. The poly(ADP-ribose) polymerase inhibitor niraparib (MK4827) in BRCA mutation carriers and patients with sporadic cancer: a phase 1 dose-escalation trial. Lancet Oncol. 2013;14(9):882-92.\u003c/li\u003e\n\u003cli\u003eGorren AC, Schrammel A, Schmidt K, Mayer B. Thiols and neuronal nitric oxide synthase: complex formation, competitive inhibition, and enzyme stabilization. Biochemistry. 1997;36(14):4360-6.\u003c/li\u003e\n\u003cli\u003eLedermann J, Harter P, Gourley C. Correction to Lancet Oncol 2014; 15: 856. Olaparib maintenance therapy in patients with platinum-sensitive relapsed serous ovarian cancer: a preplanned retrospective analysis of outcomes by BRCA status in a randomised phase 2 trial. Lancet Oncol. 2015;16(4):e158.\u003c/li\u003e\n\u003cli\u003eRobson M, Im SA, Senkus E, Xu B, Domchek SM, Masuda N, et al. Olaparib for Metastatic Breast Cancer in Patients with a Germline BRCA Mutation. N Engl J Med. 2017;377(6):523-33.\u003c/li\u003e\n\u003cli\u003eAbida W, Campbell D, Patnaik A, Shapiro JD, Sautois B, Vogelzang NJ, et al. Non-BRCA DNA Damage Repair Gene Alterations and Response to the PARP Inhibitor Rucaparib in Metastatic Castration-Resistant Prostate Cancer: Analysis From the Phase II TRITON2 Study. Clin Cancer Res. 2020;26(11):2487-96.\u003c/li\u003e\n\u003cli\u003eGolan T, Hammel P, Reni M, Van Cutsem E, Macarulla T, Hall MJ, et al. Maintenance Olaparib for Germline BRCA-Mutated Metastatic Pancreatic Cancer. N Engl J Med. 2019;381(4):317-27.\u003c/li\u003e\n\u003cli\u003eShen Y, Rehman FL, Feng Y, Boshuizen J, Bajrami I, Elliott R, et al. BMN 673, a novel and highly potent PARP1/2 inhibitor for the treatment of human cancers with DNA repair deficiency. Clin Cancer Res. 2013;19(18):5003-15.\u003c/li\u003e\n\u003cli\u003eY Z, B Z, L P, M C, Id- Orcid X, AH K, et al. - Metascape provides a biologist-oriented resource for the analysis of systems-level. D - 101528555. (- 2041-1723 (Electronic)):- 1523.\u003c/li\u003e\n\u003cli\u003eServant N, Gravier E, Gestraud P, Laurent C, Paccard C, Biton A, et al. EMA - A R package for Easy Microarray data analysis. BMC Res Notes. 2010;3:277.\u003c/li\u003e\n\u003cli\u003eTibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997;16(4):385-95.\u003c/li\u003e\n\u003cli\u003eFriedman J, Hastie T, Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw. 2010;33(1):1-22.\u003c/li\u003e\n\u003cli\u003eHeagerty PJ, Lumley T, Pepe MS. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics. 2000;56(2):337-44.\u003c/li\u003e\n\u003cli\u003eR BB, L BC, D MA, L N, J PR, B R, et al. CHARMM: the biomolecular simulation program. Journal of computational chemistry. 2009;30(10).\u003c/li\u003e\n\u003cli\u003eRay Chaudhuri A, Nussenzweig A. The multifaceted roles of PARP1 in DNA repair and chromatin remodelling. Nat Rev Mol Cell Biol. 2017;18(10):610-21.\u003c/li\u003e\n\u003cli\u003eR P, P L, N S, L S, MR M, RH W, et al., inventors- A phase II study of the potent PARP inhibitor, Rucaparib (PF-01367338, AG014699).\u003c/li\u003e\n\u003cli\u003eTang X, Xu P, Wang B, Luo J, Fu R, Huang K, et al. Identification of a Specific Gene Module for Predicting Prognosis in Glioblastoma Patients. Front Oncol. 2019;9:812.\u003c/li\u003e\n\u003cli\u003eLiang P, Chai Y, Zhao H, Wang G. Predictive Analyses of Prognostic-Related Immune Genes and Immune Infiltrates for Glioblastoma. Diagnostics (Basel). 2020;10(3).\u003c/li\u003e\n\u003cli\u003eGuo XY, Zhang GH, Wang ZN, Duan H, Xie T, Liang L, et al. A novel Foxp3-related immune prognostic signature for glioblastoma multiforme based on immunogenomic profiling. Aging (Albany NY). 2021;13(3):3501-17.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Top 20 ranked compounds with higher libdock scores than Lynparza.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.98185117967332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.86388384754991%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompounds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.970961887477314%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLibdock score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.800362976406534%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.682395644283122%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompounds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.7005444646098%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLibdock score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.98185117967332%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.86388384754991%\"\u003e\n \u003cp\u003eZINC000003995616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.970961887477314%\"\u003e\n \u003cp\u003e197.589\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.800362976406534%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.682395644283122%\"\u003e\n \u003cp\u003eZINC000021992902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.7005444646098%\"\u003e\n \u003cp\u003e169.035\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.98185117967332%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.86388384754991%\"\u003e\n \u003cp\u003eZINC000011616634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.970961887477314%\"\u003e\n \u003cp\u003e183.062\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.800362976406534%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.682395644283122%\"\u003e\n \u003cp\u003eZINC000012495612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.7005444646098%\"\u003e\n \u003cp\u003e165.482\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.98185117967332%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.86388384754991%\"\u003e\n \u003cp\u003eZINC000011616633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.970961887477314%\"\u003e\n \u003cp\u003e180.699\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.800362976406534%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.682395644283122%\"\u003e\n \u003cp\u003eZINC000031298217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.7005444646098%\"\u003e\n \u003cp\u003e162.833\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.98185117967332%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.86388384754991%\"\u003e\n \u003cp\u003eZINC000017654900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.970961887477314%\"\u003e\n \u003cp\u003e179.771\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.800362976406534%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.682395644283122%\"\u003e\n \u003cp\u003eZINC000044306670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.7005444646098%\"\u003e\n \u003cp\u003e162.746\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.98185117967332%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.86388384754991%\"\u003e\n \u003cp\u003eZINC000028968107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.970961887477314%\"\u003e\n \u003cp\u003e173.664\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.800362976406534%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.682395644283122%\"\u003e\n \u003cp\u003eZINC000003979028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.7005444646098%\"\u003e\n \u003cp\u003e162.196\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.98185117967332%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.86388384754991%\"\u003e\n \u003cp\u003eZINC000049872065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.970961887477314%\"\u003e\n \u003cp\u003e172.943\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.800362976406534%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.682395644283122%\"\u003e\n \u003cp\u003eZINC000002033589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.7005444646098%\"\u003e\n \u003cp\u003e161.044\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.98185117967332%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.86388384754991%\"\u003e\n \u003cp\u003eZINC000002528509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.970961887477314%\"\u003e\n \u003cp\u003e171.533\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.800362976406534%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.682395644283122%\"\u003e\n \u003cp\u003eZINC000044086691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.7005444646098%\"\u003e\n \u003cp\u003e160.416\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.98185117967332%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.86388384754991%\"\u003e\n \u003cp\u003eZINC000073280937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.970961887477314%\"\u003e\n \u003cp\u003e171.524\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.800362976406534%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.682395644283122%\"\u003e\n \u003cp\u003eZINC000034944433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.7005444646098%\"\u003e\n \u003cp\u003e159.795\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.98185117967332%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.86388384754991%\"\u003e\n \u003cp\u003eZINC000014951634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.970961887477314%\"\u003e\n \u003cp\u003e170.928\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.800362976406534%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.682395644283122%\"\u003e\n \u003cp\u003eZINC000038143594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.7005444646098%\"\u003e\n \u003cp\u003e159.372\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.98185117967332%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.86388384754991%\"\u003e\n \u003cp\u003eZINC000053057130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.970961887477314%\"\u003e\n \u003cp\u003e170.314\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.800362976406534%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.682395644283122%\"\u003e\n \u003cp\u003eZINC000002528486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.7005444646098%\"\u003e\n \u003cp\u003e158.692\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. ADME (Adsorption, Distribution, Metabolism, Excretion) properties of compounds.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompounds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSolubility Level\u003c/strong\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBBB level\u003c/strong\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCYP2D6\u003c/strong\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHepatotoxicity\u003c/strong\u003ed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbsorption Level\u003c/strong\u003ee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPB Level\u003c/strong\u003ef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000003995616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000011616634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000011616633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000017654900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000028968107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000049872065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000002528509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000073280937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000014951634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000053057130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000021992902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000012495612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000031298217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000044306670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000003979028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000002033589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000044086691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000034944433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000038143594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eZINC000002528486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66869300911854%\"\u003e\n \u003cp\u003eLynparza\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.310030395136778%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814589665653495%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.62917933130699%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.118541033434651%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;a Aqueous-solubility level: 0 (extremely low); 1 (very low, but possible); 2 (low); 3 (good)\u003c/p\u003e\n\u003cp\u003eb Blood Brain Barrier level: 0 (Very high penetrant); 1 (High); 2 (Medium); 3 (Low); 4 (Undefined)\u003c/p\u003e\n\u003cp\u003ec Cytochrome P450 2D6 level: 0 (Non-inhibitor); 1 (Inhibitor)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ed Hepatotoxicity: 0 (Nontoxic); 1 (Toxic)\u003c/p\u003e\n\u003cp\u003ee Human-intestinal absorption level: 0 (good); 1 (moderate); 2 (poor); 3 (very poor)\u003c/p\u003e\n\u003cp\u003ef Plasma Protein Binding: 0 (Absorbent weak); 1 (Absorbent strong)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Toxicities of compounds.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"10%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"21.475409836065573%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompounds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"21.9672131147541%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMouse NTP\u003c/strong\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"21.9672131147541%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRat NTP\u003c/strong\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"12.950819672131148%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAMES\u003c/strong\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"11.639344262295081%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDTP\u003c/strong\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.238805970149254%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.761194029850746%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.238805970149254%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.761194029850746%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.475409836065573%\"\u003e\n \u003cp\u003eZINC000003995616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.235\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.245\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.300\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.950819672131148%\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.639344262295081%\"\u003e\n \u003cp\u003e0.252\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.475409836065573%\"\u003e\n \u003cp\u003eZINC000011616634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.761\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.509\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.308\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.583\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.950819672131148%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.639344262295081%\"\u003e\n \u003cp\u003e0.493\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.475409836065573%\"\u003e\n \u003cp\u003eZINC000011616633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.761\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.509\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.308\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.583\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.950819672131148%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.639344262295081%\"\u003e\n \u003cp\u003e0.493\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.475409836065573%\"\u003e\n \u003cp\u003eZINC000017654900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.572\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.162\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.517\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.950819672131148%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.639344262295081%\"\u003e\n \u003cp\u003e0.321\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.475409836065573%\"\u003e\n \u003cp\u003eZINC000028968107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.110\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.321\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.336\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.045\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.950819672131148%\"\u003e\n \u003cp\u003e0.115\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.639344262295081%\"\u003e\n 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\u003cp\u003eZINC000044086691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.562\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.829\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.193\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.281\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.950819672131148%\"\u003e\n \u003cp\u003e0.031\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.639344262295081%\"\u003e\n \u003cp\u003e0.823\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.475409836065573%\"\u003e\n \u003cp\u003eZINC000034944433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.502\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.433\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.327\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.526\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.950819672131148%\"\u003e\n \u003cp\u003e0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.639344262295081%\"\u003e\n \u003cp\u003e0.836\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.475409836065573%\"\u003e\n \u003cp\u003eZINC000038143594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.384\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.405\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.265\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.300\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.950819672131148%\"\u003e\n \u003cp\u003e0.178\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.639344262295081%\"\u003e\n \u003cp\u003e0.614\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.475409836065573%\"\u003e\n \u003cp\u003eZINC000002528486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.275\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.564\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.462\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.443\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.950819672131148%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.639344262295081%\"\u003e\n \u003cp\u003e0.587\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.475409836065573%\"\u003e\n \u003cp\u003eLynparza\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.665\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.311\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.967213114754099%\"\u003e\n \u003cp\u003e0.440\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e0.627\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.950819672131148%\"\u003e\n \u003cp\u003e0.368\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.639344262295081%\"\u003e\n \u003cp\u003e0.672\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;a \u0026lt;0.3 (Non-Carcinogen); \u0026gt;0.7 (Carcinogen)\u003c/p\u003e\n\u003cp\u003eb \u0026lt;0.3 (Non-Mutagen); \u0026gt;0.7 (Mutagen)\u003c/p\u003e\n\u003cp\u003ec \u0026lt;0.3 (Non-Toxic); \u0026gt;0.7 (Toxic)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e CDOCKER interaction energy of compounds with PARP-1.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.40659340659341%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompounds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.59340659340659%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCDOCKER Interaction energy (Kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.40659340659341%\"\u003e\n \u003cp\u003eZINC000014951634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.59340659340659%\"\u003e\n \u003cp\u003e-72.8455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.40659340659341%\"\u003e\n \u003cp\u003eZINC000053057130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.59340659340659%\"\u003e\n \u003cp\u003e-70.3196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.40659340659341%\"\u003e\n \u003cp\u003eLynparza\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.59340659340659%\"\u003e\n \u003cp\u003e-54.2416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Hydrogen bond interaction parameters for each compound and PARP-1 residues.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.260162601626018%\"\u003e\n \u003cp\u003e\u003cstrong\u003eReceptor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.552845528455286%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.723577235772357%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDonor atom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.10569105691057%\"\u003e\n \u003cp\u003e\u003cstrong\u003eReceptor Atom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.357723577235772%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistances (\u0026Aring;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"9\" width=\"16.260162601626018%\"\u003e\n \u003cp\u003ePARP-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" width=\"24.552845528455286%\"\u003e\n \u003cp\u003eZINC000014951634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.723577235772357%\"\u003e\n \u003cp\u003eB:ARG878:HH21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.10569105691057%\"\u003e\n \u003cp\u003eZINC000014951634:O40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.357723577235772%\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.945054945054945%\"\u003e\n \u003cp\u003eB:TYR896:O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.175824175824175%\"\u003e\n \u003cp\u003eZINC000014951634:H44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.87912087912088%\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.945054945054945%\"\u003e\n \u003cp\u003eB:TRP861:O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.175824175824175%\"\u003e\n \u003cp\u003eZINC000014951634:H43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.87912087912088%\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.945054945054945%\"\u003e\n \u003cp\u003eB:SER904:HG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.175824175824175%\"\u003e\n \u003cp\u003eZINC000014951634:O3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.87912087912088%\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"29.320388349514563%\"\u003e\n \u003cp\u003eZINC000053057130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.16504854368932%\"\u003e\n \u003cp\u003eB:GLY863:O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.75728155339806%\"\u003e\n \u003cp\u003eZINC000053057130:H39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.757281553398059%\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.945054945054945%\"\u003e\n \u003cp\u003eB:ASP766:OD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.175824175824175%\"\u003e\n \u003cp\u003eZINC000053057130:H66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.87912087912088%\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.945054945054945%\"\u003e\n \u003cp\u003eB:ARG878:HH21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.175824175824175%\"\u003e\n \u003cp\u003eZINC000053057130:O36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.87912087912088%\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"29.320388349514563%\"\u003e\n \u003cp\u003eLynparza\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.16504854368932%\"\u003e\n \u003cp\u003eB:SER904:HG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.75728155339806%\"\u003e\n \u003cp\u003eMolecule：O27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.757281553398059%\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.945054945054945%\"\u003e\n \u003cp\u003eB:GLN759:HE21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.175824175824175%\"\u003e\n \u003cp\u003eMolecule：O11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.87912087912088%\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6: Pi-Pi interaction, Pi-Alkyl interaction, Pi-Anion interaction and Alkyl interaction parameters for each compound and PARP-1 residues.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.093514328808446%\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteraction parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.105580693815988%\"\u003e\n \u003cp\u003e\u003cstrong\u003eReceptor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361990950226243%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.574660633484163%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDonor\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eatom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.457013574660632%\"\u003e\n \u003cp\u003e\u003cstrong\u003eReceptor Atom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.407239819004525%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistances (\u0026Aring;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.093514328808446%\"\u003e\n \u003cp\u003ePi-Pi staked\u0026nbsp;\u003c/p\u003e\n \u003cp\u003einteraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"18\" width=\"10.105580693815988%\"\u003e\n \u003cp\u003ePARP-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361990950226243%\"\u003e\n \u003cp\u003eZINC000014951634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.574660633484163%\"\u003e\n \u003cp\u003eB:TYR896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.457013574660632%\"\u003e\n \u003cp\u003eZINC000014951634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.407239819004525%\"\u003e\n \u003cp\u003e4.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.026845637583893%\"\u003e\n \u003cp\u003ePi-Pi T-shaped interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.651006711409394%\"\u003e\n \u003cp\u003eZINC000053057130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.100671140939598%\"\u003e\n \u003cp\u003eB:TYR889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.644295302013422%\"\u003e\n \u003cp\u003eZINC000053057130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.577181208053691%\"\u003e\n \u003cp\u003e5.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"29.026845637583893%\"\u003e\n \u003cp\u003ePi-Alkyl interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"22.651006711409394%\"\u003e\n \u003cp\u003eZINC000014951634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.100671140939598%\"\u003e\n \u003cp\u003eB:TYR889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.644295302013422%\"\u003e\n \u003cp\u003eZINC000014951634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.577181208053691%\"\u003e\n \u003cp\u003e4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\"\u003e\n \u003cp\u003eB:ARG878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.791666666666664%\"\u003e\n \u003cp\u003eZINC000014951634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\"\u003e\n \u003cp\u003e5.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\"\u003e\n \u003cp\u003eB:LEU877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.791666666666664%\"\u003e\n \u003cp\u003eZINC000014951634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\"\u003e\n \u003cp\u003e5.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" width=\"31.914893617021278%\"\u003e\n \u003cp\u003eZINC000053057130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n 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width=\"31.25%\"\u003e\n \u003cp\u003eB:ARG878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.791666666666664%\"\u003e\n \u003cp\u003eZINC000053057130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\"\u003e\n \u003cp\u003e4.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"31.914893617021278%\"\u003e\n \u003cp\u003eLynparza\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003eB:TYR907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.49645390070922%\"\u003e\n \u003cp\u003eMolecule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.31205673758865%\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\"\u003e\n \u003cp\u003eB:HIS862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.791666666666664%\"\u003e\n \u003cp\u003eMolecule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\"\u003e\n \u003cp\u003e4.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\"\u003e\n \u003cp\u003eB:ALA762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.791666666666664%\"\u003e\n \u003cp\u003eMolecule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\"\u003e\n \u003cp\u003e5.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"29.026845637583893%\"\u003e\n \u003cp\u003ePi-Anion interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.651006711409394%\"\u003e\n \u003cp\u003eZINC000014951634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.100671140939598%\"\u003e\n \u003cp\u003eB:GLU988:OE1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.644295302013422%\"\u003e\n \u003cp\u003eZINC000014951634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.577181208053691%\"\u003e\n \u003cp\u003e4.18\u003c/p\u003e\n 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width=\"22.651006711409394%\"\u003e\n \u003cp\u003eZINC000053057130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.100671140939598%\"\u003e\n \u003cp\u003eB:ALA898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.644295302013422%\"\u003e\n \u003cp\u003eZINC000053057130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.577181208053691%\"\u003e\n \u003cp\u003e5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"31.914893617021278%\"\u003e\n \u003cp\u003eLynparza\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003eB:ALA898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.49645390070922%\"\u003e\n \u003cp\u003eMolecule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.31205673758865%\"\u003e\n \u003cp\u003e4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\"\u003e\n \u003cp\u003eB:LYS903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.791666666666664%\"\u003e\n \u003cp\u003eMolecule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\"\u003e\n \u003cp\u003e4.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Glioma, PARP-1 (Poly ADP-ribose polymerase 1), DNA damage repair, Inhibitors","lastPublishedDoi":"10.21203/rs.3.rs-1714523/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1714523/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGliomas are the most common primary intracranial malignancies. Current standard treatments include surgical resection, supplemented by radiotherapy and chemotherapy, but the prognosis is poor. PARP-1 (Poly ADP-ribose polymerase 1) inhibitors have become a hotspot in cancer treatment by affecting DNA damage repair pathways, such as metastatic breast cancer, advanced prostate cancer, ovarian cancer and pancreatic cancer. Recently, a study found that the expression level of PARP-1 in glioma cell lines was significantly increased; and PARP-1 inhibitor significantly suppressed the proliferation of glioma cells and aggravated the DNA damage effect of temozolomide(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Therefore, PARP-1 is expected to become a therapeutic target of molecular targeted therapy for glioma and a sensitizer for radiotherapy and chemotherapy. Moreover, the expression level of PARP-1 is expected to become an evaluation indicator for the prognosis of patients. Firstly, we downloaded RNA-seq data from 416 glioma samples from the GEO (Gene Expression Omnibus) database and divided them into PARP1_H and PARP1_L, according to the expression level of PARP-1. The overall prognosis of PARP1_L patients was better than that of PARP1_H. The effect of radiotherapy and chemotherapy in PARP1_L was better. Results also showed that the expression level of 278 genes of DNA damage repair in PARP1_H is higher than that in PARP1_L. Next, LASSO (Least Absolute Shrinkage and Selection Operator) Cox analysis was carried out for genes of DNA-repair proteins differentially expressed between PARP1_H and PARP1_L patients. According to the developed four-gene DPS (DNA-repair prognostic signature), glioma patients were divided into high-risk and low-risk groups. Then, we developed a new nomogram to assess overall survival in glioma patients. Furthermore, to search for more effective PARP-1 inhibitors with fewer side effects, we used a series of computer-aided techniques such as Discovery Studio 4.5, Schrodinger and PyMol for screening and evaluation. Finally, ZINC000014951634 and ZINC000053057130 proved to be favorable PARP-1 inhibitors by analyzing pharmacological and toxicological properties, ligand-protein complex affinity, and stability. In conclusion, this study investigated the effect of PARP-1 expression on prognosis and the sensitization effect of radiotherapy and chemotherapy in glioma and further screened PARP-1 targeted inhibitors to improve the prognosis of glioma patients.\u003c/p\u003e","manuscriptTitle":"The Study on the Expression Level of PARP-1 in Glioma and Computational Screening of PARP-1 Inhibitors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-17 20:12:15","doi":"10.21203/rs.3.rs-1714523/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4f171193-f3ae-4dcd-a9b0-def0f4b740dc","owner":[],"postedDate":"June 17th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-09-14T11:22:16+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-17 20:12:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1714523","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1714523","identity":"rs-1714523","version":["v1"]},"buildId":"GqpaHPwrfC8PjnIFayRh5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}