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

Representativeness of variation benchmark datasets

Background

Benchmark datasets are essential for both method development and performance assessment. These datasets have numerous requirements, representativeness being one. In the case of variant tolerance/pathogenicity prediction, representativeness means that the dataset covers the space of variations and their effects.

Results

We performed the first analysis of the representativeness of variation benchmark datasets. We used statistical approaches to investigate how proteins in the benchmark datasets were representative for the entire human protein universe. We investigated the distributions of variants in chromosomes, protein structures, CATH domains and classes, Pfam protein families, Enzyme Commission (EC) classifications and Gene Ontology annotations in 24 datasets that have been used for training and testing variant tolerance prediction methods. All the datasets were available in VariBench or VariSNP databases. We tested also whether the pathogenic variant datasets contained neutral variants defined as those that have high minor allele frequency in the ExAC database. The distributions of variants over the chromosomes and proteins varied greatly between the datasets.

Conclusions

None of the datasets was found to be well representative. Many of the tested datasets had quite good coverage of the different protein characteristics. Dataset size correlates to representativeness but only weakly to the performance of methods trained on them. The results imply that dataset representativeness is an important factor and should be taken into account in predictor development and testing.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2478-6) contains supplementary material, which is available to authorized users.

NDDVD: an integrated and manually curated Neurodegenerative Diseases Variation Database

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

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

A review study: Computational techniques for expecting the impact of non-synonymous single nucleotide variants in human diseases

Non-Synonymous Single-Nucleotide Variants (nsSNVs) and mutations can create a diversity effect on proteins as changing genotype and phenotype, which interrupts its stability. The alterations in the protein stability may cause diseases like cancer. Discovering of nsSNVs and mutations can be a useful tool for diagnosing the disease at a beginning stage. Many studies introduced the various predicting singular and consensus tools that based on different Machine Learning Techniques (MLTs) using diverse datasets. Therefore, we introduce the current comprehensive review of the most popular and recent unique tools that predict pathogenic variations and Meta-tool that merge some of them for enhancing their predictive power. Also, we scanned the several types computational techniques in the state-of-the-art and methods for predicting the effect both of coding and noncoding variants. We then displayed, the protein stability predictors. We offer the details of the most common benchmark database for variations including the main predictive features used by the different methods. Finally, we address the most common fundamental criteria for performance assessment of predictive tools. This review is targeted at bioinformaticians attentive in the characterization of regulatory variants, geneticists, molecular biologists attentive in understanding more about the nature and effective role of such variants from a functional point of views, and clinicians who may hope to learn about variants in human associated with a specific disease and find out what to do next to uncover how they impact on the underlying mechanisms.

RheoScale: A tool to aggregate and quantify experimentally determined substitution outcomes for multiple variants at individual protein positions

Human mutations often cause amino acid changes (variants) that can alter protein function or stability. Some variants fall at protein positions that experimentally exhibit "rheostatic" mutation outcomes (different amino acid substitutions lead to a range of functional outcomes). In ongoing studies of rheostat positions, we encountered the need to aggregate experimental results from multiple variants, to describe the overall roles of individual positions. Here, we present "RheoScale" which generates quantitative scores to discriminate rheostat positions from those with "toggle" (most substitutions abolish function) or "neutral" (most substitutions have wild-type function) outcomes. RheoScale scores facilitate correlations of experimental data (such as binding affinity or stability) with structural and bioinformatic analyses. The RheoScale calculator is encoded into a Microsoft Excel workbook and an R script. Example analyses are shown for three model protein systems, including one assessed via deep mutational scanning. The RheoScale calculator quickly and efficiently provided quantitative descriptions that were in good agreement with prior qualitative observations. As an example application, scores were compared to the example proteins' structures; strong rheostat positions tended to occur in dynamic locations. In the future, RheoScale scores can be easily integrated into computational studies to facilitate improved algorithms for predicting outcomes of human variants.

PON-tstab: Protein Variant Stability Predictor. Importance of Training Data Quality

Several methods have been developed to predict effects of amino acid substitutions on protein stability. Benchmark datasets are essential for method training and testing and have numerous requirements including that the data is representative for the investigated phenomenon. Available machine learning algorithms for variant stability have all been trained with ProTherm data. We noticed a number of issues with the contents, quality and relevance of the database. There were errors, but also features that had not been clearly communicated. Consequently, all machine learning variant stability predictors have been trained on biased and incorrect data. We obtained a corrected dataset and trained a random forests-based tool, PON-tstab, applicable to variants in any organism. Our results highlight the importance of the benchmark quality, suitability and appropriateness. Predictions are provided for three categories: stability decreasing, increasing and those not affecting stability.

Characteristics of MUTYH variants in Japanese colorectal polyposis patients

BACKGROUND

The base excision repair gene MUTYH is the causative gene of colorectal polyposis syndrome, which is an autosomal recessive disorder associated with a high risk of colorectal cancer. Since few studies have investigated the genotype-phenotype association in Japanese patients with MUTYH variants, the aim of this study was to clarify the clinicopathological findings in Japanese patients with MUTYH gene variants who were detected by screening causative genes associated with hereditary colorectal polyposis.

METHODS

After obtaining informed consent, genetic testing was performed using target enrichment sequencing of 26 genes, including MUTYH.

RESULTS

Of the 31 Japanese patients with suspected hereditary colorectal polyposis, eight MUTYH variants were detected in five patients. MUTYH hotspot variants known for Caucasians, namely p.G396D and p.Y179D, were not among the detected variants.Of five patients, two with biallelic MUTYH variants were diagnosed with MUTYH-associated polyposis, while two others had monoallelic MUTYH variants. One patient had the p.P18L and p.G25D variants on the same allele; however, supportive data for considering these two variants 'pathogenic' were lacking.

CONCLUSIONS

Two patients with biallelic MUTYH variants and two others with monoallelic MUTYH variants were identified among Japanese colorectal polyposis patients. Hotspot variants of the MUTYH gene for Caucasians were not hotspots for Japanese patients.

Exome array analysis identifies GPR35 as a novel susceptibility gene for anthracycline-induced cardiotoxicity in childhood cancer

OBJECTIVES

Pediatric cancer survivors are a steadily growing population; however, chronic anthracycline-induced cardiotoxicity (AIC) is a serious long-term complication leading to considerable morbidity. We aimed to identify new genes and low-frequency variants influencing the susceptibility to AIC for pediatric cancer patients.

PATIENTS AND METHODS

We studied the association of variants on the Illumina HumanExome BeadChip array in 83 anthracycline-treated pediatric cancer patients. In addition to single-variant association tests, we carried out a gene-based analysis to investigate the combined effects of common and low-frequency variants to chronic AIC.

RESULTS

Although no single-variant showed an association with chronic AIC that was statistically significant after correction for multiple testing, we identified a novel significant association for G protein-coupled receptor 35 (GPR35) by gene-based testing, a gene with potential roles in cardiac physiology and pathology (P=7.0×10), which remained statistically significant after correction for multiple testing (PFDR=0.03). The greatest contribution to this observed association was made by rs12468485, a missense variant (p.Thr253Met, c.758C>T, minor allele frequency=0.04), with the T allele associated with an increased risk of chronic AIC and more severe symptomatic cardiac manifestations at low anthracycline doses.

CONCLUSION

Using exome array data, we identified GPR35 as a novel susceptibility gene associated with chronic AIC in pediatric cancer patients.

PON-SC - program for identifying steric clashes caused by amino acid substitutions

Background

Amino acid substitutions due to DNA nucleotide replacements are frequently disease-causing because of affecting functionally important sites. If the substituting amino acid does not fit into the protein, it causes structural alterations that are often harmful. Clashes of amino acids cause local or global structural changes. Testing structural compatibility of variations has been difficult due to the lack of a dedicated method that could handle vast amounts of variation data produced by next generation sequencing technologies.

Results

We developed a method, PON-SC, for detecting protein structural clashes due to amino acid substitutions. The method utilizes side chain rotamer library and tests whether any of the common rotamers can be fitted into the protein structure. The tool was tested both with variants that cause and do not cause clashes and found to have accuracy of 0.71 over five test datasets.

Conclusions

We developed a fast method for residue side chain clash detection. The method provides in addition to the prediction also visualization of the variant in three dimensional structure.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1947-7) contains supplementary material, which is available to authorized users.

Fatal CTLA-4 heterozygosity with autoimmunity and recurrent infections: a de novo mutation

Primary immunodeficiency disorders are rarely diagnosed in adults but must be considered in the differential diagnosis of combined recurrent infections and autoimmune disease. We describe a patient with CTLA-4 haploinsufficiency and an abnormal regulatory T-cell phenotype. Unusually, infections were more severe than autoimmunity, illustrating therapeutic challenges in disease course.

Assessing predictions of fitness effects of missense mutations in SUMO-conjugating enzyme UBE2I

The exponential growth of genomic variants uncovered by next-generation sequencing necessitates efficient and accurate computational analyses to predict their functional effects. A number of computational methods have been developed for the task, but few unbiased comparisons of their performance are available. To fill the gap, The Critical Assessment of Genome Interpretation (CAGI) comprehensively assesses phenotypic predictions on newly collected experimental datasets. Here, we present the results of the SUMO conjugase challenge where participants were predicting functional effects of missense mutations in human SUMO-conjugating enzyme UBE2I. The performance of the predictors is similar to each other and is far from perfection. Evolutionary information from sequence alignments dominates the success: deleterious mutations at conserved positions and benign mutations at variable positions are accurately predicted. Prediction accuracy of other mutations remains unsatisfactory, and this fast-growing field of research is yet to learn the use of spatial structure information to improve the predictions significantly.

Development of pathogenicity predictors specific for variants that do not comply with clinical guidelines for the use of computational evidence

Background

Strict guidelines delimit the use of computational information in the clinical setting, due to the still moderate accuracy of in silico tools. These guidelines indicate that several tools should always be used and that full coincidence between them is required if we want to consider their results as supporting evidence in medical decision processes. Application of this simple rule certainly decreases the error rate of in silico pathogenicity assignments. However, when predictors disagree this rule results in the rejection of potentially valuable information for a number of variants. In this work, we focus on these variants of the protein sequence and develop specific predictors to help improve the success rate of their annotation.

Results

We have used a set of 59,442 protein sequence variants (15,723 pathological and 43,719 neutral) from 228 proteins to identify those cases for which pathogenicity predictors disagree. We have repeated this process for all the possible combinations of five known methods (SIFT, PolyPhen-2, PON-P2, CADD and MutationTaster2). For each resulting subset we have trained a specific pathogenicity predictor. We find that these specific predictors are able to discriminate between neutral and pathogenic variants, with a success rate different from random. They tend to outperform the constitutive methods but this trend decreases as the performance of the constitutive predictor improves (e.g. with PON-P2 and PolyPhen-2). We also find that specific methods outperform standard consensus methods (Condel and CAROL).

Conclusion

Focusing development efforts on the case of variants for which known methods disagree we may obtain pathogenicity predictors with improved performances. Although we have not yet reached the success rate that allows the use of this computational evidence in a clinical setting, the simplicity of the approach indicates that more advanced methods may reach this goal in a close future.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3914-0) contains supplementary material, which is available to authorized users.

Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges

Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships.

Ensemble variant interpretation methods to predict enzyme activity and assign pathogenicity in the CAGI4 NAGLU (Human N-acetyl-glucosaminidase) and UBE2I (Human SUMO-ligase) challenges

CAGI (Critical Assessment of Genome Interpretation) conducts community experiments to determine the state of the art in relating genotype to phenotype. Here, we report results obtained using newly developed ensemble methods to address two CAGI4 challenges: enzyme activity for population missense variants found in NAGLU (Human N-acetyl-glucosaminidase) and random missense mutations in Human UBE2I (Human SUMO E2 ligase), assayed in a high-throughput competitive yeast complementation procedure. The ensemble methods are effective, ranked second for SUMO-ligase and third for NAGLU, according to the CAGI independent assessors. However, in common with other methods used in CAGI, there are large discrepancies between predicted and experimental activities for a subset of variants. Analysis of the structural context provides some insight into these. Post-challenge analysis shows that the ensemble methods are also effective at assigning pathogenicity for the NAGLU variants. In the clinic, providing an estimate of the reliability of pathogenic assignments is the key. We have also used the NAGLU dataset to show that ensemble methods have considerable potential for this task, and are already reliable enough for use with a subset of mutations.

Variants of the ABCA3 gene might contribute to susceptibility to interstitial lung diseases in the Chinese population

ATP-binding cassette A3 (ABCA3) is a phospholipid carrier that is mainly expressed in the alveolar epithelium. Biallelic mutations of ABCA3 has been associated with fatal respiratory distress syndrome and interstitial lung disease (ILD) in children. However, whether variations in ABCA3 have a role in the development of adult ILD, including idiopathic pulmonary fibrosis (IPF), remains to be addressed. In this study, we screened for germline variants of ABCA3 by exons-sequencing in 30 patients with sporadic IPF and in 30 matched healthy controls. Eleven missense variants, predominantly in heterozygous, were found in 13 of these patients, but only two missenses in 2 healthy controls. We then selected four of the detected missense variants (p.L39V, p.S828F, p.V968M and p.G1205R) to performed cohort analysis in 1,024 ILD patients, containing 250 IPF and 774 connective tissue disease-ILD (CTD-ILD) patients, and 1,054 healthy individuals. Our results showed that the allele frequency of p.G1205R, but not p.L39V, was significantly higher in ILD patients than in healthy controls. However, no additional subject carrying the variant p.S828F or p.V968M was detected in the cohort analysis. These results indicate that the heterozygous ABCA3 gene variants may contribute to susceptibility to diseases in the Chinese population.

Objective assessment of the evolutionary action equation for the fitness effect of missense mutations across CAGI-blinded contests

A major challenge in genome interpretation is to estimate the fitness effect of coding variants of unknown significance (VUS). Labor, limited understanding of protein functions, and lack of assays generally limit direct experimental assessment of VUS, and make robust and accurate computational approaches a necessity. Often, however, algorithms that predict mutational effect disagree among themselves and with experimental data, slowing their adoption for clinical diagnostics. To objectively assess such methods, the Critical Assessment of Genome Interpretation (CAGI) community organizes contests to predict unpublished experimental data, available only to CAGI assessors. We review here the CAGI performance of evolutionary action (EA) predictions of mutational impact. EA models the fitness effect of coding mutations analytically, as a product of the gradient of the fitness landscape times the perturbation size. In practice, these terms are computed from phylogenetic considerations as the functional sensitivity of the mutated site and as the magnitude of amino acid substitution, respectively, and yield the percentage loss of wild-type activity. In five CAGI challenges, EA consistently performed on par or better than sophisticated machine learning approaches. This objective assessment suggests that a simple differential model of evolution can interpret the fitness effect of coding variations, opening diverse clinical applications.

In silico analyses of deleterious missense SNPs of human apolipoprotein E3

ApoE3 is the major chylomicron apolipoprotein, binding in a specific liver peripheral cell receptor, allowing transport and normal catabolism of triglyceride-rich lipoprotein constituents. Point mutations in ApoE3 have been associated with Alzheimer's disease, type III hyperlipoproteinemia, atherosclerosis, telomere shortening and impaired cognitive function. Here, we evaluate the impact of missense SNPs in APOE retrieved from dbSNP through 16 computational prediction tools, and further evaluate the structural impact of convergent deleterious changes using 100 ns molecular dynamics simulations. We have found structural changes in four analyzed variants (Pro102Arg, Arg132Ser, Arg176Cys and Trp294Cys), two of them (Pro102Arg and Arg176Cys) being previously associated with human diseases. In all cases, except for Trp294Cys, there was a loss in the number of hydrogen bonds between CT and NT domains that could result in their detachment. In conclusion, data presented here could increase the knowledge of ApoE3 activity and be a starting point for the study of the impact of variations on APOE gene.

PON-P and PON-P2 predictor performance in CAGI challenges: Lessons learned

Computational tools are widely used for ranking and prioritizing variants for characterizing their disease relevance. Since numerous tools have been developed, they have to be properly assessed before being applied. Critical Assessment of Genome Interpretation (CAGI) experiments have significantly contributed toward the assessment of prediction methods for various tasks. Within and outside the CAGI, we have addressed several questions that facilitate development and assessment of variation interpretation tools. These areas include collection and distribution of benchmark datasets, their use for systematic large-scale method assessment, and the development of guidelines for reporting methods and their performance. For us, CAGI has provided a chance to experiment with new ideas, test the application areas of our methods, and network with other prediction method developers. In this article, we discuss our experiences and lessons learned from the various CAGI challenges. We describe our approaches, their performance, and impact of CAGI on our research. Finally, we discuss some of the possibilities that CAGI experiments have opened up and make some suggestions for future experiments.

Pathogenicity in POLG syndromes: DNA polymerase gamma pathogenicity prediction server and database

DNA polymerase gamma (POLG) is the replicative polymerase responsible for maintaining mitochondrial DNA (mtDNA). Disorders related to its functionality are a major cause of mitochondrial disease. The clinical spectrum of POLG syndromes includes Alpers-Huttenlocher syndrome (AHS), childhood myocerebrohepatopathy spectrum (MCHS), myoclonic epilepsy myopathy sensory ataxia (MEMSA), the ataxia neuropathy spectrum (ANS) and progressive external ophthalmoplegia (PEO). We have collected all publicly available POLG-related patient data and analyzed it using our pathogenic clustering model to provide a new research and clinical tool in the form of an online server. The server evaluates the pathogenicity of both previously reported and novel mutations. There are currently 176 unique point mutations reported and found in mitochondrial patients in the gene encoding the catalytic subunit of POLG, POLG. The mutations are distributed nearly uniformly along the length of the primary amino acid sequence of the gene. Our analysis shows that most of the mutations are recessive, and that the reported dominant mutations cluster within the polymerase active site in the tertiary structure of the POLG enzyme. The POLG Pathogenicity Prediction Server (http://polg.bmb.msu.edu) is targeted at clinicians and scientists studying POLG disorders, and aims to provide the most current available information regarding the pathogenicity of POLG mutations.

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Multi-level access to crucial data supporting diagnosis/prognosis of POLG syndromes

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Clustering protocol enables identification of novel neutral polymorphisms

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Identical alleles displaying variable symptoms evidence unidentified components

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POLG enzymes with premature stop codons, insertions/deletions group biochemically

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Dominant POLG mutations all lie within a critical location in the structure

Semantic prioritization of novel causative genomic variants

Discriminating the causative disease variant(s) for individuals with inherited or de novo mutations presents one of the main challenges faced by the clinical genetics community today. Computational approaches for variant prioritization include machine learning methods utilizing a large number of features, including molecular information, interaction networks, or phenotypes. Here, we demonstrate the PhenomeNET Variant Predictor (PVP) system that exploits semantic technologies and automated reasoning over genotype-phenotype relations to filter and prioritize variants in whole exome and whole genome sequencing datasets. We demonstrate the performance of PVP in identifying causative variants on a large number of synthetic whole exome and whole genome sequences, covering a wide range of diseases and syndromes. In a retrospective study, we further illustrate the application of PVP for the interpretation of whole exome sequencing data in patients suffering from congenital hypothyroidism. We find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants.

We address the problem of how to distinguish which of the many thousands of DNA sequence variants carried by an individual with a rare disease is responsible for the disease phenotypes. This can help clinicians arrive at a diagnosis, but also can be instrumental in improving our understanding of the pathobiology of the disease. Many methods are currently available to help with the problem of determining causative variant, using information about evolutionary conservation and prediction of the functional consequences of the sequence variant. We have developed a novel algorithm (PVP) which augments existing strategies by using the similarity of the patients phenotype to known phenotype-genotype data in human and model organism databases to further rank potential candidate genes. In a retrospective study, we apply PVP to the interpretation of whole exome sequencing data in patients suffering from congenital hypothyroidism, and find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants.

TK, C. GPD, R. S, Zayed H. Determining the role of missense mutations in the POU domain of HNF1A that reduce the DNA-binding affinity: A computational approach

Maturity-onset diabetes of the young type 3 (MODY3) is a non-ketotic form of diabetes associated with poor insulin secretion. Over the past years, several studies have reported the association of missense mutations in the Hepatocyte Nuclear Factor 1 Alpha (HNF1A) with MODY3. Missense mutations in the POU homeodomain (POUH) of HNF1A hinder binding to the DNA, thereby leading to a dysfunctional protein. Missense mutations of the HNF1A were retrieved from public databases and subjected to a three-step computational mutational analysis to identify the underlying mechanism. First, the pathogenicity and stability of the mutations were analyzed to determine whether they alter protein structure and function. Second, the sequence conservation and DNA-binding sites of the mutant positions were assessed; as HNF1A protein is a transcription factor. Finally, the biochemical properties of the biological system were validated using molecular dynamic simulations in Gromacs 4.6.3 package. Two arginine residues (131 and 203) in the HNF1A protein are highly conserved residues and contribute to the function of the protein. Furthermore, the R131W, R131Q, and R203C mutations were predicted to be highly deleterious by in silico tools and showed lower binding affinity with DNA when compared to the native protein using the molecular docking analysis. Triplicate runs of molecular dynamic (MD) simulations (50ns) revealed smaller changes in patterns of deviation, fluctuation, and compactness, in complexes containing the R131Q and R131W mutations, compared to complexes containing the R203C mutant complex. We observed reduction in the number of intermolecular hydrogen bonds, compactness, and electrostatic potential, as well as the loss of salt bridges, in the R203C mutant complex. Substitution of arginine with cysteine at position 203 decreases the affinity of the protein for DNA, thereby destabilizing the protein. Based on our current findings, the MD approach is an important tool for elucidating the impact and affinity of mutations in DNA-protein interactions and understanding their function.

Prediction of the impact of coding missense and nonsense single nucleotide polymorphisms on HD5 and HBD1 antibacterial activity against Escherichia coli

Defensins confer host defense against microorganisms and are important for human health. Single nucleotide polymorphisms (SNPs) in defensin gene-coding regions could lead to less active variants. Using SNP data available at the dbSNP database and frequency information from the 1000 Genomes Project, two DEFA5 (L26I and R13H) and eight DEFB1 (C35S, K31T, K33R, R29G, V06I, C12Y, Y28* and C05*) missense and nonsense SNPs that are located within mature regions of the coded defensins were retrieved. Such SNPs are rare and population restricted. In order to assess their antibacterial activity against Escherichia coli, two linear regression models were used from a previous work, which models the antibacterial activity as a function of solvation potential energy, using molecular dynamics data. Regarding only the antibacterial predictions, for HD5, no biological differences between wild-type and its variants were observed; while for HBD1, the results suggest that the R29G, K31T, Y28* and C05* variants could be less active than the wild-type one. The data here reported could lead to a substantial improvement in knowledge about the impact of missense SNPs in human defensins and their world distribution. © 2016 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 106: 633-644, 2016.

Predicting Severity of Disease-Causing Variants

Most diseases, including those of genetic origin, express a continuum of severity. Clinical interventions for numerous diseases are based on the severity of the phenotype. Predicting severity due to genetic variants could facilitate diagnosis and choice of therapy. Although computational predictions have been used as evidence for classifying the disease relevance of genetic variants, special tools for predicting disease severity in large scale are missing. Here, we manually curated a dataset containing variants leading to severe and less severe phenotypes and studied the abilities of variation impact predictors to distinguish between them. We found that these tools cannot separate the two groups of variants. Then, we developed a novel machine-learning-based method, PON-PS (http://structure.bmc.lu.se/PON-PS), for the classification of amino acid substitutions associated with benign, severe, and less severe phenotypes. We tested the method using an independent test dataset and variants in four additional proteins. For distinguishing severe and nonsevere variants, PON-PS showed an accuracy of 61% in the test dataset, which is higher than for existing tolerance prediction methods. PON-PS is the first generic tool developed for this task. The tool can be used together with other evidence for improving diagnosis and prognosis and for prioritization of preventive interventions, clinical monitoring, and molecular tests.

GAVIN: Gene-Aware Variant INterpretation for medical sequencing

We present Gene-Aware Variant INterpretation (GAVIN), a new method that accurately classifies variants for clinical diagnostic purposes. Classifications are based on gene-specific calibrations of allele frequencies from the ExAC database, likely variant impact using SnpEff, and estimated deleteriousness based on CADD scores for >3000 genes. In a benchmark on 18 clinical gene sets, we achieve a sensitivity of 91.4% and a specificity of 76.9%. This accuracy is unmatched by 12 other tools. We provide GAVIN as an online MOLGENIS service to annotate VCF files and as an open source executable for use in bioinformatic pipelines. It can be found at http://molgenis.org/gavin.

Predicting Secretory Proteins with SignalP

SignalP is the currently most widely used program for prediction of signal peptides from amino acid sequences. Proteins with signal peptides are targeted to the secretory pathway, but are not necessarily secreted. After a brief introduction to the biology of signal peptides and the history of signal peptide prediction, this chapter will describe all the options of the current version of SignalP and the details of the output from the program. The chapter includes a case study where the scores of SignalP were used in a novel way to predict the functional effects of amino acid substitutions in signal peptides.

The Mutational Landscape of the Oncogenic MZF1 SCAN Domain in Cancer

SCAN domains in zinc-finger transcription factors are crucial mediators of protein-protein interactions. Up to 240 SCAN-domain encoding genes have been identified throughout the human genome. These include cancer-related genes, such as the myeloid zinc finger 1 (MZF1), an oncogenic transcription factor involved in the progression of many solid cancers. The mechanisms by which SCAN homo- and heterodimers assemble and how they alter the transcriptional activity of zinc-finger transcription factors in cancer and other diseases remain to be investigated. Here, we provide the first description of the conformational ensemble of the MZF1 SCAN domain cross-validated against NMR experimental data, which are probes of structure and dynamics on different timescales. We investigated the protein-protein interaction network of MZF1 and how it is perturbed in different cancer types by the analyses of high-throughput proteomics and RNASeq data. Collectively, we integrated many computational approaches, ranging from simple empirical energy functions to all-atom microsecond molecular dynamics simulations and network analyses to unravel the effects of cancer-related substitutions in relation to MZF1 structure and interactions.

The novel homozygous KCNJ10 c.986T>C (p.(Leu329Pro)) variant is pathogenic for the SeSAME/EAST homologue in Malinois dogs

SeSAME/EAST syndrome is a multisystemic disorder in humans, characterised by seizures, sensorineural deafness, ataxia, developmental delay and electrolyte imbalance. It is exclusively caused by homozygous or compound heterozygous variations in the KCNJ10 gene. Here we describe a similar syndrome in two families belonging to the Malinois dog breed, based on clinical, neurological, electrodiagnostic and histopathological examination. Genetic analysis detected a novel pathogenic KCNJ10 c.986T>C (p.(Leu329Pro)) variant that is inherited in an autosomal recessive way. This variant has an allele frequency of 2.9% in the Belgian Malinois population, but is not found in closely related dog breeds or in dog breeds where similar symptoms have been already described. The canine phenotype is remarkably similar to humans, including ataxia and seizures. In addition, in half of the dogs clinical and electrophysiological signs of neuromyotonia were observed. Because there is currently no cure and treatment is nonspecific and unsatisfactory, this canine translational model could be used for further elucidating the genotype/phenotype correlation of this monogenic multisystem disorder and as an excellent intermediate step for drug safety testing and efficacy evaluations before initiating human studies.

How to Define Pathogenicity, Health, and Disease?

Scientific and clinical communities produce ever increasing amounts of data and details about health and disease. Our ability to understand and utilize this information is limited because of imprecise language and lack of well-defined concepts. This problem involves also the principal concepts of health, disease, and pathogenicity. Here, a systematic model is presented for pathogenicity, as well as for health and disease. It has three components: extent, modulation, and severity, which jointly define the continuum of pathogenicity. The model is population based, and once implemented, it can be used for numerous purposes such as diagnosis, patient stratification, prognosis, finding phenotype-genotype correlations, or explaining adverse drug reactions. The new model has several benefits including health economy by allowing evidence-based personalized/precision medicine.

Investigating the Molecular Mechanisms Behind Uncharacterized Cysteine Losses from Prediction of Their Oxidation State

Cysteines are among the rarest amino acids in nature, and are both functionally and structurally very important for proteins. The ability of cysteines to form disulfide bonds is especially relevant, both for constraining the folded state of the protein and for performing enzymatic duties. But how does the variation record of human proteins reflect their functional importance and structural role, especially with regard to deleterious mutations? We created HUMCYS, a manually curated dataset of single amino acid variants that (1) have a known disease/neutral phenotypic outcome and (2) cause the loss of a cysteine, in order to investigate how mutated cysteines relate to structural aspects such as surface accessibility and cysteine oxidation state. We also have developed a sequence-based in silico cysteine oxidation predictor to overcome the scarcity of experimentally derived oxidation annotations, and applied it to extend our analysis to classes of proteins for which the experimental determination of their structure is technically challenging, such as transmembrane proteins. Our investigation shows that we can gain insights into the reason behind the outcome of cysteine losses in otherwise uncharacterized proteins, and we discuss the possible molecular mechanisms leading to deleterious phenotypes, such as the involvement of the mutated cysteine in a structurally or enzymatically relevant disulfide bond.

REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants

The vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP, SiPhy, phyloP, and phastCons. REVEL was trained with recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. When applied to two independent test sets, REVEL had the best overall performance (p < 10^-12) as compared to any individual tool and seven ensemble methods: MetaSVM, MetaLR, KGGSeq, Condel, CADD, DANN, and Eigen. Importantly, REVEL also had the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies <0.5%. The area under the receiver operating characteristic curve (AUC) for REVEL was 0.046-0.182 higher in an independent test set of 935 recent SwissVar disease variants and 123,935 putatively neutral exome sequencing variants and 0.027-0.143 higher in an independent test set of 1,953 pathogenic and 2,406 benign variants recently reported in ClinVar than the AUCs for other ensemble methods. We provide pre-computed REVEL scores for all possible human missense variants to facilitate the identification of pathogenic variants in the sea of rare variants discovered as sequencing studies expand in scale.

Structural impact analysis of missense SNPs present in the uroguanylin gene by long-term molecular dynamics simulations

The guanylate cyclase activator 2B, also known as uroguanylin, is part of the guanylin peptide family, which includes peptides such as guanylin and lymphoguanylin. The guanylin peptides could be related to sodium absorption inhibition and water secretion induction and their dysfunction may be related to various pathologies such as chronic renal failure, congestive heart failure and nephrotic syndrome. Besides, uroguanylin point mutations have been associated with essential hypertension. However, currently there are no studies on the impact of missense SNPs on uroguanylin structure. This study applied in silico SNP impact prediction tools to evaluate the impact of uroguanylin missense SNPs and to filter those considered as convergent deleterious, which were then further analyzed through long-term molecular dynamics simulations of 1μs of duration. The simulations suggested that all missense SNPs considered as convergent deleterious caused some kind of structural change to the uroguanylin peptide. Additionally, four of these SNPs were also shown to cause modifications in peptide flexibility, possibly resulting in functional changes.

Acute Intermittent Porphyria: Predicted Pathogenicity of HMBS Variants Indicates Extremely Low Penetrance of the Autosomal Dominant Disease

Acute intermittent porphyria results from hydroxymethylbilane synthase (HMBS) mutations that markedly decrease HMBS enzymatic activity. This dominant disease is diagnosed when heterozygotes have life-threatening acute attacks, while most heterozygotes remain asymptomatic and undiagnosed. Although >400 HMBS mutations have been reported, the prevalence of pathogenic HMBS mutations in genomic/exomic databases, and the actual disease penetrance are unknown. Thus, we interrogated genomic/exomic databases, identified non-synonymous variants (NSVs) and consensus splice-site variants (CSSVs) in various demographic/racial groups, and determined the NSV's pathogenicity by prediction algorithms and in vitro expression assays. Caucasians had the most: 58 NSVs and two CSSVs among ∼92,000 alleles, a 0.00575 combined allele frequency. In silico algorithms predicted 14 out of 58 NSVs as "likely-pathogenic." In vitro expression identified 10 out of 58 NSVs as likely-pathogenic (seven predicted in silico), which together with two CSSVs had a combined allele frequency of 0.00056. Notably, six presumably pathogenic mutations/NSVs in the Human Gene Mutation Database were benign. Compared with the recent prevalence estimate of symptomatic European heterozygotes (∼0.000005), the prevalence of likely-pathogenic HMBS mutations among Caucasians was >100 times more frequent. Thus, the estimated penetrance of acute attacks was ∼1% of heterozygotes with likely-pathogenic mutations, highlighting the importance of predisposing/protective genes and environmental modifiers that precipitate/prevent the attacks.

Recurrent HOXB13 mutations in the Dutch population do not associate with increased breast cancer risk

The HOXB13 p.G84E mutation has been firmly established as a prostate cancer susceptibility allele. Although HOXB13 also plays a role in breast tumor progression, the association of HOXB13 p.G84E with breast cancer risk is less evident. Therefore, we comprehensively interrogated the entire HOXB13 coding sequence for mutations in 1,250 non-BRCA1/2 familial breast cancer cases and 800 controls. We identified two predicted deleterious missense mutations, p.G84E and p.R217C, that were recurrent among breast cancer cases and further evaluated their association with breast cancer risk in a larger study. Taken together, 4,520 familial non-BRCA1/2 breast cancer cases and 3,127 controls were genotyped including the cases and controls of the whole gene screen. The concordance rate for the genotyping assays compared with Sanger sequencing was 100%. The prostate cancer risk allele p.G84E was identified in 18 (0.56%) of 3,187 cases and 16 (0.70%) of 2,300 controls (OR = 0.81, 95% CI = 0.41–1.59, P = 0.54). Additionally, p.R217C was identified in 10 (0.31%) of 3,208 cases and 2 (0.087%) of 2,288 controls (OR = 3.57, 95% CI = 0.76–33.57, P = 0.14). These results imply that none of the recurrent HOXB13 mutations in the Dutch population are associated with breast cancer risk, although it may be worthwhile to evaluate p.R217C in a larger study.

Computational assessment of feature combinations for pathogenic variant prediction

BACKGROUND

Although several methods have been proposed for predicting the effects of genetic variants and their role in disease, it is still a challenge to identify and prioritize pathogenic variants within sequencing studies.

METHODS

Here, we compare different variant and gene-specific features as well as existing methods and investigate their best combination to explore potential performance gains.

RESULTS

We found that combining the number of "biological process" Gene Ontology annotations of a gene with the methods PON-P2, and PROVEAN significantly improves prediction of pathogenic variants, outperforming all individual methods. A comprehensive analysis of the Gene Ontology feature suggests that it is not a variant-dependent annotation bias but reflects the multifunctional nature of disease genes. Furthermore, we identified a set of difficult variants where different prediction methods fail.

CONCLUSION

Existing pathogenicity prediction methods can be further improved.

KinMutRF: a random forest classifier of sequence variants in the human protein kinase superfamily

Background

The association between aberrant signal processing by protein kinases and human diseases such as cancer was established long time ago. However, understanding the link between sequence variants in the protein kinase superfamily and the mechanistic complex traits at the molecular level remains challenging: cells tolerate most genomic alterations and only a minor fraction disrupt molecular function sufficiently and drive disease.

Results

KinMutRF is a novel random-forest method to automatically identify pathogenic variants in human kinases. Twenty six decision trees implemented as a random forest ponder a battery of features that characterize the variants: a) at the gene level, including membership to a Kinbase group and Gene Ontology terms; b) at the PFAM domain level; and c) at the residue level, the types of amino acids involved, changes in biochemical properties, functional annotations from UniProt, Phospho.ELM and FireDB. KinMutRF identifies disease-associated variants satisfactorily (Acc: 0.88, Prec:0.82, Rec:0.75, F-score:0.78, MCC:0.68) when trained and cross-validated with the 3689 human kinase variants from UniProt that have been annotated as neutral or pathogenic. All unclassified variants were excluded from the training set. Furthermore, KinMutRF is discussed with respect to two independent kinase-specific sets of mutations no included in the training and testing, Kin-Driver (643 variants) and Pon-BTK (1495 variants). Moreover, we provide predictions for the 848 protein kinase variants in UniProt that remained unclassified.

A public implementation of KinMutRF, including documentation and examples, is available online (http://kinmut2.bioinfo.cnio.es). The source code for local installation is released under a GPL version 3 license, and can be downloaded from https://github.com/Rbbt-Workflows/KinMut2.

Conclusions

KinMutRF is capable of classifying kinase variation with good performance. Predictions by KinMutRF compare favorably in a benchmark with other state-of-the-art methods (i.e. SIFT, Polyphen-2, MutationAssesor, MutationTaster, LRT, CADD, FATHMM, and VEST). Kinase-specific features rank as the most elucidatory in terms of information gain and are likely the improvement in prediction performance. This advocates for the development of family-specific classifiers able to exploit the discriminatory power of features unique to individual protein families.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2723-1) contains supplementary material, which is available to authorized users.

Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation

As personal genome sequencing becomes a reality, understanding the effects of genetic variants on phenotype-particularly the impact of germline variants on disease risk and the impact of somatic variants on cancer development and treatment-continues to increase in importance. Because of their clear potential for affecting phenotype, nonsynonymous genetic variants (variants that cause a change in the amino acid sequence of a protein encoded by a gene) have long been the target of efforts to predict the effects of genetic variation. Whole-genome sequencing is identifying large numbers of nonsynonymous variants in each genome, intensifying the need for computational methods that accurately predict which of these are likely to impact disease phenotypes. This review focuses on nonsynonymous variant prediction with two aims in mind: (1) to review the prioritization methods that have been developed to date and the principles on which they are based and (2) to discuss the challenges to further improving these methods.

Screening for GPR101 defects in pediatric pituitary corticotropinomas

Cushing disease (CD) in children is caused by adrenocorticotropic hormone (ACTH)-secreting pituitary adenomas. Germline or somatic mutations in genes such as MEN1, CDKIs, AIP, and USP8 have been identified in pediatric CD, but the genetic defects in a significant percentage of cases are still unknown. We investigated the orphan G protein-coupled receptor GPR101, a gene known to be involved in somatotropinomas, for its possible involvement in corticotropinomas. We performed GPR101 sequencing, expression analyses by RT-qPCR and immunostaining, and functional studies (cell proliferation, pituitary hormones secretion, and cAMP measurement) in a series of patients with sporadic CD secondary to ACTH-secreting adenomas in whom we had peripheral and tumor DNA (N=36). No increased GPR101 expression was observed in tumors compared to normal pituitary (NP) tissues, nor did we find a correlation between GPR101 and ACTH expression levels. Sequence analysis revealed a very rare germline heterozygous GPR101 variant (p.G31S) in one patient with CD. Overexpression of the p.G31S variant did not lead to increased growth and proliferation, although modest effects on cAMP signaling were seen. GPR101 is not overexpressed in ACTH-secreting tumors compared to NPs. A rare germline GPR101 variant was found in one patient with CD but in vitro studies did not support a consistent pathogenic effect. GPR101 is unlikely to be involved in the pathogenesis of CD.

Substitution scanning identifies a novel, catalytically active ibrutinib-resistant BTK cysteine 481 to threonine (C481T) variant

Irreversible Bruton tyrosine kinase (BTK) inhibitors, ibrutinib and acalabrutinib have demonstrated remarkable clinical responses in multiple B-cell malignancies. Acquired resistance has been identified in a sub-population of patients in which mutations affecting BTK predominantly substitute cysteine 481 in the kinase domain for catalytically active serine, thereby ablating covalent binding of inhibitors. Activating substitutions in the BTK substrate phospholipase Cγ2 (PLCγ2) instead confers resistance independent of BTK. Herein, we generated all six possible amino acid substitutions due to single nucleotide alterations for the cysteine 481 codon, in addition to threonine, requiring two nucleotide substitutions, and performed functional analysis. Replacement by arginine, phenylalanine, tryptophan or tyrosine completely inactivated the catalytic activity, whereas substitution with glycine caused severe impairment. BTK with threonine replacement was catalytically active, similar to substitution with serine. We identify three potential ibrutinib resistance scenarios for cysteine 481 replacement: (1) Serine, being catalytically active and therefore predominating among patients. (2) Threonine, also being catalytically active, but predicted to be scarce, because two nucleotide changes are needed. (3) As BTK variants replaced with other residues are catalytically inactive, they presumably need compensatory mutations, therefore being very scarce. Glycine and tryptophan variants were not yet reported but likely also provide resistance.

Structural insights and functional implications of inter-individual variability in β2-adrenergic receptor

The human β₂-adrenergic receptor (β₂AR) belongs to the G protein-coupled receptor (GPCR) family and due to its central role in bronchodilation, is an important drug target. The inter-individual variability in β₂AR has been implicated in disease susceptibility and differential drug response. In this work, we identified nine potentially deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) using a consensus approach. The deleterious nsSNPs were found to cluster near the ligand binding site and towards the G-protein binding site. To assess their molecular level effects, we built structural models of these receptors and performed atomistic molecular dynamics simulations. Most notably, in the Phe290Ser variant we observed the rotameric flip of Trp286^6.48, a putative activation switch that has not been reported in β₂AR thus far. In contrast, the variant Met82Lys was found to be the most detrimental to epinephrine binding. Additionally, a few of the nsSNPs were seen to cause perturbations to the lipid bilayer, while a few lead to differences at the G-protein coupling site. We are thus able to classify the variants as ranging from activating to damaging, prioritising them for experimental studies.

Clinical relevance of short-chain acyl-CoA dehydrogenase (SCAD) deficiency: Exploring the role of new variants including the first SCAD-disease-causing allele carrying a synonymous mutation

Short-chain acyl-coA dehydrogenase deficiency (SCADD) is an autosomal recessive inborn error of mitochondrial fatty acid oxidation caused by ACADS gene alterations. SCADD is a heterogeneous condition, sometimes considered to be solely a biochemical condition given that it has been associated with variable clinical phenotypes ranging from no symptoms or signs to metabolic decompensation occurring early in life.

A reason for this variability is due to SCAD alterations, such as the common p.Gly209Ser, that confer a disease susceptibility state but require a complex multifactorial/polygenic condition to manifest clinically.

Our study focuses on 12 SCADD patients carrying 11 new ACADS variants, with the purpose of defining genotype–phenotype correlations based on clinical data, metabolite evaluation, molecular analyses, and in silico functional analyses.

Interestingly, we identified a synonymous variant, c.765G > T (p.Gly255Gly) that influences ACADS mRNA splicing accuracy. mRNA characterisation demonstrated that this variant leads to an aberrant splicing product, harbouring a premature stop codon.

Molecular analysis and in silico tools are able to characterise ACADS variants, identifying the severe mutations and consequently indicating which patients could benefit from a long term follow- up. We also emphasise that synonymous mutations can be relevant features and potentially associated with SCADD.

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Molecular and functional analysis can identify severe genotypes

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Patients with severe genotype should receive a long term follow-up.

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We identified the first SCAD synonymous variant.

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The first SCAD silent variant was proven to achieve into a disease causing allele.