mutation3D: Cancer Gene Prediction Through Atomic Clustering of Coding Variants in the Structural Proteome

Comprehensive in silico characterization of nonsynonymous SNPs in the human ezrin (EZR) gene and their role in disease pathogenesis

Ezrin (EZR) is a crucial linker between the actin cytoskeleton and the plasma membrane. It interacts with proteins involved in cancer-related signaling pathways. To assess the impact of nonsynonymous single nucleotide polymorphisms (nsSNPs) on EZR structure and function, we employed bioinformatics tools (SIFT, PolyPhen-2, PROVEAN, PhD-SNP, SNPs&GO, SuSPect, and FATHMM) and identified deleterious variants. Stability analyses using MUpro, mCSM, I-Mutant 2.0, and DynaMut2 revealed six destabilizing nsSNPs (F240S, H288D, I248T, L59Q, L125S, and L225P). Structural modeling using HOPE, MutPred2, AlphaFold, Swiss-Model, and protein-protein docking using HADDOCK 2.4 assessed the impact on the EZR-EBP50 complex. Binding free energy calculations, salt bridge analysis, and interface residue mapping further confirmed that the L225P, F240S, and I248T mutations significantly impaired EZR-EBP50 interaction, potentially disrupting key signaling pathways. Molecular dynamics simulations indicated that mutant EZR proteins exhibited reduced stability, flexibility, and hydrogen bonding. This first comprehensive in silico analysis of EZR highlights pathogenic nsSNPs that may contribute to disease progression. These findings provide a foundation for experimental validation and may inform targeted therapies for EZR-related pathologies.

In-silico analysis of nsSNPs in BCL-2 family proteins: Implications for colorectal cancer pathogenesis and therapeutics

Colorectal cancer (CRC) is a multifaceted disease characterized by abnormal cell proliferation in the colon and rectum. The BCL-2 family proteins are implicated in CRC pathogenesis, yet the impacts of genetic variations within these proteins remains elusive. This in-silico study employs diverse sequence- and structure-based bioinformatics tools to identify potentially pathogenic nonsynonymous single nucleotide polymorphisms (nsSNPs) in BCL-2 family proteins. Leveraging computational tools including SIFT, PolyPhen-2, SNPs&GO, PhD-SNP, PANTHER, and Condel, 94 nsSNPs were predicted as deleterious, damaging, and disease-associated by at least five tools. Stability analysis with I-Mutant2.0, MutPred, and PredictSNP further identified 31 nsSNPs that reduce protein stability. Conservation analysis highlighted highly functional, exposed variants (rs960653284, rs758817904, rs1466732626, rs569276903, rs746711568, rs764437421, rs779690846, and rs2038330314) and structural, buried variants (rs376149674, rs1375767408, rs1582066443, rs367558446, rs367558446, rs1319541919, and rs1370070128). To explore the functional effects of these mutations, molecular docking and molecular dynamics simulations were conducted. G233D (rs376149674) and R12G (rs960653284) mutations in the BCL2 protein exhibited the greatest differences in docking scores with d-α-Tocopherol and Tocotrienol, suggesting enhanced protein-ligand interactions. The simulations revealed that d-α-Tocopherol and Tocotrienol (strong binders) contributed to greater stability of BCL-2 family proteins, while Fluorouracil, though weaker, still demonstrated selective binding stability. This work represents the first comprehensive computational analysis of functional nsSNPs in BCL-2 family proteins, providing insights into their roles in CRC pathogenesis. While these findings demand experimental validation, they hold great promise for guiding future large-scale population studies, facilitating drug repurposing efforts, and advancing the development of targeted diagnostic and therapeutic modalities for CRC.

In silico report on five high-risk protein C pathogenic variants: G403R, P405S, S421N, C238S, and I243T

In this study, we propose reclassification of 5 out of 16 PROC VUS (variants of uncertain significance): C238S, I243T, G403R, P405S, and S421N, as pathogenic variants, associated with thrombophilia due to PROC deficiency. The obtained results are based on in silico analysis, which enables a detailed assessment of variants' impact, despite limited clinical evidence. In particular, the G403R substitution, next to the S402-active site, is expected to reduce the flexibility of the local coil domain, affecting the catalytic activity of serine protease. The P405S substitution may imply B-factor gain (P = 0.24; p-value=0.040). On the other hand, the S421N variant causes phosphorylation site disruption at S421, which serves as a target for CK2 phosphorylation. C238S substitution alters metal binding, while the I243T variant may alter transmembrane properties (P = 0.27, P-value=0.00071). All five PROC variants hold promise as diagnostic markers for protein C deficiency and may also serve as potential drug targets for therapeutic intervention.

Age-related cerebral ventriculomegaly occurs in patients with primary ciliary dyskinesia

Primary ciliary dyskinesia (PCD) is a genetic disorder causing motile ciliary dysfunction primarily affecting the respiratory and reproductive systems. However, the impact of PCD on the central nervous system remains poorly understood. Rodent models of PCD exhibit marked hydrocephalus leading to early animal mortality, however, most humans with PCD do not develop hydrocephalus for unknown reasons. We hypothesized that patients with PCD exhibit sub-clinical ventriculomegaly related to ependymal motile ciliary dysfunction. We demonstrated highly specific expression levels of known PCD-related genes in human brain multiciliated ependymal cells (p < 0.0001). To assess ventricular size, computed tomography sinus images from patients with PCD (n = 33) and age/sex-matched controls (n = 64) were analysed. Patients with PCD displayed significantly larger ventricular areas (p < 0.0001) and Evans index (p < 0.01), indicating ventriculomegaly that was consistent across all genetic subgroups. Ventricular enlargement correlated positively with increasing age in patients with PCD compared to controls (p < 0.001). Additionally, chart review demonstrated a high prevalence (39%) of neuropsychiatric/neurological disorders in adult PCD patients that did not correlate with degree of ventriculomegaly. Our findings suggest that patients with PCD may have unrecognized, mild ventriculomegaly which correlates with ageing, potentially attributable to ependymal ciliary dysfunction. Further study is required to determine causality, and whether ventricular enlargement contributes to neuropsychiatric/neurological or other morbidity in PCD.

A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology

A major goal of cancer biology is to understand the mechanisms driven by somatically acquired mutations. Two distinct methodologies-one analyzing mutation clustering within protein sequences and 3D structures, the other leveraging protein-protein interaction network topology-offer complementary strengths. We present NetFlow3D, a unified, end-to-end 3D structurally-informed protein interaction network propagation framework that maps the multiscale mechanistic effects of mutations. Built upon the Human Protein Structurome, which incorporates the 3D structures of every protein and the binding interfaces of all known protein interactions, NetFlow3D integrates atomic, residue, protein and network-level information: It clusters mutations on 3D protein structures to identify driver mutations and propagates their impacts anisotropically across the protein interaction network, guided by the involved interaction interfaces, to reveal systems-level impacts. Applied to 33 cancer types, NetFlow3D identifies 2 times more 3D clusters and incorporates 8 times more proteins in significantly interconnected network modules compared to traditional methods.

Comprehensive characterization of high-risk coding and non-coding single nucleotide polymorphisms of human CXCR4 gene

CXCR4, a chemokine receptor known as Fusin or CD184, spans the outer membrane of various human cells, including leukocytes. This receptor is essential for HIV infection as well as for many vital cellular processes and is implicated to be associated with multiple pathologies, including cancers. This study employs various computational tools to investigate the molecular effects of disease-vulnerable germ-line missense and non-coding SNPs of the CXCR4 gene. In this investigation, the tools SIFT, PROVEAN, PolyPhen-2, PANTHER, SNAP 2.0, PhD-SNP, and SNPs&GO were used to predict potentially harmful and disease-causing nsSNPs in CXCR4. Additionally, their impact on protein stability was examined by I-mutant 3.0, MUpro, Consurf, and Netsurf 2.0, combined with conservation and solvent accessibility analyses. Structural analysis with normal and mutant residues of the protein harboring these disease-associated functional SNPs was conducted using TM-align and SWIS MODEL, with visualization aided by PyMOL and the BIOVINA Discovery Studio Visualizer. The functional impact of wild-type and mutated CXCR4 variants was evaluated through molecular docking with its natural ligand CXCR4-modulator 1, using the PyRx tool. Non-coding SNPs in the 3' -UTR were investigated for their regulatory effects on miRNA binding sites using PolymiRTS. Five non-coding SNPs were identified in the 3'-UTR that can disrupt existing miRNA binding sites or create new ones. Non-coding SNPs in the 5' and 3'-UTRs, as well as in intronic regions, were also examined for their potential roles in gene expression regulation. Furthermore, RegulomeDB databases were employed to assess the regulatory potential of these non-coding SNPs based on chromatin state and protein binding regulation. In the mostly annotated variant (ENSP00000241393) of the CXCR4 gene, we found 23 highly deleterious and pathogenic nsSNPs and these were selected for in-depth analysis. Among the 23 nsSNPs, five (G55V, H79P, L80P, H113P, and P299L) displayed notable structural alternation, with elevated RMSD values and reduced TM (TM-score) values. A molecular docking study revealed the significant impact of the H113P variant on the protein-ligand binding affinity, supported by MD simulation over 100 nanoseconds, which highlighted substantial stability differences between wild-type and H113P mutated proteins during ligand binding. This comprehensive analysis shed light on the potential functional consequences of genetic variation in the CXCR genes, offering valuable insights into the implications of disease susceptibility and may pave the way for future therapeutic interventions.

Protein structural context of cancer mutations reveals molecular mechanisms and candidate driver genes

Advances in protein structure determination and modeling allow us to study the structural context of human genetic variants on an unprecedented scale. Here, we analyze millions of cancer-associated missense mutations based on their structural locations and predicted perturbative effects. By considering the collective properties of mutations at the level of individual proteins, we identify distinct patterns associated with tumor suppressors and oncogenes. Tumor suppressors are enriched in structurally damaging mutations, consistent with loss-of-function mechanisms, while oncogene mutations tend to be structurally mild, reflecting selection for gain-of-function driver mutations and against loss-of-function mutations. Although oncogenes are difficult to distinguish from genes with no role in cancer using only structural damage, we find that the three-dimensional clustering of mutations is highly predictive. These observations allow us to identify candidate driver genes and speculate about their molecular roles, which we expect will have general utility in the analysis of cancer sequencing data.

Bioinformatic Characterization of the Functional and Structural Effect of Single Nucleotide Mutations in Patients with High-Grade Glioma

Background: Gliomas are neoplasms of the central nervous system that originate in glial cells. The genetic characteristics of this type of neoplasm are the loss of function of tumor suppressor genes such as TP53 and somatic mutations in genes such as IDH1/2. Additionally, in clinical cases, de novo single nucleotide polymorphisms (SNP) are reported, of which their pathogenicity and their effects on the function and stability of the protein are known. Methodology: Non-synonymous SNPs were analyzed for their structural and functional effect on proteins using a set of bioinformatics tools such as SIFT, PolyPhen-2, PhD-SNP, I-Mutant 3.0, MUpro, and mutation3D. A structural comparison between normal and mutated residues for disease-associated coding SNPs was performed using TM-aling and the SWISS MODEL. Results: A total of 13 SNPs were obtained for the TP53 gene, 1 SNP for IDH1, and 1 for IDH2, which would be functionally detrimental and associated with disease. Additionally, these changes compromise the structure and function of the protein; the A161S SNP for TP53 that has not been reported in any databases was classified as detrimental. Conclusions: All non-synonymous SNPs reported for TP53 were in the region of the deoxyribonucleic acid (DNA) binding domain and had a great impact on the function and stability of the protein. In addition, the two polymorphisms detected in IDH1 and IDH2 genes compromise the structure and activity of the protein. Both genes are related to the development of high-grade gliomas. All the data obtained in this study must be validated through experimental approaches.

Unraveling the potential effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on the Protein structure and function of the human SLC30A8 gene on type 2 diabetes and colorectal cancer: An In silico approach

Background and aims

The single nucleotide polymorphisms (SNPs) in SLC30A8 gene have been recognized as contributing to type 2 diabetes (T2D) susceptibility and colorectal cancer. This study aims to predict the structural stability, and functional impacts on variations in non-synonymous SNPs (nsSNPs) in the human SLC30A8 gene using various computational techniques.

Materials and methods

Several in silico tools, including SIFT, Predict-SNP, SNPs&GO, MAPP, SNAP2, PhD-SNP, PANTHER, PolyPhen-1,PolyPhen-2, I-Mutant 2.0, and MUpro, have been used in our study.

Results

After data analysis, out of 336 missenses, the eight nsSNPs, namely R138Q, I141N, W136G, I349N, L303R, E140A, W306C, and L308Q, were discovered by ConSurf to be in highly conserved regions, which could affect the stability of their proteins. Project HOPE determines any significant molecular effects on the structure and function of eight mutated proteins and the three-dimensional (3D) structures of these proteins. The two pharmacologically significant compounds, Luzonoid B and Roseoside demonstrate strong binding affinity to the mutant proteins, and they are more efficient in inhibiting them than the typical SLC30A8 protein using Autodock Vina and Chimera. Increased binding affinity to mutant SLC30A8 proteins has been determined not to influence drug resistance. Ultimately, the Kaplan-Meier plotter study revealed that alterations in SLC30A8 gene expression notably affect the survival rates of patients with various cancer types.

Conclusion

Finally, the study found eight highly deleterious missense nsSNPs in the SLC30A8 gene that can be helpful for further proteomic and genomic studies for T2D and colorectal cancer diagnosis. These findings also pave the way for personalized treatments using biomarkers and more effective healthcare strategies.

A comprehensive in silico investigation into the pathogenic SNPs in the RTEL1 gene and their biological consequences

The Regulator of Telomere Helicase 1 (RTEL1) gene encodes a critical DNA helicase intricately involved in the maintenance of telomeric structures and the preservation of genomic stability. Germline mutations in the RTEL1 gene have been clinically associated with Hoyeraal-Hreidarsson syndrome, a more severe version of Dyskeratosis Congenita. Although various research has sought to link RTEL1 mutations to specific disorders, no comprehensive investigation has yet been conducted on missense mutations. In this study, we attempted to investigate the functionally and structurally deleterious coding and non-coding SNPs of the RTEL1 gene using an in silico approach. Initially, out of 1392 nsSNPs, 43 nsSNPs were filtered out through ten web-based bioinformatics tools. With subsequent analysis using nine in silico tools, these 43 nsSNPs were further shortened to 11 most deleterious nsSNPs. Furthermore, analyses of mutated protein structures, evolutionary conservancy, surface accessibility, domains & PTM sites, cancer susceptibility, and interatomic interaction revealed the detrimental effect of these 11 nsSNPs on RTEL1 protein. An in-depth investigation through molecular docking with the DNA binding sequence demonstrated a striking change in the interaction pattern for F15L, M25V, and G706R mutant proteins, suggesting the more severe consequences of these mutations on protein structure and functionality. Among the non-coding variants, two had the highest likelihood of being regulatory variants, whereas one variant was predicted to affect the target region of a miRNA. Thus, this study lays the groundwork for extensive analysis of RTEL1 gene variants in the future, along with the advancement of precision medicine and other treatment modalities.

A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology

A major goal of cancer biology is to understand the mechanisms underlying tumorigenesis driven by somatically acquired mutations. Two distinct types of computational methodologies have emerged: one focuses on analyzing clustering of mutations within protein sequences and 3D structures, while the other characterizes mutations by leveraging the topology of protein-protein interaction network. Their insights are largely non-overlapping, offering complementary strengths. Here, we established a unified, end-to-end 3D structurally-informed protein interaction network propagation framework, NetFlow3D, that systematically maps the multiscale mechanistic effects of somatic mutations in cancer. The establishment of NetFlow3D hinges upon the Human Protein Structurome, a comprehensive repository we compiled that incorporates the 3D structures of every single protein as well as the binding interfaces of all known protein interactions in humans. NetFlow3D leverages the Structurome to integrate information across atomic, residue, protein and network levels: It conducts 3D clustering of mutations across atomic and residue levels on protein structures to identify potential driver mutations. It then anisotropically propagates their impacts across the protein interaction network, with propagation guided by the specific 3D structural interfaces involved, to identify significantly interconnected network "modules", thereby uncovering key biological processes underlying disease etiology. Applied to 1,038,899 somatic protein-altering mutations in 9,946 TCGA tumors across 33 cancer types, NetFlow3D identified 1,4444 significant 3D clusters throughout the Human Protein Structurome, of which ~55% would not have been found if using only experimentally-determined structures. It then identified 26 significantly interconnected modules that encompass ~8-fold more proteins than applying standard network analyses. NetFlow3D and our pan-cancer results can be accessed from http://netflow3d.yulab.org.

Prediction of the most deleterious non-synonymous SNPs in the human IL1B gene: evidence from bioinformatics analyses

BACKGROUND

Polymorphisms in IL1B play a significant role in depression, multiple inflammatory-associated disorders, and susceptibility to infection. Functional non-synonymous SNPs (nsSNPs) result in changes in the encoded amino acids, potentially leading to structural and functional alterations in the mutant proteins. So far, most genetic studies have concentrated on SNPs located in the IL1B promoter region, without addressing nsSNPs and their association with multifactorial diseases. Therefore, this study aimed to explore the impact of deleterious nsSNPs retrieved from the dbSNP database on the structure and functions of the IL1B protein.

RESULTS

Six web servers (SIFT, PolyPhen-2, PROVEAN, SNPs&GO, PHD-SNP, PANTHER) were used to analyze the impact of 222 missense SNPs on the function and structure of IL1B protein. Five novel nsSNPs (E100K, T240I, S53Y, D128Y, and F228S) were found to be deleterious and had a mutational impact on the structure and function of the IL1B protein. The I-mutant v2.0 and MUPro servers predicted that these mutations decreased the stability of the IL1B protein. Additionally, these five mutations were found to be conserved, underscoring their significance in protein structure and function. Three of them (T240I, D128Y, and F228S) were predicted to be cancer-causing nsSNPs. To analyze the behavior of the mutant structures under physiological conditions, we conducted a 50 ns molecular dynamics simulation using the WebGro online tool. Our findings indicate that the mutant values differ from those of the IL1B wild type in terms of RMSD, RMSF, Rg, SASA, and the number of hydrogen bonds.

CONCLUSIONS

This study provides valuable insights into nsSNPs located in the coding regions of IL1B, which lead to direct deleterious effects on the functional and structural aspects of the IL1B protein. Thus, these nsSNPs could be considered significant candidates in the pathogenesis of disorders caused by IL1B dysfunction, contributing to effective drug discovery and the development of precision medications. Thorough research and wet lab experiments are required to verify our findings. Moreover, bioinformatic tools were found valuable in the prediction of deleterious nsSNPs.

A computational approach for structural and functional analyses of disease-associated mutations in the human CYLD gene

Tumor suppressor cylindromatosis protein (CYLD) regulates NF-κB and JNK signaling pathways by cleaving K63-linked poly-ubiquitin chain from its substrate molecules and thus preventing the progression of tumorigenesis and metastasis of the cancer cells. Mutations in CYLD can cause aberrant structure and abnormal functionality leading to tumor formation. In this study, we utilized several computational tools such as PANTHER, PROVEAN, PredictSNP, PolyPhen-2, PhD-SNP, PON-P2, and SIFT to find out deleterious nsSNPs. We also highlighted the damaging impact of those deleterious nsSNPs on the structure and function of the CYLD utilizing ConSurf, I-Mutant, SDM, Phyre2, HOPE, Swiss-PdbViewer, and Mutation 3D. We shortlisted 18 high-risk nsSNPs from a total of 446 nsSNPs recorded in the NCBI database. Based on the conservation profile, stability status, and structural impact analysis, we finalized 13 nsSNPs. Molecular docking analysis and molecular dynamic simulation concluded the study with the findings of two significant nsSNPs (R830K, H827R) which have a remarkable impact on binding affinity, RMSD, RMSF, radius of gyration, and hydrogen bond formation during CYLD-ubiquitin interaction. The principal component analysis compared native and two mutants R830K and H827R of CYLD that signify structural and energy profile fluctuations during molecular dynamic (MD) simulation. Finally, the protein-protein interaction network showed CYLD interacts with 20 proteins involved in several biological pathways that mutations can impair. Considering all these in silico analyses, our study recommended conducting large-scale association studies of nsSNPs of CYLD with cancer as well as designing precise medications against diseases associated with these polymorphisms.

In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene

Single nucleotide polymorphisms (SNPs) are one of the most common determinants and potential biomarkers of human disease pathogenesis. SNPs could alter amino acid residues, leading to the loss of structural and functional integrity of the encoded protein. In humans, members of the minichromosome maintenance (MCM) family play a vital role in cell proliferation and have a significant impact on tumorigenesis. Among the MCM members, the molecular mechanism of how missense SNPs of minichromosome maintenance complex component 6 (MCM6) contribute to DNA replication and tumor pathogenesis is underexplored and needs to be elucidated. Hence, a series of sequence and structure-based computational tools were utilized to determine how mutations affect the corresponding MCM6 protein. From the dbSNP database, among 15,009 SNPs in the MCM6 gene, 642 missense SNPs (4.28%), 291 synonymous SNPs (1.94%), and 12,500 intron SNPs (83.28%) were observed. Out of the 642 missense SNPs, 33 were found to be deleterious during the SIFT analysis. Among these, 11 missense SNPs (I123S, R207C, R222C, L449F, V456M, D463G, H556Y, R602H, R633W, R658C, and P815T) were found as deleterious, probably damaging, affective and disease-associated. Then, I123S, R207C, R222C, V456M, D463G, R602H, R633W, and R658C missense SNPs were found to be highly harmful. Six missense SNPs (I123S, R207C, V456M, D463G, R602H, and R633W) had the potential to destabilize the corresponding protein as predicted by DynaMut2. Interestingly, five high-risk mutations (I123S, V456M, D463G, R602H, and R633W) were distributed in two domains (PF00493 and PF14551). During molecular dynamics simulations analysis, consistent fluctuation in RMSD and RMSF values, high Rg and hydrogen bonds in mutant proteins compared to wild-type revealed that these mutations might alter the protein structure and stability of the corresponding protein. Hence, the results from the analyses guide the exploration of the mechanism by which these missense SNPs of the MCM6 gene alter the structural integrity and functional properties of the protein, which could guide the identification of ways to minimize the harmful effects of these mutations in humans.

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks

The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.

Comprehensive characterization of coding and non-coding single nucleotide polymorphisms of the Myoneurin (MYNN) gene using molecular dynamics simulation and docking approaches

Genome-wide association studies (GWAS) identified a coding single nucleotide polymorphism, MYNN rs10936599, at chromosome 3q. MYNN gene encodes myoneurin protein, which has been associated with several cancer pathogenesis and disease development processes. However, there needed to be a more detailed characterization of this polymorphism's (and other coding and non-coding polymorphisms) structural, functional, and molecular impact. The current study addressed this gap and analyzed different properties of rs10936599 and non-coding SNPs of MYNN via a thorough computational method. The variant, rs10936599, was predicted functionally deleterious by nine functionality prediction approaches, like SIFT, PolyPhen-2, and REVEL, etc. Following that, structural modifications were estimated through the HOPE server and Mutation3D. Moreover, the mutation was found in a conserved and active residue, according to ConSurf and CPORT. Further, the secondary structures were predicted, followed by tertiary structures, and there was a significant deviation between the native and variant models. Similarly, molecular simulation also showed considerable differences in the dynamic pattern of the wildtype and mutant structures. Molecular docking revealed that the variant binds with better docking scores with ligand NOTCH2. In addition to that, non-coding SNPs located at the MYNN locus were retrieved from the ENSEMBL database. These were found to disrupt the transcription factor binding regulatory regions; nonetheless, only two affect miRNA target sites. Again, eight non-coding variants were detected in the testes with normalized expression, whereas HaploReg v4.1 unveiled annotations for non-coding variants. In summary, in silico comprehensive characterization of coding and non-coding single nucleotide polymorphisms of MYNN gene will assist researchers to work on MYNN gene and establish their association with certain types of cancers.

Computational approaches for identifying disease-causing mutations in proteins

Advancements in genome sequencing have expanded the scope of investigating mutations in proteins across different diseases. Amino acid mutations in a protein alter its structure, stability and function and some of them lead to diseases. Identification of disease-causing mutations is a challenging task and it will be helpful for designing therapeutic strategies. Hence, mutation data available in the literature have been curated and stored in several databases, which have been effectively utilized for developing computational methods to identify deleterious mutations (drivers), using sequence and structure-based properties of proteins. In this chapter, we describe the contents of specific databases that have information on disease-causing and neutral mutations followed by sequence and structure-based properties. Further, characteristic features of disease-causing mutations will be discussed along with computational methods for identifying cancer hotspot residues and disease-causing mutations in proteins.

Genotyping, in silico screening and molecular dynamics simulation of SNPs of MGMT and ERCC1 gene in lung cancer patients treated with platinum-based doublet chemotherapy

Lung cancer, the leading cause of death worldwide, arises from an intricate combination of genetic and environmental factors. Genetic variations can influence the chemotherapeutic response of lung cancer patients in DNA repair genes. This study examines the response to platinum-based drugs among lung cancer patients of North Indian descent who possess genetic variations in the MGMT and ERCC1 genes. P CR-RFLP method was used for genotypic analysis. MedCalc statistical software was used to calculate odds ratios and Median Survival Time (MST). GROMACS software was used to perform Molecular dynamic simulation. ADCC Patients revealed a significant association with MGMT in the heterozygous genotype (HR= 1.56, p=0.02) and also with ERCC1 in both mutant and combined variants (HR= 1.25, p=0.01; HR=0.78, p=0.03). SQCC subjects harbouring ERCC1 polymorphism also reported a 2-fold increase in hazard ratio and a corresponding decrease in survival time for heterozygous and combined variants (HR= 2.55, p=0.02; HR 2.33, p=0.01, respectively). MD simulation results demonstrate a lower RMSD, stable radius of gyration, and lower RMSF, indicating the mutated MGMT protein is more stable than the wild. Further, the docking score for DNA-Wild and DNA-L84F mutants are -201.6 and -131.8, respectively. MD Simulation of the complexes further validated the results. Our study concludes that MGMT and ERCC1 polymorphisms are associated with decreased overall survival. Further, computational analysis of MGMT (rs12917) polymorphism revealed that mutated MGMT cannot bind properly to the DNA and hence cannot properly repair DNA, resulting in lower overall survival.Communicated by Ramaswamy H. Sarma.

Genetic polymorphisms in Nrf2 and FoxO1: implications for antioxidant enzyme activity in diabetes

In diabetes, persistent hyperglycemia generates excess reactive oxygen species (ROS), leading to oxidative stress (OS). In response to OS, transcription factors (TFs) Nrf2 and FoxO1 get activated, which induce the expression of antioxidant enzymes catalase (CAT) and superoxide dismutase (SOD). It is well documented that the antioxidant response in diabetic individuals is very low. Since Nrf2 and FoxO1 are the major TFs activating these genes, we were interested in determining if single nucleotide polymorphisms (SNPs) in genes for these TFs have any association with lowered antioxidant enzyme activity in diabetic individuals. The activity of CAT and SOD and total antioxidant capacity (TAC) were quantified from the serum samples of diabetic (n = 98) and non-diabetic (n = 90) individuals. Genomic DNA was isolated, and Nrf2 and FoxO1 were amplified and sequenced by Illumina NextSeq500. Data were screened for SNPs in amplified regions. An independent samples t-test to find an association between CAT, SOD, and TAC and allele frequency of SNP with the diabetic condition was carried out. We found decreased CAT and SOD activity and significantly low TAC in diabetic individuals. Thirty-two and thirty-four SNPs and Single-nucleotide variants (SNVs) were observed in Nrf2 and FoxO1, respectively. However, a statistically significant difference in the allele frequency distribution between study groups was observed only in two intronic SNPs, rs17524059:A > C and rs60373589:Indel(A) of Nrf2 and FoxO1, respectively. SNPs, rs17524059 in the Nrf2 and rs60373589 of FoxO1, were not associated with reduced CAT and SOD activity and level of TAC in Indian diabetic individuals.Communicated by Ramaswamy H. Sarma.

Comprehensive analysis predicting effects of deleterious SNPs of human progesterone receptor gene on its structure and functions: a computational approach

Progesterone receptor plays a crucial role in the development of the mammary gland and breast cancer. Single nucleotide polymorphisms (SNPs) within its gene, PGR, are associated with the risk of miscarriages and preterm birth as well as many cancers across different populations. The main aim of this work is to investigate the most deleterious SNPs in the PGR gene to identify potential biomarkers for various disease susceptibility and treatments. Both sequence and structure-based computational approaches were adopted and in total 11 nsSNPs have been filtered out of 674 nsSNPs along with seven non-coding SNPs. R740Q, I744T and D746E belonged to a mutation cluster. R740Q, D746E along with S865L altered H-bond interactions within the receptor. The same mutations have been found to be associated with several cancers including uterine and breast cancer among others. It is, therefore, possible that the high-risk SNPs associated with cancers may exert their effect by causing changes in the protein structure, particularly in its bonding patterns, and thus affecting its function. In addition, seven non-coding SNPs that were located in the UTR region created a new miRNA site while three SNPs disrupted a conserved miRNA site. These high-risk SNPs can play an instrumental role in generating a dataset of the PGR gene's SNPs. Thus, the present study may pave the way to design and develop novel therapeutics for overcoming the challenges associated with certain cancers and pregnancy that result from a change in the protein structure and function due to the SNP mutations in the PGR gene.Communicated by Ramaswamy H. Sarma.

POU3F3-related disorder: Defining the phenotype and expanding the molecular spectrum

POU3F3 variants cause developmental delay, behavioral problems, hypotonia and dysmorphic features. We investigated the phenotypic and genetic landscape, and genotype-phenotype correlations in individuals with POU3F3-related disorders. We recruited unpublished individuals with POU3F3 variants through international collaborations and obtained updated clinical data on previously published individuals. Trio exome sequencing or single exome sequencing followed by segregation analysis were performed in the novel cohort. Functional effects of missense variants were investigated with 3D protein modeling. We included 28 individuals (5 previously published) from 26 families carrying POU3F3 variants; 23 de novo and one inherited from an affected parent. Median age at study inclusion was 7.4 years. All had developmental delay mainly affecting speech, behavioral difficulties, psychiatric comorbidities and dysmorphisms. Additional features included gastrointestinal comorbidities, hearing loss, ophthalmological anomalies, epilepsy, sleep disturbances and joint hypermobility. Autism, hearing and eye comorbidities, dysmorphisms were more common in individuals with truncating variants, whereas epilepsy was only associated with missense variants. In silico structural modeling predicted that all (likely) pathogenic variants destabilize the DNA-binding region of POU3F3. Our study refined the phenotypic and genetic landscape of POU3F3-related disorders, it reports the functional properties of the identified pathogenic variants, and delineates some genotype-phenotype correlations.

Structure-based pathogenicity relationship identifier for predicting effects of single missense variants and discovery of higher-order cancer susceptibility clusters of mutations

We report the structure-based pathogenicity relationship identifier (SPRI), a novel computational tool for accurate evaluation of pathological effects of missense single mutations and prediction of higher-order spatially organized units of mutational clusters. SPRI can effectively extract properties determining pathogenicity encoded in protein structures, and can identify deleterious missense mutations of germ line origin associated with Mendelian diseases, as well as mutations of somatic origin associated with cancer drivers. It compares favorably to other methods in predicting deleterious mutations. Furthermore, SPRI can discover spatially organized pathogenic higher-order spatial clusters (patHOS) of deleterious mutations, including those of low recurrence, and can be used for discovery of candidate cancer driver genes and driver mutations. We further demonstrate that SPRI can take advantage of AlphaFold2 predicted structures and can be deployed for saturation mutation analysis of the whole human proteome.

Impact of highly deleterious non-synonymous polymorphisms on GRIN2A protein's structure and function

GRIN2A is a gene that encodes NMDA receptors found in the central nervous system and plays a pivotal role in excitatory synaptic transmission, plasticity and excitotoxicity in the mammalian central nervous system. Changes in this gene have been associated with a spectrum of neurodevelopmental disorders such as epilepsy. Previous studies on GRIN2A suggest that non-synonymous single nucleotide polymorphisms (nsSNPs) can alter the protein's structure and function. To gain a better understanding of the impact of potentially deleterious variants of GRIN2A, a range of bioinformatics tools were employed in this study. Out of 1320 nsSNPs retrieved from the NCBI database, initially 16 were predicted as deleterious by 9 tools. Further assessment of their domain association, conservation profile, homology models, interatomic interaction, and Molecular Dynamic Simulation revealed that the variant I463S is likely to be the most deleterious for the structure and function of the protein. Despite the limitations of computational algorithms, our analyses have provided insights that can be a valuable resource for further in vitro and in vivo research on GRIN2A-associated diseases.

Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools

Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.

Delineation of functionally essential protein regions for 242 neurodevelopmental genes

Neurodevelopmental disorders (NDDs), including severe paediatric epilepsy, autism and intellectual disabilities are heterogeneous conditions in which clinical genetic testing can often identify a pathogenic variant. For many of them, genetic therapies will be tested in this or the coming years in clinical trials. In contrast to first-generation symptomatic treatments, the new disease-modifying precision medicines require a genetic test-informed diagnosis before a patient can be enrolled in a clinical trial. However, even in 2022, most identified genetic variants in NDD genes are 'variants of uncertain significance'. To safely enrol patients in precision medicine clinical trials, it is important to increase our knowledge about which regions in NDD-associated proteins can 'tolerate' missense variants and which ones are 'essential' and will cause a NDD when mutated. In addition, knowledge about functionally indispensable regions in the 3D structure context of proteins can also provide insights into the molecular mechanisms of disease variants. We developed a novel consensus approach that overlays evolutionary, and population based genomic scores to identify 3D essential sites (Essential3D) on protein structures. After extensive benchmarking of AlphaFold predicted and experimentally solved protein structures, we generated the currently largest expert curated protein structure set for 242 NDDs and identified 14 377 Essential3D sites across 189 gene disorders associated proteins. We demonstrate that the consensus annotation of Essential3D sites improves prioritization of disease mutations over single annotations. The identified Essential3D sites were enriched for functional features such as intermembrane regions or active sites and discovered key inter-molecule interactions in protein complexes that were otherwise not annotated. Using the currently largest autism, developmental disorders, and epilepsies exome sequencing studies including >360 000 NDD patients and population controls, we found that missense variants at Essential3D sites are 8-fold enriched in patients. In summary, we developed a comprehensive protein structure set for 242 NDDs and identified 14 377 Essential3D sites in these. All data are available at https://es-ndd.broadinstitute.org for interactive visual inspection to enhance variant interpretation and development of mechanistic hypotheses for 242 NDDs genes. The provided resources will enhance clinical variant interpretation and in silico drug target development for NDD-associated genes and encoded proteins.

Faltwerk: a library for spatial exploratory data analysis of protein structures

Summary

Proteins are fundamental building blocks of life and are investigated in a broad range of scientific fields, especially in the context of recent progress using in silico structure prediction models and the surge of resulting protein structures in public databases. However, exploratory data analysis of these proteins can be slow because of the need for several methods, ranging from geometric and spatial analysis to visualization. The Python library faltwerk provides an integrated toolkit to perform explorative work with rapid feedback. This toolkit includes support for protein complexes, spatial analysis (point density or spatial autocorrelation), ligand binding site prediction and an intuitive visualization interface based on the grammar of graphics.

Availability and implementation

faltwerk is distributed under the permissive BSD-3 open source license. Source code and documentation, including an extensive common-use case tutorial, can be found at github.com/phiweger/faltwerk; binaries are available from the pypi repository.

Identification of deleterious nsSNPs in human HGF gene: in silico approach

HGF is a protein that binds to the hepatocyte growth factor receptor to regulate cell growth, cell motility and morphogenesis in different cells and tissues. Several bioinformatics tools and in silico methods were used to identify most deleterious nsSNPs that might change the structure and function of HGF protein. The in silico tools such as SIFT, SNP&GO and PolyPhen2 were used to distinguish deleterious nsSNPs from neutral ones. Protein stability is analysed by I-Mutant, MUpro and iStable. The functional and structural effects are predicted by other tools like MutPred2, Maestro, DUET etc. Analysis of structure was performed by HOPE and Mutation3D. SWISS-MODEL. server, was used for wild type and mutant proteins 3-D Modelling. Gene-gene and protein-protein interaction were predicted by GeneMANIA and STRING, respectively. The wildtype HGF protein and these three variants were independently docked with their close interactor protein MET by the use of ClusPro. Our study suggested that out of 392 missense nsSNPs of the HGF gene, five nsSNPs (D358G, G648R, I550N, N175S and R220Q), are the most deleterious in HGF gene. Gene-gene interactions showed relation of HGF with other genes depicting its importance in several pathways and co-expressions. The protein-protein interacting network is composed of 11 nodes. Analysis of protein stability by different tools indicated that the five nsSNPS decreased the stability of the protein. Anyway these nsSNPs need a confirmation analysis by experimental investigation and GWAS studiesCommunicated by Ramaswamy H. Sarma.

Predicting mutational function using machine learning

Genetic variations are one of the major causes of phenotypic variations between human individuals. Although beneficial as being the substrate of evolution, germline mutations may cause diseases, including Mendelian diseases and complex diseases such as diabetes and heart diseases. Mutations occurring in somatic cells are a main cause of cancer and likely cause age-related phenotypes and other age-related diseases. Because of the high abundance of genetic variations in the human genome, i.e., millions of germline variations per human subject and thousands of additional somatic mutations per cell, it is technically challenging to experimentally verify the function of every possible mutation and their interactions. Significant progress has been made to solve this problem using computational approaches, especially machine learning (ML). Here, we review the progress and achievements made in recent years in this field of research. We classify the computational models in two ways: one according to their prediction goals including protein structural alterations, gene expression changes, and disease risks, and the other according to their methodologies, including non-machine learning methods, classical machine learning methods, and deep neural network methods. For models in each category, we discuss their architecture, prediction accuracy, and potential limitations. This review provides new insights into the applications and future directions of computational approaches in understanding the role of mutations in aging and disease.

Deleterious single nucleotide polymorphisms (SNPs) of human IFNAR2 gene facilitate COVID-19 severity in patients: a comprehensive in silico approach

In humans, the dimeric receptor complex IFNAR2-IFNAR1 accelerates cellular response triggered by type I interferon (IFN) family proteins in response to viral infection including Coronavirus infection. Studies have revealed the association of the IFNAR2 gene with severe illness in Coronavirus infection and indicated the association of genomic variants, i.e. single nucleotide polymorphisms (SNPs). However, comprehensive analysis of SNPs of the IFNAR2 gene has not been performed in both coding and non-coding region to find the causes of loss of function of IFNAR2 in COVID-19 patients. In this study, we have characterized coding SNPs (nsSNPs) of IFNAR2 gene using different bioinformatics tools and identified deleterious SNPs. We found 9 nsSNPs as pathogenic and disease-causing along with a decrease in protein stability. We employed molecular docking analysis that showed 5 nsSNPs to decrease binding affinity to IFN. Later, MD simulations showed that P136R mutant may destabilize crucial binding with the IFN molecule in response to COVID-19. Thus, P136R is likely to have a high impact on disrupting the structure of the IFNAR2 protein. GTEx portal analysis predicted 14 sQTLs and 5 eQTLs SNPs in lung tissues hampering the post-transcriptional modification (splicing) and altering the expression of the IFNAR2 gene. sQTLs and eQTLs SNPs potentially explain the reduced IFNAR2 production leading to severe diseases. These mutants in the coding and non-coding region of the IFNAR2 gene can help to recognize severe illness due to COVID 19 and consequently assist to develop an effective drug against the infection.Communicated by Ramaswamy H. Sarma.

Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs

Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques, such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologs. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologs in the Protein Databank. We noticed that the model quality was higher and the Root mean square deviation (RMSD) lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein-protein interfaces and conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, whether they destabilized the protein structure based on ddG calculations or whether they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms, a larger percentage of disease-associated missense mutations were buried, closer to predicted functional sites, predicted as destabilizing and pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.

Brain radiotoxicity-related 15CAcBRT gene expression signature predicts survival prognosis of glioblastoma patients

BACKGROUND

Glioblastoma is the most common and devastating primary brain cancer. Radiotherapy is standard of care; however, it is associated with brain radiation toxicity (BRT). This study used a multi-omics approach to determine whether BRT-related genes (RGs) harbor survival prognostic value and whether their encoded proteins represent novel therapeutic targets for glioblastoma.

METHODS

RGs were identified through analysis of single-nucleotide variants associated with BRT (R-SNVs). Functional relationships between RGs were established using Protein-Protein Interaction networks. The influence of RGs and their functional groups on glioblastoma prognosis was evaluated using clinical samples from the Glioblastoma Bio-Discovery Portal database and validated using the Chinese Glioma Genome Atlas dataset. The identification of clusters of radiotoxic and putative pathogenic variants in proteins encoded by RGs was achieved by computational 3D structural analysis.

RESULTS

We identified the BRT-related 15CAcBRT molecular signature with prognostic value in glioblastoma, by analysis of the COMT and APOE protein functional groups. Its external validation confirmed clinical relevance independent of age, MGMT promoter methylation status, and IDH mutation status. Interestingly, the genes IL6, APOE, and MAOB documented significant gene expression levels alteration, useful for drug repositioning. Biological networks associated with 15CAcBRT signature involved pathways relevant to cancer and neurodegenerative diseases. Analysis of 3D clusters of radiotoxic and putative pathogenic variants in proteins coded by RGs unveiled potential novel therapeutic targets in neuro-oncology.

CONCLUSIONS

15CAcBRT is a BRT-related molecular signature with prognostic significance for glioblastoma patients and represents a hub for drug repositioning and development of novel therapies.

In silico comprehensive analysis of coding and non-coding SNPs in human mTOR protein

The mammalian/mechanistic target of rapamycin (mTOR) protein is an important growth regulator and has been linked with multiple diseases including cancer and diabetes. Non-synonymous mutations of this gene have already been found in patients with renal clear cell carcinoma, melanoma, and acute lymphoid leukemia among many others. Such mutations can potentially affect a protein’s structure and hence its functions. In this study, therefore, the most deleterious SNPs of mTOR protein have been determined to identify potential biomarkers for various disease treatments. The aim is to generate a structured dataset of the mTOR gene’s SNPs that may prove to be an asset for the identification and treatment of multiple diseases associated with the target gene. Both sequence and structure-based approaches were adopted and a wide variety of bioinformatics tools were applied to analyze the SNPs of mTOR protein. In total 11 nsSNPs have been filtered out of 2178 nsSNPs along with two non-coding variations. All of the nsSNPs were found to destabilize the protein structure and disrupt its function. While R619C, A1513D, and T1977R mutations were shown to alter C alpha distances and bond angles of the mTOR protein, L509Q, R619C and N2043S were predicted to disrupt the mTOR protein’s interaction with NBS1 protein and FKBP1A/rapamycin complex. In addition, one of the non-coding SNPs was shown to alter miRNA binding sites. Characterizing nsSNPs and non-coding SNPs and their harmful effects on a protein’s structure and functions will enable researchers to understand the critical impact of mutations on the molecular mechanisms of various diseases. This will ultimately lead to the identification of potential targets for disease diagnosis and therapeutic interventions.

The 3D mutational constraint on amino acid sites in the human proteome

Quantification of the tolerance of protein sites to genetic variation has become a cornerstone of variant interpretation. We hypothesize that the constraint on missense variation at individual amino acid sites is largely shaped by direct interactions with 3D neighboring sites. To quantify this constraint, we introduce a framework called COntact Set MISsense tolerance (or COSMIS) and comprehensively map the landscape of 3D mutational constraint on 6.1 million amino acid sites covering 16,533 human proteins. We show that 3D mutational constraint is pervasive and that the level of constraint is strongly associated with disease relevance both at the site and the protein level. We demonstrate that COSMIS performs significantly better at variant interpretation tasks than other population-based constraint metrics while also providing structural insight into the functional roles of constrained sites. We anticipate that COSMIS will facilitate the interpretation of protein-coding variation in evolution and prioritization of sites for mechanistic investigation.

Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation

The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integrate these data sources will play increasingly important roles in disease gene discovery and variant interpretation. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

In silico screening and analysis of single-nucleotide polymorphic variants of the ABCC2 gene affecting Dubin-Johnson syndrome

BACKGROUND AND STUDY AIMS

Dubin-Johnson syndrome (DJS) is a benevolent genetic disorder of the liver with autosomal inheritance. It is a rare disorder characterized by an increase in conjugated bilirubin and anomaly in coproporphyrin clearance. DJS is caused by deleterious mutations in the ABCC2 gene. A polymorphism in the ABCC2 gene causes malfunctions in its ability to regulate the efflux of different organic anions, such as bilirubin, from hepatocytes to the canaliculi. Multidrug resistance protein 2 (MRP2) encoded by the ABCC2 gene is one of the main regulators of the export of bilirubin to respective sites. ABCC2 gene mutations have widely drawn attention in the pathology of DJS in various populations.

PATIENTS AND METHODS

The ABCC2 gene was subjected to the National Center for Biotechnology Information (NCBI) database in 2020, and non-synonymous single-nucleotide polymorphisms (nsSNPs) and variants in untranslated regions were studied using different computational servers. SIFT, Protein variation effect analyzer, and PolyPhen-2 were used to retrieve the damaging Single-nucleotide polymorphisms (SNPs); PhD-SNP, SNPs&GO, and Protein Analysis Through Evolutionary Relationships were used to predict the association of nsSNPs with DJS; Mutation3D illustrated the location of variants in the protein; SNAP2, MutPred2, ELASPIC, and HOPE were used to predict the structural and functional effects of these mutations on MRP2; and I-mutant 3.0 and MuPro were used to determine the effects of polymorphism on the function of MRP2.

RESULTS

In this study, 18,947 SNPs were screened from the NCBI database, followed by a series of refinement of variants using online available servers. We concluded that 41 ABCC2 gene variants are vital etiological candidates for DJS in humans. These 41 variants had highly damaging effects on the MRP2 protein, which may lead to deficient transportation capacity, thereby affecting the efflux of bilirubin across the canalicular membrane.

CONCLUSION

In silico tools are an alternative approach for predicting the target SNPs. Hence, previously unreported variants can be considered strong etiological candidates for diseases related to MRP2.

The structural coverage of the human proteome before and after AlphaFold

The protein structure field is experiencing a revolution. From the increased throughput of techniques to determine experimental structures, to developments such as cryo-EM that allow us to find the structures of large protein complexes or, more recently, the development of artificial intelligence tools, such as AlphaFold, that can predict with high accuracy the folding of proteins for which the availability of homology templates is limited. Here we quantify the effect of the recently released AlphaFold database of protein structural models in our knowledge on human proteins. Our results indicate that our current baseline for structural coverage of 48%, considering experimentally-derived or template-based homology models, elevates up to 76% when including AlphaFold predictions. At the same time the fraction of dark proteome is reduced from 26% to just 10% when AlphaFold models are considered. Furthermore, although the coverage of disease-associated genes and mutations was near complete before AlphaFold release (69% of Clinvar pathogenic mutations and 88% of oncogenic mutations), AlphaFold models still provide an additional coverage of 3% to 13% of these critically important sets of biomedical genes and mutations. Finally, we show how the contribution of AlphaFold models to the structural coverage of non-human organisms, including important pathogenic bacteria, is significantly larger than that of the human proteome. Overall, our results show that the sequence-structure gap of human proteins has almost disappeared, an outstanding success of direct consequences for the knowledge on the human genome and the derived medical applications.

Protein structures are key to understand many biological phenomena at the molecular scale: from the effects of genetic variation to how different proteins interact with each other to create molecular pathways that, together, have a biological function. Obtaining experimental structures, however, is extremely consuming in terms of both, time and resources.

For this and other reasons, scientists have long worked to develop computational approaches that predict the structure of a protein using only its sequence as input. Recently, a group of scientists at Deepmind have developed AlphaFold2, a computational tool that is extremely accurate at this task. Moreover, they have used this tool to predict the structures of all human proteins.

In this manuscript we provide an overview of the structural coverage of the human proteome before AlphaFold models were released and how much we have gained thanks to these models. We also show how the gain affects our understanding of human pathogenic variants, both germline and somatic. Finally, we provide evidence suggesting that the gain in non-human organisms is larger than for the human proteome, particularly in the case of bacteria.

A full-proteome, interaction-specific characterization of mutational hotspots across human cancers

Rapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically informed computational framework that characterizes the functional relevance of all 1107 published mutational hotspots identified in approximately 25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein-protein interactions is critical for the oncogenicity of many hotspot mutations. Our framework enables, for the first time, systematic identification of specific protein interactions affected by hotspot mutations at the full proteome scale. Furthermore, by constructing a hotspot-affected network that connects all hotspot-affected interactions throughout the whole-human interactome, we uncover genome-wide relationships among hotspots and implicate novel cancer proteins that do not harbor hotspot mutations themselves. Moreover, applying our network-based framework to specific cancer types identifies clinically significant hotspots that can be used for prognosis and therapy targets. Overall, we show that our framework bridges the gap between the statistical significance of mutational hotspots and their biological and clinical significance in human cancers.

Comprehensive Characterization of the Coding and Non-Coding Single Nucleotide Polymorphisms in the Tumor Protein p63 (TP63) Gene Using In Silico Tools

Single nucleotide polymorphisms (SNPs) help to understand the phenotypic variations in humans. Genome-wide association studies (GWAS) have identified SNPs located in the tumor protein 63 (TP63) locus to be associated with the genetic susceptibility of cancers. However, there is a lack of in-depth characterization of the structural and functional impacts of the SNPs located at the TP63 gene. The current study was designed for the comprehensive characterization of the coding and non-coding SNPs in the human TP63 gene for their functional and structural significance. The functional and structural effects of the SNPs were investigated using a wide variety of computational tools and approaches, including molecular dynamics (MD) simulation. The deleterious impact of eight nonsynonymous SNPs (nsSNPs) affecting protein stability, structure, and functions was measured by using 13 bioinformatics tools. These eight nsSNPs are in highly conserved positions in protein and were predicted to decrease protein stability and have a deleterious impact on the TP63 protein function. Molecular docking analysis showed five nsSNPs to reduce the binding affinity of TP63 protein to DNA with significant results for three SNPs (R319H, G349E, and C347F). Further, MD simulations revealed the possible disruption of TP63 and DNA binding, hampering the essential protein function. PolymiRTS study found five non-coding SNPs in miRNA binding sites, and the GTEx portal recognized five eQTLs SNPs in single tissue of the lung, heart (LV), and cerebral hemisphere (brain). Characterized nsSNPs and non-coding SNPs will help researchers to focus on TP63 gene loci and ascertain their association with certain diseases.

A computational in silico approach to predict high-risk coding and non-coding SNPs of human PLCG1 gene

PLCG1 gene is responsible for many T-cell lymphoma subtypes, including peripheral T-cell lymphoma (PTCL), angioimmunoblastic T-cell lymphoma (AITL), cutaneous T-cell lymphoma (CTCL), adult T-cell leukemia/lymphoma along with other diseases. Missense mutations of this gene have already been found in patients of CTCL and AITL. The non-synonymous single nucleotide polymorphisms (nsSNPs) can alter the protein structure as well as its functions. In this study, probable deleterious and disease-related nsSNPs in PLCG1 were identified using SIFT, PROVEAN, PolyPhen-2, PhD-SNP, Pmut, and SNPS&GO tools. Further, their effect on protein stability was checked along with conservation and solvent accessibility analysis by I-mutant 2.0, MUpro, Consurf, and Netsurf 2.0 server. Some SNPs were finalized for structural analysis with PyMol and BIOVIA discovery studio visualizer. Out of the 16 nsSNPs which were found to be deleterious, ten nsSNPs had an effect on protein stability, and six mutations (L411P, R355C, G493D, R1158H, A401V and L455F) were predicted to be highly conserved. Among the six highly conserved mutations, four nsSNPs (R355C, A401V, L411P and L455F) were part of the catalytic domain. L411P, L455F and G493D made significant structural change in the protein structure. Two mutations-Y210C and R1158H had post-translational modification. In the 5' and 3' untranslated region, three SNPs, rs139043247, rs543804707, and rs62621919 showed possible miRNA target sites and DNA binding sites. This in silico analysis has provided a structured dataset of PLCG1 gene for further in vivo researches. With the limitation of computational study, it can still prove to be an asset for the identification and treatment of multiple diseases associated with the target gene.

Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection

Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome-scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence-level evolution has shaped reaction networks towards new metabolic functions. Strikingly, using GEMs, we can mechanistically map different evolutionary events, e.g. horizontal gene transfer and gene duplication, onto relevant subpathways to explain metabolic plasticity. This demonstrates that gene family expansion and enzyme promiscuity are prominent mechanisms for metabolic trait gains, while GEM simulations reveal that additional factors, such as gene loss from distant pathways, contribute to trait losses. Furthermore, our analysis could pinpoint to specific genes and pathways that have been under positive selection and relevant for the formulation of complex metabolic traits, i.e. thermotolerance and the Crabtree effect. Our findings illustrate how multidimensional evolution in both metabolic network structure and individual enzymes drives phenotypic variations.

The investigation of nonsynonymous SNPs of human SLC6A4 gene associated with depression: An in silico approach

Genetic polymorphism of the SLC6A4 gene is associated with several behavioral disorders, including depression. Since studying the total nonsynonymous single nucleotide polymorphisms (nsSNPs) of the SLC6A4 gene at the population level is a difficult task, we aim to utilize in silico approach to detect the most deleterious nsSNPs of the SLC6A4 gene. In our study, 7 computational tools were used in the initial stage, including SIFT, Polyphen-2, PROVEAN, SNAP2, PhD-SNP, PANTHER, and SNPs&GO to find out the most damaging nsSNPs. In the second phase, we performed structural, functional, and stability analysis of SLC6A4 protein by popular computation tools, including I-Mutant 2.0 and MutPred2. Also, the ConSurf server was utilized to find the conserved region of the SLC6A4 protein to determine the relationship between these conserved regions with high-risk nsSNPs. Based on these analyses, 5 high-risk mutations of the SLC6A4 protein were selected. Then, we carried out comparative modeling by using the Robetta server and aligned the mutant protein model with the native protein structure. Later, we performed the post-translational modification and functional domain analysis of the SLC6A4 protein. This study concludes that Arginine → Tryptophan at position 79 and Arginine → Cysteine at position 104 are the two significant mutations in SLC6A4 protein which might play an essential role in causing diseases. Future studies should take these high-risk nsSNPs (rs1221448303, rs200953188) into consideration while exploring diseases related to the SLC6A4 gene. Besides, our research is the first-ever comprehensive in silico investigation of the SLC6A4 gene. Thus, the findings of this study could be beneficial for developing precision medicines against diseases caused by SLC6A4 malfunction. Furthermore, extensive wet-lab research and experiments on various model organisms might be helpful to investigate the precise role of these damaging nsSNPs of the SLC6A4 gene.

A computational and structural analysis of germline and somatic variants affecting the DDR mechanism, and their impact on human diseases

DNA-Damage Response (DDR) proteins are crucial for maintaining the integrity of the genome by identifying and repairing errors in DNA. Variants affecting their function can have severe consequences since failure to repair damaged DNA can result in cells turning cancerous. Here, we compare germline and somatic variants in DDR genes, specifically looking at their locations in the corresponding three-dimensional (3D) structures, Pfam domains, and protein–protein interaction interfaces. We show that somatic variants in metastatic cases are more likely to be found in Pfam domains and protein interaction interfaces than are pathogenic germline variants or variants of unknown significance (VUS). We also show that there are hotspots in the structures of ATM and BRCA2 proteins where pathogenic germline, and recurrent somatic variants from primary and metastatic tumours, cluster together in 3D. Moreover, in the ATM, BRCA1 and BRCA2 genes from prostate cancer patients, the distributions of germline benign, pathogenic, VUS, and recurrent somatic variants differ across Pfam domains. Together, these results provide a better characterisation of the most recurrent affected regions in DDRs and could help in the understanding of individual susceptibility to tumour development.

The Diagnostic Journey of a Patient with Prader-Willi-Like Syndrome and a Unique Homozygous SNURF-SNRPN Variant; Bio-Molecular Analysis and Review of the Literature

Prader-Willi syndrome (PWS) is a rare genetic condition characterized by hypotonia, intellectual disability, and hypothalamic dysfunction, causing pituitary hormone deficiencies and hyperphagia, ultimately leading to obesity. PWS is most often caused by the loss of expression of a cluster of genes on chromosome 15q11.2-13. Patients with Prader-Willi-like syndrome (PWLS) display features of the PWS phenotype without a classical PWS genetic defect. We describe a 46-year-old patient with PWLS, including hypotonia, intellectual disability, hyperphagia, and pituitary hormone deficiencies. Routine genetic tests for PWS were normal, but a homozygous missense variant NM_003097.3(SNRPN):c.193C>T, p.(Arg65Trp) was identified. Single nucleotide polymorphism array showed several large regions of homozygosity, caused by high-grade consanguinity between the parents. Our functional analysis, the 'Pipeline for Rapid in silico, in vivo, in vitro Screening of Mutations' (PRiSM) screen, showed that overexpression of SNRPN-p.Arg65Trp had a dominant negative effect, strongly suggesting pathogenicity. However, it could not be confirmed that the variant was responsible for the phenotype of the patient. In conclusion, we present a unique homozygous missense variant in SNURF-SNRPN in a patient with PWLS. We describe the diagnostic trajectory of this patient and the possible contributors to her phenotype in light of the current literature on the genotype-phenotype relationship in PWS.

Clinical, neuroimaging, and molecular spectrum of TECPR2-associated hereditary sensory and autonomic neuropathy with intellectual disability

Bi-allelic TECPR2 variants have been associated with a complex syndrome with features of both a neurodevelopmental and neurodegenerative disorder. Here, we provide a comprehensive clinical description and variant interpretation framework for this genetic locus. Through international collaboration, we identified 17 individuals from 15 families with bi-allelic TECPR2-variants. We systemically reviewed clinical and molecular data from this cohort and 11 cases previously reported. Phenotypes were standardized using Human Phenotype Ontology terms. A cross-sectional analysis revealed global developmental delay/intellectual disability, muscular hypotonia, ataxia, hyporeflexia, respiratory infections, and central/nocturnal hypopnea as core manifestations. A review of brain magnetic resonance imaging scans demonstrated a thin corpus callosum in 52%. We evaluated 17 distinct variants. Missense variants in TECPR2 are predominantly located in the N- and C-terminal regions containing β-propeller repeats. Despite constituting nearly half of disease-associated TECPR2 variants, classifying missense variants as (likely) pathogenic according to ACMG criteria remains challenging. We estimate a pathogenic variant carrier frequency of 1/1221 in the general and 1/155 in the Jewish Ashkenazi populations. Based on clinical, neuroimaging, and genetic data, we provide recommendations for variant reporting, clinical assessment, and surveillance/treatment of individuals with TECPR2-associated disorder. This sets the stage for future prospective natural history studies.

Okur-Chung neurodevelopmental syndrome-linked CK2α variants have reduced kinase activity

The Okur-Chung neurodevelopmental syndrome, or OCNDS, is a newly discovered rare neurodevelopmental disorder. It is characterized by developmental delay, intellectual disability, behavioral problems (hyperactivity, repetitive movements and social interaction deficits), hypotonia, epilepsy and language/verbalization deficits. OCNDS is linked to de novo mutations in CSNK2A1, that lead to missense or deletion/truncating variants in the encoded protein, the protein kinase CK2α. Eighteen different missense CK2α mutations have been identified to date; however, no biochemical or cell biological studies have yet been performed to clarify the functional impact of such mutations. Here, we show that 15 different missense CK2α mutations lead to varying degrees of loss of kinase activity as recombinant purified proteins and when mutants are ectopically expressed in mammalian cells. We further detect changes in the phosphoproteome of three patient-derived fibroblast lines and show that the subcellular localization of CK2α is altered for some of the OCNDS-linked variants and in patient-derived fibroblasts. Our data argue that reduced kinase activity and abnormal localization of CK2α may underlie the OCNDS phenotype.

Structure analysis of deleterious nsSNPs in human PALB2 protein for functional inference

Partner and Localizer of BRCA2 or PALB2 is a typical tumor suppressor protein, that responds to DNA double stranded breaks through homologous recombination repair. Heterozygous mutations in PALB2 are known to contribute to the susceptibility of breast and ovarian cancer. However, there is no comprehensive study characterizing the structural and functional impacts of SNPs located in the PALB2 gene. Therefore, it is of interest to document a comprehensive analysis of coding and non-coding SNPs located at the PALB2 loci using in silico tools. The data for 1455 non-synonymous SNPs (nsSNPs) located in the PALB2 loci were retrieved from the dbSNP database. Comprehensive characterization of the SNPs using a combination of in silico tools such as SIFT, PROVEAN, PolyPhen, PANTHER, PhD-SNP, Pmut, MutPred 2.0 and SNAP-2, identified 28 functionally important SNPs. Among these, 16 nsSNPs were further selected for structural analysis using conservation profile and protein stability. The most deleterious nsSNPs were documented within the WD40 domain of PALB2. A general outline of the structural consequences of each variant was developed using the HOPE project data. These 16 mutant structures were further modelled using SWISS Model and three most damaging mutant models (rs78179744, rs180177123 and rs45525135) were identified. The non-coding SNPs in the 3' UTR region of the PALB2 gene were analyzed for altered miRNA target sites. The comprehensive characterization of the coding and non-coding SNPs in the PALB2 locus has provided a list of damaging SNPs with potential disease association. Further validation through genetic association study will reveal their clinical significance.

In-depth and 3-dimensional exploration of the budding yeast phosphoproteome

Phosphorylation is one of the most dynamic and widespread post-translational modifications regulating virtually every aspect of eukaryotic cell biology. Here, we assemble a dataset from 75 independent phosphoproteomic experiments performed in our laboratory using Saccharomyces cerevisiae. We report 30,902 phosphosites identified from cells cultured in a range of DNA damage conditions and/or arrested in distinct cell cycle stages. To generate a comprehensive resource for the budding yeast community, we aggregate our dataset with the Saccharomyces Genome Database and another recently published study, resulting in over 46,000 budding yeast phosphosites. With the goal of enhancing the identification of functional phosphorylation events, we perform computational positioning of phosphorylation sites on available 3D protein structures and systematically identify events predicted to regulate protein complex architecture. Results reveal hundreds of phosphorylation sites mapping to or near protein interaction interfaces, many of which result in steric or electrostatic "clashes" predicted to disrupt the interaction. With the advancement of Cryo-EM and the increasing number of available structures, our approach should help drive the functional and spatial exploration of the phosphoproteome.