Pathogenic or not? And if so, then how? Studying the effects of missense mutations using bioinformatics methods

In Silico Functional and Structural Analysis of Non-synonymous Single Nucleotide Polymorphisms (nsSNPs) in Human Paired Box 4 Gene

In human genome, members of Paired box (PAX) transcription factor family are highly sequence-specific DNA-binding proteins. Among PAX gene family members, PAX4 gene has significant role in growth, proliferation, differentiation, and insulin secretion of pancreatic β-cells. Single nucleotide polymorphisms (SNPs) in PAX4 gene progress in the pathogenesis of various human diseases. Hence, the molecular mechanism of how these SNPs in PAX4 gene significantly progress diseases pathogenesis needs to be elucidated. For the reason, a series of bioinformatic analyzes were done to identify the SNPs of PAX4 gene that contribute in diseases pathogenesis. From the analyzes, 4145 SNPs (rsIDs) in PAX4 gene were obtained, where, 362 missense (8.73%), 169 synonymous (4.08%), and 2323 intron variants (56.04%). The rest SNPs were unspecified. Among the 362 missense variants, 118 nsSNPs were found as deleterious in SIFT analysis. Among those, 25 nsSNPs were most probably damaging and 23 were deleterious as observed in PolyPhen-2 and PROVEAN analyzes, respectively. Following all analyzes, 14 nsSNPs (rs149708455, rs115887120, rs147279315, rs35155575, rs370095957, rs373939873, rs145468905, rs121917718, rs2233580, rs3824004, rs372751660, rs369459316, rs375472849, rs372497946) were common and observed as deleterious, probably damaging, affective and diseases associated. Following structural analyzes, 11 nsSNPs guided proteins were found as most unstable and highly conserved. Among these, R20W, R39Q, R45Q, R60H, G65D, and A223D mutated proteins were highly harmful. Hence, the results from above-mentioned integrated comprehensive bioinformatic analyzes guide how different nsSNPs in PAX4 gene alter structural and functional characteristics of the protein that might progress diseases pathogenesis in human including type 2 diabetes.

PHACTboost: A Phylogeny-Aware Pathogenicity Predictor for Missense Mutations via Boosting

Most algorithms that are used to predict the effects of variants rely on evolutionary conservation. However, a majority of such techniques compute evolutionary conservation by solely using the alignment of multiple sequences while overlooking the evolutionary context of substitution events. We had introduced PHACT, a scoring-based pathogenicity predictor for missense mutations that can leverage phylogenetic trees, in our previous study. By building on this foundation, we now propose PHACTboost, a gradient boosting tree-based classifier that combines PHACT scores with information from multiple sequence alignments, phylogenetic trees, and ancestral reconstruction. By learning from data, PHACTboost outperforms PHACT. Furthermore, the results of comprehensive experiments on carefully constructed sets of variants demonstrated that PHACTboost can outperform 40 prevalent pathogenicity predictors reported in the dbNSFP, including conventional tools, metapredictors, and deep learning-based approaches as well as more recent tools such as AlphaMissense, EVE, and CPT-1. The superiority of PHACTboost over these methods was particularly evident in case of hard variants for which different pathogenicity predictors offered conflicting results. We provide predictions of 215 million amino acid alterations over 20,191 proteins. PHACTboost is available at https://github.com/CompGenomeLab/PHACTboost. PHACTboost can improve our understanding of genetic diseases and facilitate more accurate diagnoses.

Prediction and assessment of deleterious and disease causing nonsynonymous single nucleotide polymorphisms (nsSNPs) in human FOXP4 gene: An in - silico study

In humans, FOXP gene family is involved in embryonic development and cancer progression. The FOXP4 (Forkhead box protein P4) gene belongs to this FOXP gene family. FOXP4 gene plays a crucial role in oncogenesis. Single nucleotide polymorphisms are biological markers and common determinants of human diseases. Mutations can largely affect the function of the corresponding protein. Therefore, the molecular mechanism of nsSNPs in the FOXP4 gene needs to be elucidated. Initially, the SNPs of the FOXP4 gene were extracted from the dbSNP database and a total of 23124 SNPs was found, where 555 nonsynonymous, 20525 intronic, 1114 noncoding transcript, 334 synonymous were obtained and the rest were unspecified. Then, a series of bioinformatics tools (SIFT, PolyPhen2, SNAP2, PhD SNP, PANTHER, I-Mutant2.0, MUpro, GOR IV, ConSurf, NetSurfP 2.0, HOPE, DynaMut2, GeneMANIA, STRING and Schrodinger) were used to explore the effect of nsSNPs on FOXP4 protein function and structural stability. First, 555 nsSNPs were analyzed using SIFT, of which 57 were found as deleterious. Following, PolyPhen2, SNAP2, PhD SNP and PANTHER analyses, 10 nsSNPs (rs372762294, rs141899153, rs142575732, rs376938850, rs367607523, rs112517943, rs140387832, rs373949416, rs373949416 and rs376160648) were common and observed as deleterious, damaging and diseases associated. Following that, using I-Mutant2.0 and MUpro servers, 7 nsSNPs were found to be the most unstable. GOR IV predicted that these seven nsSNPs affect protein structure by altering the protein contents of alpha helixes, extended strands, and random coils. Following DynaMut2, 5 nsSNPs showed a decrease in the ΔΔG value compared with the wild-type and were found to be responsible for destabilizing the corresponding protein. GeneMANIA and STRING network revealed interaction of FOXP4 with other genes. Finally, molecular dynamics simulation analysis revealed consistent fluctuation in RMSD and RMSF values, Rg and hydrogen bonds in the mutant proteins compared with WT, which might alter the functional and structural stability of the corresponding protein. As a result, the aforementioned integrated comprehensive bioinformatic analyses provide insight into how various nsSNPs of the FOXP4 gene change the structural and functional properties of the corresponding protein, potentially proceeding with the pathophysiology of human diseases.

Discovery of oncogenic ROS1 missense mutations with sensitivity to tyrosine kinase inhibitors

ROS1 is the largest receptor tyrosine kinase in the human genome. Rearrangements of the ROS1 gene result in oncogenic ROS1 kinase fusion proteins that are currently the only validated biomarkers for targeted therapy with ROS1 TKIs in patients. While numerous somatic missense mutations in ROS1 exist in the cancer genome, their impact on catalytic activity and pathogenic potential is unknown. We interrogated the AACR Genie database and identified 34 missense mutations in the ROS1 tyrosine kinase domain for further analysis. Our experiments revealed that these mutations have varying effects on ROS1 kinase function, ranging from complete loss to significantly increased catalytic activity. Notably, Asn and Gly substitutions at Asp2113 in the ROS1 kinase domain were found to be TKI-sensitive oncogenic variants in cell-based model systems. In vivo experiments showed that ROS1 D2113N induced tumor formation that was sensitive to crizotinib and lorlatinib, FDA-approved ROS1-TKIs. Collectively, these findings highlight the tumorigenic potential of specific point mutations within the ROS1 kinase domain and their potential as therapeutic targets with FDA-approved ROS1-TKIs.

Identification of High-Risk Single Nucleotide Polymorphisms in the Human CYB5R3 Gene Responsible for Recessive Congenital Methemoglobinemia: A Computational Approach

Introduction

NADH-cytochrome b5 reductase deficiency due to pathogenic variants in the CYB5R3 gene causes recessive congenital methemoglobinemia (RCM) type I or type II. In type I, cyanosis from birth is the only major symptom, and the enzyme deficiency is restricted only to erythrocytes. Whereas in type II, cyanosis is associated with severe neurological manifestations, and the enzyme deficiency is generalized to all tissues.

Methods

In this study, several computational methods (SIFT, Polyphen-2, PROVEAN, Mutation Assessor, Panther, Phd-SNP, SNPs&GO, SNAP2, Align, GVGD, MutPred2, I-Mutant 2.0, MUpro, Duet, ConSurf and Netsurf-2.0 tools) were used to find the most deleterious nsSNPs in the CYB5R3 gene. Furthermore, structural analysis by Swiss-PDB viewer, protein-ligand docking using FTSite, and protein-protein interaction using STRING were carried out to evaluate the impact of these nsSNPs on the protein structure and function.

Results

Our in silico analysis suggested that out of 339 nsSNPs of the CYB5R3 gene, 17 (L47H, L47P, R61P, L73R G76D, G76C, P96H, G104C, S128P, G144D, P145S, L149P, Y151H, M177T, I178T, I216N, and G251V), are the most deleterious. Among them, two (P96H and S128P) were reported to be associated with the severe form RCM type II, six are related to RCM type I (G104C, G144D, P145S, L149P, M177T, and I178T), and the remaining nine high-risk nsSNPs have not yet been reported in RCM patients.

Discussion

This study highlighted the potential pathogenic nsSNPs of the CYB5R3 gene. To comprehend how these most harmful nsSNPs contribute to disease, it is crucial to experimentally validate their functional effects.

Computational screening and analysis of deleterious nsSNPs in human p14ARF (CDKN2A gene) protein using molecular dynamic simulation approach

Cyclin-dependent kinase inhibitor 2 A (CDKN2A) gene belongs to the cyclin-dependent kinase family that code for two transcripts (p16INK4A and p14ARF), both work as tumor suppressors proteins. The mutation that occurs in the p14ARF protein can lead to different types of cancers. Single nucleotide polymorphisms (SNPs) are an important type of genetic alteration that can lead to different types of diseases. In this study, we applied the computational strategy on human p14ARF protein to identify the potential deleterious nsSNPs and check their impact on the structure, function, and protein stability. We applied more than ten prediction tools to screen the retrieved 288 nsSNPs, consequently extracting four deleterious nsSNPs i.e., rs139725688 (R10G), rs139725688 (R21W), rs374360796 (F23L) and rs747717236 (L124R). Homology modeling, conservation and conformational analysis of mutant models were performed to examine the divergence of these variants from the native p14ARF structure. All-atom molecular dynamics simulation revealed a significant impact of these mutations on protein stability, compactness, globularity, solvent accessibility and secondary structure elements. Protein-protein interactions indicated that p14ARF operates as a hub linking clusters of different proteins and that changes in p14ARF may result in the disassociation of numerous signal cascades. Our current study is the first survey of computational analysis on p14ARF protein that determines the association of these nsSNPs with the altered function of p14ARF protein and leads to the development of various types of cancers. This research proposes the described functional SNPs as possible targets for proteomic investigations, diagnostic procedures, and treatments.Communicated by Ramaswamy H. Sarma.

Genetic Variants and Their Putative Effects on microRNA-Seed Sites: Characterization of the 3' Untranslated Region of Genes Associated with Temperament

The 3' untranslated region has an important role in gene regulation through microRNAs, and it has been estimated that microRNAs regulate up to 50% of coding genes in mammals. With the aim of allelic variant identification of 3' untranslated region microRNA seed sites, the 3' untranslated region was searched for seed sites of four temperament-associated genes (CACNG4, EXOC4, NRXN3, and SLC9A4). The microRNA seed sites were predicted in the four genes, and the CACNG4 gene had the greatest number with 12 predictions. To search for variants affecting the predicted microRNA seed sites, the four 3' untranslated regions were re-sequenced in a Brahman cattle population. Eleven single nucleotide polymorphisms were identified in the CACNG4, and eleven in the SLC9A4. Rs522648682:T>G of the CACNG4 gene was located at the predicted seed site for bta-miR-191. Rs522648682:T>G evidenced an association with both exit velocity (p = 0.0054) and temperament score (p = 0.0097). The genotype TT had a lower mean exit velocity (2.93 ± 0.4 m/s) compared with the TG and GG genotypes (3.91 ± 0.46 m/s and 3.67 ± 0.46 m/s, respectively). The allele associated with the temperamental phenotype antagonizes the seed site, disrupting the bta-miR-191 recognition. The G allele of CACNG4-rs522648682 has the potential to influence bovine temperament through a mechanism associated with unspecific recognition of bta-miR-191.

Functional Assessment of Cancer-Linked Mutations in Sensitive Regions of Regulators of G Protein Signaling Predicted by Three-Dimensional Missense Tolerance Ratio Analysis

Regulators of G protein signaling (RGS) proteins modulate G protein-coupled receptor (GPCR) signaling by acting as negative regulators of G proteins. Genetic variants in RGS proteins are associated with many diseases, including cancers, although the impact of these mutations on protein function is uncertain. Here we analyze the RGS domains of 15 RGS protein family members using a novel bioinformatic tool that measures the missense tolerance ratio (MTR) using a three-dimensional (3D) structure (3DMTR). Subsequent permutation analysis can define the protein regions that are most significantly intolerant (P < 0.05) in each dataset. We further focused on RGS14, RGS10, and RGS4. RGS14 exhibited seven significantly tolerant and seven significantly intolerant residues, RGS10 had six intolerant residues, and RGS4 had eight tolerant and six intolerant residues. Intolerant and tolerant-control residues that overlap with pathogenic cancer mutations reported in the COSMIC cancer database were selected to define the functional phenotype. Using complimentary cellular and biochemical approaches, proteins were tested for effects on GPCR-Gα activation, Gα binding properties, and downstream cAMP levels. Identified intolerant residues with reported cancer-linked mutations RGS14-R173C/H and RGS4-K125Q/E126K, and tolerant RGS14-S127P and RGS10-S64T resulted in a loss-of-function phenotype in GPCR-G protein signaling activity. In downstream cAMP measurement, tolerant RGS14-D137Y and RGS10-S64T and intolerant RGS10-K89M resulted in change of function phenotypes. These findings show that 3DMTR identified intolerant residues that overlap with cancer-linked mutations cause phenotypic changes that negatively impact GPCR-G protein signaling and suggests that 3DMTR is a potentially useful bioinformatics tool for predicting functionally important protein residues. SIGNIFICANCE STATEMENT: Human genetic variant/mutation information has expanded rapidly in recent years, including cancer-linked mutations in regulator of G protein signaling (RGS) proteins. However, experimental testing of the impact of this vast catalogue of mutations on protein function is not feasible. We used the novel bioinformatics tool three-dimensional missense tolerance ratio (3DMTR) to define regions of genetic intolerance in RGS proteins and prioritize which cancer-linked mutants to test. We found that 3DMTR more accurately classifies loss-of-function mutations in RGS proteins than other databases thereby offering a valuable new research tool.

Genomic analysis of breast cancer patients from Kerala: A novel BRCA1 mutation detected

BACKGROUND

Breast cancer is the most common cancer among females, with an incidence of 6,41,000 cases annually. The genetic makeup of the individuals, ethnicity, geographical location, lifestyle, and BMI are some well-described factors associated with breast cancer. It is well known that pathogenic variants in BRCA1 and BRCA2 are associated with a majority of hereditary breast cancer. Genome-wide association studies (GWAS) have identified more than 80 germline susceptibility loci responsible for hereditary breast cancer.

METHODS

In the present study, analysis of 94 genes associated with hereditary cancer was performed using next generation sequencing (NGS) in twelve patients having breast cancer and suspected with hereditary association.

RESULTS

Four out of twelve (33%) patients harbored pathogenic mutation of the BRCA1 gene. Two patients was identified p. E23Vfs*17 mutation in BRCA1, one patient had p.Glu1580Gln in BRCA1, and a novel frameshift variant p.T1456Ifs*9(c.4367Cdel) in one patient.

CONCLUSION

In the present study, out of four detected mutations in the BRCA1 gene, three were known and one was a novel BRCA1 mutation. It is advised to perform NGS-based genome sequencing to identify the genetic predisposition in breast cancer patients.

Assessing and Engineering Antibody Stability Using Experimental and Computational Methods

Engineering increased stability into antibodies can improve their developability. While a range of properties need to be optimized, thermal stability and aggregation are two key factors that affect the antibody yield, purity, and specificity throughout the development and manufacturing pipeline. Therefore, an ideal goal would be to apply protein engineering methods early-on, such as in parallel to affinity maturation, to screen out potential drug molecules with the desired conformational and colloidal stability. This chapter introduces our methods to computationally characterize an antibody Fab fragment, propose stabilizing variants, and then experimentally verify these predictions.

Computational screening and structural analysis of Gly201Arg and Gly201Asp missense mutations in human cyclin-dependent kinase 4 protein

The regulatory proteins, cyclins, and cyclin-dependent kinases (CDKs) control the cell cycle progression. CDK4 gene mutations are associated with certain cancers such as melanoma, breast cancer, and rhabdomyosarcoma. Therefore, understanding the mechanisms of cell cycle control and cell proliferation is essential in developing cancer treatment regimens. In this study, we obtained cancer-causing CDK4 mutations from the COSMIC database and subjected them to a series of in silico analyses to identify the most significant mutations. An overall of 238 mutations (119 missense mutations) retrieved from the COSMIC database were investigated for the pathogenic and destabilizing properties using the PredictSNP and iStable algorithms. Further, the amino acid position of the most pathogenic and destabilizing mutations were analyzed to understand the nature of amino acid conservation across the species during the evolution. We observed that the missense mutations G201R and G201D were more significant and the Glycine at position 201 was found to highly conserved. These significant mutations were subjected to molecular dynamics simulation analysis to understand the protein's structural changes. The results from molecular dynamics simulations revealed that both G201R and G201D of CDK4 are capable of altering the protein's native form. On comparison among the most significant mutations, G201R disrupted the protein structure higher than the protein with G201D.

Pathogenicity Prediction of Single Amino Acid Variants With Machine Learning Model Based on Protein Structural Energies

The most popular tools for predicting pathogenicity of single amino acid variants (SAVs) were developed based on sequence-based techniques. SAVs may change protein structure and function. In the context of van der Waals force and disulfide bridge calculations, no method directly predicts the impact of mutations on the energies of the protein structure. Here, we combined machine learning methods and energy scores of protein structures calculated by Rosetta Energy Function 2015 to predict SAV pathogenicity. The accuracy level of our model (0.76) is higher than that of six prediction tools. Further analyses revealed that the differential reference energies, attractive energies, and solvation of polar atoms between wildtype and mutant side-chains played essential roles in distinguishing benign from pathogenic variants. These features indicated the physicochemical properties of amino acids, which were observed in 3D structures instead of sequences. We added 16 features to Rhapsody (the prediction tool we used for our data set) and consequently improved its performance. The results indicated that these energy scores were more appropriate and more detailed representations of the pathogenicity of SAVs.

Computational Insights into the Structural and Functional Impacts of nsSNPs of Bone Morphogenetic Proteins

BMPs (bone morphogenetic proteins) are multipurpose (transforming growth factor)TGF-superfamily released cytokines. These glycoproteins, acting as disulfide-linked homo- or heterodimers, are highly potent regulators of bone and cartilage production and repair, cell proliferation throughout embryonic development, and bone homeostasis in the adults. Due to the fact that genetic variation might influence structural functions, this study is aimed to determine the pathogenic effect of nonsynonymous single-nucleotide polymorphisms (nsSNPs) in BMP genes. The implications of these variations, investigated using computational analysis and molecular models of the mature TGF-β domain, revealed the impact of modifications on the function of BMP protein. The three-dimensional (3D) structure analysis was performed on the nsSNP Y316S, V386G, E387G, C389G, and C391G nsSNP in the TGF-β domain of chicken BMP2 and H344P, S347P, V357A nsSNP in the TGF-β domain of chicken BMP4 protein that was anticipated to be harmful and of high risk. The ability of the proteins to perform variety of tasks interact with other molecules depends on their tertiary structural composition. The current analysis revealed the four most damaging variants (Y316S, V386G, E387G, C389G, and C391G), highly conserved and functional and are located in the TGF-beta domain of BMP2 and BMP4. The amino acid substitutions E387G, C389G, and C391G are discovered in the binding region. It was observed that the mutations in the TGF-beta domain caused significant changes in its structural organization including the substrate binding sites. Current findings will assist future research focused on the role of these variants in BMP function loss and their role in skeletal disorders, and this will possibly help to develop practical strategies for treating bone-related conditions.

Mutation-Specific Differences in Kv7.1 (KCNQ1) and Kv11.1 (KCNH2) Channel Dysfunction and Long QT Syndrome Phenotypes

The electrocardiogram (ECG) empowered clinician scientists to measure the electrical activity of the heart noninvasively to identify arrhythmias and heart disease. Shortly after the standardization of the 12-lead ECG for the diagnosis of heart disease, several families with autosomal recessive (Jervell and Lange-Nielsen Syndrome) and dominant (Romano-Ward Syndrome) forms of long QT syndrome (LQTS) were identified. An abnormally long heart rate-corrected QT-interval was established as a biomarker for the risk of sudden cardiac death. Since then, the International LQTS Registry was established; a phenotypic scoring system to identify LQTS patients was developed; the major genes that associate with typical forms of LQTS were identified; and guidelines for the successful management of patients advanced. In this review, we discuss the molecular and cellular mechanisms for LQTS associated with missense variants in KCNQ1 (LQT1) and KCNH2 (LQT2). We move beyond the "benign" to a "pathogenic" binary classification scheme for different KCNQ1 and KCNH2 missense variants and discuss gene- and mutation-specific differences in K+ channel dysfunction, which can predispose people to distinct clinical phenotypes (e.g., concealed, pleiotropic, severe, etc.). We conclude by discussing the emerging computational structural modeling strategies that will distinguish between dysfunctional subtypes of KCNQ1 and KCNH2 variants, with the goal of realizing a layered precision medicine approach focused on individuals.

Comparative analysis of web-based programs for single amino acid substitutions in proteins

Single amino-acid substitution in a protein affects its structure and function. These changes are the primary reasons for the advent of many complex diseases. Analyzing single point mutations in a protein is crucial to see their impact and to understand the disease mechanism. This has given many biophysical resources, including databases and web-based tools to explore the effects of mutations on the structure and function of human proteins. For a given mutation, each tool provides a score-based outcomes which indicate deleterious probability. In recent years, developments in existing programs and the introduction of new prediction algorithms have transformed the state-of-the-art protein mutation analysis. In this study, we have performed a systematic study of the most commonly used mutational analysis programs (10 sequence-based and 5 structure-based) to compare their prediction efficiency. We have carried out extensive mutational analyses using these tools for previously known pathogenic single point mutations of five different proteins. These analyses suggested that sequence-based tools, PolyPhen2, PROVEAN, and PMut, and structure-based web tool, mCSM have a better prediction accuracy. This study indicates that the employment of more than one program based on different approaches should significantly improve the prediction power of the available methods.

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.

MutateX: an automated pipeline for in silico saturation mutagenesis of protein structures and structural ensembles

Mutations, which result in amino acid substitutions, influence the stability of proteins and their binding to biomolecules. A molecular understanding of the effects of protein mutations is both of biotechnological and medical relevance. Empirical free energy functions that quickly estimate the free energy change upon mutation (ΔΔG) can be exploited for systematic screenings of proteins and protein complexes. In silico saturation mutagenesis can guide the design of new experiments or rationalize the consequences of known mutations. Often software such as FoldX, while fast and reliable, lack the necessary automation features to apply them in a high-throughput manner. We introduce MutateX, a software to automate the prediction of ΔΔGs associated with the systematic mutation of each residue within a protein, or protein complex to all other possible residue types, using the FoldX energy function. MutateX also supports ΔΔG calculations over protein ensembles, upon post-translational modifications and in multimeric assemblies. At the heart of MutateX lies an automated pipeline engine that handles input preparation, parallelization and outputs publication-ready figures. We illustrate the MutateX protocol applied to different case studies. The results of the high-throughput scan provided by our tools can help in different applications, such as the analysis of disease-associated mutations, to complement experimental deep mutational scans, or assist the design of variants for industrial applications. MutateX is a collection of Python tools that relies on open-source libraries. It is available free of charge under the GNU General Public License from https://github.com/ELELAB/mutatex.

A comparison on predicting functional impact of genomic variants

Single-nucleotide polymorphism (SNPs) may cause the diverse functional impact on RNA or protein changing genotype and phenotype, which may lead to common or complex diseases like cancers. Accurate prediction of the functional impact of SNPs is crucial to discover the ‘influential’ (deleterious, pathogenic, disease-causing, and predisposing) variants from massive background polymorphisms in the human genome. Increasing computational methods have been developed to predict the functional impact of variants. However, predictive performances of these computational methods on massive genomic variants are still unclear. In this regard, we systematically evaluated 14 important computational methods including specific methods for one type of variant and general methods for multiple types of variants from several aspects; none of these methods achieved excellent (AUC ≥ 0.9) performance in both data sets. CADD and REVEL achieved excellent performance on multiple types of variants and missense variants, respectively. This comparison aims to assist researchers and clinicians to select appropriate methods or develop better predictive methods.

New variants of alpha-1-antitrypsin: structural simulations and clinical expression

BACKGROUND

Alpha-1 antitrypsin deficiency (AATD) is characterized by reduced serum levels of the AAT protein and predisposes to liver and lung disease. The characterization at structural level of novel pathogenic SERPINA1 mutants coding for circulating AAT could provide novel insights into the mechanisms of AAT misfolding. The present study aimed to provide a practical framework for the identification and analysis of new AAT mutations, combining structural simulations and clinical data.

METHODS

We analysed a total of five mutations (four not previously described) in a total of six subjects presenting moderate to severe AATD: Gly95Alafs*18, Val210Glu, Asn247Ser, Pi*S + Asp341His and Pi*S + Leu383Phe + Lys394Ile. Clinical data, genotyping and phenotyping assays, structural mapping, and conformational characterization through molecular dynamic (MD) simulations were developed and combined.

RESULTS

Newly discovered AAT missense variants were localized both on the interaction surface and the hydrophobic core of the protein. Distribution of mutations across the structure revealed Val210Glu at the solvent exposed s4C strand and close to the "Gate" region. Asn247Ser was located on the accessible surface, which is important for glycan attachment. On the other hand, Asp341His, Leu383Phe were mapped close to the "breach" and "shutter" regions. MD analysis revealed the reshaping of local interactions around the investigated substitutions that have varying effects on AAT conformational flexibility, hydrophobic packing, and electronic surface properties. The most severe structural changes were observed in the double- and triple-mutant (Pi*S + Asp341His and Pi*S + Leu383Phe + Lys394Ile) molecular models. The two carriers presented impaired lung function.

CONCLUSIONS

The results characterize five variants, four of them previously unknown, of the SERPINA1 gene, which define new alleles contributing to the deficiency of AAT. Rare variants might be more frequent than expected, and therefore, in discordant cases, standardized screening of the S and Z alleles needs complementation with gene sequencing and structural approaches. The utility of computational modelling for providing supporting evidence of the pathogenicity of rare single nucleotide variations is discussed.

Pierce into Structural Changes of Interactions Between Mutated Spike Glycoproteins and ACE2 to Evaluate Its Potential Biological and Therapeutic Consequences

The structural consequences of ongoing mutations on the SARS-CoV-2 spike-protein remains to be fully elucidated. These mutations could change the binding affinity between the virus and its target cell. Moreover, obtaining new mutations would also change the therapeutic efficacy of the designed drug candidates. To evaluate these consequences, 3D structure of a mutant spike protein was predicted and checked for stability, cavity sites, and residue depth. The docking analyses were performed between the 3D model of the mutated spike protein and the ACE2 protein and an engineered therapeutic ACE2 against COVID-19. The obtained results revealed that the N501Y substitution has altered the interaction orientation, augmented the number of interface bonds, and increased the affinity against the ACE2. On the other hand, the P681H mutation contributed to the increased cavity size and relatively higher residue depth. The binding affinity between the engineered therapeutic ACE2 and the mutant spike was significantly higher with a distinguished binding orientation. It could be concluded that the mutant spike protein increased the affinity, preserved the location, changed the orientation, and altered the interface amino acids of its interaction with both the ACE2 and its therapeutic engineered version. The obtained results corroborate the more aggressive nature of mutated SARS-CoV-2 due to their higher binding affinity. Moreover, designed ACe2-baased therapeutics would be still highly effective against covid-19, which could be the result of conserved nature of cellular ACE2.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10989-021-10346-1.

Prediction of the Effects of Variants and Differential Expression of Key Host Genes ACE2, TMPRSS2, and FURIN in SARS-CoV-2 Pathogenesis: An In Silico Approach

A new strain of the beta coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is solely responsible for the ongoing coronavirus disease 2019 (COVID-19) pandemic. Although several studies suggest that the spike protein of this virus interacts with the cell surface receptor, angiotensin-converting enzyme 2 (ACE2), and is subsequently cleaved by TMPRSS2 and FURIN to enter into the host cell, conclusive insight about the interaction pattern of the variants of these proteins is still lacking. Thus, in this study, we analyzed the functional conjugation among the spike protein, ACE2, TMPRSS2, and FURIN in viral pathogenesis as well as the effects of the mutations of the proteins through the implementation of several bioinformatics approaches. Analysis of the intermolecular interactions revealed that T27A (ACE2), G476S (receptor-binding domain [RBD] of the spike protein), C297T (TMPRSS2), and P812S (cleavage site for TMPRSS2) coding variants may render resistance in viral infection, whereas Q493L (RBD), S477I (RBD), P681R (cleavage site for FURIN), and P683W (cleavage site for FURIN) may lead to increase viral infection. Genotype-specific expression analysis predicted several genetic variants of ACE2 (rs2158082, rs2106806, rs4830971, and rs4830972), TMPRSS2 (rs458213, rs468444, rs4290734, and rs6517666), and FURIN (rs78164913 and rs79742014) that significantly alter their normal expression which might affect the viral spread. Furthermore, we also found that ACE2, TMPRSS2, and FURIN proteins are functionally co-related with each other, and several genes are highly co-expressed with them, which might be involved in viral pathogenesis. This study will thus help in future genomics and proteomics studies of SARS-CoV-2 and will provide an opportunity to understand the underlying molecular mechanism during SARS-CoV-2 pathogenesis.

A computational methodology to diagnose sequence-variant dynamic perturbations by comparing atomic protein structures

MOTIVATION

The objective is to diagnose dynamics perturbations caused by amino-acid mutations as prerequisite to assess protein functional health or drug failure, simply using network models of protein X-ray structures.

RESULTS

We find that the differences in the allocation of the atomic interactions of each amino acid to 1D, 2D, 3D, 4D structural levels between variants structurally robust, recover experimental dynamic perturbations. The allocation measure validated on two B-pentamers variants of AB5 toxins having 17 mutations, also distinguishes dynamic perturbations of pathogenic and non-pathogenic Transthyretin single-mutants. Finally, the main proteases of the coronaviruses SARS-CoV and SARS-CoV-2 exhibit changes in the allocation measure, raising the possibility of drug failure despite the main proteases structural similarity.

AVAILABILITY AND IMPLEMENTATION

The Python code used for the production of the results is available at github.com/lorpac/protein_partitioning_atomic_contacts. The authors will run the analysis on any PDB structures of protein variants upon request.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A scalable Drosophila assay for clinical interpretation of human PTEN variants in suppression of PI3K/AKT induced cellular proliferation

Gene variant discovery is becoming routine, but it remains difficult to usefully interpret the functional consequence or disease relevance of most variants. To fill this interpretation gap, experimental assays of variant function are becoming common place. Yet, it remains challenging to make these assays reproducible, scalable to high numbers of variants, and capable of assessing defined gene-disease mechanism for clinical interpretation aligned to the ClinGen Sequence Variant Interpretation (SVI) Working Group guidelines for 'well-established assays'. Drosophila melanogaster offers great potential as an assay platform, but was untested for high numbers of human variants adherent to these guidelines. Here, we wished to test the utility of Drosophila as a platform for scalable well-established assays. We took a genetic interaction approach to test the function of ~100 human PTEN variants in cancer-relevant suppression of PI3K/AKT signaling in cellular growth and proliferation. We validated the assay using biochemically characterized PTEN mutants as well as 23 total known pathogenic and benign PTEN variants, all of which the assay correctly assigned into predicted functional categories. Additionally, function calls for these variants correlated very well with our recent published data from a human cell line. Finally, using these pathogenic and benign variants to calibrate the assay, we could set readout thresholds for clinical interpretation of the pathogenicity of 70 other PTEN variants. Overall, we demonstrate that Drosophila offers a powerful assay platform for clinical variant interpretation, that can be used in conjunction with other well-established assays, to increase confidence in the accurate assessment of variant function and pathogenicity.

Mutation Edgotype Drives Fitness Effect in Human

Missense mutations are known to perturb protein-protein interaction networks (known as interactome networks) in different ways. However, it remains unknown how different interactome perturbation patterns (“edgotypes”) impact organismal fitness. Here, we estimate the fitness effect of missense mutations with different interactome perturbation patterns in human, by calculating the fractions of neutral and deleterious mutations that do not disrupt PPIs (“quasi-wild-type”), or disrupt PPIs either by disrupting the binding interface (“edgetic”) or by disrupting overall protein stability (“quasi-null”). We first map pathogenic mutations and common non-pathogenic mutations onto homology-based three-dimensional structural models of proteins and protein-protein interactions in human. Next, we perform structure-based calculations to classify each mutation as either quasi-wild-type, edgetic, or quasi-null. Using our predicted as well as experimentally determined interactome perturbation patterns, we estimate that >∼40% of quasi-wild-type mutations are effectively neutral and the remaining are mostly mildly deleterious, that >∼75% of edgetic mutations are only mildly deleterious, and that up to ∼75% of quasi-null mutations may be strongly detrimental. These estimates are the first such estimates of fitness effect for different network perturbation patterns in any interactome. Our results suggest that while mutations that do not disrupt the interactome tend to be effectively neutral, the majority of human PPIs are under strong purifying selection and the stability of most human proteins is essential to human life.

MAPS: machine-assisted phenotype scoring enables rapid functional assessment of genetic variants by high-content microscopy

BACKGROUND

Genetic testing is widely used in evaluating a patient's predisposition to hereditary diseases. In the case of cancer, when a functionally impactful mutation (i.e. genetic variant) is identified in a disease-relevant gene, the patient is at elevated risk of developing a lesion in their lifetime. Unfortunately, as the rate and coverage of genetic testing has accelerated, our ability to assess the functional status of new variants has fallen behind. Therefore, there is an urgent need for more practical, streamlined and cost-effective methods for classifying variants.

RESULTS

To directly address this issue, we designed a new approach that uses alterations in protein subcellular localization as a key indicator of loss of function. Thus, new variants can be rapidly functionalized using high-content microscopy (HCM). To facilitate the analysis of the large amounts of imaging data, we developed a new software toolkit, named MAPS for machine-assisted phenotype scoring, that utilizes deep learning to extract and classify cell-level features. MAPS helps users leverage cloud-based deep learning services that are easy to train and deploy to fit their specific experimental conditions. Model training is code-free and can be done with limited training images. Thus, MAPS allows cell biologists to easily incorporate deep learning into their image analysis pipeline. We demonstrated an effective variant functionalization workflow that integrates HCM and MAPS to assess missense variants of PTEN, a tumor suppressor that is frequently mutated in hereditary and somatic cancers.

CONCLUSIONS

This paper presents a new way to rapidly assess variant function using cloud deep learning. Since most tumor suppressors have well-defined subcellular localizations, our approach could be widely applied to functionalize variants of uncertain significance and help improve the utility of genetic testing.

ThermoScan: Semi-automatic Identification of Protein Stability Data From PubMed

During the last years, the increasing number of DNA sequencing and protein mutagenesis studies has generated a large amount of variation data published in the biomedical literature. The collection of such data has been essential for the development and assessment of tools predicting the impact of protein variants at functional and structural levels. Nevertheless, the collection of manually curated data from literature is a highly time consuming and costly process that requires domain experts. In particular, the development of methods for predicting the effect of amino acid variants on protein stability relies on the thermodynamic data extracted from literature. In the past, such data were deposited in the ProTherm database, which however is no longer maintained since 2013. For facilitating the collection of protein thermodynamic data from literature, we developed the semi-automatic tool ThermoScan. ThermoScan is a text mining approach for the identification of relevant thermodynamic data on protein stability from full-text articles. The method relies on a regular expression searching for groups of words, including the most common conceptual words appearing in experimental studies on protein stability, several thermodynamic variables, and their units of measure. ThermoScan analyzes full-text articles from the PubMed Central Open Access subset and calculates an empiric score that allows the identification of manuscripts reporting thermodynamic data on protein stability. The method was optimized on a set of publications included in the ProTherm database, and tested on a new curated set of articles, manually selected for presence of thermodynamic data. The results show that ThermoScan returns accurate predictions and outperforms recently developed text-mining algorithms based on the analysis of publication abstracts. Availability: The ThermoScan server is freely accessible online at https://folding.biofold.org/thermoscan. The ThermoScan python code and the Google Chrome extension for submitting visualized PMC web pages to the ThermoScan server are available at https://github.com/biofold/ThermoScan.

Comprehensive in silico analysis and molecular dynamics of the superoxide dismutase 1 (SOD1) variants related to amyotrophic lateral sclerosis

Amyotrophic Lateral Sclerosis (ALS) is the most frequent motor neuron disorder, with a significant social and economic burden. ALS remains incurable, and the only drugs approved for its treatments confers a survival benefit of a few months for the patients. Missense mutations in superoxide dismutase 1 (SOD1), a major cytoplasmic antioxidant enzyme, has been associated with ALS development, accounting for 23% of its familial cases and 7% of all sporadic cases. This work aims to characterize in silico the structural and functional effects of SOD1 protein variants. Missense mutations in SOD1 were compiled from the literature and databases. Twelve algorithms were used to predict the functional and stability effects of these mutations. ConSurf was used to estimate the evolutionary conservation of SOD1 amino-acids. GROMACS was used to perform molecular dynamics (MD) simulations of SOD1 wild-type and variants A4V, D90A, H46R, and I113T, which account for approximately half of all ALS-SOD1 cases in the United States, Europe, Japan, and United Kingdom, respectively. 233 missense mutations in SOD1 protein were compiled from the databases and literature consulted. The predictive analyses pointed to an elevated rate of deleterious and destabilizing predictions for the analyzed variants, indicating their harmful effects. The ConSurf analysis suggested that mutations in SOD1 mainly affect conserved and possibly functionally essential amino acids. The MD analyses pointed to flexibility and essential dynamics alterations at the electrostatic and metal-binding loops of variants A4V, D90A, H46R, and I113T that could lead to aberrant interactions triggering toxic protein aggregation. These alterations may have harmful implications for SOD1 and explain their association with ALS. Understanding the effects of SOD1 mutations on protein structure and function facilitates the design of further experiments and provides relevant information on the molecular mechanism of pathology, which may contribute to improvements in existing treatments for ALS.

Type IV Collagen Variants in CKD: Performance of Computational Predictions for Identifying Pathogenic Variants

Rationale & Objective

Pathogenic variants in type IV collagen have been reported to account for a significant proportion of chronic kidney disease. Accordingly, genetic testing is increasingly used to diagnose kidney diseases, but testing also may reveal rare missense variants that are of uncertain clinical significance. To aid in interpretation, computational prediction (called in silico) programs may be used to predict whether a variant is clinically important. We evaluate the performance of in silico programs for COL4A3/A4/A5 variants.

Study Design, Setting, & Participants

Rare missense variants in COL4A3/A4/A5 were identified in disease cohorts, including a local focal segmental glomerulosclerosis (FSGS) cohort and publicly available disease databases, in which they are categorized as pathogenic or benign based on clinical criteria.

Tests Compared & Outcomes

All rare missense variants identified in the 4 disease cohorts were subjected to in silico predictions using 12 different programs. Comparisons between the predictions were compared with: (1) variant classification (pathogenic or benign) in the cohorts and (2) functional characterization in a randomly selected smaller number (17) of pathogenic or uncertain significance variants obtained from the local FSGS cohort.

Results

In silico predictions correctly classified 75% to 97% of pathogenic and 57% to 100% of benign COL4A3/A4/A5 variants in public disease databases. The congruency of in silico predictions was similar for variants categorized as pathogenic and benign, with the exception of benign COL4A5 variants, in which disease effects were overestimated. By contrast, in silico predictions and functional characterization classified all 9 pathogenic COL4A3/A4/A5 variants correctly that were obtained from a local FSGS cohort. However, these programs also overestimated the effects of genomic variants of uncertain significance when compared with functional characterization. Each of the 12 in silico programs used yielded similar results.

Limitations

Overestimation of in silico program sensitivity given that they may have been used in the categorization of variants labeled as pathogenic in disease repositories.

Conclusions

Our results suggest that in silico predictions are sensitive but not specific to assign COL4A3/A4/A5 variant pathogenicity, with misclassification of benign variants and variants of uncertain significance. Thus, we do not recommend in silico programs but instead recommend pursuing more objective levels of evidence suggested by medical genetics guidelines.

In Silico Investigation of the New UK (B.1.1.7) and South African (501Y.V2) SARS-CoV-2 Variants with a Focus at the ACE2-Spike RBD Interface

SARS-CoV-2 exploits angiotensin-converting enzyme 2 (ACE2) as a receptor to invade cells. It has been reported that the UK and South African strains may have higher transmission capabilities, eventually in part due to amino acid substitutions on the SARS-CoV-2 Spike protein. The pathogenicity seems modified but is still under investigation. Here we used the experimental structure of the Spike RBD domain co-crystallized with part of the ACE2 receptor, several in silico methods and numerous experimental data reported recently to analyze the possible impacts of three amino acid replacements (Spike K417N, E484K, N501Y) with regard to ACE2 binding. We found that the N501Y replacement in this region of the interface (present in both the UK and South African strains) should be favorable for the interaction with ACE2, while the K417N and E484K substitutions (South African strain) would seem neutral or even unfavorable. It is unclear if the N501Y substitution in the South African strain could counterbalance the K417N and E484K Spike replacements with regard to ACE2 binding. Our finding suggests that the UK strain should have higher affinity toward ACE2 and therefore likely increased transmissibility and possibly pathogenicity. If indeed the South African strain has a high transmission level, this could be due to the N501Y replacement and/or to substitutions in regions located outside the direct Spike-ACE2 interface but not so much to the K417N and E484K replacements. Yet, it should be noted that amino acid changes at Spike position 484 can lead to viral escape from neutralizing antibodies. Further, these amino acid substitutions do not seem to induce major structural changes in this region of the Spike protein. This structure-function study allows us to rationalize some observations made for the UK strain but raises questions for the South African strain.

SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability

Modeling the effect of mutations on protein thermodynamics stability is useful for protein engineering and understanding molecular mechanisms of disease-causing variants. Here, we report a new development of the SAAFEC method, the SAAFEC-SEQ, which is a gradient boosting decision tree machine learning method to predict the change of the folding free energy caused by amino acid substitutions. The method does not require the 3D structure of the corresponding protein, but only its sequence and, thus, can be applied on genome-scale investigations where structural information is very sparse. SAAFEC-SEQ uses physicochemical properties, sequence features, and evolutionary information features to make the predictions. It is shown to consistently outperform all existing state-of-the-art sequence-based methods in both the Pearson correlation coefficient and root-mean-squared-error parameters as benchmarked on several independent datasets. The SAAFEC-SEQ has been implemented into a web server and is available as stand-alone code that can be downloaded and embedded into other researchers’ code.

Comprehensive in-silico analysis of damage associated SNPs in hOCT1 affecting Imatinib response in chronic myeloid leukemia

Non-synonymous single nucleotide polymorphisms (nsSNPs) in hOCT1 (encoded by SLC22A1 gene) are expected to affect Imatinib uptake in chronic myeloid leukemia (CML). In this study, sequence homology-based genetic analysis of a set of 270 coding SNPs identified 18 nsSNPs to be putatively damaging/deleterious using eight different algorithms. Subsequently, based on conservation of amino acid residues, stability analysis, posttranscriptional modifications, and solvent accessibility analysis, the possible structural-functional relationship was established for high-confidence nsSNPs. Furthermore, based on the modeling results, some dissimilarities of mutant type amino acids from wild-type amino acids such as size, charge, interaction and hydrophobicity were revealed. Three highly deleterious mutations consisting of P283L, G401S and R402G in SLC22A1 gene that may alter the protein structure, function and stability were identified. These results provide a filtered data to explore the effect of uncharacterized nsSNP and find their association with Imatinib resistance in CML.

An Ensemble Approach to Predict the Pathogenicity of Synonymous Variants

Single-nucleotide variants (SNVs) are a major form of genetic variation in the human genome that contribute to various disorders. There are two types of SNVs, namely non-synonymous (missense) variants (nsSNVs) and synonymous variants (sSNVs), predominantly involved in RNA processing or gene regulation. sSNVs, unlike missense or nsSNVs, do not alter the amino acid sequences, thereby making challenging candidates for downstream functional studies. Numerous computational methods have been developed to evaluate the clinical impact of nsSNVs, but very few methods are available for understanding the effects of sSNVs. For this analysis, we have downloaded sSNVs from the ClinVar database with various features such as conservation, DNA-RNA, and splicing properties. We performed feature selection and implemented an ensemble random forest (RF) classification algorithm to build a classifier to predict the pathogenicity of the sSNVs. We demonstrate that the ensemble predictor with selected features (20 features) enhances the classification of sSNVs into two categories, pathogenic and benign, with high accuracy (87%), precision (79%), and recall (91%). Furthermore, we used this prediction model to reclassify sSNVs with unknown clinical significance. Finally, the method is very robust and can be used to predict the effect of other unknown sSNVs.

Prediction and evaluation of deleterious and disease causing non-synonymous SNPs (nsSNPs) in human NF2 gene responsible for neurofibromatosis type 2 (NF2)

The majority of genetic variations in the human genome that lead to variety of different diseases are caused by non-synonymous single nucleotide polymorphisms (nsSNPs). Neurofibromatosis type 2 (NF2) is a deadly disease caused by nsSNPs in the NF2 gene that encodes for a protein called merlin. This study used various in silico methods, SIFT, Polyphen-2, PhD-SNP and MutPred, to investigate the pathogenic effect of 14 nsSNPs in the merlin FERM domain. The G197C and L234R mutations were found to be two deleterious and disease mutations associated with the mild and severe forms of NF2, respectively. Molecular dynamics (MD) simulations were conducted to understand the stability, structure and dynamics of these mutations. Both mutant structures experienced larger flexibility compared to the wildtype. The L234R mutant suffered from more prominent structural instability, which may help to explain why it is associated with the more severe form of NF2. The intramolecular hydrogen bonding in L234R mutation decreased from the wildtype, while intermolecular hydrogen bonding of L234R mutation with solvent greatly increased. The native contacts were also found to be important. Protein-protein docking revealed that L234R mutation decreased the binding complementarity and binding affinity of LATS2 to merlin, which may have an impact on merlin's ability to regulate the Hippo signaling pathway. The calculated binding affinity of the LATS2 to L234R mutant and wildtype merlin protein is found to be 21.73 and -11 kcal/mol, respectively. The binding affinity of the wildtype merlin agreed very well with the experimental value, -8 kcal/mol.Communicated by Ramaswamy H. Sarma.

A Premalignant Cell-Based Model for Functionalization and Classification of PTEN Variants

As sequencing becomes more economical, we are identifying sequence variations in the population faster than ever. For disease-associated genes, it is imperative that we differentiate a sequence variant as either benign or pathogenic, such that the appropriate therapeutic interventions or surveillance can be implemented. PTEN is a frequently mutated tumor suppressor that has been linked to the PTEN hamartoma tumor syndrome. Although the domain structure of PTEN and the functional impact of a number of its most common tumor-linked mutations have been characterized, there is a lack of information about many recently identified clinical variants. To address this challenge, we developed a cell-based assay that utilizes a premalignant phenotype of normal mammary epithelial cells lacking PTEN. We measured the ability of PTEN variants to rescue the spheroid formation phenotype of PTEN^-/- MCF10A cells maintained in suspension. As proof of concept, we functionalized 47 missense variants using this assay, only 19 of which have clear classifications in ClinVar. We utilized a machine learning model trained with annotated genotypic data to classify variants as benign or pathogenic based on our functional scores. Our model predicted with high accuracy that loss of PTEN function was indicative of pathogenicity. We also determined that the pathogenicity of certain variants may have arisen from reduced stability of the protein product. Overall, this assay outperformed computational predictions, was scalable, and had a short run time, serving as an ideal alternative for annotating the clinical significance of cancer-associated PTEN variants. SIGNIFICANCE: Combined three-dimensional tumor spheroid modeling and machine learning classifies PTEN missense variants, over 70% of which are currently listed as variants of uncertain significance. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/13/2775/F1.large.jpg.

Analysis of protein missense alterations by combining sequence- and structure-based methods

BACKGROUND

Different types of in silico approaches can be used to predict the phenotypic consequence of missense variants. Such algorithms are often categorized as sequence based or structure based, when they necessitate 3D structural information. In addition, many other in silico tools, not dedicated to the analysis of variants, can be used to gain additional insights about the possible mechanisms at play.

METHODS

Here we applied different computational approaches to a set of 20 known missense variants present on different proteins (CYP, complement factor B, antithrombin and blood coagulation factor VIII). The tools that were used include fast computational approaches and web servers such as PolyPhen-2, PopMusic, DUET, MaestroWeb, SAAFEC, Missense3D, VarSite, FlexPred, PredyFlexy, Clustal Omega, meta-PPISP, FTMap, ClusPro, pyDock, PPM, RING, Cytoscape, and ChannelsDB.

RESULTS

We observe some conflicting results among the methods but, most of the time, the combination of several engines helped to clarify the potential impacts of the amino acid substitutions.

CONCLUSION

Combining different computational approaches including some that were not developed to investigate missense variants help to predict the possible impact of the amino acid substitutions. Yet, when the modified residues are involved in a salt-bridge, the tools tend to fail, even when the analysis is performed in 3D. Thus, interactive structural analysis with molecular graphics packages such as Chimera or PyMol or others are still needed to clarify automatic prediction.

Deciphering the impact of missense mutations on structure and dynamics of SMAD4 protein involved in pathogenesis of gall bladder cancer

Gall bladder cancer (GBC) is the most common malignancy of biliary tract cancer associated with high mortality rate and poor prognosis due to lack of suitable biomarkers. In this study, we explored the structural and functional effects of different missense mutations occurs in SMAD4 that was associated with the development of GBC. We utilized in silico methods to predict the harmful effects of nonsynonymous missense mutations and monitored the stability of protein. We found that all mutations (D351N, G352E, R361C, R361H, E526Q) associated with SMAD4 were deleterious in nature resulting in the formation of deformed or unstable protein structure. Molecular dynamics simulation studies revealed how these mutations affect protein stability, structure, conformation and function. We observed, different mutants increase the compactness and rigidity of SMAD4 protein, alter secondary structure composition, decrease the surface area and protein-ligand interaction and affect its conformation. Findings of current work indicated that the analyzed mutations might affect the structure of protein and its caliber to interact with other molecules, which probably related to functional impairment of SMAD4 upon D351N, G352E, R361C, R361H, E526Q mutations and their involvement in cancer. Hence, the present study has significance of rational drug design and further increase our understanding of GBC development.Communicated by Ramaswamy H. Sarma.

In silico analysis of the tryptophan hydroxylase 2 (TPH2) protein variants related to psychiatric disorders

The tryptophan hydroxylase 2 (TPH2) enzyme catalyzes the first step of serotonin biosynthesis. Serotonin is known for its role in several homeostatic systems related to sleep, mood, and food intake. As the reaction catalyzed by TPH2 is the rate-limiting step of serotonin biosynthesis, mutations in TPH2 have been associated with several psychiatric disorders (PD). This work undertakes an in silico analysis of the effects of genetic mutations in the human TPH2 protein. Ten algorithms were used to predict the functional and stability effects of the TPH2 mutations. ConSurf was used to estimate the evolutionary conservation of TPH2 amino acids. GROMACS was used to perform molecular dynamics (MD) simulations of TPH2 WT and P260S, R303W, and R441H, which had already been associated with the development of PD. Forty-six TPH2 variants were compiled from the literature. Among the analyzed variants, those occurring at the catalytic domain were shown to be more damaging to protein structure and function. The ConSurf analysis indicated that the mutations affecting the catalytic domain were also more conserved throughout evolution. The variants S364K and S383F were predicted to be deleterious by all the functional algorithms used and occurred at conserved positions, suggesting that they might be deleterious. The MD analyses indicate that the mutations P206S, R303W, and R441H affect TPH2 flexibility and essential mobility at the catalytic and oligomerization domains. The variants P206S, R303W, and R441H also exhibited alterations in dimer binding affinity and stability throughout the simulations. Thus, these mutations may impair TPH2 functional interactions and, consequently, its function, leading to the development of PD. Furthermore, we developed a database, SNPMOL (http://www.snpmol.org/), containing the results presented in this paper. Understanding the effects of TPH2 mutations on protein structure and function may lead to improvements in existing treatments for PD and facilitate the design of further experiments.

Genetic Predisposition to Breast and Ovarian Cancers: How Many and Which Genes to Test?

Breast and ovarian cancers are some of the most common tumors in females, and the genetic predisposition is emerging as one of the key risk factors in the development of these two malignancies. BRCA1 and BRCA2 are the best-known genes associated with hereditary breast and ovarian cancer. However, recent advances in molecular techniques, Next-Generation Sequencing in particular, have led to the identification of many new genes involved in the predisposition to breast and/or ovarian cancer, with different penetrance estimates. TP53, PTEN, STK11, and CDH1 have been identified as high penetrance genes for the risk of breast/ovarian cancers. Besides them, PALB2, BRIP1, ATM, CHEK2, BARD1, NBN, NF1, RAD51C, RAD51D and mismatch repair genes have been recognized as moderate and low penetrance genes, along with other genes encoding proteins involved in the same pathways, possibly associated with breast/ovarian cancer risk. In this review, we summarize the past and more recent findings in the field of cancer predisposition genes, with insights into the role of the encoded proteins and the associated genetic disorders. Furthermore, we discuss the possible clinical utility of genetic testing in terms of prevention protocols and therapeutic approaches.

Evaluation of performance of leading algorithms for variant pathogenicity predictions and designing a combinatory predictor method: application to Rett syndrome variants

Background

Genomics diagnostic tests are done for a wide spectrum of complex genetics conditions such as autism and cancer. The growth of technology has not only aided in successfully decoding the genetic variants that causes or trigger these disorders. However, interpretation of these variants is not a trivial task even at a level of distinguish pathogenic vs benign variants.

Methods

We used the clinically significant variants from ClinVar database to evaluate the performance of 14 most popular in-silico predictors using supervised learning methods. We implemented a feature selection and random forest classification algorithm to identify the best combination of predictors to evaluate the pathogenicity of a variant. Finally, we have also utilized this combination of predictors to reclassify the variants of unknown significance in MeCP2 gene that are associated with the Rett syndrome.

Results

The results from analysis shows an optimized selection of prediction algorithm and developed a combinatory predictor method. Our combinatory approach of using both best performing independent and ensemble predictors reduces any algorithm biases in variant characterization. The reclassification of variants (such as VUS) in MECP2 gene associated with RETT syndrome suggest that the combinatory in-silico predictor approach had a higher success rate in categorizing their pathogenicity.

In silico analysis of PFN1 related to amyotrophic lateral sclerosis

Profilin 1 (PFN1) protein plays key roles in neuronal growth and differentiation, membrane trafficking, and regulation of the actin cytoskeleton. Four natural variants of PFN1 were described as related to ALS, the most common adult-onset motor neuron disorder. However, the pathological mechanism of PFN1 in ALS is not yet completely understood. The goal of this work is to thoroughly analyze the effects of the ALS-related mutations on PFN1 structure and function using computational simulations. Here, PhD-SNP, PMUT, PolyPhen-2, SIFT, SNAP, SNPS&GO, SAAP, nsSNPAnalyzer, SNPeffect4.0 and I-Mutant2.0 were used to predict the functional and stability effects of PFN1 mutations. ConSurf was used for the evolutionary conservation analysis, and GROMACS was used to perform the MD simulations. The mutations C71G, M114T, and G118V, but not E117G, were predicted as deleterious by most of the functional prediction algorithms that were used. The stability prediction indicated that the ALS-related mutations could destabilize PFN1. The ConSurf analysis indicated that the mutation C71G, M114T, E117G, and G118V occur in highly conserved positions. The MD results indicated that the studied mutations could affect the PFN1 flexibility at the actin and PLP-binding domains, and consequently, their intermolecular interactions. It may be therefore related to the functional impairment of PFN1 upon C71G, M114T, E117G and G118V mutations, and their involvement in ALS development. We also developed a database, SNPMOL (http://www.snpmol.org/), containing the results presented on this paper for biologists and clinicians to exploit PFN1 and its natural variants.

Structural prediction, whole exome sequencing and molecular dynamics simulation confirms p.G118D somatic mutation of PIK3CA as functionally important in breast cancer patients

To understand the structural and functional importance of PIK3CA somatic mutations, whole exome sequencing, molecular dynamics simulation techniques in combination with in silico prediction algorithms such as SIFT, PolyPhen, Provean and CADD were employed. Twenty out of eighty missense somatic mutations in PIK3CA gene were found to be pathogenic by all the four algorithms. Most recurrent mutations found were known hotspot PIK3CA mutations with known clinical significance like p.E545 K, p.E545A, p.E545 G and p.C420R. A missense mutation p.G118D was found to be recurrently mutated in 5 cases. Interestingly, this mutation was observed in one of the patients who underwent whole exome sequencing and was completely absent from the controls. To see the effect of this mutation on the structure of PIK3CA protein, molecular dynamics simulation was performed. By molecular dynamics approach, we have shown that p.G118D mutation deviated from the native structure which was supported by the decrease in the number of hydrogen bonds, difference in hydrogen bond distance and angle, difference in root mean square deviation between the native and the mutant structures.

Comparison of Predictive In Silico Tools on Missense Variants in GJB2, GJB6, and GJB3 Genes Associated with Autosomal Recessive Deafness 1A (DFNB1A)

In silico predictive software allows assessing the effect of amino acid substitutions on the structure or function of a protein without conducting functional studies. The accuracy of in silico pathogenicity prediction tools has not been previously assessed for variants associated with autosomal recessive deafness 1A (DFNB1A). Here, we identify in silico tools with the most accurate clinical significance predictions for missense variants of the GJB2 (Cx26), GJB6 (Cx30), and GJB3 (Cx31) connexin genes associated with DFNB1A. To evaluate accuracy of selected in silico tools (SIFT, FATHMM, MutationAssessor, PolyPhen-2, CONDEL, MutationTaster, MutPred, Align GVGD, and PROVEAN), we tested nine missense variants with previously confirmed clinical significance in a large cohort of deaf patients and control groups from the Sakha Republic (Eastern Siberia, Russia): Сх26: p.Val27Ile, p.Met34Thr, p.Val37Ile, p.Leu90Pro, p.Glu114Gly, p.Thr123Asn, and p.Val153Ile; Cx30: p.Glu101Lys; Cx31: p.Ala194Thr. We compared the performance of the in silico tools (accuracy, sensitivity, and specificity) by using the missense variants in GJB2 (Cx26), GJB6 (Cx30), and GJB3 (Cx31) genes associated with DFNB1A. The correlation coefficient (r) and coefficient of the area under the Receiver Operating Characteristic (ROC) curve as alternative quality indicators of the tested programs were used. The resulting ROC curves demonstrated that the largest coefficient of the area under the curve was provided by three programs: SIFT (AUC = 0.833, p = 0.046), PROVEAN (AUC = 0.833, p = 0.046), and MutationAssessor (AUC = 0.833, p = 0.002). The most accurate predictions were given by two tested programs: SIFT and PROVEAN (Ac = 89%, Se = 67%, Sp = 100%, r = 0.75, AUC = 0.833). The results of this study may be applicable for analysis of novel missense variants of the GJB2 (Cx26), GJB6 (Cx30), and GJB3 (Cx31) connexin genes.

Identification and structural characterization of deleterious non-synonymous single nucleotide polymorphisms in the human SKP2 gene

In SCF (Skp, Cullin, F-box) ubiquitin-protein ligase complexes, S-phase kinase 2 (SKP2) is one of the major players of F-box family, that is responsible for the degradation of several important cell regulators and tumor suppressor proteins. Despite of having significant evidence for the role of SKP2 on tumorgenesis, there is a lack of available data regarding the effect of non-synonymous polymorphisms. In this communication, the structural and functional consequences of non-synonymous single nucleotide polymorphisms (nsSNPs) of SKP2 have been reported by employing various computational approaches and molecular dynamics simulation. Initially, several computational tools like SIFT, PolyPhen-2, PredictSNP, I-Mutant 2.0 and ConSurf have been implicated in this study to explore the damaging SNPs. In total of 172 nsSNPs, 5 nsSNPs were identified as deleterious and 3 of them were predicted to be decreased the stability of protein. Guided from ConSurf analysis, P101L (rs761253702) and Y346C (rs755010517) were categorized as the highly conserved and functional disrupting mutations. Therefore, these mutations were subjected to three dimensional model building and molecular dynamics simulation study for the detailed structural consequences upon the mutations. The study revealed that P101L and Y346C mutations increased the flexibility and changed the structural dynamics. As both these mutations are located in the most functional regions of SKP2 protein, these computational insights might be helpful to consider these nsSNPs for wet-lab confirmatory analysis as well as in rationalizing future population based studies and structure based drug design against SKP2.

Sequence variant analysis of RNA sequences in severe equine asthma

Background

Severe equine asthma is a chronic inflammatory disease of the lung in horses similar to low-Th2 late-onset asthma in humans. This study aimed to determine the utility of RNA-Seq to call gene sequence variants, and to identify sequence variants of potential relevance to the pathogenesis of asthma.

Methods

RNA-Seq data were generated from endobronchial biopsies collected from six asthmatic and seven non-asthmatic horses before and after challenge (26 samples total). Sequences were aligned to the equine genome with Spliced Transcripts Alignment to Reference software. Read preparation for sequence variant calling was performed with Picard tools and Genome Analysis Toolkit (GATK). Sequence variants were called and filtered using GATK and Ensembl Variant Effect Predictor (VEP) tools, and two RNA-Seq predicted sequence variants were investigated with both PCR and Sanger sequencing. Supplementary analysis of novel sequence variant selection with VEP was based on a score of <0.01 predicted with Sorting Intolerant from Tolerant software, missense nature, location within the protein coding sequence and presence in all asthmatic individuals. For select variants, effect on protein function was assessed with Polymorphism Phenotyping 2 and screening for non-acceptable polymorphism 2 software. Sequences were aligned and 3D protein structures predicted with Geneious software. Difference in allele frequency between the groups was assessed using a Pearson’s Chi-squared test with Yates’ continuity correction, and difference in genotype frequency was calculated using the Fisher’s exact test for count data.

Results

RNA-Seq variant calling and filtering correctly identified substitution variants in PACRG and RTTN. Sanger sequencing confirmed that the PACRG substitution was appropriately identified in all 26 samples while the RTTN substitution was identified correctly in 24 of 26 samples. These variants of uncertain significance had substitutions that were predicted to result in loss of function and to be non-neutral. Amino acid substitutions projected no change of hydrophobicity and isoelectric point in PACRG, and a change in both for RTTN. For PACRG, no difference in allele frequency between the two groups was detected but a higher proportion of asthmatic horses had the altered RTTN allele compared to non-asthmatic animals.

Discussion

RNA-Seq was sensitive and specific for calling gene sequence variants in this disease model. Even moderate coverage (<10–20 counts per million) yielded correct identification in 92% of samples, suggesting RNA-Seq may be suitable to detect sequence variants in low coverage samples. The impact of amino acid alterations in PACRG and RTTN proteins, and possible association of the sequence variants with asthma, is of uncertain significance, but their role in ciliary function may be of future interest.

NDDVD: an integrated and manually curated Neurodegenerative Diseases Variation Database

Neurodegenerative diseases (NDDs) are associated with genetic variations including point substitutions, copy number alterations, insertions and deletions. At present, a few genetic variation repositories for some individual NDDs have been created, however, these databases are needed to be integrated and expanded to all the NDDs for systems biological investigation. We here build a relational database termed as NDDVD to integrate all the variations of NDDs using Leiden Open Variation Database (LOVD) platform. The items in the NDDVD are collected manually from PubMed or extracted from the existed variation databases. The cross-disease database includes over 6374 genetic variations of 289 genes associated with 37 different NDDs. The patterns, conservations and biological functions for variations in different NDDs are statistically compared and a user-friendly interface is provided for NDDVD at: http://bioinf.suda.edu.cn/NDDvarbase/LOVDv.3.0.

URL: http://bioinf.suda.edu.cn/NDDvarbase/LOVDv.3.0