Physicochemical constraint violation by missense substitutions mediates impairment of protein function and disease severity

AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning

Amino acid scales are crucial for protein prediction tasks, many of them being curated in the AAindex database. Despite various clustering attempts to organize them and to better understand their relationships, these approaches lack the fine-grained classification necessary for satisfactory interpretability in many protein prediction problems. To address this issue, we developed AAontology-a two-level classification for 586 amino acid scales (mainly from AAindex) together with an in-depth analysis of their relations-using bag-of-word-based classification, clustering, and manual refinement over multiple iterations. AAontology organizes physicochemical scales into 8 categories and 67 subcategories, enhancing the interpretability of scale-based machine learning methods in protein bioinformatics. Thereby it enables researchers to gain a deeper biological insight. We anticipate that AAontology will be a building block to link amino acid properties with protein function and dysfunctions as well as aid informed decision-making in mutation analysis or protein drug design.

Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors

BACKGROUND

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

RESULTS

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

CONCLUSIONS

VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at  https://genomeinterpretation.org/vipdb.

In Silico Deciphering of the Potential Impact of Variants of Uncertain Significance in Hereditary Colorectal Cancer Syndromes

Colorectal cancer (CRC) ranks third in terms of cancer incidence worldwide and is responsible for 8% of all deaths globally. Approximately 10% of CRC cases are caused by inherited pathogenic mutations in driver genes involved in pathways that are crucial for CRC tumorigenesis and progression. These hereditary mutations significantly increase the risk of initial benign polyps or adenomas developing into cancer. In recent years, the rapid and accurate sequencing of CRC-specific multigene panels by next-generation sequencing (NGS) technologies has enabled the identification of several recurrent pathogenic variants with established functional consequences. In parallel, rare genetic variants that are not characterized and are, therefore, called variants of uncertain significance (VUSs) have also been detected. The classification of VUSs is a challenging task because each amino acid has specific biochemical properties and uniquely contributes to the structural stability and functional activity of proteins. In this scenario, the ability to computationally predict the effect of a VUS is crucial. In particular, in silico prediction methods can provide useful insights to assess the potential impact of a VUS and support additional clinical evaluation. This approach can further benefit from recent advances in artificial intelligence-based technologies. In this review, we describe the main in silico prediction tools that can be used to evaluate the structural and functional impact of VUSs and provide examples of their application in the analysis of gene variants involved in hereditary CRC syndromes.

Dark Morph of the Oriental Honey-Buzzard (Pernis ptilorhynchus orientalis) is Attributable to Specific MC1R Haplotypes

A thorough understanding of the development of complex plumages in birds necessitates the acquisition of genetic data pertaining to the mechanism underlying this phenomenon from various avian species. The oriental honey-buzzard (Pernis ptilorhynchus orientalis), a tropical summer migrant to Northeast Asia, including Japan, exemplifies this aspect owing to the diversity of its ventral coloration and intra-feather barring patterns. However, genetic polymorphism responsible for this diversity has not been identified yet. This study aimed to investigate the link between dark-plumed phenotypes of this subspecies and haplotypes of the melanocortin-1-receptor (MC1R) gene. A draft sequence of MC1R was constructed using next generation sequencing and subsequently amplified using designed polymerase chain reaction (PCR) primers. The genome sequences of 32 honey-buzzard individuals were determined using PCR, and 12 MC1R haplotype sequences were obtained. Among these haplotypes, we found that unique haplotypes with nine non-synonymous substitutions and four or five synonymous substitutions in the coding region had a perfect correlation with the dark-plumed phenotype. The lack of correlation between the genotype of ASIP coding region and plumage phenotype reiterated that the dark morph is attributable to specific MC1R haplotypes. The absence of a correlation between genetic polymorphisms of MC1R and the intra-feather barring patterns, as well as the diversity observed within lighter ground color classes (pale and intermediate), implies the involvement of alternative molecular mechanisms in the manifestation of the aforementioned phenotypes.

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

Prediction of Deleterious Single Amino Acid Polymorphisms with a Consensus Holdout Sampler

Background

Single Amino Acid Polymorphisms (SAPs) or nonsynonymous Single Nucleotide Variants (nsSNVs) are the most common genetic variations. They result from missense mutations where a single base pair substitution changes the genetic code in such a way that the triplet of bases (codon) at a given position is coding a different amino acid. Since genetic mutations sometimes cause genetic diseases, it is important to comprehend and foresee which variations are harmful and which ones are neutral (not causing changes in the phenotype). This can be posed as a classification problem.

Methods

Computational methods using machine intelligence are gradually replacing repetitive and exceedingly overpriced mutagenic tests. By and large, uneven quality, deficiencies, and irregularities of nsSNVs datasets debase the convenience of artificial intelligence-based methods. Subsequently, strong and more exact approaches are needed to address these problems. In the present work paper, we show a consensus classifier built on the holdout sampler, which appears strong and precise and outflanks all other popular methods.

Results

We produced 100 holdouts to test the structures and diverse classification variables of diverse classifiers during the training phase. The finest performing holdouts were chosen to develop a consensus classifier and tested using a k-fold (1 ≤ k ≤5) cross-validation method. We also examined which protein properties have the biggest impact on the precise prediction of the effects of nsSNVs.

Conclusion

Our Consensus Holdout Sampler outflanks other popular algorithms, and gives excellent results, highly accurate with low standard deviation. The advantage of our method emerges from using a tree of holdouts, where diverse LM/AI-based programs are sampled in diverse ways.

Potential new cancer biomarkers revealed by quantum chemistry associated with bioinformatics in the study of selectin polymorphisms

Understanding the complex mechanisms involved in diseases caused by or related to important genetic variants has led to the development of clinically useful biomarkers. However, the increasing number of described variants makes it difficult to identify variants worthy of investigation, and poses challenges to their validation. We combined publicly available datasets and open source robust bioinformatics tools with molecular quantum chemistry methods to investigate the involvement of selectins, important molecules in the cell adhesion process that play a fundamental role in the cancer metastasis process. We applied this strategy to investigate single nucleotide variants (SNPs) in the intronic and UTR regions and missense SNPs with amino acid changes in the SELL, SELP, SELE, and SELPLG genes. We then focused on thyroid cancer, seeking these SNPs potential to identify biomarkers for susceptibility, diagnosis, prognosis, and therapeutic targets. We demonstrated that SELL gene polymorphisms rs2229569, rs1131498, rs4987360, rs4987301 and rs2205849; SELE gene polymorphisms rs1534904 and rs5368; rs3917777, rs2205894 and rs2205893 of SELP gene; and rs7138370, rs7300972 and rs2228315 variants of SELPLG gene may produce important alterations in the DNA structure and consequent changes in the morphology and function of the corresponding proteins. In conclusion, we developed a strategy that may save valuable time and resources in future investigations, as we were able to provide a solid foundation for the selection of selectin gene variants that may become important biomarkers and deserve further investigation in cancer patients. Large-scale clinical studies in different ethnic populations and laboratory experiments are needed to validate our results.

Deep mutational scanning of the RNase III-like domain in Trypanosoma brucei RNA editing protein KREPB4

Kinetoplastid pathogens including Trypanosoma brucei, T. cruzi, and Leishmania species, are early diverged, eukaryotic, unicellular parasites. Functional understanding of many proteins from these pathogens has been hampered by limited sequence homology to proteins from other model organisms. Here we describe the development of a high-throughput deep mutational scanning approach in T. brucei that facilitates rapid and unbiased assessment of the impacts of many possible amino acid substitutions within a protein on cell fitness, as measured by relative cell growth. The approach leverages several molecular technologies: cells with conditional expression of a wild-type gene of interest and constitutive expression of a library of mutant variants, degron-controlled stabilization of I-SceI meganuclease to mediate highly efficient transfection of a mutant allele library, and a high-throughput sequencing readout for cell growth upon conditional knockdown of wild-type gene expression and exclusive expression of mutant variants. Using this method, we queried the effects of amino acid substitutions in the apparently non-catalytic RNase III-like domain of KREPB4 (B4), which is an essential component of the RNA Editing Catalytic Complexes (RECCs) that carry out mitochondrial RNA editing in T. brucei. We measured the impacts of thousands of B4 variants on bloodstream form cell growth and validated the most deleterious variants containing single amino acid substitutions. Crucially, there was no correlation between phenotypes and amino acid conservation, demonstrating the greater power of this method over traditional sequence homology searching to identify functional residues. The bloodstream form cell growth phenotypes were combined with structural modeling, RECC protein proximity data, and analysis of selected substitutions in procyclic form T. brucei. These analyses revealed that the B4 RNaseIII-like domain is essential for maintenance of RECC integrity and RECC protein abundances and is also involved in changes in RECCs that occur between bloodstream and procyclic form life cycle stages.

Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine-learning approaches

Background

Pyrazinamide is one of four first-line antibiotics used to treat tuberculosis; however, antibiotic susceptibility testing for pyrazinamide is challenging. Resistance to pyrazinamide is primarily driven by genetic variation in pncA, encoding an enzyme that converts pyrazinamide into its active form.

Methods

We curated a dataset of 664 non-redundant, missense amino acid mutations in PncA with associated high-confidence phenotypes from published studies and then trained three different machine-learning models to predict pyrazinamide resistance. All models had access to a range of protein structural-, chemical- and sequence-based features.

Results

The best model, a gradient-boosted decision tree, achieved a sensitivity of 80.2% and a specificity of 76.9% on the hold-out test dataset. The clinical performance of the models was then estimated by predicting the binary pyrazinamide resistance phenotype of 4027 samples harbouring 367 unique missense mutations in pncA derived from 24 231 clinical isolates.

Conclusions

This work demonstrates how machine learning can enhance the sensitivity/specificity of pyrazinamide resistance prediction in genetics-based clinical microbiology workflows, highlights novel mutations for future biochemical investigation, and is a proof of concept for using this approach in other drugs.

Computational Tools to Assist in Analyzing Effects of the SERPINA1 Gene Variation on Alpha-1 Antitrypsin (AAT)

In the rapidly advancing field of bioinformatics, the development and application of computational tools to predict the effects of single nucleotide variants (SNVs) are shedding light on the molecular mechanisms underlying disorders. Also, they hold promise for guiding therapeutic interventions and personalized medicine strategies in the future. A comprehensive understanding of the impact of SNVs in the SERPINA1 gene on alpha-1 antitrypsin (AAT) protein structure and function requires integrating bioinformatic approaches. Here, we provide a guide for clinicians to navigate through the field of computational analyses which can be applied to describe a novel genetic variant. Predicting the clinical significance of SERPINA1 variation allows clinicians to tailor treatment options for individuals with alpha-1 antitrypsin deficiency (AATD) and related conditions, ultimately improving the patient's outcome and quality of life. This paper explores the various bioinformatic methodologies and cutting-edge approaches dedicated to the assessment of molecular variants of genes and their product proteins using SERPINA1 and AAT as an example.

Analysis of E2F1 single-nucleotide polymorphisms reveals deleterious non-synonymous substitutions that disrupt E2F1-RB protein interaction in cancer

Cancer is a medical condition that is caused by the abnormal growth and division of cells, leading to the formation of tumors. The E2F1 and RB pathways are critical in regulating cell cycle, and their dysregulation can contribute to the development of cancer. In this study, we analyzed experimentally reported SNPs in E2F1 and assessed their effects on the binding affinity with RB. Out of 46, nine mutations were predicted as deleterious, and further analysis revealed four highly destabilizing mutations (L206W, R232C, I254T, A267T) that significantly altered the protein structure. Molecular docking of wild-type and mutant E2F1 with RB revealed a docking score of -242 kcal/mol for wild-type, while the mutant complexes had scores ranging from -217 to -220 kcal/mol. Molecular simulation analysis revealed variations in the dynamics features of both mutant and wild-type complexes due to the acquired mutations. Furthermore, the total binding free energy for the wild-type E2F1-RB complex was -64.89 kcal/mol, while those of the L206W, R232C, I254T, and A267T E2F1-RB mutants were -45.90 kcal/mol, -53.52 kcal/mol, -55.67 kcal/mol, and -61.22 kcal/mol, respectively. Our study is the first to extensively analyze E2F1 gene mutations and identifies candidate mutations for further validation and potential targeting for cancer therapeutics.

Structural effect of the H992D/H418D mutation of angiotensin-converting enzyme in the Indian population: implications for health and disease

The Non synonymous SNPs (nsSNPs) of the renin-angiotensin-system (RAS) pathway, unique to the Indian population were investigated in view of its importance as an endocrine system. nsSNPs of the RAS pathway genes were mined from the IndiGenome database. Damaging nsSNPs were predicted using SIFT, PredictSNP, SNP and GO, Snap2 and Protein Variation Effect Analyzer. Loss of function was predicted based on protein stability change using I mutant, PremPS and CONSURF. The structural impact of the nsSNPs was predicted using HOPE and Missense3d followed by modeling, refinement, and energy minimization. Molecular Dynamics studies were carried out using Gromacsv2021.1. 23 Indian nsSNPs of the RAS pathway genes were selected for structural analysis and 8 were predicted to be damaging. Further sequence analysis showed that HEMGH zinc binding motif changes to HEMGD in somatic ACE-C domain (sACE-C) H992D and Testis ACE (tACE) H418D resulted in loss of zinc coordination, which is essential for enzymatic activity in this metalloprotease. There was a loss of internal interactions around the zinc coordination residues in the protein structural network. This was also confirmed by Principal Component Analysis, Free Energy Landscape and residue contact maps. Both mutations lead to broadening of the AngI binding cavity. The H992D mutation in sACE-C is likely to be favorable for cardiovascular health, but may lead to renal abnormalities with secondary impact on the heart. H418D in tACE is potentially associated with male infertility.Communicated by Ramaswamy H. Sarma.

FCMSTrans: Accurate Prediction of Disease-Associated nsSNPs by Utilizing Multiscale Convolution and Deep Feature Combination within a Transformer Framework

Nonsynonymous single-nucleotide polymorphisms (nsSNPs), implicated in over 6000 diseases, necessitate accurate prediction for expedited drug discovery and improved disease diagnosis. In this study, we propose FCMSTrans, a novel nsSNP predictor that innovatively combines the transformer framework and multiscale modules for comprehensive feature extraction. The distinctive attribute of FCMSTrans resides in a deep feature combination strategy. This strategy amalgamates evolutionary-scale modeling (ESM) and ProtTrans (PT) features, providing an understanding of protein biochemical properties, and position-specific scoring matrix, secondary structure, predicted relative solvent accessibility, and predicted disorder (PSPP) features, which are derived from four protein sequences and structure-oriented characteristics. This feature combination offers a comprehensive view of the molecular dynamics involving nsSNPs. Our model employs the transformer's self-attention mechanisms across multiple layers, extracting higher-level and abstract representations. Simultaneously, varied-level features are captured by multiscale convolutions, enriching feature abstraction at multiple echelons. Our comparative analyses with existing methodologies highlight significant improvements made possible by the integrated feature fusion approach adopted in FCMSTrans. This is further substantiated by performance assessments based on diverse data sets, such as PredictSNP, MMP, and PMD, with areas under the curve (AUCs) of 0.869, 0.819, and 0.693, respectively. Furthermore, FCMSTrans shows robustness and superiority by outperforming the current best predictor, PROVEAN, in a blind test conducted on a third-party data set, achieving an impressive AUC score of 0.7838. The Python code of FCMSTrans is available at https://github.com/gc212/FCMSTrans for academic usage.

A mutational atlas for Parkin proteostasis

Proteostasis can be disturbed by mutations affecting folding and stability of the encoded protein. An example is the ubiquitin ligase Parkin, where gene variants result in autosomal recessive Parkinsonism. To uncover the pathological mechanism and provide comprehensive genotype-phenotype information, variant abundance by massively parallel sequencing (VAMP-seq) is leveraged to quantify the abundance of Parkin variants in cultured human cells. The resulting mutational map, covering 9219 out of the 9300 possible single-site amino acid substitutions and nonsense Parkin variants, shows that most low abundance variants are proteasome targets and are located within the structured domains of the protein. Half of the known disease-linked variants are found at low abundance. Systematic mapping of degradation signals (degrons) reveals an exposed degron region proximal to the so-called "activation element". This work provides examples of how missense variants may cause degradation either via destabilization of the native protein, or by introducing local signals for degradation.

Relevance of augmented kisspeptin signaling through H364 KISS1R in central precocious puberty

Understanding the pathophysiology of idiopathic central precocious puberty (ICPP) is essential, in view of its consequences on reproductive health and metabolic disorders in later life. Towards this, estimation of circulating levels of the neuropeptides, viz; Kisspeptin (Kp-10), Neurokinin B (NKB) and Neuropeptide Y (NPY), acting upstream to Gonadotropin-Releasing Hormone (GnRH), has shown promise. Insights can also be gained from functional studies on genetic variations implicated in ICPP. This study investigated the pathophysiology of ICPP in a girl by exploring the therapeutic relevance of the circulating levels of Kp-10, NKB, NPY and characterizing the nonsynonymous KISS1R variant, L364H, that she harbours, in a homozygous condition. Plasma levels of Kp-10, NKB and NPY before and after GnRH analog (GnRHa) treatment, were determined by ELISA. It was observed that GnRHa treatment resulted in suppression of circulating levels of Kp-10, NKB and NPY. Further, the H364 variant in KISS1R was generated by site directed mutagenesis. Post transient transfection of either L364 or H364 KISS1R variant in CHO cells, receptor expression was ascertained by western blotting, indirect immunofluorescence and flow cytometry. Kp-10 stimulated signalling response was also determined by phospho-ERK and inositol phosphate production. Structure-function studies revealed that, although the receptor expression in H364 KISS1R was comparable to L364 KISS1R, there was an enhanced signalling response through this variant at high doses of Kp-10. Thus, elevated levels of Kp-10, acting through H364 KISS1R, contributed to the manifestation of ICPP, providing further evidence that dysregulation of Kp-10/KISS1R axis impacts the onset of puberty.

A novel NPHP4 homozygous missense variant identified in infertile brothers with multiple morphological abnormalities of the sperm flagella

PURPOSE

Asthenozoospermia is an important cause of male infertility, and the most serious type is characterized by multiple morphological abnormalities of the sperm flagella (MMAF). However, the precise etiology of MMAF remains unknown. In the current study, we recruited a consanguineous Pakistani family with two infertile brothers suffering from primary infertility due to MMAF without obvious signs of PCD.

METHODS

We performed whole-exome sequencing on DNAs of the patients, their parents, and a fertile brother and identified the homozygous missense variant (c.1490C > G (p.P497R) in NPHP4 as the candidate mutation for male infertility in this family.

RESULTS

Sanger sequencing confirmed that this mutation recessively co-segregated with the MMAF in this family. In silico analysis revealed that the mutation site is conserved across different species, and the identified mutation also causes abnormalities in the structure and hydrophobic interactions of the NPHP4 protein. Different bioinformatics tools predict that NPHP4p.P497R mutation is pathogenic. Furthermore, Papanicolaou staining and scanning electron microscopy of sperm revealed that affected individuals displayed typical MMAF phenotype with a high percentage of coiled, bent, short, absent, and/or irregular flagella. Transmission electron microscopy images of the patient's spermatozoa revealed significant anomalies in the sperm flagella with the absence of a central pair of microtubules (9 + 0) in every section scored.

CONCLUSIONS

Taken together, these results show that the homozygous missense mutation in NPHP4 is associated with MMAF.

Exploring Novel Variants of the Cytochrome P450 Reductase Gene (POR) from the Genome Aggregation Database by Integrating Bioinformatic Tools and Functional Assays

Cytochrome P450 oxidoreductase (POR) is an essential redox partner for steroid and drug-metabolizing cytochromes P450 located in the endoplasmic reticulum. Mutations in POR lead to metabolic disorders, including congenital adrenal hyperplasia, and affect the metabolism of steroids, drugs, and xenobiotics. In this study, we examined approximately 450 missense variants of the POR gene listed in the Genome Aggregation Database (gnomAD) using eleven different in silico prediction tools. We found that 64 novel variants were consistently predicted to be disease-causing by most tools. To validate our findings, we conducted a population analysis and selected two variations in POR for further investigation. The human POR wild type and the R268W and L577P variants were expressed in bacteria and subjected to enzyme kinetic assays using a model substrate. We also examined the activities of several cytochrome P450 proteins in the presence of POR (WT or variants) by combining P450 and reductase proteins in liposomes. We observed a decrease in enzymatic activities (ranging from 35% to 85%) of key drug-metabolizing enzymes, supported by POR variants R288W and L577P compared to WT-POR. These results validate our approach of curating a vast amount of data from genome projects and provide an updated and reliable reference for diagnosing POR deficiency.

PredictONCO: a web tool supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning

PredictONCO 1.0 is a unique web server that analyzes effects of mutations on proteins frequently altered in various cancer types. The server can assess the impact of mutations on the protein sequential and structural properties and apply a virtual screening to identify potential inhibitors that could be used as a highly individualized therapeutic approach, possibly based on the drug repurposing. PredictONCO integrates predictive algorithms and state-of-the-art computational tools combined with information from established databases. The user interface was carefully designed for the target specialists in precision oncology, molecular pathology, clinical genetics and clinical sciences. The tool summarizes the effect of the mutation on protein stability and function and currently covers 44 common oncological targets. The binding affinities of Food and Drug Administration/ European Medicines Agency -approved drugs with the wild-type and mutant proteins are calculated to facilitate treatment decisions. The reliability of predictions was confirmed against 108 clinically validated mutations. The server provides a fast and compact output, ideal for the often time-sensitive decision-making process in oncology. Three use cases of missense mutations, (i) K22A in cyclin-dependent kinase 4 identified in melanoma, (ii) E1197K mutation in anaplastic lymphoma kinase 4 identified in lung carcinoma and (iii) V765A mutation in epidermal growth factor receptor in a patient with congenital mismatch repair deficiency highlight how the tool can increase levels of confidence regarding the pathogenicity of the variants and identify the most effective inhibitors. The server is available at https://loschmidt.chemi.muni.cz/predictonco.

Some common deleterious mutations are shared in SARS-CoV-2 genomes from deceased COVID-19 patients across continents

The identification of deleterious mutations in different variants of SARS-CoV-2 and their roles in the morbidity of COVID-19 patients has yet to be thoroughly investigated. To unravel the spectrum of mutations and their effects within SARS-CoV-2 genomes, we analyzed 5,724 complete genomes from deceased COVID-19 patients sourced from the GISAID database. This analysis was conducted using the Nextstrain platform, applying a generalized time-reversible model for evolutionary phylogeny. These genomes were compared to the reference strain (hCoV-19/Wuhan/WIV04/2019) using MAFFT v7.470. Our findings revealed that SARS-CoV-2 genomes from deceased individuals belonged to 21 Nextstrain clades, with clade 20I (Alpha variant) being the most predominant, followed by clade 20H (Beta variant) and clade 20J (Gamma variant). The majority of SARS-CoV-2 genomes from deceased patients (33.4%) were sequenced in North America, while the lowest percentage (0.98%) came from Africa. The 'G' clade was dominant in the SARS-CoV-2 genomes of Asian, African, and North American regions, while the 'GRY' clade prevailed in Europe. In our analysis, we identified 35,799 nucleotide (NT) mutations throughout the genome, with the highest frequency (11,402 occurrences) found in the spike protein. Notably, we observed 4150 point-specific amino acid (AA) mutations in SARS-CoV-2 genomes, with D614G (20%) and N501Y (14%) identified as the top two deleterious mutations in the spike protein on a global scale. Furthermore, we detected five common deleterious AA mutations, including G18V, W45S, I33T, P30L, and Q418H, which play a key role in defining each clade of SARS-CoV-2. Our novel findings hold potential value for genomic surveillance, enabling the monitoring of the evolving pattern of SARS-CoV-2 infection, its emerging variants, and their impact on the development of effective vaccination and control strategies.

DNA Mismatch Repair Gene Variant Classification: Evaluating the Utility of Somatic Mutations and Mismatch Repair Deficient Colonic Crypts and Endometrial Glands

Germline pathogenic variants in the DNA mismatch repair (MMR) genes (Lynch syndrome) predispose to colorectal (CRC) and endometrial (EC) cancer. Lynch syndrome specific tumor features were evaluated for their ability to support the ACMG/InSiGHT framework in classifying variants of uncertain clinical significance (VUS) in the MMR genes. Twenty-eight CRC or EC tumors from 25 VUS carriers (6xMLH1, 9xMSH2, 6xMSH6, 4xPMS2), underwent targeted tumor sequencing for the presence of microsatellite instability/MMR-deficiency (MSI-H/dMMR) status and identification of a somatic MMR mutation (second hit). Immunohistochemical testing for the presence of dMMR crypts/glands in normal tissue was also performed. The ACMG/InSiGHT framework reclassified 7/25 (28%) VUS to likely pathogenic (LP), three (12%) to benign/likely benign, and 15 (60%) VUS remained unchanged. For the seven re-classified LP variants comprising nine tumors, tumor sequencing confirmed MSI-H/dMMR (8/9, 88.9%) and a second hit (7/9, 77.8%). Of these LP reclassified variants where normal tissue was available, the presence of a dMMR crypt/gland was found in 2/4 (50%). Furthermore, a dMMR endometrial gland in a carrier of an MSH2 exon 1-6 duplication provides further support for an upgrade of this VUS to LP. Our study confirmed that identifying these Lynch syndrome features can improve MMR variant classification, enabling optimal clinical care.

ASCARIS: Positional feature annotation and protein structure-based representation of single amino acid variations

Background

Genomic variations may cause deleterious effects on protein functionality and perturb biological processes. Elucidating the effects of variations is critical for developing novel treatment strategies for diseases of genetic origin. Computational approaches have been aiding the work in this field by modeling and analyzing the mutational landscape. However, new approaches are required, especially for accurate representation and data-centric analysis of sequence variations.

Method

In this study, we propose ASCARIS (Annotation and StruCture-bAsed RepresentatIon of Single amino acid variations), a method for the featurization (i.e., quantitative representation) of single amino acid variations (SAVs), which could be used for a variety of purposes, such as predicting their functional effects or building multi-omics-based integrative models. ASCARIS utilizes the direct and spatial correspondence between the location of the SAV on the sequence/structure and 30 different types of positional feature annotations (e.g., active/lipidation/glycosylation sites; calcium/metal/DNA binding, inter/transmembrane regions, etc.), along with structural features and physicochemical properties. The main novelty of this method lies in constructing reusable numerical representations of SAVs via functional annotations.

Results

We statistically analyzed the relationship between these features and the consequences of variations and found that each carries information in this regard. To investigate potential applications of ASCARIS, we trained variant effect prediction models that utilize our SAV representations as input. We carried out an ablation study and a comparison against the state-of-the-art methods and observed that ASCARIS has a competing and complementary performance against widely-used predictors. ASCARIS can be used alone or in combination with other approaches to represent SAVs from a functional perspective. ASCARIS is available as a programmatic tool at https://github.com/HUBioDataLab/ASCARIS and as a web-service at https://huggingface.co/spaces/HUBioDataLab/ASCARIS.

Evolutionary history of MEK1 illuminates the nature of deleterious mutations

Mutations in signal transduction pathways lead to various diseases including cancers. MEK1 kinase, encoded by the human MAP2K1 gene, is one of the central components of the MAPK pathway and more than a hundred somatic mutations in the MAP2K1 gene were identified in various tumors. Germline mutations deregulating MEK1 also lead to congenital abnormalities, such as the cardiofaciocutaneous syndrome and arteriovenous malformation. Evaluating variants associated with a disease is a challenge, and computational genomic approaches aid in this process. Establishing evolutionary history of a gene improves computational prediction of disease-causing mutations; however, the evolutionary history of MEK1 is not well understood. Here, by revealing a precise evolutionary history of MEK1, we construct a well-defined dataset of MEK1 metazoan orthologs, which provides sufficient depth to distinguish between conserved and variable amino acid positions. We matched known and predicted disease-causing and benign mutations to evolutionary changes observed in corresponding amino acid positions and found that all known and many suspected disease-causing mutations are evolutionarily intolerable. We selected several variants that cannot be unambiguously assessed by automated prediction tools but that are confidently identified as "damaging" by our approach, for experimental validation in Drosophila. In all cases, evolutionary intolerant variants caused increased mortality and severe defects in fruit fly embryos confirming their damaging nature. We anticipate that our analysis will serve as a blueprint to help evaluate known and novel missense variants in MEK1 and that our approach will contribute to improving automated tools for disease-associated variant interpretation.

SARS-CoV-2 vaccine breakthrough infections (VBI) by Omicron variant (B.1.1.529) and consequences in structural and functional impact

This study investigated the efficacy of existing vaccines against hospitalization and infection due to the Omicron variant of COVID-19, particularly for those who received two doses of Moderna or Pfizer vaccines and one dose of Johnson & Johnson vaccine or who were vaccinated more than five months before. A total of 36 variants in Omicron's spike protein, targeted by all three vaccinations, have made antibodies less effective at neutralizing the virus. The genotyping of the SARS-CoV-2 viral sequence revealed clinically significant variants such as E484K in three genetic mutations (T95I, D614G, and del142-144). A woman showed two of these mutations, indicating a potential risk of infection after successful immunization, as recently reported by Hacisuleyman (2021). We examine the effects of mutations on domains (NID, RBM, and SD2) found at the interfaces of the spike domains Omicron B.1.1529, Delta/B.1.1529, Alpha/B.1.1.7, VUM B.1.526, B.1.575.2, and B.1.1214 (formerly VOI Iota). We tested the affinity of Omicron for ACE2 and found that the wild- and mutant-spike proteins were using atomistic molecular dynamics simulations. According to the binding free energies calculated during mutagenesis, the ACE2 bound Omicron spikes more strongly than the wild strain SARS-CoV-2. T95I, D614G, and E484K are three substitutions that significantly contribute to RBD, corresponding to ACE2 binding energies and a doubling of the electrostatic potential of Omicron spike proteins. The Omicron appears to bind to ACE2 with greater affinity, increasing its infectivity and transmissibility. The spike virus was designed to strengthen antibody immune evasion through binding while boosting receptor binding by enhancing IgG and IgM antibodies that stimulate human β-cell, as opposed to the wild strain, which has more vital stimulation of both antibodies.

A comprehensive map of human glucokinase variant activity

BACKGROUND

Glucokinase (GCK) regulates insulin secretion to maintain appropriate blood glucose levels. Sequence variants can alter GCK activity to cause hyperinsulinemic hypoglycemia or hyperglycemia associated with GCK-maturity-onset diabetes of the young (GCK-MODY), collectively affecting up to 10 million people worldwide. Patients with GCK-MODY are frequently misdiagnosed and treated unnecessarily. Genetic testing can prevent this but is hampered by the challenge of interpreting novel missense variants.

RESULT

Here, we exploit a multiplexed yeast complementation assay to measure both hyper- and hypoactive GCK variation, capturing 97% of all possible missense and nonsense variants. Activity scores correlate with in vitro catalytic efficiency, fasting glucose levels in carriers of GCK variants and with evolutionary conservation. Hypoactive variants are concentrated at buried positions, near the active site, and at a region of known importance for GCK conformational dynamics. Some hyperactive variants shift the conformational equilibrium towards the active state through a relative destabilization of the inactive conformation.

CONCLUSION

Our comprehensive assessment of GCK variant activity promises to facilitate variant interpretation and diagnosis, expand our mechanistic understanding of hyperactive variants, and inform development of therapeutics targeting GCK.

NR3C1 rs6198 Variant May Be Involved in the Relationship of Graves' Disease with Stressful Events

Although stressful events are known to trigger Graves' disease (GD), the mechanisms involved in this process are not well understood. The NR3C1 gene, encoding for the glucocorticoid receptor (GR), presents single nucleotide polymorphisms (SNPs) that are associated with stress-related diseases. To investigate the relationship between NR3C1 SNPs, GD susceptibility, and clinical features, we studied 792 individuals, including 384 patients, among which 209 presented with Graves' orbitopathy (GO), and 408 paired healthy controls. Stressful life events were evaluated in a subset of 59 patients and 66 controls using the IES-R self-report questionnaire. SNPs rs104893913, rs104893909, and rs104893911 appeared at low frequencies and presented similar profiles in patients and controls. However, variant forms of rs6198 were rarer in GD patients, suggesting a protective effect. Stressful events were more common in patients than controls, and were reported to have clearly occurred immediately before the onset of GD symptoms in 23 cases. However, no association was found between these events and rs6198 genotypes or GD/GO characteristics. We suggest that the NR3C1 rs6198 polymorphism may be an important protective factor against GD, but its relationship with stressful events needs further investigation.

Computational insight into in silico analysis and molecular dynamics simulation of the dimer interface residues of ALS-linked hSOD1 forms in apo/holo states: a combined experimental and bioinformatic perspective

The aggregation of misfolded SOD1 proteins in neurodegenerative illnesses is a key pathological hallmark in amyotrophic lateral sclerosis (ALS). SOD1 is stabilized and enzymatically activated after binding to Cu/Zn and forming intramolecular disulfide. SOD1 aggregation/oligomerization is triggered by the dissociation of Cu and/or Zn ions. Therefore, we compared the possible effects of ALS-associated point mutations of the holo/apo forms of WT/I149T/V148G SOD1 variants located at the dimer interface to determine structural characterization using spectroscopic methods, computational approaches as well as molecular dynamics (MD) simulations. Predictive results of computational analysis of single-nucleotide polymorphisms (SNPs) suggested that mutant SOD1 has a deleterious effect on activity and structure destabilization. MD data analysis indicated that changes in flexibility, stability, hydrophobicity of the protein as well as increased intramolecular interactions of apo-SOD1 were more than holo-SOD1. Furthermore, a decrease in enzymatic activity in apo-SOD1 was observed compared to holo-SOD1. Comparative intrinsic and ANS fluorescence results of holo/apo-WT-hSOD1 and mutants indicated structural alterations in the local environment of tryptophan residue and hydrophobic patches, respectively. Experimental and MD data supported that substitution effect and metal deficiency of mutants (apo forms) in the dimer interface may promote the tendency to protein mis-folding and aggregation, consequently disrupting the dimer-monomer equilibrium and increased propensity to dissociation dimer into SOD-monomer ultimately leading to loss of stability and function. Overall, data analysis of apo/holo SOD1 forms on protein structure and function using computational and experimental studies will contribute to a better understanding of ALS pathogenicity.

Molecular dynamics simulations reveal the effect of mutations in the RING domains of BRCA1-BARD1 complex and its relevance to the prognosis of breast cancer

The N-terminal RING-RING domain of BRCA1-BARD1 is an E3 ubiquitin ligase complex that plays a critical role in tumor suppression through DNA double stranded repair mechanism. Mutations in the BRCA1-BARD1 heterodimer RING domains were found to have an association with breast and ovarian cancer by a way of hampering the E3 ubiquitin ligase activity. Herein, the molecular mechanism of interaction, conformational change due to the specific mutations on the BRCA1-BARD1 complex at atomic level has been examined by employing molecular modeling techniques. Sixteen mutations have been selected for the study. Molecular dynamics simulation results reveal that the mutant complexes have more local perturbation with a high residual fluctuation in the zinc binding sites and central helix. A few of the BRCA1 (V11A, I21V, I42V, R71G, I31M and L51W) mutants have been experimentally identified that do not impair E3 ligase activity, display an enhanced number of H-bonds and non-bonded contacts at the interacting interface as revealed by MD simulation. The mutation of BRCA1 (C61G, C64Y, C39Y and C24R) and BARD1 (C53W, C71Y and C83R) zinc binding residues displayed a smaller number of significant H-bonds, other interactions and also loss of some of the hotspot residues. Additionally, most of the mutant complexes display relatively lower electrostatic energy, H-bonding and total stabilizing energy as compared to wild-type. The current study attempts to unravel the role of BRCA1-BARD1 mutations and delineates the structural and conformational dynamics in the progression of breast cancer.Communicated by Ramaswamy H. Sarma.

Energetic and frustration analysis of SARS-CoV-2 nucleocapsid protein mutations

The ongoing COVID-19 spreads worldwide with the ability to evolve in diverse human populations. The nucleocapsid (N) protein is one of the mutational hotspots in the SARS-CoV-2 genome. The N protein is an abundant RNA-binding protein critical for viral genome packaging. It comprises two large domains including the N-terminal domain (NTD) and the C-terminal domain (CTD) linked by the centrally located linker region. Mutations in N protein have been reported to increase the severity of disease by modulating viral transmissibility, replication efficiency as well as virulence properties of the virus in different parts of the world. To study the effect of N protein missense mutations on protein stability, function, and pathogenicity, we analyzed 228 mutations from each domain of N protein. Further, we have studied the effect of mutations on local residual frustration changes in N protein. Out of 228 mutations, 11 mutations were predicted to be deleterious and destabilized. Among these mutations, R32C, R191C, and R203 M mutations fall into disordered regions and show significant change in frustration state. Overall, this work reveals that by altering the energetics and residual frustration, N protein mutations might affect the stability, function, and pathogenicity of the SARS-CoV-2.

Comparison of In Vitro and In Silico Assessments of Human Galactose-1-Phosphate Uridylyltransferase Coding Variants

Introduction Human pathogenic coding variations of the galactose-1-phosphate uridylyltransferase (GALT) gene cause classic galactosemia, a recessive disease of galactose metabolism. Unfortunately, there are many variants of uncertain significance (VUS) that need to be characterized in order to be able to predict the likelihood of classic galactosemia for all possible genotypes. There are many bioinformatic resources available that attempt to predict the pathogenicity of a human variant, but it is unclear if these methods realistically predict the consequence of these variants. To determine the clinical application of these resources, we compared the results of in vitro enzymatic assays with in silico predictive models. Methods In all assays, we compared the activity of the three human GALT VUS (Alanine81Threonine, Histidine47Aspartate, Glutamate58Lysine) to native GALT (nGALT) and to a variant of known pathogenic clinical significance (Glutamine188Arginine). The enzymatic activities of VUS recombinant proteins were compared to the results of in silico analytical methods. The in silico methods included the comparison of molecular dynamic simulation root-mean-square deviation (RMSD) results and the results from predictive programs PredictSNP, evolutionary model of variant effect (EVE), ConSurf, and sorting intolerant from tolerant (SIFT). Results The enzymatic assays showed that the variants tested had diminished Vmax relative to the native protein. The VUS RMSD data for both the whole protein and individual residues in the molecular dynamics simulations were not significantly different when compared to nGALT. The other predictive programs had mixed results for each VUS and were not consistent with the enzyme activity or simulation results. Conclusions Our experiments indicated a statistically significant decrease in enzymatic activity of the VUS when compared to nGALT. These experiments also demonstrated significant differences between in silico predictions and in vitro results. These results suggest that the in silico tools used may not be beneficial in determining the pathogenicity of GALT VUS.

Unraveling the Structural Changes in the DNA-Binding Region of Tumor Protein p53 (TP53) upon Hotspot Mutation p53 Arg248 by Comparative Computational Approach

The vital tissue homeostasis regulator p53 forms a tetramer when it binds to DNA and regulates the genes that mediate essential biological processes such as cell-cycle arrest, senescence, DNA repair, and apoptosis. Missense mutations in the core DNA-binding domain (109-292) simultaneously cause the loss of p53 tumor suppressor function and accumulation of the mutant p53 proteins that are carcinogenic. The most common p53 hotspot mutation at codon 248 in the DNA-binding region, where arginine (R) is substituted by tryptophan (W), glycine (G), leucine (L), proline (P), and glutamine (Q), is reported in various cancers. However, it is unclear how the p53 Arg248 mutation with distinct amino acid substitution affects the structure, function, and DNA binding affinity. Here, we characterized the pathogenicity and protein stability of p53 hotspot mutations at codon 248 using computational tools PredictSNP, Align GVGD, HOPE, ConSurf, and iStable. We found R248W, R248G, and R248P mutations highly deleterious and destabilizing. Further, we subjected all five R248 mutant-p53-DNA and wt-p53-DNA complexes to molecular dynamics simulation to investigate the structural stability and DNA binding affinity. From the MD simulation analysis, we observed increased RMSD, RMSF, and Rg values and decreased protein-DNA intermolecular hydrogen bonds in the R248-p53-DNA than the wt-p53-DNA complexes. Likewise, due to high SASA values, we observed the shrinkage of proteins in R248W, R248G, and R248P mutant-p53-DNA complexes. Compared to other mutant p53-DNA complexes, the R248W, R248G, and R248P mutant-p53-DNA complexes showed more structural alteration. MM-PBSA analysis showed decreased binding energies with DNA in all five R248-p53-DNA mutants than the wt-p53-DNA complexes. Henceforth, we conclude that the amino acid substitution of Arginine with the other five amino acids at codon 248 reduces the p53 protein's affinity for DNA and may disrupt cell division, resulting in a gain of p53 function. The proposed study influences the development of rationally designed molecular-targeted treatments that improve p53-based therapeutic outcomes in cancer.

Insights on variant analysis in silico tools for pathogenicity prediction

Molecular biology is currently a fast-advancing science. Sequencing techniques are getting cheaper, but the interpretation of genetic variants requires expertise and computational power, therefore is still a challenge. Next-generation sequencing releases thousands of variants and to classify them, researchers propose protocols with several parameters. Here we present a review of several in silico pathogenicity prediction tools involved in the variant prioritization/classification process used by some international protocols for variant analysis and studies evaluating their efficiency.

Gene family evolution and natural selection signatures in Datura spp. (Solanaceae)

Elucidating the diversification process of congeneric species makes it necessary to identify the factors promoting species variation and diversification. Comparative gene family analysis allows us to elucidate the evolutionary history of species by identifying common genetic/genomic mechanisms underlying species responses to biotic and abiotic environments at the genomic level. In this study, we analyzed the high-quality transcriptomes of four Datura species, D. inoxia, D. pruinosa, D. stramonium, and D. wrightii. We performed a thorough comparative gene family analysis to infer the role of selection in molecular variation, changes in protein physicochemical properties, and gain/loss of genes during their diversification processes. The results revealed common and species-specific signals of positive selection, physicochemical divergence and/or expansion of metabolic genes (e.g., transferases and oxidoreductases) associated with terpene and tropane metabolism and some resistance genes (R genes). The gene family analysis presented here is a valuable tool for understanding the genome evolution of economically and ecologically significant taxa such as the Solanaceae family.

An in silico analysis of rpoB mutations to affect Chlamydia trachomatis sensitivity to rifamycin

Background

Chlamydia trachomatis is an obligate intracellular gram-negative pathogen, responsible for diverse affections, mainly trachoma and sexually transmitted diseases. Antibiotics are the commonly used drugs to tackle chlamydiae infections. However, when overused or wrongly used this may lead to strains’ resistance to antibiotics, this phenomenon represents a real health problem worldwide. Numerous studies showed the association of Chlamydia trachomatis resistance with mutations in different genes; these mutations could have a deleterious or neutral impacts on the encoded proteins. The aim of this study is to perform an in silico analysis of C. trachomatis rpoB-encoded proteins using numerous bioinformatics tools and to identify the functional and structural-related effects of the mutations and consequently their impact on the bacteria sensitivity to antibiotics.

Results

The analysis revealed that the prediction of the damaging impact related to the mutations in rpoB-encoded proteins showed eight mutations: V136F, Q458K, V466A, A467T, H471N, H471Y, H471L, and I517M with big deleterious effects. Among them, six mutations, V136F, Q458K, V466A, A467T, H471N, and I517M, are located in a highly conserved regions decreasing the protein’s stability. Furthermore, the structures analysis showed that the mutations A467T, H471N, I517M, and V136F models had a high deviation compared to the wild type. Moreover, the prediction of protein-protein network indicated that rpoB wild type interacts strongly with 10 proteins of C. trachomatis, which are playing different roles at different levels.

Conclusion

As conclusion, the present study revealed that the changes observed in the encoded proteins can affect their functions and structures, in addition to their interactions with other proteins which impact the bacteria sensitivity to antibiotics. Consequently, the information revealed through this in silico analysis would be useful for deeper exploration to understand the mechanisms of C. trachomatis resistance and enable managing the infection to avoid its complications. We recommend further investigations and perform deeper experimental analysis with collaboration between bioinformaticians, physicians, biologists, pharmacists, and chemistry and biochemistry scientists.

Variant predictions in congenital adrenal hyperplasia caused by mutations in CYP21A2

CYP21A2 deficiency represents 95% of congenital adrenal hyperplasia (CAH) cases, a group of genetic disorders that affect steroid biosynthesis. The genetic and functional analysis provide critical tools to elucidate complex CAH cases. One of the most accessible tools to infer the pathogenicity of new variants is in silico prediction. Here, we analyzed the performance of in silico prediction tools to categorize missense single nucleotide variants (SNVs) of CYP21A2. SNVs of CYP21A2 characterized in vitro by functional assays were selected to assess the performance of online single and meta predictors. SNVs were tested separately or in combination with the related phenotype (severe or mild CAH form). In total, 103 SNVs of CYP21A2 (90 pathogenic and 13 neutral) were used to test the performance of 13 single-predictors and four meta-predictors. All SNVs associated with the severe phenotypes were well categorized by all tools, with an accuracy of between 0.69 (PredictSNP2) and 0.97 (CADD), and Matthews’ correlation coefficient (MCC) between 0.49 (PoredicSNP2) and 0.90 (CADD). However, SNVs related to the mild phenotype had more variation, with the accuracy between 0.47 (S3Ds&GO and MAPP) and 0.88 (CADD), and MCC between 0.18 (MAPP) and 0.71 (CADD). From our analysis, we identified four predictors of CYP21A2 variant pathogenicity with good performance, CADD, ConSurf, DANN, and PolyPhen2. These results can be used for future analysis to infer the impact of uncharacterized SNVs in CYP21A2.

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.

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.

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.

Structural and Dynamic Investigation of non-synonymous variations in Renin-AGT complex revealed altered binding via hydrogen bonding network reprogramming to accelerate the hypertension pathway

Hypertension is one of the major issues worldwide and one of the main factors involved in heart and kidney failure. Angiotensinogen and renin are key components of the renin-angiotensin-aldosterone system (RAAS), which plays an indispensable role in hypertension. The aimed of this study to find out the non-synonymous mutations and structure-based mutation-function correlation in the Renin-AGT complex and reveal the most deleterious mutations to accelerated hypertension. In the current study, we employed computational modelling and molecular simulation approaches to demonstrate the impact of specific mutations in the REN-AGT interface in hypertension. Computational algorithms i.e. PhD-SNP, PolyPhen-1, MAPP, SIFT, SNAP, PredictSNP, PolyPhen-2, and PANTHER predicted 20 mutations as deleterious in AGT while only five mutations were conformed as deleterious in the Renin protein. Investigation of the bonding analysis revealed that two mutations S107L and V193F in Renin altered the hydrogen-bonding paradigm at the interface site. Furthermore, exploration of structural-dynamic behaviors demonstrated by that these mutations also increases the structural stability to regulate the expression of disease pathway. The flexibility index of each residues and structural compactness analysis further validated the findings by portraying the difference in the dynamic behavior in contrast to the wild type. Binding energy calculations based on molecular mechanics/generalized Born surface area (MM/GBSA) methods were used which further established the binding differences between the wild type, S107L, and V193F mutant variants. The total binding energy for wild type, S107L, and V193F were reported to be -27.79 kcal/mol, -47.72 kcal/mol, and -38.25 kcal/mol respectively. In conclusion, these two mutations increase the binding free energy alongside the docking score to enhance the binding between Renin and AGT to overexpress this pathway in a hypertension disease condition. Patients with these mutations may be screened for potential therapeutic intervention.

Whole-exome sequencing analysis of NSCLC reveals the pathogenic missense variants from cancer-associated genes

BACKGROUND

Non-small-cell lung cancer (NSCLC) is the most common type of lung cancer. NSCLC accounts for 84% of all lung cancer cases. In recent years, advances in pathway understanding, methods for discovering novel genetic biomarkers, and new drugs designed to inhibit the signaling cascades have enabled clinicians to personalize therapy for NSCLC.

OBJECTIVES

The primary aim of this study is to identify the genes associated with NSCLC that harbor pathogenic variants that could be causative for NSCLC. The second aim is to investigate their roles in different pathways that lead to NSCLC.

METHODS

We examined exome-sequencing datasets from 54 NSCLC patients to characterize the variants associated with NSCLC.

RESULTS

Our findings revealed that 17 variants in 14 genes were considered highly pathogenic, including CDKN2A, ERBB2, FOXP1, IDH1, JAK3, KMT2D, K-Ras, MSH3, MSH6, POLE, RNF43, TCF7L2, TP53, and TSC1. Gene set enrichment analysis revealed the involvement of transmembrane receptor protein tyrosine kinase activity, protein binding, ATP binding, phosphatidylinositol-4,5-bisphosphate 3-kinase, and Ras guanyl-nucleotide exchange factor activity. Pathway analysis of these genes yielded different cancer-related pathways, including colorectal, prostate, endometrial, pancreatic, PI3K-Akt signaling pathways, and signaling pathways regulating pluripotency of stem cells. Module 1 from protein-protein interactions (PPIs) identified genes that harbor pathogenic SNPs. Three of the most deleterious SNPs are ERBB2 (rs1196929947), K-Ras (rs121913529), and POLE (rs751425952). Interestingly, one patient has a pathogenic K-Ras variant (rs121913529) co-occurred with the missense variant (rs752054698) inTSC1 gene.

CONCLUSION

This study maps highly pathogenic variants associated with NSCLC and investigates their contributions to the pathogenesis of NSCLC. This study sheds light on the potential applications of precision medicine in patients with NSCLC.

Evidence of selection in the ectodysplasin pathway among endangered aquatic mammals

Synopsis

The ectodysplasin pathway has been a target of evolution repeatedly. Genetic variation in the key genes of this pathway (EDA, EDAR, and EDARADD) results in a rich source of pleiotropic effects across ectodermally-derived structures, including teeth, hair, sweat glands, and mammary glands. In addition, a non-canonical Wnt pathway has a very similar functional role, making variation in the WNT10A gene also of evolutionary significance. The adaptation of mammals to aquatic environments has occurred independently in at least 4 orders, whose species occupy a wide geographic range (from equatorial to polar regions) and exhibit great phenotypic variation in ectodermally-derived structures, including the presence or absence of fur and extreme lactational strategies. The role of the ectodysplasin pathway in the adaptation to aquatic environments has been never explored in mammalian species. In the present study, we analyze the genetic variation in orthologous coding sequences from EDA, EDAR, EDARADD, and WNT10A genes together with ectodermally-derived phenotypic variation from 34 aquatic and non-aquatic mammalian species to assess signals of positive selection, gene-trait coevolution, and genetic convergence. Our study reveals strong evidence of positive selection in a proportion of coding sites in EDA and EDAR genes in 3 endangered aquatic mammals (the Hawaiian monk seal, the Yangtze finless porpoise, and the sea otter). We hypothesize functional implications potentially related to the adaptation to the low-latitude aquatic environment in the Hawaiian monk seal and the freshwater in the Yangtze finless porpoise. The signal in the sea otter is likely the result of an increased genetic drift after an intense bottleneck and reduction of genetic diversity. Besides positive selection, we have not detected robust signals of gene-trait coevolution or convergent amino acid shifts in the ectodysplasin pathway associated with shared phenotypic traits among aquatic mammals. This study provides new evidence of the evolutionary role of the ectodysplasin pathway and encourages further investigation, including functional studies, to fully resolve its relationship with mammalian aquatic adaptation.

Spanish

La vía de la ectodisplasina ha sido objeto de la evolución repetidamente. La variación genética en los principales genes de esta vía (EDA, EDAR y EDARADD) da como resultado una gran diversidad de efectos pleiotrópicos en las estructuras derivadas del ectodermo, incluidos los dientes, el cabello, las glándulas sudoríparas y las glándulas mamarias. Además, una vía wnt no canónica tiene un papel funcional muy similar, por lo que la variación en el gen WNT10A también tiene importancia evolutiva. La adaptación de los mamíferos a los entornes acuáticos se ha producido de forma independiente en al menos cuatro órdenes, cuyas especies ocupan un amplio rango geográfico (desde regiones ecuatoriales a polares) y presentan una gran variación fenotípica en las estructuras derivadas del ectodermo, incluyendo la presencia o ausencia de pelaje y estrategias de lactancia muy diferentes. El papel de la vía de la ectodisplasina en la adaptación a entornos acuáticos no se ha explorado nunca en especies de mamíferos. En este estudio, analizamos la variación genética en las secuencias codificantes ortólogas de los genes EDA, EDAR, EDARADD y WNT10A junto con la variación fenotípica derivada del ectodermo de 34 especies de mamíferos acuáticos y no acuáticos para evaluar señales de selección positiva, coevolución gen-rasgo y convergencia genética. Nuestro estudio revela señales de selección positiva en regiones de las secuencias codificantes de los genes EDA y EDAR en tres mamíferos acuáticos en peligro de extinción (la foca monje de Hawái, la marsopa lisa y la nutria marina). Estas señales podrían tener implicaciones funcionales potencialmente relacionadas con la adaptación al entorno acuático de baja latitud en la foca monje de Hawái y el agua dulce en la marsopa lisa. La señal en la nutria marina es probablemente el resultado de una mayor deriva genética tras un intenso un cuello de botella y una reducción de la diversidad genética. A parte de selección positiva, no hemos detectado señales sólidas de coevolución gen-rasgo o cambios convergentes de aminoácidos en la vía de la ectodisplasina asociados a rasgos fenotípicos compartidos entre mamíferos acuáticos. Este estudio proporciona nuevas evidencias del papel evolutivo de la vía de la ectodisplasina y quiere promover futuras investigaciones con estudios funcionales para acabar de resolver la relación de esta vía con la adaptación acuática de los mamíferos.

Hypergastrinemia, a clue leading to the identification of an atypical form of diabetes mellitus type 2

BACKGROUND

A hitherto undescribed form of diabetes mellitus type 2 is reported in a Flemish family. In these patients, markedly elevated gastrin levels were observed, which could not be linked to gastrointestinal symptoms.

MATERIALS AND METHODS

Gel permeation chromatography was performed for gastrin, insulin, and proinsulin. Proprotein convertase subtilisin/kexin type (PCSK1 and PCSK2)] were sequenced. Whole-exome sequencing was performed on the genomic DNA extracted from leukocytes of the proband of the family.

RESULTS

Gel permeation chromatography revealed that the apparent hypergastrinemia was caused by the accumulation of biologically inactive progastrin. Besides, high serum concentrations of proinsulin and intact fibroblast growth factor 23 (FGF23) were also detected. Sequencing of PCSK1 and PCSK2 genes did not reveal any mutations in these genes. Whole exome sequencing revealed a c.1150C>T (p.Pro384Ser) mutation in G protein-coupled receptor kinase 6 (GRK6), which cosegregated with the disease. Expression of the mutant enzyme in mammalian cells revealed that it was mislocalized compared to the wild-type GRK6.

CONCLUSIONS

In the affected patients, prohormone processing is impaired likely due to the altered function of mutant GRK6. Delayed pro-insulin processing causes hypoglycaemia episodes a couple of hours following meals. In addition, increased plasma concentrations of progastrin and intact FGF23 in the affected individuals can be explained by incomplete processing of the precursor hormones.

Unraveling structural and conformational dynamics of DGAT1 missense nsSNPs in dairy cattle

Cattle are domestic animals that have been nourishing humans for thousands of years. Milk from cattle represents a key source of high-quality protein, fat, and other nutrients. The nutritional value of milk and dairy products is closely associated with the fat content, providing up to 30% of the total fat consumed in the human diet. The fat content in cattle milk represents a major concern for the scientific community due to its association with human health. The relationship between milk fat content and diacylglycerol o-acyltransferase 1 gene (DGAT1) is well described in literature. Several studies demonstrated the difference in fat contents and other milk production traits in a wide range of cattle breeds, to be associated with missense non-synonymous single nucleotide polymorphisms (nsSNPs) of the DGAT1 gene. As a result, an nsSNPs analysis is crucial for unraveling the DGAT1 structural and conformational dynamics linked to milk fat content. DGAT1-nsSNPs are yet to be studied in terms of their structural and functional impact. Therefore, state-of-the-art computational and structural genomic methods were used to analyze five selected variants (W128R, W214R, C215G, P245R, and W459G), along with the wild type DGAT1. Significant structural and conformational changes in the variants were observed. We illustrate how single amino acid substitutions affect DGAT1 function, how this contributes to our understanding of the molecular basis of variations in DGAT1, and ultimately its impact in improving fat quality in milk.

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.

A comprehensive WGS-based pipeline for the identification of new candidate genes in inherited retinal dystrophies

To enhance the use of Whole Genome Sequencing (WGS) in clinical practice, it is still necessary to standardize data analysis pipelines. Herein, we aimed to define a WGS-based algorithm for the accurate interpretation of variants in inherited retinal dystrophies (IRD). This study comprised 429 phenotyped individuals divided into three cohorts. A comparison of 14 pathogenicity predictors, and the re-definition of its cutoffs, were performed using panel-sequencing curated data from 209 genetically diagnosed individuals with IRD (training cohort). The optimal tool combinations, previously validated in 50 additional IRD individuals, were also tested in patients with hereditary cancer (n = 109), and with neurological diseases (n = 47) to evaluate the translational value of this approach (validation cohort). Then, our workflow was applied for the WGS-data analysis of 14 individuals from genetically undiagnosed IRD families (discovery cohort). The statistical analysis showed that the optimal filtering combination included CADDv1.6, MAPP, Grantham, and SIFT tools. Our pipeline allowed the identification of one homozygous variant in the candidate gene CFAP20 (c.337 C > T; p.Arg113Trp), a conserved ciliary gene, which was abundantly expressed in human retina and was located in the photoreceptors layer. Although further studies are needed, we propose CFAP20 as a candidate gene for autosomal recessive retinitis pigmentosa. Moreover, we offer a translational strategy for accurate WGS-data prioritization, which is essential for the advancement of personalized medicine.

Ankfn1-mutant vestibular defects require loss of both ancestral and derived paralogs for penetrance in zebrafish

How and to what degree gene duplication events create regulatory innovation, redundancy, or neofunctionalization remain important questions in animal evolution and comparative genetics. Ankfn1 genes are single copy in most invertebrates, partially duplicated in jawed vertebrates, and only the derived copy retained in most mammals. Null mutations in the single mouse homolog have vestibular and neurological abnormalities. Null mutation of the single Drosophila homolog is typically lethal with severe sensorimotor deficits in rare survivors. The functions and potential redundancy of paralogs in species with two copies are not known. Here, we define a vestibular role for Ankfn1 homologs in zebrafish based on the simultaneous disruption of each locus. Zebrafish with both paralogs disrupted showed vestibular defects and early lethality from swim bladder inflation failure. One intact copy at either locus was sufficient to prevent major phenotypes. Our results show that vertebrate Ankfn1 genes are required for vestibular-related functions, with at least partial redundancy between ancestral and derived paralogs.

Fitness Effects of Mutations: An Assessment of PROVEAN Predictions Using Mutation Accumulation Data

Predicting fitness in natural populations is a major challenge in biology. It may be possible to leverage fast-accumulating genomic data sets to infer the fitness effects of mutant alleles, allowing evolutionary questions to be addressed in any organism. In this paper, we investigate the utility of one such tool, called PROVEAN. This program compares a query sequence with existing data to provide an alignment-based score for any protein variant, with scores categorized as neutral or deleterious based on a pre-set threshold. PROVEAN has been used widely in evolutionary studies, for example, to estimate mutation load in natural populations, but has not been formally tested as a predictor of aggregate mutational effects on fitness. Using three large published data sets on the genome sequences of laboratory mutation accumulation lines, we assessed how well PROVEAN predicted the actual fitness patterns observed, relative to other metrics. In most cases, we find that a simple count of the total number of mutant proteins is a better predictor of fitness than the number of proteins with variants scored as deleterious by PROVEAN. We also find that the sum of all mutant protein scores explains variation in fitness better than the number of mutant proteins in one of the data sets. We discuss the implications of these results for studies of populations in the wild.

Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion

More than 6000 human diseases have been recorded to be caused by non-synonymous single nucleotide polymorphisms (nsSNPs). Rapid and accurate prediction of pathogenic nsSNPs can improve our understanding of the principle and design of new drugs, which remains an unresolved challenge. In the present work, a new computational approach, termed MSRes-MutP, is proposed based on ResNet blocks with multi-scale kernel size to predict disease-associated nsSNPs. By feeding the serial concatenation of the extracted four types of features, the performance of MSRes-MutP does not obviously improve. To address this, a second model FFMSRes-MutP is developed, which utilizes deep feature fusion strategy and multi-scale 2D-ResNet and 1D-ResNet blocks to extract relevant two-dimensional features and physicochemical properties. FFMSRes-MutP with the concatenated features achieves a better performance than that with individual features. The performance of FFMSRes-MutP is benchmarked on five different datasets. It achieves the Matthew's correlation coefficient (MCC) of 0.593 and 0.618 on the PredictSNP and MMP datasets, which are 0.101 and 0.210 higher than that of the existing best method PredictSNP1. When tested on the HumDiv and HumVar datasets, it achieves MCC of 0.9605 and 0.9507, and area under curve (AUC) of 0.9796 and 0.9748, which are 0.1747 and 0.2669, 0.0853 and 0.1335, respectively, higher than the existing best methods PolyPhen-2 and FATHMM (weighted). In addition, on blind test using a third-party dataset, FFMSRes-MutP performs as the second-best predictor (with MCC and AUC of 0.5215 and 0.7633, respectively), when compared with the other four predictors. Extensive benchmarking experiments demonstrate that FFMSRes-MutP achieves effective feature fusion and can be explored as a useful approach for predicting disease-associated nsSNPs. The webserver is freely available at http://csbio.njust.edu.cn/bioinf/ffmsresmutp/ for academic use.

MutTMPredictor: Robust and accurate cascade XGBoost classifier for prediction of mutations in transmembrane proteins

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Prediction of mutations in transmembrane proteins is of significance for diseases diagnosis.

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Building on the evolutionary information, proposed the Gaussian WAPSSM algorithm.

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Based on WAPSSM and sequence and structure-based features, proposed the cascade XGBoost algorithm.

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Webserver is freely at (http://csbio.njust.edu.cn/bioinf/ffmsresmutp/).

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Implement MutTMPredictor to predict mutations in transmembrane proteins.

Transmembrane proteins have critical biological functions and play a role in a multitude of cellular processes including cell signaling, transport of molecules and ions across membranes. Approximately 60% of transmembrane proteins are considered as drug targets. Missense mutations in such proteins can lead to many diverse diseases and disorders, such as neurodegenerative diseases and cystic fibrosis. However, there are limited studies on mutations in transmembrane proteins. In this work, we first design a new feature encoding method, termed weight attenuation position-specific scoring matrix (WAPSSM), which builds upon the protein evolutionary information. Then, we propose a new mutation prediction algorithm (cascade XGBoost) by leveraging the idea learned from consensus predictors and gcForest. Multi-level experiments illustrate the effectiveness of WAPSSM and cascade XGBoost algorithms. Finally, based on WAPSSM and other three types of features, in combination with the cascade XGBoost algorithm, we develop a new transmembrane protein mutation predictor, named MutTMPredictor. We benchmark the performance of MutTMPredictor against several existing predictors on seven datasets. On the 546 mutations dataset, MutTMPredictor achieves the accuracy (ACC) of 0.9661 and the Matthew’s Correlation Coefficient (MCC) of 0.8950. While on the 67,584 dataset, MutTMPredictor achieves an MCC of 0.7523 and area under curve (AUC) of 0.8746, which are 0.1625 and 0.0801 respectively higher than those of the existing best predictor (fathmm). Besides, MutTMPredictor also outperforms two specific predictors on the Pred-MutHTP datasets. The results suggest that MutTMPredictor can be used as an effective method for predicting and prioritizing missense mutations in transmembrane proteins. The MutTMPredictor webserver and datasets are freely accessible at http://csbio.njust.edu.cn/bioinf/muttmpredictor/ for academic use.

Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learning

Genomic data analysis is a fundamental system for monitoring pathogen evolution and the outbreak of infectious diseases. Based on bioinformatics and deep learning, this study was designed to identify the genomic variability of SARS-CoV-2 worldwide and predict the impending mutation rate. Analysis of 259044 SARS-CoV-2 isolates identified 3334545 mutations with an average of 14.01 mutations per isolate. Globally, single nucleotide polymorphism (SNP) is the most prevalent mutational event. The prevalence of C > T (52.67%) was noticed as a major alteration across the world followed by the G > T (14.59%) and A > G (11.13%). Strains from India showed the highest number of mutations (48) followed by Scotland, USA, Netherlands, Norway, and France having up to 36 mutations. D416G, F106F, P314L, UTR:C241T, L93L, A222V, A199A, V30L, and A220V mutations were found as the most frequent mutations. D1118H, S194L, R262H, M809L, P314L, A8D, S220G, A890D, G1433C, T1456I, R233C, F263S, L111K, A54T, A74V, L183A, A316T, V212F, L46C, V48G, Q57H, W131R, G172V, Q185H, and Y206S missense mutations were found to largely decrease the structural stability of the corresponding proteins. Conversely, D3L, L5F, and S97I were found to largely increase the structural stability of the corresponding proteins. Multi-nucleotide mutations GGG > AAC, CC > TT, TG > CA, and AT > TA have come up in our analysis which are in the top 20 mutational cohort. Future mutation rate analysis predicts a 17%, 7%, and 3% increment of C > T, A > G, and A > T, respectively in the future. Conversely, 7%, 7%, and 6% decrement is estimated for T > C, G > A, and G > T mutations, respectively. T > G\A, C > G\A, and A > T\C are not anticipated in the future. Since SARS-CoV-2 is mutating continuously, our findings will facilitate the tracking of mutations and help to map the progression of the COVID-19 intensity worldwide.

A domain damage index to prioritizing the pathogenicity of missense variants

Prioritizing causal variants is one major challenge for the clinical application of sequencing data. Prompted by the observation that 74.3% of missense pathogenic variants locate in protein domains, we developed an approach named domain damage index (DDI). DDI identifies protein domains depleted of rare missense variations in the general population, which can be further used as a metric to prioritize variants. DDI is significantly correlated with phylogenetic conservation, variant-level metrics, and reported pathogenicity. DDI achieved great performance for distinguishing pathogenic variants from benign ones in three benchmark datasets. The combination of DDI with the other two best approaches improved the performance of each individual method considerably, suggesting DDI provides a powerful and complementary way of variant prioritization.