PON-P2: prediction method for fast and reliable identification of harmful variants

Structural dynamics of clinically-reported VUS in the BARD1 ARD-BRCT region to predict the molecular basis of alterations

The functional domains of BARD1, comprise the Ankyrin Repeat Domain (ARD), C-Terminal domains (BRCTs), and a linker between ARD and the BRCTs, which are known to bind to Cleavage stimulation Factor complex-subunit of 50 kDa (CstF-50). The pathogenic mutation Q564H in the BARD1 ARD-linker-BRCT region has been reported to abrogate the binding between BARD1 and CstF-50. Intermediate penetrance variants of BARD1 are associated with the occurrence of breast cancer. Therefore, seven missense variants of unknown significance (VUS), L447V, P454L, N470S, V507M, I509T, C557S, and Q564H of BARD1, reported in the ARD domain and the linker region were evaluated via molecular dynamics (MD) simulations. The mutants revealed statistically significantly different distributions of RMSD (root mean square deviation), residuewise RMSF (root mean square fluctuation), Rg (radius of gyration), SASA (solvent accessible surface area), and COM (centre of mass)-to-COM distance between the ARD and the BRCT repeat, between the wild type and each mutant. The secondary structural composition of the mutants was slightly altered relative to that of the wild type. However, the reported in-silico based prediction require further validation using in-vitro, biophysical and structure-based approachCommunicated by Ramaswamy H. Sarma.

Subset of retinoblastoma tumours is associated with BRCA1/2 mutations

BACKGROUND

We investigated the potential association between pathogenic BRCA1/2 variants and retinoblastoma pathogenicity.

METHODS

In this single-centre, retrospective case series, we performed hereditary cancer panel tests using blood samples for patients with retinoblastoma diagnosed between March 2017 and October 2021. Bioinformatics prediction tools were then used to conduct in silico pathogenicity assessments for patients with BRCA1/2 family variants, in addition to the American College of Medical Genetics and Genomics (ACMG) variant classification. One patient with a germline BRCA1 variant was analysed with whole-genome sequencing (WGS), mutational signature analysis and methylation analysis for RB1 and BRCA using the patient's tumour and blood samples.

RESULTS

Of 30 retinoblastoma patients who underwent panel sequencing, six (20%) were found to carry germline variants in the BRCA1/2 or BRIP1 genes. Among these six patients, two had pathogenic or likely pathogenic variants as per the ACMG variant classification. Additionally, three patients showed potential pathogenic BRCA1/2 family variants through further analysis with alternative bioinformatics prediction tools. In the WGS analysis of a tumour from a patient with a germline likely pathogenic BRCA1 variant in one allele, we observed the loss of one RB1 allele due to a large deletion. No somatic non-synonymous mutations or frameshift indels were detected in the RB1 locus of the remaining allele. This sample also showed BRCA1 gene promoter hypermethylation in the tumour, indicating additional epigenetic silencing.

CONCLUSION

This study demonstrated that some retinoblastoma patients harboured germline BRCA1/2 family variants, which may be associated with the development of retinoblastoma along with RB1 mutations.

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.

Delineating the mechanistic relevance of the TP53 gene and its mutational impact on gene expression and patients' survival in bladder cancer

Bladder carcinoma (BLCA) is a widespread urological malignancy causing significant global mortality, often hindered by delayed diagnosis and limited treatments. BLCA frequently exhibits TP53 mutations, playing a pivotal role in its pathogenesis and underscoring the potential of targeting TP53 as a therapeutic approach for this prevalent urological malignancy. Tumor tissues from 50 bladder cancer patients were used for mutational analysis in TP53's mutation-rich exons (5, 7, & 8). The gene expression of the TP53 gene, along with a TP53-target gene B-cell translocation gene 2 (BTG2) was also assessed in the cDNA samples from the same BLCA tissues and 15 urine controls of healthy people. The analysis revealed 22 % of patients with somatic hotspot mutations, 18 % with pathogenic missense mutations, and 12 % with intronic variants. Patients with somatic mutations exhibited the worst prognosis, supported by survival analysis from The Cancer Genome Atlas (TCGA) BLCA data. Interestingly, H296Y missense mutation correlated with higher TP53 expression and improved survival, while intronic SNPs were linked to worse outcomes. Additionally, upregulated BTG2 expression in mutated patients was observed which was correlated with poor prognosis, emphasizing the role of TP53 mutations in bladder cancer progression. The multivariate analysis highlighted the predictive power of TP53 mutations, with a high frequency of high-grade tumors (78.57 %) in mutated patients, underscoring their role in cancer progression. In conclusion, this study emphasizes the crucial role of TP53 mutations in bladder cancer patients from Bangladesh.

Predicting hotspots for disease-causing single nucleotide variants using sequences-based coevolution, network analysis, and machine learning

To enable personalized medicine, it is important yet highly challenging to accurately predict disease-causing mutations in target proteins at high throughput. Previous computational methods have been developed using evolutionary information in combination with various biochemical and structural features of protein residues to discriminate neutral vs. deleterious mutations. However, the power of these methods is often limited because they either assume known protein structures or treat residues independently without fully considering their interactions. To address the above limitations, we build upon recent progress in machine learning, network analysis, and protein language models, and develop a sequences-based variant site prediction workflow based on the protein residue contact networks: 1. We employ and integrate various methods of building protein residue networks using state-of-the-art coevolution analysis tools (RaptorX, DeepMetaPSICOV, and SPOT-Contact) powered by deep learning. 2. We use machine learning algorithms (Random Forest, Gradient Boosting, and Extreme Gradient Boosting) to optimally combine 20 network centrality scores to jointly predict key residues as hot spots for disease mutations. 3. Using a dataset of 107 proteins rich in disease mutations, we rigorously evaluate the network scores individually and collectively (via machine learning). This work supports a promising strategy of combining an ensemble of network scores based on different coevolution analysis methods (and optionally predictive scores from other methods) via machine learning to predict hotspot sites of disease mutations, which will inform downstream applications of disease diagnosis and targeted drug design.

Investigating the Influence of ANTXR2 Gene Mutations on Protective Antigen Binding for Heightened Anthrax Resistance

Bacillus anthracis is the bacterium responsible for causing the zoonotic disease called anthrax. The disease presents itself in different forms like gastrointestinal, inhalation, and cutaneous. Bacterial spores are tremendously adaptable, can persist for extended periods and occasionally endanger human health. The Anthrax Toxin Receptor-2 (ANTXR2) gene acts as membrane receptor and facilitates the entry of the anthrax toxin into host cells. Additionally, mutations in the ANTXR2 gene have been linked to various autoimmune diseases, including Hyaline Fibromatosis Syndrome (HFS), Ankylosing Spondylitis (AS), Juvenile Hyaline Fibromatosis (JHF), and Infantile Systemic Hyalinosis (ISH). This study delves into the genetic landscape of ANTXR2, aiming to comprehend its associations with diverse disorders, elucidate the impacts of its mutations, and pinpoint minimal non-pathogenic mutations capable of reducing the binding affinity of the ANTXR2 gene with the protective antigen. Recognizing the pivotal role of single-nucleotide polymorphisms (SNPs) in shaping genetic diversity, we conducted computational analyses to discern highly deleterious and tolerated non-synonymous SNPs (nsSNPs) in the ANTXR2 gene. The Mutpred2 server determined that the Arg465Trp alteration in the ANTXR2 gene leads to altered DNA binding (p = 0.22) with a probability of a deleterious mutation of 0.808; notably, among the identified deleterious SNPs, rs368288611 (Arg465Trp) stands out due to its significant impact on altering the DNA-binding ability of ANTXR2. We propose these SNPs as potential candidates for hypertension linked to the ANTXR2 gene, which is implicated in blood pressure regulation. Noteworthy among the tolerated substitutions is rs200536829 (Ala33Ser), recognized as less pathogenic; this highlights its potential as a valuable biomarker, potentially reducing side effects on the host while also reducing binding with the protective antigen protein. Investigating these SNPs holds the potential to correlate with several autoimmune disorders and mitigate the impact of anthrax disease in humans.

Empowering Protein Engineering through Recombination of Beneficial Substitutions

Directed evolution stands as a seminal technology for generating novel protein functionalities, a cornerstone in biocatalysis, metabolic engineering, and synthetic biology. Today, with the development of various mutagenesis methods and advanced analytical machines, the challenge of diversity generation and high-throughput screening platforms is largely solved, and one of the remaining challenges is: how to empower the potential of single beneficial substitutions with recombination to achieve the epistatic effect. This review overviews experimental and computer-assisted recombination methods in protein engineering campaigns. In addition, integrated and machine learning-guided strategies were highlighted to discuss how these recombination approaches contribute to generating the screening library with better diversity, coverage, and size. A decision tree was finally summarized to guide the further selection of proper recombination strategies in practice, which was beneficial for accelerating protein engineering.

TransEFVP: A Two-Stage Approach for the Prediction of Human Pathogenic Variants Based on Protein Sequence Embedding Fusion

Studying the effect of single amino acid variations (SAVs) on protein structure and function is integral to advancing our understanding of molecular processes, evolutionary biology, and disease mechanisms. Screening for deleterious variants is one of the crucial issues in precision medicine. Here, we propose a novel computational approach, TransEFVP, based on large-scale protein language model embeddings and a transformer-based neural network to predict disease-associated SAVs. The model adopts a two-stage architecture: the first stage is designed to fuse different feature embeddings through a transformer encoder. In the second stage, a support vector machine model is employed to quantify the pathogenicity of SAVs after dimensionality reduction. The prediction performance of TransEFVP on blind test data achieves a Matthews correlation coefficient of 0.751, an F1-score of 0.846, and an area under the receiver operating characteristic curve of 0.871, higher than the existing state-of-the-art methods. The benchmark results demonstrate that TransEFVP can be explored as an accurate and effective SAV pathogenicity prediction method. The data and codes for TransEFVP are available at https://github.com/yzh9607/TransEFVP/tree/master for academic use.

A computational study on structural and functional consequences of nsSNPs in human dopa decarboxylase

The Dopa Decarboxylase (DDC) gene plays an important role in the synthesis of biogenic amines such as dopamine, serotonin, and histamine. Non-synonymous single nucleotide polymorphisms (nsSNPs) in the DDC gene have been linked with various neurodegenerative disorders. In this study, a comprehensive in silico analysis of nsSNPs in the DDC gene was conducted to assess their potential functional consequences and associations with disease outcomes. Using publicly available databases, a complete list of nsSNPs in the DDC gene was obtained. 29 computational tools and algorithms were used to characterise the effects of these nsSNPs on protein structure, function, and stability. In addition, the population-based association studies were performed to investigate possible associations between specific nsSNPs and arthritis. Our research identified four novel DDC gene nsSNPs that have a major impact on the structure and function of proteins. Through molecular dynamics simulations (MDS), we observed changes in the stability of the DDC protein induced by specific nsSNPs. Furthermore, population-based association studies have revealed potential associations between certain DDC nsSNPs and various neurological disorders, including Parkinson's disease and dementia. The in silico approach used in this study offers insightful information about the functional effects of nsSNPs in the DDC gene. These discoveries provide insight into the cellular processes that underlie cognitive disorders. Furthermore, the detection of disease-associated nsSNPs in the DDC gene may facilitate the development of tailored and targeted therapy approaches.Communicated by Ramaswamy H. Sarma.

Unraveling the function and structure impact of deleterious missense SNPs in the human OX1R receptor by computational analysis

The orexin/hypocretin receptor type 1 (OX1R) plays a crucial role in regulating various physiological functions, especially feeding behavior, addiction, and reward. Genetic variations in the OX1R have been associated with several neurological disorders. In this study, we utilized a combination of sequence and structure-based computational tools to identify the most deleterious missense single nucleotide polymorphisms (SNPs) in the OX1R gene. Our findings revealed four highly conserved and structurally destabilizing missense SNPs, namely R144C, I148N, S172W, and A297D, located in the GTP-binding domain. Molecular dynamics simulations analysis demonstrated that all four most detrimental mutant proteins altered the overall structural flexibility and dynamics of OX1R protein, resulting in significant changes in the structural organization and motion of the protein. These findings provide valuable insights into the impact of missense SNPs on OX1R function loss and their potential contribution to the development of neurological disorders, thereby guiding future research in this field.

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.

A New Set of in Silico Tools to Support the Interpretation of ATM Missense Variants Using Graphical Analysis

Establishing the pathogenic nature of variants in ATM, a gene associated with breast cancer and other hereditary cancers, is crucial for providing patients with adequate care. Unfortunately, achieving good variant classification is still difficult. To address this challenge, we extended the range of in silico tools with a series of graphical tools devised for the analysis of computational evidence by health care professionals. We propose a family of fast and easy-to-use graphical representations in which the impact of a variant is considered relative to other pathogenic and benign variants. To illustrate their value, the representations are applied to three problems in variant interpretation. The assessment of computational pathogenicity predictions showed that the graphics provide an intuitive view of prediction reliability, complementing and extending conventional numerical reliability indexes. When applied to variant of unknown significance populations, the representations shed light on the nature of these variants and can be used to prioritize variants of unknown significance for further studies. In a third application, the graphics were used to compare the two versions of the ATM-adapted American College of Medical Genetics and Genomics and Association for Molecular Pathology guidelines, obtaining valuable information on their relative virtues and weaknesses. Finally, a server [ATMision (ATM missense in silico interpretation online)] was generated for users to apply these representations in their variant interpretation problems, to check the ATM-adapted guidelines' criteria for computational evidence on their variant(s) and access different sources of information.

Independent COL17A1 Variants in Cats with Junctional Epidermolysis Bullosa

Epidermolysis bullosa (EB), characterized by defective adhesion of the epidermis to the dermis, is a heterogeneous disease with many subtypes in human patients and domestic animals. We investigated two unrelated cats with recurring erosions and ulcers on ear pinnae, oral mucosa, and paw pads that were suggestive of EB. Histopathology confirmed the diagnosis of EB in both cats. Case 1 was severe and had to be euthanized at 5 months of age. Case 2 had a milder course and was alive at 11 years of age at the time of writing. Whole genome sequencing of both affected cats revealed independent homozygous variants in COL17A1 encoding the collagen type XVII alpha 1 chain. Loss of function variants in COL17A1 lead to junctional epidermolysis bullosa (JEB) in human patients. The identified splice site variant in case 1, c.3019+1del, was predicted to lead to a complete deficiency in collagen type XVII. Case 2 had a splice region variant, c.769+5G>A. Assessment of the functional impact of this variant on the transcript level demonstrated partial aberrant splicing with residual expression of wildtype transcript. Thus, the molecular analyses provided a plausible explanation of the difference in clinical severity between the two cases and allowed the refinement of the diagnosis in the affected cats to JEB. This study highlights the complexity of EB in animals and contributes to a better understanding of the genotype-phenotype correlation in COL17A1-related JEB.

PON-Fold: Prediction of Substitutions Affecting Protein Folding Rate

Most proteins fold into characteristic three-dimensional structures. The rate of folding and unfolding varies widely and can be affected by variations in proteins. We developed a novel machine-learning-based method for the prediction of the folding rate effects of amino acid substitutions in two-state folding proteins. We collected a data set of experimentally defined folding rates for variants and used them to train a gradient boosting algorithm starting with 1161 features. Two predictors were designed. The three-class classifier had, in blind tests, specificity and sensitivity ranging from 0.324 to 0.419 and from 0.256 to 0.451, respectively. The other tool was a regression predictor that showed a Pearson correlation coefficient of 0.525. The error measures, mean absolute error and mean squared error, were 0.581 and 0.603, respectively. One of the previously presented tools could be used for comparison with the blind test data set, our method called PON-Fold showed superior performance on all used measures. The applicability of the tool was tested by predicting all possible substitutions in a protein domain. Predictions for different conformations of proteins, open and closed forms of a protein kinase, and apo and holo forms of an enzyme indicated that the choice of the structure had a large impact on the outcome. PON-Fold is freely available.

Prediction of pathogenic single amino acid substitutions using molecular fragment descriptors

MOTIVATION

Next Generation Sequencing technologies make it possible to detect rare genetic variants in individual patients. Currently, more than a dozen software and web services have been created to predict the pathogenicity of variants related with changing of amino acid residues. Despite considerable efforts in this area, at the moment there is no ideal method to classify pathogenic and harmless variants, and the assessment of the pathogenicity is often contradictory. In this article, we propose to use peptides structural formulas of proteins as an amino acid residues substitutions description, rather than a single-letter code. This allowed us to investigate the effectiveness of chemoinformatics approach to assess the pathogenicity of variants associated with amino acid substitutions.

RESULTS

The structure-activity relationships analysis relying on protein-specific data and atom centric substructural multilevel neighborhoods of atoms (MNA) descriptors of molecular fragments appeared to be suitable for predicting the pathogenic effect of single amino acid variants. MNA-based Naïve Bayes classifier algorithm, ClinVar and humsavar data were used for the creation of structure-activity relationships models for 10 proteins. The performance of the models was compared with 11 different predicting tools: 8 individual (SIFT 4G, Polyphen2 HDIV, MutationAssessor, PROVEAN, FATHMM, MVP, LIST-S2, MutPred) and 3 consensus (M-CAP, MetaSVM, MetaLR). The accuracy of MNA-based method varies for the proteins (AUC: 0.631-0.993; MCC: 0.191-0.891). It was similar for both the results of comparisons with the other individual predictors and third-party protein-specific predictors. For several proteins (BRCA1, BRCA2, COL1A2, and RYR1), the performance of the MNA-based method was outstanding, capable of capturing the pathogenic effect of structural changes in amino acid substitutions.

AVAILABILITY AND IMPLEMENTATION

The datasets are available as supplemental data at Bioinformatics online. A python script to convert amino acid and nucleotide sequences from single-letter codes to SD files is available at https://github.com/SmirnygaTotoshka/SequenceToSDF. The authors provide trial licenses for MultiPASS software to interested readers upon request.

Choosing Variant Interpretation Tools for Clinical Applications: Context Matters

Pathogenicity predictors are computational tools that classify genetic variants as benign or pathogenic; this is currently a major challenge in genomic medicine. With more than fifty such predictors available, selecting the most suitable tool for clinical applications like genetic screening, molecular diagnostics, and companion diagnostics has become increasingly challenging. To address this issue, we have developed a cost-based framework that naturally considers the various components of the problem. This framework encodes clinical scenarios using a minimal set of parameters and treats pathogenicity predictors as rejection classifiers, a common practice in clinical applications where low-confidence predictions are routinely rejected. We illustrate our approach in four examples where we compare different numbers of pathogenicity predictors for missense variants. Our results show that no single predictor is optimal for all clinical scenarios and that considering rejection yields a different perspective on classifiers.

Discovering Deleterious Single Nucleotide Polymorphisms of Human AKT1 Oncogene: An In Silico Study

BACKGROUND

AKT1 is a serine/threonine kinase necessary for the mediation of apoptosis, angiogenesis, metabolism, and cell proliferation in both normal and cancerous cells. The mutations in the AKT1 gene have been associated with different types of cancer. Further, the AKT1 gene mutations are also reported to be associated with other diseases such as Proteus syndrome and Cowden syndromes. Hence, this study aims to identify the deleterious AKT1 missense SNPs and predict their effect on the function and structure of the AKT1 protein using various computational tools.

METHODS

Extensive in silico approaches were applied to identify deleterious SNPs of the human AKT1 gene and assessment of their impact on the function and structure of the AKT1 protein. The association of these highly deleterious missense SNPs with different forms of cancers was also analyzed. The in silico approach can help in reducing the cost and time required to identify SNPs associated with diseases.

RESULTS

In this study, 12 highly deleterious SNPs were identified which could affect the structure and function of the AKT1 protein. Out of the 12, four SNPs-namely, G157R, G159V, G336D, and H265Y-were predicted to be located at highly conserved residues. G157R could affect the ligand binding to the AKT1 protein. Another highly deleterious SNP, R273Q, was predicted to be associated with liver cancer.

CONCLUSIONS

This study can be useful for pharmacogenomics, molecular diagnosis of diseases, and developing inhibitors of the AKT1 oncogene.

In-silico assessment of high-risk non-synonymous SNPs in ADAMTS3 gene associated with Hennekam syndrome and their impact on protein stability and function

Hennekam Lymphangiectasia-Lymphedema Syndrome 3 (HKLLS3) is a rare genetical disorder caused by mutations in a few genes including ADAMTS3. It is characterized by lymphatic dysplasia, intestinal lymphangiectasia, severe lymphedema and distinctive facial appearance. Up till now, no extensive studies have been conducted to elucidate the mechanism of the disease caused by various mutations. As a preliminary investigation of HKLLS3, we sorted out the most deleterious nonsynonymous single nucleotide polymorphisms (nsSNPs) that might affect the structure and function of ADAMTS3 protein by using a variety of in silico tools. A total of 919 nsSNPs in the ADAMTS3 gene were identified. 50 nsSNPs were predicted to be deleterious by multiple computational tools. 5 nsSNPs (G298R, C567Y, A370T, C567R and G374S) were found to be the most dangerous and can be associated with the disease as predicted by different bioinformatics tools. Modelling of the protein shows it can be divided into segments 1, 2 and 3, which are connected by short loops. Segment 3 mainly consists of loops without substantial secondary structures. With prediction tools and molecular dynamics simulation, some SNPs were found to significantly destabilize the protein structure and disrupt the secondary structures, especially in segment 2. The deleterious effects of mutations in segment 1 are possibly not from destabilization but from other factors such as the change in phosphorylation as suggested by post-translational modification (PTM) studies. This is the first-ever study of ADAMTS3 gene polymorphism, and the predicted nsSNPs in ADAMST3, some of which have not been reported yet in patients, will serve for diagnostic purposes and further therapeutic implications in Hennekam syndrome, contributing to better diagnosis and treatment.

Resources and tools for rare disease variant interpretation

Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

Impact of SERPINC1 mutation on thrombotic phenotype in children with congenital antithrombin deficiency-first analysis of the International Society on Thrombosis and Haemostasis pediatric antithrombin deficiency database and biorepository

BACKGROUND

The natural history and genotype-phenotype correlation of congenital antithrombin (AT) deficiency in children are unknown.

OBJECTIVES

To describe the clinical presentation of congenital AT deficiency in children and evaluate its correlation to specific mutations in SERPINC1.

METHODS

In 2017, a prospective pediatric database and DNA biorepository for congenital AT deficiency was established. During the pilot phase, the database was opened at 4 tertiary care centers in Canada and US. Approval from research ethics board was obtained at each participating center. Written consent/assent was obtained from guardians/subjects who met eligibility. Demographic/clinical data were uploaded into a database. DNA extraction and SERPINC1 sequencing were centralized for US centers. Standard statistical methods were used to summarize parameters. Probability of VTE-free survival was assessed using the Kaplan-Meier method.

RESULTS

Overall, 43 participants (25 females) from 31 unique kindreds were enrolled. Median age (range) at enrollment was 14.8 years (1-21 years). Median AT activity was 52% (24%-87%), and median AT antigen (n = 20) was 55% (38%-110%). Nineteen (44%) participants had a history of venous thromboembolism (VTE). Median age at VTE diagnosis was 12.8 years (0.1-19.2 years). SERPINC1 sequencing was completed for 31 participants and 21 unique mutations were identified, including 5 novel variants. Probability of 5-year VTE-free survival (95% CI) for carriers of missense mutations (92.0% [95% CI: 71.6%-97.9%]) was significantly higher compared with carriers of null mutations (66.7% [95% CI: 19.5%-90.4%]); p = .0012.

CONCLUSION

To our knowledge, this is the first pediatric study to document a severe thrombotic phenotype in carriers of null mutations in SERPINC1, when compared with carriers of missense mutations; underscoring the importance of genetic testing.

Missense polymorphisms potentially involved in mandibular prognathism

Objective

The current study aimed to identify and analyze missense single nucleotide polymorphisms (SNPs) that can potentially cause mandibular prognathism.

Methods

After reviewing the articles, 56 genes associated with mandibular prognathism were identified and their missense SNPs were retrieved from the NCBI website. Several web-based tools including CADD, PolyPhen-2, PROVEAN, SNAP2, PANTHER, FATHMM, and PON-P2 were used to filter out harmful SNPs. Additionally, ConSurf determined the level of evolutionary conservation at positions where SNPs occur. I-Mutant2 and MUpro predicted the effect of SNPs on protein stability. Furthermore, to investigate the structural and functional changes of proteins, HOPE and LOMETS tools were utilized.

Results

Based on predictions in at least four web-based tools, the results indicated that PLXNA2-rs4844658, DUSP6-rs2279574, and FBN3-rs33967815 are harmful. These SNPs are located at positions with variable or average conservation and have the potential to reduce the stability of their respective proteins. Moreover, they may impair protein activity by causing structural and functional changes.

Conclusions

In this study, we identified PLXNA2-rs4844658, DUSP6-rs2279574, and FBN3-rs33967815 as potential risk factors for mandibular prognathism using several web-based tools. According to the possible roles of PLXNA2, DUSP6, and FBN3 proteins in ossification pathways, we recommend that these SNPs be investigated further in experimental research. Through such studies, we hope to gain a better understanding of the molecular mechanisms involved in mandible formation.

Rare deleterious mutations in Bruton's tyrosine kinase as biomarkers for ibrutinib-based therapy: an in silico insight

CONTEXT

Squamous cell carcinoma (SCC) is the second most common type of skin cancer caused by malignant keratinocytes. Multiple studies have shown that protein mutations have a significant impact on the development and progression of cancer, including SCC. We attempted to decode the effect of single amino acid mutations in the Bruton's tyrosine kinase (BTK) protein in this study. Molecular dynamic (MD) simulations were performed on selected deleterious mutations of the BTK protein, revealing that the variants adversely affect the protein, indicating that they may contribute to the prognosis of SCC by making the protein unstable. Then, we investigated the interaction between the protein and its mutants with ibrutinib, a drug designed to treat SCC. Even though the mutations have deleterious effects on protein structure, they bind to ibrutinib similarly to their wild type counterpart. This study demonstrates that the effect of detected missense mutations is unfavorable and can result in function loss, which is severe for SCC, but that ibrutinib-based therapy can still be effective on them, and the mutations can be used as biomarkers for Ibrutinib-based treatment.

METHODS

Seven different computational techniques were used to compute the effect of SAVs in accordance with the experimental requirements of this study. To understand the differences in protein and mutant dynamics, MD simulation and trajectory analysis, including RMSD, RMSF, PCA, and contact analysis, were performed. The free binding energy and its decomposition for each protein-drug complex were determined using docking, MM-GBSA, MM-PBSA, and interaction analysis (wild and mutants).

Delineating the impact of pathogenic mutations on the conformational dynamics of HDL's vital protein ApoA1: a combined computational and molecular dynamic simulation approach

Apolipoprotein A1 (ApoA1), is the important component of high-density lipoproteins (HDL), that has key role in HDL biogenesis, cholesterol trafficking, and reverse cholesterol transport (RCT). Non-synonymous Single Nucleotide Polymorphisms (nsSNPs) in ApoA1 have been linked to cardiovascular diseases and amyloidosis as they alter the protein's native structure and function. Therefore in this study, we attempted to understand the molecular pathogenicity profile of nsSNPs of ApoA1 using various computational approaches. We used state-of-the-art computational methods to thoroughly investigate the 295 ApoA1 nsSNPs at sequence and structural levels. Seven nsSNPs (L13R, L84R, L84P, L99P, R173P, L187P, and L238P) out of 295 were classified as the most deleterious and destabilizing. In order to estimate the effect of such destabilizing mutations on the protein conformation, all-atom molecular dynamics simulations (MDS) of ApoA1 wild-type (WT), L99P and R173P for 100 ns, was carried out using GROMACS 5.0.1 package. The MD simulation investigation revealed significant structural alterations in L99P and R173P. In addition, they had changed principal component analysis and electrostatic surface potential, decreased structural compactness, and intramolecular hydrogen bonds, which supported the rationale underpinning ApoA1 dysfunction with such mutations. This work sheds light on ApoA1 dysfunction due to single amino acid alterations, and offers new insight into the molecular basis of ApoA1-related diseases progression.Communicated by Ramaswamy H. Sarma.

E-SNPs&GO: embedding of protein sequence and function improves the annotation of human pathogenic variants

MOTIVATION

The advent of massive DNA sequencing technologies is producing a huge number of human single-nucleotide polymorphisms occurring in protein-coding regions and possibly changing their sequences. Discriminating harmful protein variations from neutral ones is one of the crucial challenges in precision medicine. Computational tools based on artificial intelligence provide models for protein sequence encoding, bypassing database searches for evolutionary information. We leverage the new encoding schemes for an efficient annotation of protein variants.

RESULTS

E-SNPs&GO is a novel method that, given an input protein sequence and a single amino acid variation, can predict whether the variation is related to diseases or not. The proposed method adopts an input encoding completely based on protein language models and embedding techniques, specifically devised to encode protein sequences and GO functional annotations. We trained our model on a newly generated dataset of 101 146 human protein single amino acid variants in 13 661 proteins, derived from public resources. When tested on a blind set comprising 10 266 variants, our method well compares to recent approaches released in literature for the same task, reaching a Matthews Correlation Coefficient score of 0.72. We propose E-SNPs&GO as a suitable, efficient and accurate large-scale annotator of protein variant datasets.

AVAILABILITY AND IMPLEMENTATION

The method is available as a webserver at https://esnpsandgo.biocomp.unibo.it. Datasets and predictions are available at https://esnpsandgo.biocomp.unibo.it/datasets.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Molecular dynamics study of tropical calcific pancreatitis (TCP) associated calcium-sensing receptor single nucleotide variation

Tropical Calcific Pancreatitis (TCP) is a chronic non-alcoholic pancreatitis characterised by extensive calcification. The disease usually appears at a younger age and is more common in tropical regions. This disease’s progression can lead to pancreatic diabetes, which can subsequently lead to pancreatic cancer. The CASR gene encodes a calcium-sensing receptor (CaSR), which is a GPCR protein of class C. It is expressed in the islets of Langerhans, the parathyroid gland, and other tissues. It primarily detects small gradients in circulating calcium concentrations and couples this information to intracellular signalling, which helps to regulate PTH (parathyroid hormone) secretion and mineral ion homeostasis. From co-leading insulin release, CaSR modulates ductal HCO₃₋ secretion, Ca²⁺ concentration, cell-cell communication, β-cell proliferation, and intracellular Ca²⁺ release. In pancreatic cancer, the CaSR limits cell proliferation. TCP-related four novel missense mutations P163R, I427S, D433H and V477A, found in CaSR extracellular domain (ECD) protein, which were reported in the mutTCPdb Database (https://lms.snu.edu.in/mutTCPDB/index.php). P163R mutation occurs in ligand-binding domain 1 (LBD-1) of the CaSR ECD. To investigate the influence of these variations on protein function and structural activity multiple in-silico prediction techniques such as SIFT, PolyPhen, CADD scores, and other methods have been utilized. A 500 ns molecular dynamic simulation was performed on the CaSR ECD crystal structure and the corresponding mutated models. Furthermore, Principal Component Analysis (PCA) and Essential Dynamics analysis were used to forecast collective motions, thermodynamic stabilities, and the critical subspace crucial to CaSR functions. The results of molecular dynamic simulations showed that the mutations P163R, I427S, D433H, and V477A caused conformational changes and decreased the stability of protein structures. This study also demonstrates the significance of TCP associated mutations. As a result of our findings, we hypothesised that the investigated mutations may have an effect on the protein’s structure and ability to interact with other molecules, which may be related to the protein’s functional impairment.

Embeddings from protein language models predict conservation and variant effects

The emergence of SARS-CoV-2 variants stressed the demand for tools allowing to interpret the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational Scanning (DMS) sets continue to expand our understanding of the mutational landscape of single proteins, the results continue to challenge analyses. Protein Language Models (pLMs) use the latest deep learning (DL) algorithms to leverage growing databases of protein sequences. These methods learn to predict missing or masked amino acids from the context of entire sequence regions. Here, we used pLM representations (embeddings) to predict sequence conservation and SAV effects without multiple sequence alignments (MSAs). Embeddings alone predicted residue conservation almost as accurately from single sequences as ConSeq using MSAs (two-state Matthews Correlation Coefficient-MCC-for ProtT5 embeddings of 0.596 ± 0.006 vs. 0.608 ± 0.006 for ConSeq). Inputting the conservation prediction along with BLOSUM62 substitution scores and pLM mask reconstruction probabilities into a simplistic logistic regression (LR) ensemble for Variant Effect Score Prediction without Alignments (VESPA) predicted SAV effect magnitude without any optimization on DMS data. Comparing predictions for a standard set of 39 DMS experiments to other methods (incl. ESM-1v, DeepSequence, and GEMME) revealed our approach as competitive with the state-of-the-art (SOTA) methods using MSA input. No method outperformed all others, neither consistently nor statistically significantly, independently of the performance measure applied (Spearman and Pearson correlation). Finally, we investigated binary effect predictions on DMS experiments for four human proteins. Overall, embedding-based methods have become competitive with methods relying on MSAs for SAV effect prediction at a fraction of the costs in computing/energy. Our method predicted SAV effects for the entire human proteome (~ 20 k proteins) within 40 min on one Nvidia Quadro RTX 8000. All methods and data sets are freely available for local and online execution through bioembeddings.com, https://github.com/Rostlab/VESPA , and PredictProtein.

Computational approaches for predicting variant impact: An overview from resources, principles to applications

One objective of human genetics is to unveil the variants that contribute to human diseases. With the rapid development and wide use of next-generation sequencing (NGS), massive genomic sequence data have been created, making personal genetic information available. Conventional experimental evidence is critical in establishing the relationship between sequence variants and phenotype but with low efficiency. Due to the lack of comprehensive databases and resources which present clinical and experimental evidence on genotype-phenotype relationship, as well as accumulating variants found from NGS, different computational tools that can predict the impact of the variants on phenotype have been greatly developed to bridge the gap. In this review, we present a brief introduction and discussion about the computational approaches for variant impact prediction. Following an innovative manner, we mainly focus on approaches for non-synonymous variants (nsSNVs) impact prediction and categorize them into six classes. Their underlying rationale and constraints, together with the concerns and remedies raised from comparative studies are discussed. We also present how the predictive approaches employed in different research. Although diverse constraints exist, the computational predictive approaches are indispensable in exploring genotype-phenotype relationship.

ProTstab2 for Prediction of Protein Thermal Stabilities

The stability of proteins is an essential property that has several biological implications. Knowledge about protein stability is important in many ways, ranging from protein purification and structure determination to stability in cells and biotechnological applications. Experimental determination of thermal stabilities has been tedious and available data have been limited. The introduction of limited proteolysis and mass spectrometry approaches has facilitated more extensive cellular protein stability data production. We collected melting temperature information for 34,913 proteins and developed a machine learning predictor, ProTstab2, by utilizing a gradient boosting algorithm after testing seven algorithms. The method performance was assessed on a blind test data set and showed a Pearson correlation coefficient of 0.753 and root mean square error of 7.005. Comparison to previous methods indicated that ProTstab2 had superior performance. The method is fast, so it was applied to predict and compare the stabilities of all proteins in human, mouse, and zebrafish proteomes for which experimental data were not determined. The tool is freely available.

Vasor: Accurate prediction of variant effects for amino acid substitutions in multidrug resistance protein 3

The phosphatidylcholine floppase multidrug resistance protein 3 (MDR3) is an essential hepatobiliary transport protein. MDR3 dysfunction is associated with various liver diseases, ranging from severe progressive familial intrahepatic cholestasis to transient forms of intrahepatic cholestasis of pregnancy and familial gallstone disease. Single amino acid substitutions are often found as causative of dysfunction, but identifying the substitution effect in in vitro studies is time and cost intensive. We developed variant assessor of MDR3 (Vasor), a machine learning-based model to classify novel MDR3 missense variants into the categories benign or pathogenic. Vasor was trained on the largest data set to date that is specific for benign and pathogenic variants of MDR3 and uses general predictors, namely Evolutionary Models of Variant Effects (EVE), EVmutation, PolyPhen-2, I-Mutant2.0, MUpro, MAESTRO, and PON-P2 along with other variant properties, such as half-sphere exposure and posttranslational modification site, as input. Vasor consistently outperformed the integrated general predictors and the external prediction tool MutPred2, leading to the current best prediction performance for MDR3 single-site missense variants (on an external test set: F1-score, 0.90; Matthew's correlation coefficient, 0.80). Furthermore, Vasor predictions cover the entire sequence space of MDR3. Vasor is accessible as a webserver at https://cpclab.uni-duesseldorf.de/mdr3_predictor/ for users to rapidly obtain prediction results and a visualization of the substitution site within the MDR3 structure. The MDR3-specific prediction tool Vasor can provide reliable predictions of single-site amino acid substitutions, giving users a fast way to initially assess whether a variant is benign or pathogenic.

Individual Genetic Heterogeneity

Genetic variation has been widely covered in literature, however, not from the perspective of an individual in any species. Here, a synthesis of genetic concepts and variations relevant for individual genetic constitution is provided. All the different levels of genetic information and variation are covered, ranging from whether an organism is unmixed or hybrid, has variations in genome, chromosomes, and more locally in DNA regions, to epigenetic variants or alterations in selfish genetic elements. Genetic constitution and heterogeneity of microbiota are highly relevant for health and wellbeing of an individual. Mutation rates vary widely for variation types, e.g., due to the sequence context. Genetic information guides numerous aspects in organisms. Types of inheritance, whether Mendelian or non-Mendelian, zygosity, sexual reproduction, and sex determination are covered. Functions of DNA and functional effects of variations are introduced, along with mechanism that reduce and modulate functional effects, including TARAR countermeasures and intraindividual genetic conflict. TARAR countermeasures for tolerance, avoidance, repair, attenuation, and resistance are essential for life, integrity of genetic information, and gene expression. The genetic composition, effects of variations, and their expression are considered also in diseases and personalized medicine. The text synthesizes knowledge and insight on individual genetic heterogeneity and organizes and systematizes the central concepts.

Altered Expression of TMEM43 Causes Abnormal Cardiac Structure and Function in Zebrafish

Arrhythmogenic cardiomyopathy (ACM) is an inherited heart muscle disease caused by heterozygous missense mutations within the gene encoding for the nuclear envelope protein transmembrane protein 43 (TMEM43). The disease is characterized by myocyte loss and fibro-fatty replacement, leading to life-threatening ventricular arrhythmias and sudden cardiac death. However, the role of TMEM43 in the pathogenesis of ACM remains poorly understood. In this study, we generated cardiomyocyte-restricted transgenic zebrafish lines that overexpress eGFP-linked full-length human wild-type (WT) TMEM43 and two genetic variants (c.1073C>T, p.S358L; c.332C>T, p.P111L) using the Tol2-system. Overexpression of WT and p.P111L-mutant TMEM43 was associated with transcriptional activation of the mTOR pathway and ribosome biogenesis, and resulted in enlarged hearts with cardiomyocyte hypertrophy. Intriguingly, mutant p.S358L TMEM43 was found to be unstable and partially redistributed into the cytoplasm in embryonic and adult hearts. Moreover, both TMEM43 variants displayed cardiac morphological defects at juvenile stages and ultrastructural changes within the myocardium, accompanied by dysregulated gene expression profiles in adulthood. Finally, CRISPR/Cas9 mutants demonstrated an age-dependent cardiac phenotype characterized by heart enlargement in adulthood. In conclusion, our findings suggest ultrastructural remodeling and transcriptomic alterations underlying the development of structural and functional cardiac defects in TMEM43-associated cardiomyopathy.

Screening of Pathogenic Missense Single Nucleotide Variants From LHPP Gene Associated With the Hepatocellular Carcinoma: An In silico Approach

LHPP gene encodes a phospholysine phosphohistidine inorganic pyrophosphate phosphatase, which functions as a tumor-suppressor protein. The tumor suppression by this protein has been confirmed in various cancers, including hepatocellular carcinoma (HCC). LHPP downregulation promotes cell growth and proliferation by modulating the PI3K/AKT signaling pathway. This study identifies potentially deleterious missense single nucleotide variants (SNVs) associated with the LHPP gene using multiple computational tools based on different algorithms. A total of 4 destabilizing mutants are identified as L22P, I212T, G227R, and G236R, from the conserved region of the phosphatase. The 3-dimensional (3D) modeling and structural comparison of variants with the native protein reveals significant structural and conformational variations after mutations, suggesting disruption in the function of phospholysine phosphohistidine inorganic pyrophosphate phosphatase. The identified mutations might, therefore, participate in the cause of HCC.

A comprehensive in silico analysis of the deleterious nonsynonymous SNPs of human FOXP2 protein

FOXP2 encodes the forkhead transcription factor that plays a significant role in language development. Single nucleotide polymorphisms in FOXP2 have been linked to speech- language disorder, autism, cancer and schizophrenia. So, scrutinizing the functional SNPs to better understand their association in disease is an uphill task. The purpose of the current study was to identify the missense SNPs which have detrimental structural and functional effects on the FOXP2 protein. Multiple computational tools were employed to investigate the deleterious role of non-synonymous SNPs. Five variants as Y531H, L558P, R536G and R553C were found to be associated with diseases and located at the forkhead domain of the FOXP2 protein. Molecular docking analysis of FOXP2 DNA binding domain with its most common target sequence 5’-CAAATT-3’ predicted that R553C and L558P mutant variants destabilize protein structure by changing protein-DNA interface interactions and disruption of hydrogen bonds that may reduce the specificity and affinity of the binding. Further experimental investigations may need to verify whether this kind of structural and functional variations dysregulate protein activities and induce formation of disease.

PPVED: A machine learning tool for predicting the effect of single amino acid substitution on protein function in plants

Single amino acid substitution (SAAS) produces the most common variant of protein function change under physiological conditions. As the number of SAAS events in plants has increased exponentially, an effective prediction tool is required to help identify and distinguish functional SAASs from the whole genome as either potentially causal traits or as variants. Here, we constructed a plant SAAS database that stores 12 865 SAASs in 6172 proteins and developed a tool called Plant Protein Variation Effect Detector (PPVED) that predicts the effect of SAASs on protein function in plants. PPVED achieved an 87% predictive accuracy when applied to plant SAASs, an accuracy that was much higher than those from six human database software: SIFT, PROVEAN, PANTHER-PSEP, PhD-SNP, PolyPhen-2, and MutPred2. The predictive effect of six SAASs from three proteins in Arabidopsis and maize was validated with wet lab experiments, of which five substitution sites were accurately predicted. PPVED could facilitate the identification and characterization of genetic variants that explain observed phenotype variations in plants, contributing to solutions for challenges in functional genomics and systems biology. PPVED can be accessed under a CC-BY (4.0) license via http://www.ppved.org.cn.

PON-All: Amino Acid Substitution Tolerance Predictor for All Organisms

Genetic variations are investigated in human and many other organisms for many purposes (e.g., to aid in clinical diagnosis). Interpretation of the identified variations can be challenging. Although some dedicated prediction methods have been developed and some tools for human variants can also be used for other organisms, the performance and species range have been limited. We developed a novel variant pathogenicity/tolerance predictor for amino acid substitutions in any organism. The method, PON-All, is a machine learning tool trained on human, animal, and plant variants. Two versions are provided, one with Gene Ontology (GO) annotations and another without these details. GO annotations are not available or are partial for many organisms of interest. The methods provide predictions for three classes: pathogenic, benign, and variants of unknown significance. On the blind test, when using GO annotations, accuracy was 0.913 and MCC 0.827. When GO features were not used, accuracy was 0.856 and MCC 0.712. The performance is the best for human and plant variants and somewhat lower for animal variants because the number of known disease-causing variants in animals is rather small. The method was compared to several other tools and was found to have superior performance. PON-All is freely available at http://structure.bmc.lu.se/PON-All and http://8.133.174.28:8999/.

Interpreting protein variant effects with computational predictors and deep mutational scanning

Computational predictors of genetic variant effect have advanced rapidly in recent years. These programs provide clinical and research laboratories with a rapid and scalable method to assess the likely impacts of novel variants. However, it can be difficult to know to what extent we can trust their results. To benchmark their performance, predictors are often tested against large datasets of known pathogenic and benign variants. These benchmarking data may overlap with the data used to train some supervised predictors, which leads to data re-use or circularity, resulting in inflated performance estimates for those predictors. Furthermore, new predictors are usually found by their authors to be superior to all previous predictors, which suggests some degree of computational bias in their benchmarking. Large-scale functional assays known as deep mutational scans provide one possible solution to this problem, providing independent datasets of variant effect measurements. In this Review, we discuss some of the key advances in predictor methodology, current benchmarking strategies and how data derived from deep mutational scans can be used to overcome the issue of data circularity. We also discuss the ability of such functional assays to directly predict clinical impacts of mutations and how this might affect the future need for variant effect predictors.

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.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

Identification of the most damaging nsSNPs in the human CFL1 gene and their functional and structural impacts on cofilin-1 protein

The cofilin-1 protein, encoded by CFL1, is an actin-binding protein that regulates F-actin depolymerization and nucleation activity through phosphorylation and dephosphorylation. CFL1 has been implicated in the development of neurodegenerative diseases (Alzheimer's disease and Huntington's disease), neuronal migration disorders (lissencephaly, epilepsy, and schizophrenia), and neural tube closure defects. Mutations in CFL1 have been associated with impaired neural crest cell migration and neural tube closure defects. In our study, various computational approaches were utilized to explore single-nucleotide polymorphisms (SNPs) in CFL1. The Variation Viewer and gnomAD databases were used to retrieve CFL1 SNPs, including 46 nonsynonymous SNPs (nsSNPs). The functional and structural annotation of SNPs was performed using 12 sequence-based web applications, which identified 20 nsSNPs as being the most likely to be deleterious or disease-causing. The conservation of cofilin-1 protein structures was illustrated using the ConSurf and PROSITE web servers, which projected the 12 most deleterious nsSNPs onto conserved domains, with the potential to disrupt the protein's functionality. These 12 nsSNPs were selected for protein structure construction, and the DynaMut/DUET servers predicted that the protein variants V7G, L84P, and L99A were the most likely to be damaging to the cofilin-1 protein structure or function. The evaluation of molecular docking studies demonstrated that the L99A and L84P cofilin-1 variants reduce the binding affinity for actin compared with the native cofilin-1 structure, and molecular dynamic simulation studies confirmed that these variants might destabilize the protein structure. The consequences of putative mutations on protein-protein interactions and post-translational modification sites in the cofilin-1 protein structure were analyzed. This study represents the first complete approach to understanding the effects of nsSNPs within the actin-depolymerizing factor/cofilin family, which suggested that SNPs resulting in L84P (rs199716082) and L99A (rs267603119) variants represent significant CFL1 mutations associated with disease development.

Contiguously hydrophobic sequences are functionally significant throughout the human exome

SignificanceProteins rely on the hydrophobic effect to maintain structure and interactions with the environment. Surprisingly, natural selection on amino acid hydrophobicity has not been detected using modern genetic data. Analyses that treat each amino acid separately do not reveal significant results, which we confirm here. However, because the hydrophobic effect becomes more powerful as more hydrophobic molecules are introduced, we tested whether unbroken stretches of hydrophobic amino acids are under selection. Using genetic variant data from across the human genome, we find evidence that selection increases with the length of the unbroken hydrophobic sequence. These results could lead to improvements in a wide range of genomic tools as well as insights into protein-aggregation disease etiology and protein evolutionary history.

Genomic Variation Prediction: A Summary From Different Views

Structural variations in the genome are closely related to human health and the occurrence and development of various diseases. To understand the mechanisms of diseases, find pathogenic targets, and carry out personalized precision medicine, it is critical to detect such variations. The rapid development of high-throughput sequencing technologies has accelerated the accumulation of large amounts of genomic mutation data, including synonymous mutations. Identifying pathogenic synonymous mutations that play important roles in the occurrence and development of diseases from all the available mutation data is of great importance. In this paper, machine learning theories and methods are reviewed, efficient and accurate pathogenic synonymous mutation prediction methods are developed, and a standardized three-level variant analysis framework is constructed. In addition, multiple variation tolerance prediction models are studied and integrated, and new ideas for structural variation detection based on deep information mining are explored.

Which Is the Best In Silico Program for the Missense Variations in IDUA Gene? A Comparison of 33 Programs Plus a Conservation Score and Evaluation of 586 Missense Variants

Mucopolysaccharidosis type I (MPS I) is an autosomal recessive disease characterized by the deficiency of alpha-L-iduronidase (IDUA), an enzyme involved in glycosaminoglycan degradation. More than 200 disease-causing variants have been reported and characterized in the IDUA gene. It also has several variants of unknown significance (VUS) and literature conflicting interpretations of pathogenicity. This study evaluated 586 variants obtained from the literature review, five population databases, in addition to dbSNP, Human Genome Mutation Database (HGMD), and ClinVar. For the variants described in the literature, two datasets were created based on the strength of the criteria. The stricter criteria subset had 108 variants with expression study, analysis of healthy controls, and/or complete gene sequence. The less stringent criteria subset had additional 52 variants found in the literature review, HGMD or ClinVar, and dbSNP with an allele frequency higher than 0.001. The other 426 variants were considered VUS. The two strength criteria datasets were used to evaluate 33 programs plus a conservation score. BayesDel (addAF and noAF), PON-P2 (genome and protein), and ClinPred algorithms showed the best sensitivity, specificity, accuracy, and kappa value for both criteria subsets. The VUS were evaluated with these five algorithms. Based on the results, 122 variants had total consensus among the five predictors, with 57 classified as predicted deleterious and 65 as predicted neutral. For variants not included in PON-P2, 88 variants were considered deleterious and 92 neutral by all other predictors. The remaining 124 did not obtain a consensus among predictors.

Computational and structural based approach to identify malignant nonsynonymous single nucleotide polymorphisms associated with CDK4 gene

Cycline-dependent kinase 4 (CDK4), an enzyme of the cycline dependent or Ser/Thr protein kinase family, plays a role in cell cycle progression (G1 phase) by phosphorylating a tumor suppressor protein called pRB. Alteration of this enzyme due to missense mutation/ nonsynonymous single nucleotide polymorphisms (nsSNPs) are responsible for various types of cancer progression, e.g. melanoma, lung cancer, and breast cancer. Hence, this study is designed to identify the malignant missense mutation of CDK4 from the single nucleotide polymorphism database (dbSNP) by incorporating computational algorithms. Out of 239 nsSNPs; G15S, D140Y and D140H were predicted to be highly malignant variants which may have a devastating impact on protein structure or function. We also found defective binding motif of these three mutants with the CDK4 inhibitor ribociclib and ATP. However, by incorporating molecular dynamic simulation, our study concludes that the superiority of G15S than the other two mutants (D140Y and D140H) in destabilizing proteins nature.

Impact of Deleterious Mutations on Structure, Function and Stability of Serum/Glucocorticoid Regulated Kinase 1: A Gene to Diseases Correlation

Serum and glucocorticoid-regulated kinase 1 (SGK1) is a Ser/Thr protein kinase involved in regulating cell survival, growth, proliferation, and migration. Its elevated expression and dysfunction are reported in breast, prostate, hepatocellular, lung adenoma, and renal carcinomas. We have analyzed the SGK1 mutations to explore their impact at the sequence and structure level by utilizing state-of-the-art computational approaches. Several pathogenic and destabilizing mutations were identified based on their impact on SGK1 and analyzed in detail. Three amino acid substitutions, K127M, T256A, and Y298A, in the kinase domain of SGK1 were identified and incorporated structurally into original coordinates of SGK1 to explore their time evolution impact using all-atom molecular dynamic (MD) simulations for 200 ns. MD results indicate substantial conformational alterations in SGK1, thus its functional loss, particularly upon T256A mutation. This study provides meaningful insights into SGK1 dysfunction upon mutation, leading to disease progression, including cancer, and neurodegeneration.

Measuring and interpreting pervasive heterogeneity, poikilosis

Measurements are widely used in science, engineering, industry, and trade. They form the basis for experimental scientific research, approach, and progress; however, their foundations are seldom thought or questioned. Recently poikilosis, pervasive heterogeneity ranging from subatomic level to biosphere, was introduced. Poikilosis makes single point measurements and estimates obsolete and irrelevant as measurands display intervals of magnitudes. Consideration of poikilosis requires new lines of thinking in experimental design, conduction of studies, data analysis and interpretation. Measurements of poikilosis must consider lagom, normal, variation extent. Measurements, measures, and measurands as well as the measuring systems and uncertainties are discussed from the perspective of poikilosis. New systematics is introduced for description of uncertainty in measurements and for types of experimental designs. Poikilosis-aware experimenting, data analysis and interpretation are discussed. Instructions are provided for how to measure lagom and non-lagom effects of poikilosis. Consideration of poikilosis can solve scientific controversies and enigmas and can allow novel insight into systems, processes, mechanisms, and reactions and their interpretation, understanding, and manipulation. Furthermore, it will increase reproducibility of measurements and studies.

PON-Sol2: Prediction of Effects of Variants on Protein Solubility

Genetic variations have a multitude of effects on proteins. A substantial number of variations affect protein-solvent interactions, either aggregation or solubility. Aggregation is often related to structural alterations, whereas solubilizable proteins in the solid phase can be made again soluble by dilution. Solubility is a central protein property and when reduced can lead to diseases. We developed a prediction method, PON-Sol2, to identify amino acid substitutions that increase, decrease, or have no effect on the protein solubility. The method is a machine learning tool utilizing gradient boosting algorithm and was trained on a large dataset of variants with different outcomes after the selection of features among a large number of tested properties. The method is fast and has high performance. The normalized correct prediction rate for three states is 0.656, and the normalized GC2 score is 0.312 in 10-fold cross-validation. The corresponding numbers in the blind test were 0.545 and 0.157. The performance was superior in comparison to previous methods. The PON-Sol2 predictor is freely available. It can be used to predict the solubility effects of variants for any organism, even in large-scale projects.

Prediction of driver variants in the cancer genome via machine learning methodologies

Sequencing technologies have led to the identification of many variants in the human genome which could act as disease-drivers. As a consequence, a variety of bioinformatics tools have been proposed for predicting which variants may drive disease, and which may be causatively neutral. After briefly reviewing generic tools, we focus on a subset of these methods specifically geared toward predicting which variants in the human cancer genome may act as enablers of unregulated cell proliferation. We consider the resultant view of the cancer genome indicated by these predictors and discuss ways in which these types of prediction tools may be progressed by further research.

Polymorphisms at site 469 of B-RAF protein associated with skin melanoma may be correlated with dabrafenib resistance: An in silico study

Melanoma is a type of skin cancer. Numerous genes and their proteins are strongly associated with melanoma susceptibility. This study aims to use an in silico method to identify genetic variants in the melanoma susceptibility gene. The COSMIC database was queried for genes and cross-referenced with three environment-gene interaction databases (EGP, SeattleSNPs and CTD) to identify shared genes. The majority of approved skin melanoma drugs were found to act on the protein serine/threonine-protein kinase (B-RAF) encoded by the BRAF gene, which was also present in all three referenced databases. Comprehensive computational analysis was performed to predict deleterious genetic variants associated with skin melanoma, and the nsSNPs G469V and G469E were prioritized based on their predicted deleterious effects. Molecular dynamic simulation analysis of the B-RAF protein mutants G469V and G469E reveals that variations in the amino acid conformation at the drug binding site result in inconsistency in drug interaction. Additionally, this analysis showed that the G469V and G469E mutants have lower binding energy for dabrafenib than the wild type. The population with the highest frequency of each deleterious and pathogenic variant has been determined. The study's findings would support the development of more effective treatment strategies for skin melanoma. Communicated by Ramaswamy H. Sarma.