In silico analysis of human NEK10 reveals novel domain architecture and protein-protein interactions

In silico analysis of human NEK10 reveals novel domain architecture and protein-protein interactions | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL PROTEINS: Structure, Function, and Bioinformatics This is a preprint and has not been peer reviewed. Data may be preliminary. 7 May 2025 V1 Latest version Share on In silico analysis of human NEK10 reveals novel domain architecture and protein-protein interactions Authors : Andriele S. Eichner , Nathaniel Zimmerman , and Shaneen Singh 0000-0002-4003-5756 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174665051.19843345/v1 Published Proteins: Structure, Function, and Bioinformatics Version of record Peer review timeline 446 views 243 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Cancer is the second leading cause of death worldwide, with an estimated 27.5 million new cases projected by 2040. Disruptions in cell cycle control cause DNA replication errors to accumulate during cell growth, leading to genomic instability and tumor development. Proteins that regulate cell cycle progression and checkpoint mechanisms are crucial targets for cancer therapy. NIMA-related kinases (NEKs) are a family of serine/threonine kinases involved in regulating various aspects of the cell cycle and mitotic checkpoints in humans. Among these, NEK10 is the most divergent member and has been associated with both cancer and ciliopathies, a group of disorders caused by defects in cilia structure or function. Despite its biological significance and distinctive domain architecture, the structural details of NEK10 remain largely unknown. To address this gap, we employed computational modeling techniques to predict the complete structure of the NEK10 protein. Our analysis revealed a catalytic domain flanked by two coiled-coil domains, armadillo repeats (ARM repeats), an ATP binding site, two putative ubiquitin-associated (UBA) domains, and a PEST sequence known to regulate protein degradation. Furthermore, we mapped a comprehensive interactome of NEK10, uncovering previously unreported interactions with the cancer-related proteins MAP3K1 and HSPB1. MAP3K1, a serine/threonine kinase and E3 ubiquitin ligase frequently mutated in cancers, interacts with the catalytic region of NEK10. The interaction with HSPB1, a molecular chaperone associated with poor cancer prognosis, is mediated by NEK10’s ARM repeats. Our findings highlight a potential connection between NEK10, ciliogenesis, and cancer, suggesting an important role in cancer development and progression. In silico analysis of human NEK10 reveals novel domain architecture and protein-protein interactions In Silico Study of Human NEK10 Andriele S. Eichner 1,2 , Nathaniel Zimmerman 1 , Shaneen Singh 1,2,3 1 Department of Biology, Brooklyn College, The City University of New York, Brooklyn, New York, 11210. 2 The Biochemistry PhD program, The Graduate Center of the City University of New York; New York, 10016 3 The Biology PhD program, The Graduate Center of the City University of New York; New York, 10016 * Correspondence: [email protected] ; Tel.: 718- 951-5720; Fax: 718-951-4659 Abstract Cancer is the second leading cause of death worldwide, with an estimated 27.5 million new cases projected by 2040. Disruptions in cell cycle control cause DNA replication errors to accumulate during cell growth, leading to genomic instability and tumor development. Proteins that regulate cell cycle progression and checkpoint mechanisms are crucial targets for cancer therapy. NIMA-related kinases (NEKs) are a family of serine/threonine kinases involved in regulating various aspects of the cell cycle and mitotic checkpoints in humans. Among these, NEK10 is the most divergent member and has been associated with both cancer and ciliopathies, a group of disorders caused by defects in cilia structure or function. Despite its biological significance and distinctive domain architecture, the structural details of NEK10 remain largely unknown. To address this gap, we employed computational modeling techniques to predict the complete structure of the NEK10 protein. Our analysis revealed a catalytic domain flanked by two coiled-coil domains, armadillo repeats (ARM repeats), an ATP binding site, two putative ubiquitin-associated (UBA) domains, and a PEST sequence known to regulate protein degradation. Furthermore, we mapped a comprehensive interactome of NEK10, uncovering previously unreported interactions with the cancer-related proteins MAP3K1 and HSPB1. MAP3K1, a serine/threonine kinase and E3 ubiquitin ligase frequently mutated in cancers, interacts with the catalytic region of NEK10. The interaction with HSPB1, a molecular chaperone associated with poor cancer prognosis, is mediated by NEK10’s ARM repeats. Our findings highlight a potential connection between NEK10, ciliogenesis, and cancer, suggesting an important role in cancer development and progression. Keywords NEKs, NEK10, cancer, computational modeling, interactome, cell cycle, cilia, ubiquitin Main Text 1. Introduction Ensuring genome stability is critical for preserving cell integrity and preventing mistakes during DNA replication. This stability is required to resist both internal sources of DNA damage, such as reactive oxygen species (ROS) created during cellular metabolism, and external influences such as UV light, ionizing radiation, and cancer-causing chemicals. Tumors initiation and progression are thought to occur due to acquired genomic alterations in the original normal cells, followed by the selection of more aggressive sub clones [1]. Cells, predictably, have evolved sophisticated and frequently replicated systems that enable them to undergo mitosis without error. One of the most well-studied techniques of mitotic control is reversible phosphorylation. Cyclin-dependent kinases (CDKs), in collaboration with opposing phosphatases, regulate the phosphorylation state of numerous substrate proteins, which in turn regulate the processes that coordinate mitosis. Only a few protein kinase families that govern mitosis have been found thus far, including Aurora kinases, and Polo-like kinases (PLKs). Nonetheless, there is an additional protein kinase family that is less well-defined yet performs critical roles in mitosis. This group is known as NIMA-related kinases, or NEKs [2-4]. Human cells express a total of eleven genes, specifically NEK1 through NEK11. Except for NEK10, these proteins possess a protein domain structure that encompasses an N-terminal catalytic kinase domain. Functionally, multiple studies conducted in different systems, including humans, offer evidence that most NEKs have a role, either directly or indirectly, in promoting the cell cycle and/or the development of cilia [3-4]. Hence, any change in the expression or mutation of NEKs can potentially disturb these crucial processes, indicating their potential role in both human cancer and inherited ciliopathies. While the exact roles of most mammalian NEKs are not fully understood, it is established that certain NEK family members participate in one or more of the following functions: cell cycle [5-6], centrosome regulation [7,8], primary cilia [9,10], DNA damage response (DDR) [11,12], RNA splicing [13], myogenic differentiation [14], intracellular protein transport [15], and mitochondria homeostasis [16]. Furthermore, there is a growing body of evidence indicating that NEKs play a role in the development of cancer. Elevated expression is the primary cause, while a limited number of rare mutations in NEKs have been identified through cancer genome screenings [3]. The NEK10 protein consisting of 1,172 amino acids [2] and is the most structurally divergent member of the family with a centrally located catalytic kinase domain, flanked by two large regulatory domains and a coiled-coil regions but remains the least studied members of the human NEK protein family. In its N-terminal regulatory region, NEK10 displays four ARM repeats and a PEST region, which are thought to be important for protein-protein interactions and proteolytic regulation, respectively [17]. NEK10 exhibits dual-specificity kinase activity, effectively catalyzing the phosphorylation of both itself, and peptide substrates on serine and tyrosine residues. The enzymatic function is enhanced by the process of tyrosine auto-phosphorylation. T657, I693, S696, and C697 were identified to be unique to the activation region of NEK10 and mutations in D655 and I693 lead to a kinase dead NEK10. NEK10 homologs from nematodes to humans show conservation of these four key residues [18]. Functionally, NEK10 is thought to be involved in ciliogenesis [19], DDR [11], and mitochondrial homeostasis [16]. Recent studies demonstrate NEK10’s involvement in several diseases, such bronchiectasis syndrome [20], breast cancer [11], and lung cancer [16]. Despite its functional significance, NIH has termed it an understudied kinase [21]. We hypothesize that the unique structural features of NEK10 underlie fundamental functional differences and selection of unique binding partners in the NEK family of proteins. The structural information for NEK10 and its predicted partners is either unavailable or only partially available. In this study we present a complete structural representation of NEK10 and elucidate the protein-protein binding mechanisms of NEK10 with select novel cancer related protein partners using in silico approaches. Structural modeling and in silico analysis tools provide valuable insights into knowledge gaps while also providing a cost-effective and logical starting point in experimental design. Finally, the results of this study can pave the way for future research aimed at identifying novel drug targets to control NEK10 functions in gene expression during tumor development. Materials and Methods A flowchart summarizing the general protocol of the present study with the various tools used to analyze NEK10 and its interactions (discussed in detail below) is shown in the supporting Figure S1. Modeling and characterization of NEK10 Domain Architecture To obtain domain architecture information for NEK10, we utilized its full-length amino acid sequence (1172AA; UniProt ID: Q6ZWH5) as input for following databases: PROSITE [22], PFAM [23], SMART [24], and Conserved Domain Database (CDD) [25]. The E-values of the individual results were used to distinguish between findings from each database that could have occurred by chance and results that are more likely to occur in the deposited sequence. The farthest boundary boundaries were used to map the consensus domain architecture. The secondary structure prediction was used to refine the results. To detect PEST motifs, ePESTfind [26] was used. Since there is no clear motif for a UBA domain, a manual evaluation approach was used, guided by the description of a solved structure of HHR23A UBA [27] and identified by a combination of sequence and structural analyses. Hydrophobic patches were visualized using PyMOL and Yellow-Red-Blue (YRB) script [28]. Angle between helices were measured in PyMOL using the Python script AngleBetweenHelices [29]. Surface potential maps were calculated using APBS [30]. Net charge on both putative UBA domains was calculated using a built-in PyMOL feature. Sequence conservation and multiple sequence alignment was analyzed using pBLAST [31] and UGENE [32]. Manual analysis of coincidence of UBA domain with PEST sequence was analyzed using ePESTfind, Microsoft Excel, and RSCB Protein Data Bank (PDB) public database. Secondary Structure Prediction (SSP), and prediction of Intrinsically Disordered Regions (IDRs) To obtain secondary structure predictions for NEK10, we submitted its full-length amino acid sequence (1172AA; UniProt ID: Q6ZWH5) in the SSPRED webserver [33], which forms a consensus of SSP obtained from the following SSP databases: PSIPRED [34], YASPIN [35], PSSpred [36], JPred4 [37], SABLE [38], RaptorX [39], and SCRATCH [40]. Following, the domain borders were refined using the results from consensus secondary predictions, and we used Biological Sequences (IBS) [41] to generate a new refined domain architecture graphic. Furthermore, the full-length sequence of NEK10 was utilized to predict its intrinsically disordered regions (IDRs) via the following computational tools: DISOPRED [42], IUPred2A [43], MFDp2 [44], and CSpritz [45]. Consideration was given to the predicted IDRs, results when selecting the method for predicting the tertiary structure of NEK10. Tertiary Structure Prediction of NEK10, evaluation, refining and visualization To build a full-length model for NEK10, we utilized NEK2 (PDB ID:2W5A) as a template. Because NEK10’s structure contains over a thousand residues, we used multiple modelling tools to create models by parts and then assembled the full-length model using advanced MODELLER scripts [46]. The generated final models were then evaluated for optimal quality using the following programs: VERIFY3D [47], ProsaWEB [48], VoroMQA [49], and ProQ3 [50]. Top quality models were then refined as needed using ModRefiner [51], and 3DRefine [52]. For model visualization as well as biophysical/biochemical characterization, we used both Pymol [53] and Chimera software [54]. Protein-Protein Interaction Predictions for NEK10 To obtain Protein-Protein Interaction Predictions (PPI) for NEK10, we utilized its UniProt accession number (Q6ZWH5) as the input for the following PPI prediction databases: (a) The Biological General Repository for Interaction Datasets (BioGRID), a public database that stores and shares genetic and protein interaction data from humans and other model organisms. BioGRID has more than 1,740,000 interactions, gathered from both large datasets and smaller, more focused studies. These interactions come from more than 70,000+ papers in the primary literature [55], (b) IntAct, a free, open-source database system as well as molecular interaction data analysis tool. All interactions are based on either curated literature or direct user submissions [56], (c) STRING, a database of predicted and known protein-protein interactions. The interactions include direct (physical) and indirect (functional) associations; they stem from computational prediction, from knowledge transfer between organisms, and from interactions aggregated from other (primary) databases [57], (d) PrePPI, a database of predicted protein-protein interactions. The database’s predicted interactions are determined using a Bayesian framework that incorporates structural, functional, evolutionary, and expression information [58], (e) Mentha, a protein interaction database archive information about PPI from published articles [59], and (f) InnateDB, a database that provides manually-curated knowledge of genes, proteins, and signaling responses in mammalian innate immunity and integrates interactions and pathways from many major public databases [60]. Results were then compiled and NEK10 interactors that appeared in 3 or more webservers were considered for further studies . Interactome for NEK10 In a biological network known as the ”interactome,” the molecular interactions of a specific protein can be mapped functionally. The study of the interactome could accelerate the discovery of biomarkers and therapeutics and assist in the detection of malfunctioning pathways [61]. To construct a complete interactome for NEK10 based on the obtained protein-protein predictions, we first compiled the following information from “The Human Protein Atlas” [62]: interactor protein name, molecular function, and cancer location. These data were added to a spreadsheet which served as input to Cytoscape [63]. Cytoscape allowed us to obtain a protein-protein interaction network for NEK10 and further categorize the location of each protein interactor of NEK10. We decided to focus on MAP3K1 and HSPB1 in more detail as these proteins appeared in more than three protein-protein prediction databases but have not been reported previously in the literature as interactors, lack complete structural information and are known cancer proteins. Modeling and characterization of interaction partners of NEK10 MAP3K1 (1512 aa; UniProt ID: Q13233) and HSPB1 (205 aa; UniProt ID: P04792) were investigated for domain architecture, secondary structure prediction, IDR analysis, and tertiary structure prediction/characterization using the same methodology as described for NEK10 above. To construct a full-length model of MAP3K1, we used its FASTA sequence, together with the backbone model from AlphaFold (ID: AF-Q13233-F1) for comparative modeling in the Robetta server [64]. To construct a full-length model of HSPB1 (Hsp27), we also chose the Robetta server for comparative modeling using the backbone model for HSPB1 from AlphaFold (ID: AF-P04792-F1) with HSPB1 FASTA sequence. Prediction of NEK10 – MAP3K1 interaction and NEK10 – HSPB1 interactions We utilized ClusPro [65], and HDOCK [66] to predict putative interaction scenarios of interaction of NEK10 with MAP3K1 as well as HSPB1. PDBsum [67] was used to analyze the interactions within the docked complexes and complexes were visualized in Pymol. Modeling and characterization of NEK10 Currently, the PDB does not contain any full-length structures for NEK10. Although The AlphaFold database [68] offers a three-dimensional model of the complete NEK10 structure, certain regions of the protein, specifically its N-terminus, exhibit a remarkably low quality. We employed a range of computational techniques to predict the domain architecture, secondary structure, IDRs, and tertiary structure of NEK10 to complete and detailed structural representation of the full length NEK10 protein. In this study, we used the full-length amino acid sequence of NEK10 (1172AA) to derive a consensus from a variety of domain architecture prediction tools. These findings revealed that NEK10 has Serine-Threonine Kinase domain spanning residues N518 to L791 flanked by two coiled-coil, four ARM repeats covering residues K199 to C319, two putative UBA domains located at L36-I64 and F868-E901 (Figure 1C). In addition, NEK10 has a small IDR predicted to be involved in binding (Figure S2) identified by a consensus of predictions from four disordered prediction programs. The secondary structure prediction of NEK10 conforms to expected secondary structure elements in the structured regions of the domain architecture, e.g., multiple alpha helices typical of ARM repeats . Tertiary Structure Prediction of NEK10 To build a full-length model for NEK10, we utilized NEK2 (PDB ID:2W5A) as a template. Because NEK10’s structure contains over a thousand residues, we used multiple modelling tools to create models by parts and then assembled the full-length model using advanced MODELLER scripts. Our final models were then tested for optimal quality to find the top model (Table S1). In VERIFY3D, the top model (model 5) averaged 3D-1D scores showing 71.14% of residues with >0.2 score. VoroMQA score for model 5 is 0.426 (the highest of all other models), a model scoring above 0.4 in VoroMQA is considered a high-quality model. In ProsaWeb, model 5 scored -14.2 demonstrating that our model falls within the acceptable range for similar x-ray and NMR structures. Finally, in ProQ2, model 5 scored better than all other models and was the only model to meet the 0.4 ProQ2 established threshold for a high-quality model. Our findings show that the binding motif-containing areas are centrally located, with the ARM repeats positioned near the NEK10 N-terminus (Figure 1A, 1C). Surface electrostatic profile of the UBA domains excluded one of the domains from further analysis (detailed in the result section ‘The presence of a PEST sequence and putative UBA domains suggest it is regulated via ubiquitination’). Surface electrostatic profile of the complete model shows that the ARM repeats and the second UBA domain are in a highly negative (red) region, while the kinase domain is seen in a mostly positive (blue) region (Figure 1B). Figure 1. Structural model of NEK10. A full-length NEK10 protein structure composed of 1172 amino acids predicted based on the amino-acid sequence and known structural information (PDB ID:2W5A). (B) Surface electrostatic profile of NEK10. Blue – Positive; Red – Negative (-5 to +5kT/e). (C) Domain architecture: The grey line represents the full-length of the protein. The orange rectangle represents the Serine-Threonine domain spanning residues N518 to C791, the green rectangle represents the ARM repeats covering residues K199 to C319, coiled-coils are seen as blue boxes, and the PEST motif is displayed as a yellow rectangle between positions N890 and K921. Red boxes represent putative UBA domains in regions L36-I64 and F868-E901; the F868-E901 UBA domain has an overlap with PEST sequence. Comparison between NEK10 predicted models: AlphaFold versus our model Significant advancements have been made in computational protein structure prediction, particularly in template-free modeling [69]. In the absence of experimental structures, it is preferable to obtain an estimate of the local and global quality of predicted 3D models, which is done via model quality assessment [70]. We compared our NEK10 protein model to the NEK10 model in the AlphaFold Database using various model evaluation methods (Figure S3). Our results reveal that our NEK10 model outperformed AlphaFold’s model in terms of quality, and therefore is better suited for docking and protein-protein studies. In VERIFY3D, averaged 3D-1D scores for our NEK10 model show 71.14% of residues with shows 57.29% of residues with >=0.2 score. By looking at both plots and focusing on the orange arrows, we can see that in our model for NEK10 most residues on its N-terminus score above or equal 0.2. The same is not true for AlphaFold’s NEK10 model. Most residues on its N-terminus score are lower than 0.2. Protein-Protein Interaction Predictions for NEK10: Mapping a comprehensive interactome for NEK10 Four PPI databases were probed to map a comprehensive model for the network of NEK10’s protein-protein interactions. Several predictions of NEK10’s protein-protein interactions that are relevant to a cancer phenotype can be seen in our constructed interactome (Figure 2A; Table S2), including previously reported NEK10 interactions such as GLUD1 and CS [16]. We observed that the proteins HSPB1 and MAP3K1, which are associated with cancer, were predicted to have interactions with NEK10 in more than three PPI databases (Figure 2A). Consequently, we conducted a more in-depth investigation on these novel interactions. The interactome for NEK10 visualized in Cytoscape shows the cellular localization of the NEK10 interactors and involvement in different cancers. Our prediction results indicate that the majority of NEK10 interactions localized to the nucleoplasm, cytosol and mitochondria (Figure 2B). Furthermore, NEK10 was predicted to associate with several renal, liver, colorectal, and breast cancer proteins (Figure 2C). Figure 2. Interactome of NEK 10 . (A) NEK10’s protein-protein interaction network drawn using Cytoscape. The network map was constructed from our protein-protein interaction predictions. NEK10 is predicted to interact with several cancer proteins located in different regions of the cell: cytoplasm (green), cytosol (blue), intracellular (purple), membrane (magenta) and nucleoplasm (yellow), and mitochondria (orange). The previous reported physical interactions are encircled by dotted line, while predictions are encircled by a solid line; (B) Cellular location of NEK10 interactors. (C) Cancer types associated with NEK10 and its predicted interactors . Investigation of NEK10’s previously uncharacterized protein-protein interactions of NEK10 Prediction of NEK10 – HSPB1 interaction NEK10 – HSPB1 interaction is novel, however this interaction is predicted by more than three protein-protein prediction databases (Figure 2). The small heat shock proteins (sHSPs) include HSPB1, also known as heat shock protein 27. Its function is to prevent or delay the denaturation or unfolding of cellular proteins in response to stress or high temperatures. Many pathogenic processes in cancer are regulated by HSPB1, including drug resistance, apoptosis, and metastasis. For instance, HSPB1 is regarded as a critical molecular target for tumor growth suppression and apoptosis induction [71]. However, it is still unclear what structural domains of HSPB1 are implicated in its function and which client types are bound. The current state of knowledge is that sHSPs’ natural clients remain unknown [72]. Modeling and characterization of HSPB1 The PDB Database contains seven incomplete structures for HSPB1 and the AlphaFold model for HSPB1 (AF ID P04791) displays very low confidence in several regions of the protein model. The structural components of HSPB1 that enable its interaction with NEK10 remain poorly understood. HSPB1 is composed of a lengthy intrinsically disordered region starting at its N-terminus, and most of the IDR is further predicted to be involved in binding (Figure S4). The protein also displays β sheets and a few helices characteristic of an α-crystallin domain (ACD), which matches its domain architecture (Figure 3C). Tertiary Structure Prediction of HSPB1 Our results show that HSPB1’s N-terminal IDR is predicted to assume a helical shape, followed by a classic ACD connected through a loop to the PPI hotspot IXIXV sequence (ITIPV) (Figure 3A). Surface electrostatic studies of HSPB1 reveal a positively charged cleft formed between the ACD and the PPI hotspot IXIXV sequence (ITIPV) (Figures 3B). Our models for HSPB1 were constructed utilizing the Robetta server for comparative modeling using the backbone model for HSPB1 from AlphaFold (ID: AF-P04792-F1) with HSPB1 FASTA sequence. Model quality evaluation scores were very high for all our models, specifically model 2 (Table S3) which we chose for docking with NEK10. Figure 3. Full-length structure of HSPB1 . (A) The full-length HSPB1 model contains 205 amino acids. The ACD is depicted in deep purple, the PPI hotspot IXIXV sequence (ITIPV) in red, and the N-terminal IDR in orange. (B) Surface electrostatic profile of HSPB1. Blue – Positive; Red – Negative (-5 to +5kT/e). (C) Domain Architecture: HSPB1 contains 205AA, a highly conserved ACD (purple) and a known PPI hotspot for HSPB1(red). Prediction of NEK10 – HSPB1 interaction Our results (Figures 4A, 4B) show that the distinctive ARM repeats of NEK10 participates in its interaction with HSPB1. Residue H301 located in the ARM repeat region of NEK10 forms a salt bridge with I179 of HSPB1. These results corroborate the current literature, as Isoleucine (Ile179), located within an unusual, extended IXIXV sequence (ITIPV), is a known PPI hotspot for HSPB1 [73]. Figure 4. Docking of NEK10 with HSPB1. (A) The full-length model shows the extended IXIXV sequence (ITIPV) in red. (B) A zoom-in image shows details of the docking scenario of NEK10’s ARM repeat (green) residue H301 interaction with HSPB I179 (red). Modeling and characterization of MAP3K1 Results from our predictions of protein-protein interactions for NEK10 suggest a significant likelihood of a NEK10-MAP3K1 interaction. In addition, NEK10 is known to participate in the MAPK pathway [2]. To understand the interaction between NEK10 and MAP3K1, a high-quality structure of MAP3K1 is required. Unfortunately, there is only one incomplete structure for MAP3K1 in the PDB Database, covering only 19% of the proteins length. Furthermore, the AlphaFold model for MAP3K1 displays very low confidence in multiple regions (AF ID Q13233). Therefore, to characterize MAP3K1, and further study its interaction with NEK10, we used a variety of computational tools for Secondary Structure, IDRs, and Tertiary Structure of MAP3K1 predictions, as well as Molecular Electrostatic Potential (MEP) surface analysis, docking, and docking analysis. According to IDRs and Secondary Structure predictions, MAP3K1 is composed by a few ß sheets, and several alpha helices. These results seem to be in accordance with published domain architecture. Although MAP3K1 is a very large protein containing over 1500 amino acids, MAP3K1 does not seem to display any large IDRs (Figure S5). Tertiary Structure Prediction of MAP3K1 Our models score well in every model evaluation tool used (Table S4). Our results show that the Kinase Domain and the Ubiquitin interaction motif (UIM) (Figure 5A, 5C) are in a highly negatively charged region of the protein (Figure 5B). The TOG domain (same region as ARM repeat) falls within a mostly positive region of the protein (Figure 5C), and GEF is seen in a mostly positively charged pocket near MAP3K1’s N-terminus (Figure 5B) . Figure 5. Full-length structure of MAP3K1 (A) A full-length MAP3K1 protein structure composed of 1512 amino acids was predicted based on the amino-acid sequence and known structure information. Motifs, domains, and important sites are represented in different colors: GEF (brown), SWIM (yellow), E3 (purple), RING (green), TOG (blue), UIM (orange), and KD (sand). (B) Surface electrostatic profile of MAP3K1. Blue – Positive; Red – Negative (-5 to +5 kT/e). The C-terminus of MAP3K1 is mostly negatively charged, while its N-terminus is mostly positively charged . (C) Domain Architecture: GEF (brown), SWIM (yellow), E3 (purple), RING (green), TOG (blue), UIM (orange), and KD (sand). Prediction of NEK10 – MAP3K1 interaction Details of the docking scenario reveal NEK10’s Kinase residue D655 interacting with MAP3K1’s TOG domain residue T847 (Figures 6A, 6B). Figure 6. Docking of NEK10 with MAP3K1. (A) A zoom-out image shows full-length NEK10 model (light cyan – Kinase Domain in orange) interacting with MAP3K1 (grey – TOG domain in blue). (B) A zoom-in image shows details of the docking scenario of NEK10’s Kinase Domain (orange) residue D655 interaction with MAP3K1’s TOG domain (blue) residue T847. The presence of a PEST sequence and putative UBA domains suggest it is regulated via ubiquitination. We found a potential PEST motif with 24 amino acids between position N890 and K921. The multiple sequence alignment of the region containing a putative UBA domain, and the PEST sequence (F868-K921) has a high sequence conservation amongst 1177 homologues in 331 animal species (Figure S6). Residue K896 has a conservation level of 99.8% and likely the prime candidate for interaction with ubiquitin (Figure 7) which is also corroborated with our docking predictions (Figure 8). Figure 7. Multiple sequence alignment of select NEK10 homologs. Sequence F868-K921 shows high conservation. Blue arrow indicates Lys896, a prime candidate for ubiquitin interaction. Lys896 (K29 on the figure) has a 99.8% conservation across 1177 protein species in 331 animal species (only 20 shown) in this alignment. Color scheme: HKR cyan, DE red, STNQ maroon, AVLIM pink, FYW blue, PG orange, C green. Our analysis suggests the presence of at least one UBA domain, located between residues F868 and E901 (Figure S7). The second putative UBA (L36-I64) domain was excluded from further analysis based on a few important determinants outlined in the solved crystal structure analysis [27] and a high throughput study of UBA domains [76]. The presumptive area of interaction with ubiquitin showed a net positive surface charge, which apparently prevents the interaction with a positively charged C-terminus of ubiquitin. To avoid inconsistencies in our methodology, we attempted docking of NEK10 and ubiquitin using the full model of NEK10, a fragment of a model containing full length of this putative domain, and a scenario with a full model but with explicit instruction to algorithm to attempt docking in the region of interest. All three jobs returned results that did not show the interaction of ubiquitin’s diGly terminal with any of two available lysine residues (K42 and K59) in the focused region. UBA domains allow proteins to undergo ubiquitination by mono- or poly-ubiquitin complexes. Binding of a ubiquitin complex to a protein mark it for degradation via the proteosome pathway. Ubiquitination of the proteins plays a crucial role in the recycling and cell-cycle as it allows for rapid degradation of the regulatory proteins whose function is no longer required [74, 75]. Its structure, function, and residue composition appear to be like that of PEST sequences. Analysis of NEK10 and 17 other kinases with known UBA domains, showed that 58.82% of them contain a UBA domain concurrently with a PEST sequence (in some, one PEST sequence for each UBA domain, if there are more than one). It is also of interest that both motifs have lysine as the target residue where ubiquitination takes place [76]. Our docking results show that K896 of NEK10 interacts with G76 of ubiquitin (PDB ID: 1TBE) (Figure 8). Distance of interaction between the interacting side chains measures 6.1 Å. For reference, tetraubiquitin chain interaction between Lys and Gly of adjacent subunits measures 5.07 Å. The angles between helices are listed in Table S5. Another piece of evidence that points to the existence of a putative UBA domain appears to be the local net negative charge (Figure S7A). According to a high-throughput study [76], a pronounced shift towards negative local net charge was observed surrounding lysines interacting with ubiquitin’s diglycine terminal in analysis of 2879 proteins. This evidence fits the observed complementing local net positive charge in the ubiquitin diglycine terminal (Figure S7B), which also is consistent with the docking results. The measured angles are out of the reference range of the angles presented in the HHR23A UBA(2) solved structure. The UBA domain region also contains a hydrophobic patch (Figure S8) consistent with the solved structure of the reference protein (HHR23A). Figure 8. Docking of NEK10 with ubiquitin. Full-length NEK10 model (gray – UBA Domain in yellow) interacting with ubiquitin (PDB ID:1TBE; magenta). B. Inset showing a zoomed-in with details of the docking scenario of NEK10’s UBA Domain slice (yellow) residue K896 interaction with ubiquitin’s (magenta) residue G76. The value of 6.1 Å is the distance between the side chains of K896 and G76. Discussion Functional insights of NEK10 revealed from the NEK10 full length model Our structural modeling of full-length NEK10 (Figure 1) displayed various distinct domains that are likely to contribute to its functional diversity and regulatory roles. Specifically, we found a centrally located catalytic domain and four additional regions: (1) ARM repeats, (2) two coiled-coil regions (CC), (3) a PEST motif, and (4) a UBA domain. The spatial arrangement of these domains, supported by SSP, intrinsically disordered region (IDR) analyses, and domain architecture mapping (Figure S2), highlights a particular organization that may underpin NEK10’s role in cellular signaling. Incorporating these results with prior literature, the ARM repeats are known to mediate protein-protein interactions and may regulate NEK10’s involvement in cell cycle control and the DDR [77-79]. The CC regions are usually involved in oligomerization and scaffold assembly, consistent with a role in complex formation. The PEST motif is linked with rapid protein degradation through the ubiquitin-proteasome system [2,80,27,81], while the UBA domain suggests a role in ubiquitin binding and proteostasis regulation [82]. Together, these structural insights improve our understanding of NEK10’s potential regulatory mechanisms. Furthermore, the surface electrostatic profile of the modeled NEK10 reveals a predominantly negative potential in the ARM repeat region and a positively charged kinase domain (Figure 1B). This may be significant to NEK10’s ability to engage in diverse molecular interactions, particularly in pathways related to cell cycle regulation, DNA damage response, and proteostasis. These electrostatic features and their functional implications are explored in greater detail in subsequent sections. NEK10 Interactome and Its Implications The NEK10 interactome uncovers important insights into its functional roles, subcellular localization, and cancer associations. Our analysis (Figure 2A) suggests that NEK10’s nuclear localization is key to its function, as it interacts with nucleoplasmic proteins involved in RNA processing, transcriptional regulation, chromatin remodeling, and DNA repair. Particularly, NEK10 shares similarities with NEK2, which regulates the spliceosome and alternative splicing, suggesting a potential role in post-transcriptional regulation [86]. Furthermore, NEK10’s association with DDR proteins strengthens its role in genomic stability and cell cycle checkpoint control [2]. Beyond the nucleus, NEK10 interacts with cytosolic and mitochondrial proteins (Figure 2B), supporting previous findings that link it to mitochondrial homeostasis [2]. Our interactome analysis also highlights NEK10’s relevance to cancer, with interactions linked to renal, liver, colorectal, and breast cancers (Figure 2C). Its association with breast cancer-related proteins aligns with a 2019 study linking NEK10 expression abnormalities to breast cancer [83]. As kinase dysregulation is central to oncogenesis, altered NEK10 activity may contribute to tumor progression, metastasis, or therapy resistance. Given its role in DDR, mitochondrial regulation, and RNA processing, NEK10 could serve as a biomarker for prognosis or a therapeutic target. If NEK10 regulates mitochondrial function, targeting its kinase activity could offer novel strategies for exploiting metabolic vulnerabilities in cancer cells. Further studies will be fundamental to validate these interactions and assess NEK10’s therapeutic potential in oncology. NEK10 and its protein-protein interactions Structural insights from NEK10-HSPB1 interaction Our results (Figure 4A) reveal that NEK10’s ARM repeats mediate its interaction with HSPB1, with residue H301 forming a salt bridge with I179 of HSPB1 (Figure 4B). This interaction is consistent with the electrostatic surface profile of the complex, where the negatively charged ARM region of NEK10 complements the positively charged IXIXV motif of HSPB1 (Figure 3B). These findings reveal a key PPI hotspot involving conserved residues within each protein. This is supported by previous studies identifying I179 within HSPB1’s extended IXIXV motif as a critical PPI hotspot [86]. HSPB1, a sHSP, prevents protein denaturation under stress and is implicated in cancer-related processes such as drug resistance, apoptosis, and metastasis [72]. Its ACD is essential for dimerization and is flanked by intrinsically disordered N- and C-terminal domains (NTD and CTD, Figure 3C), which can modulate client interactions [84,85]. Despite extensive study, the structural determinants of HSPB1’s binding specificity remain unclear [72]. A 2018 study showed that mutations at I179 or V181 within the IXIXV motif abolish binding to Tau, underscoring their role in interaction fidelity [86]. HSPB1 also features a disordered N-terminal region with predicted binding capacity (Figure S4), and structural modeling (Figure 3A) supports its domain architecture and electrostatic features. The positively charged cleft between the ACD and the IXIXV motif likely facilitates selective binding to negatively charged interactors such as NEK10 (Figure 3B). Our findings also suggest that HSPB1’s positively charged cleft may preferentially recruit negatively charged client proteins, raising the possibility that NEK10 competes with other such clients for binding. This competition could impact cellular stress responses and protein quality control mechanisms. These results align with prior studies showing that HSPB1 facilitates protein degradation through the Unfolded Protein–Hsp70–ADP complex, which targets substrates to the ubiquitin ligase CHIP [87]. A 2018 study further identified a stable NEK10–CHIP–HSP70 complex during cilium disassembly, where increased CHIP levels correlated with reduced NEK10 and impaired ciliogenesis in human cancers [19]. Together, these findings suggest that electrostatic regulation of NEK10–HSPB1 binding could play a key role in controlling NEK10 stability and ciliogenesis. Future studies should explore how post-translational modifications shape this interaction and whether its disruption contributes to cancer progression, offering potential therapeutic avenues in tumors with defective cilia function. Structural insights from NEK10-MAP3K1 interaction We constructed a protein model for MAP3K1 (Figure 5A) that is highly consistent with our other bioinformatics predictions. The model corroborates the Domain Architecture (Figure 5C), confirming the spatial arrangement of functional domains such as the kinase domain, SWIM, RING, ARM repeats, and TOG domains. Structurally, MAP3K1 contains a caspase-3 cleavage site, a UIM near its C-terminal kinase domain, and multiple N-terminal functional domains. These include the SWIM domain, which facilitates c-Jun ubiquitination, and the TOG domain, which binds tubulin heterodimers, though its biological significance remains unclear [91,92]. Studies in MAP3K1-deficient models show roles in immune system function, injury repair, vasculature remodeling, and tumor growth [93]. Electrostatic analysis (Figure 5B) further supports our findings by revealing distinct charge distributions across the protein surface. These charged regions likely modulate binding affinity and specificity, as electrostatic complementarity can stabilize protein-protein interactions. Our docking analysis reveals that NEK10’s D655, a catalytically essential residue within its kinase domain, interacts with T847 in the TOG domain of MAP3K1 (Figure 6). The relevance of D655 is highlighted by the fact that its mutation to asparagine (D655N) renders NEK10 kinase-dead, preventing phosphorylation activity [18]. T847 resides within the TOG domain, which has been reported to bind tubulin. Mutations disrupting this interaction have been associated with cancer progression [94]. Given that tubulin post-translational modifications are essential for ciliary dynamics [95], the NEK10–MAP3K1 interaction might connect kinase activity to microtubule regulation during ciliogenesis. Furthermore, our docking results reveal that NEK10’s K896 interacts with G76 of ubiquitin (PDB ID: 1TBE) (Figure 8), suggesting that NEK10 is regulated through ubiquitin-mediated degradation. The PEST sequence (F868-K921) is highly conserved among 1177 homologs across 331 animal species (Figure S6). Residue K896 shows 99.8% conservation and is the prime candidate for ubiquitin interaction (Figure 7), supported by our docking predictions (Figure 8). The UBA domain, which facilitates this process, is a recognized target for proteasomal degradation [96]. Mutations in UBA domains have been implicated in various cancers. For instance, mutation-induced dysfunction of the UBA domain in p62 enhances cell survival and drives ovarian cancer proliferation [97], while a mutation in the UBA domain of the tyrosine kinase ACK1 (S985N) prevents its degradation and leads to overexpression in cervical cancer cells [98]. Given that aberrant accumulation of NEK proteins has been observed in oncogenic contexts, defective ubiquitination of NEK10 could similarly contribute to cancer progression. MAP3K1, also known as MEKK1, is a key member of the MAP3K superfamily, transmitting upstream signals by phosphorylating MAP2Ks, which in turn activate MAPKs to regulate transcription and gene expression [88]. While primarily associated with the JNK/p38 pathways, MAP3K1 can also regulate ERK and NF-κB signaling, influencing cell survival and apoptosis [89]. Its full-length form promotes survival through MAP2K4/7-JNK-c-Jun and MAP2K1/2-ERK1/2 activation, while caspase cleavage generates a kinase domain fragment that drives apoptosis. Furthermore, MAP3K1 mediates c-Jun and ERK1/2 degradation via ubiquitination [90]. NEK10 is a protein kinase that primarily functions in ciliated cells and regulates the motile ciliary proteome, promoting ciliary length and mucociliary transport without altering ciliary number, radial structure, or beat frequency [20]. This role, combined with its interaction with MAP3K1’s TOG domain, implies that NEK10 may modulate microtubule dynamics in cilia via its kinase activity. The NEK10–MAP3K1 interaction seem to play a critical regulatory role in ciliary function and cellular homeostasis. First, this interaction appears to facilitate microtubule remodeling during ciliogenesis. NEK10’s kinase activity, mediated by the essential residue D655, is linked to MAP3K1’s TOG domain, which regulates tubulin binding and microtubule dynamics. NEK10 may phosphorylate substrates that modulate MAP3K1’s tubulin-binding capacity or influence TOG domain conformation, thereby impacting microtubule assembly, stability, and organization essential for proper ciliary function. Second, our findings suggest that NEK10’s stability is tightly regulated through ubiquitin-mediated degradation, ensuring controlled protein turnover. Specifically, NEK10’s K896 interacts with ubiquitin at G76, targeting it for proteasomal degradation via the UBA domain. Given MAP3K1’s role in microtubule organization, this interaction may serve as a checkpoint, linking NEK10 degradation to microtubule remodeling. Disruptions in this process, such as UBA domain mutations or impaired ubiquitin binding, could lead to abnormal NEK10 accumulation, dysregulating ciliary dynamics and altering cellular signaling pathways. Both defective microtubule remodeling and faulty NEK10 ubiquitination could have significant consequences. Impaired ciliogenesis may result in structurally defective or dysfunctional cilia, which are essential for signal transduction and maintaining cellular polarity. These defects can drive oncogenic processes by promoting uncontrolled cell proliferation, migration, or survival. Similarly, excess NEK10 due to defective degradation may exacerbate these effects, creating a cellular environment conducive to tumor progression. Conclusion In this study, we set out to elucidate the structural and functional roles of NEK10 in ciliogenesis and cancer. Using a full-length model supported by domain architecture, secondary structure, and electrostatic analyses, we identified key regulatory regions, including ARM repeats, coiled-coil motifs, a PEST region, and a UBA domain, that underpin NEK10’s function. Through interactome analysis, we found robust indication for nuclear localization and potential involvement in cancer biology, with associations to renal, liver, colorectal, and breast cancer-linked proteins. Docking and PPI predictions revealed critical interactions: NEK10’s ARM repeats bind HSPB1’s IXIXV motif, its catalytic residue D655 interacts with MAP3K1’s TOG domain, and K896 engages ubiquitin, suggesting a degradation mechanism via the proteasome. These findings link NEK10’s kinase activity and turnover to microtubule regulation and stress response, implicating its dysfunction in impaired ciliogenesis and oncogenesis. To build on these insights, future work should incorporate molecular dynamics simulations, free energy calculations, and improved sampling to evaluate complex stability and the effects of post-translational modifications. Experimental validation via site-directed mutagenesis, ubiquitination assays, and live-cell imaging will help define how these regulatory mechanisms control NEK10 function. In summary, our integrative computational method provides a framework connecting NEK10’s structure and interactions to its roles in ciliary function and cancer. Our results support further investigation of NEK10 as a therapeutic target in diseases involving dysregulated kinase signaling and cilia dysfunction. Supplementary Materials: Figure S1. The workflow and tools used in this study.; Figure S2. NEK10: Secondary structure prediction, and prediction of IDRs.; Figure S3. Evaluation: Present NEK10 model vs. AlphaFold’s NEK10 model.; Figure S4. HSPB1: Secondary structure prediction, and prediction of IDRs.; Figure S5. MAP3K1: Secondary structure prediction, and prediction of IDRs. Figure S6. Taxonomy of the putative UBA region.; Figure S7. Electrostatic maps of NEK10. Figure S8. Hydrophobic map of NEK10 Table S1. Evaluation scores for the full-length model of NEK10; Table S2. Protein-protein interaction predictions for NEK10.; Table S3. Evaluation scores for the full-length model of HSPB1.; Table S4. Evaluation scores for the full-length mode of MAP3K1.; Table S5. Angles between helices on NEK10’s UBA domain. Author Contributions: For research articles with several authors, a short paragraph specifying their individual contributions must be provided. 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Eichner Brooklyn College View all articles by this author Nathaniel Zimmerman Brooklyn College View all articles by this author Shaneen Singh 0000-0002-4003-5756 [email protected] Brooklyn College View all articles by this author Metrics & Citations Metrics Article Usage 446 views 243 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Andriele S. Eichner, Nathaniel Zimmerman, Shaneen Singh. In silico analysis of human NEK10 reveals novel domain architecture and protein-protein interactions. Authorea . 07 May 2025. DOI: https://doi.org/10.22541/au.174665051.19843345/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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