Vikas Pejaver

The structural and functional signatures of proteins that undergo multiple events of post‐translational modification

The structural, functional, and mechanistic characterization of several types of post‐translational modifications (PTMs) is well‐documented. PTMs, however, may interact or interfere with one another when regulating protein function. Yet, characterization of the structural and functional signatures of their crosstalk has been hindered by the scarcity of data. To this end, we developed a unified sequence‐based predictor of 23 types of PTM sites that, we believe, is a useful tool in guiding biological experiments and data interpretation. We then used experimentally determined and predicted PTM sites to investigate two particular cases of potential PTM crosstalk in eukaryotes. First, we identified proteins statistically enriched in multiple types of PTM sites and found that they show preferences toward intrinsically disordered regions as well as functional roles in transcriptional, posttranscriptional, and developmental processes. Second, we observed that target sites modified by more than one type of PTM, referred to as shared PTM sites, show even stronger preferences toward disordered regions than their single‐PTM counterparts; we explain this by the need for these regions to accommodate multiple partners. Finally, we investigated the influence of single and shared PTMs on differential regulation of protein–protein interactions. We provide evidence that molecular recognition features (MoRFs) show significant preferences for PTM sites, particularly shared PTM sites, implicating PTMs in the modulation of this specific type of macromolecular recognition. We conclude that intrinsic disorder is a strong structural prerequisite for complex PTM‐based regulation, particularly in context‐dependent protein–protein interactions related to transcriptional and developmental processes. Availability: www.modpred.org

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.

MutPred2: inferring the molecular and phenotypic impact of amino acid variants

We introduce MutPred2, a tool that improves the prioritization of pathogenic amino acid substitutions, generates molecular mechanisms potentially causative of disease, and returns interpretable pathogenicity score distributions on individual genomes. While its prioritization performance is state-of-the-art, a novel and distinguishing feature of MutPred2 is the probabilistic modeling of variant impact on specific aspects of protein structure and function that can serve to guide experimental studies of phenotype-altering variants. We demonstrate the utility of MutPred2 in the identification of the structural and functional mutational signatures relevant to Mendelian disorders and the prioritization of de novo mutations associated with complex neurodevelopmental disorders. We then experimentally validate the functional impact of several variants identified in patients with such disorders. We argue that mechanism-driven studies of human inherited diseases have the potential to significantly accelerate the discovery of clinically actionable variants. Availability: http://mutpred.mutdb.org/

Physicochemical sequence characteristics that influence S-palmitoylation propensity.

Over the past 30 years, several hundred eukaryotic proteins spanning from yeast to man have been shown to be S-palmitoylated. This post-translational modification involves the reversible addition of a 16-carbon saturated fatty acyl chain onto the cysteine residue of a protein where it regulates protein membrane association and distribution, conformation, and stability. However, the large-scale proteome-wide discovery of new palmitoylated proteins has been hindered by the difficulty of identifying a palmitoylation consensus sequence. Using a bioinformatics approach, we show that the enrichment of hydrophobic and basic residues, the cellular context of the protein, and the structural features of the residues surrounding the palmitoylated cysteine all influence the likelihood of palmitoylation. We developed a new palmitoylation predictor that incorporates these identified features, and this predictor achieves a Matthews Correlation Coefficient of .74 using 10-fold cross validation, and significantly outperforms existing predictors on unbiased testing sets. This demonstrates that palmitoylation sites can be predicted with accuracy by taking into account not only physiochemical properties of the modified cysteine and its surrounding residues, but also structural parameters and the subcellular localization of the modified cysteine. This will allow for improved predictions of palmitoylated residues in uncharacterized proteins. A web-based version of this predictor is currently under development.

The Loss and Gain of Functional Amino Acid Residues Is a Common Mechanism Causing Human Inherited Disease

Elucidating the precise molecular events altered by disease-causing genetic variants represents a major challenge in translational bioinformatics. To this end, many studies have investigated the structural and functional impact of amino acid substitutions. Most of these studies were however limited in scope to either individual molecular functions or were concerned with functional effects (e.g. deleterious vs. neutral) without specifically considering possible molecular alterations. The recent growth of structural, molecular and genetic data presents an opportunity for more comprehensive studies to consider the structural environment of a residue of interest, to hypothesize specific molecular effects of sequence variants and to statistically associate these effects with genetic disease. In this study, we analyzed data sets of disease-causing and putatively neutral human variants mapped to protein 3D structures as part of a systematic study of the loss and gain of various types of functional attribute potentially underlying pathogenic molecular alterations. We first propose a formal model to assess probabilistically function-impacting variants. We then develop an array of structure-based functional residue predictors, evaluate their performance, and use them to quantify the impact of disease-causing amino acid substitutions on catalytic activity, metal binding, macromolecular binding, ligand binding, allosteric regulation and post-translational modifications. We show that our methodology generates actionable biological hypotheses for up to 41% of disease-causing genetic variants mapped to protein structures suggesting that it can be reliably used to guide experimental validation. Our results suggest that a significant fraction of disease-causing human variants mapping to protein structures are function-altering both in the presence and absence of stability disruption.

Position of Proline Mediates the Reactivity of S-Palmitoylation.

Palmitoylation, a post-translational modification in which a saturated 16-carbon chain is added predominantly to a cysteine residue, participates in various biological functions. The position of proline relative to other residues being post-translationally modified has been previously reported as being important. We determined that proline is statistically enriched around cysteines known to be S-palmitoylated. The goal of this work was to determine how the position of proline influences the palmitoylation of the cysteine residue. We established a mass spectrometry-based approach to investigate time- and temperature-dependent kinetics of autopalmitoylation in vitro and to derive the thermodynamic parameters of the transition state associated with palmitoylation; to the best of our knowledge, our work is the first to study the kinetics and activation properties of the palmitoylation process. We then used these thermochemical parameters to determine if the position of proline relative to the modified cysteine is important for palmitoylation. Our results show that peptides with proline at the -1 position of cysteine in their sequence (PC) have lower enthalpic barriers and higher entropic barriers in comparison to the same peptides with proline at the +1 position of cysteine (CP); interestingly, the free-energy barriers for both pairs are almost identical. Molecular dynamics studies demonstrate that the flexibility of the cysteine backbone in the PC-containing peptide when compared to the CP-containing peptide explains the increased entropic barrier and decreased enthalpic barrier observed experimentally.

Missense variant pathogenicity predictors generalize well across a range of function-specific prediction challenges

The steady advances in machine learning and accumulation of biomedical data have contributed to the development of numerous computational models that assess the impact of missense variants. Different methods, however, operationalize impact differently. Two common tasks in this context are the prediction of the pathogenicity of variants and the prediction of their effects on a proteins function. These are related but distinct problems, and it is unclear whether methods developed for one are optimized for the other. The Critical Assessment of Genome Interpretation (CAGI) experiment provides a means to address this question empirically. To this end, we participated in various protein‐specific challenges in CAGI with two objectives in mind. First, to compare the performance of methods in the MutPred family with the state‐of‐the‐art. Second and more importantly, to investigate the applicability of general‐purpose pathogenicity predictors to the classification of specific function‐altering variants without additional training or calibration. We find that our pathogenicity predictors performed competitively with other methods, outputting score distributions in agreement with experimental outcomes. Overall, we conclude that binary classifiers learned from disease‐causing mutations are capable of modeling important aspects of the underlying biology and the alteration of protein function resulting from mutations.

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

Precision medicine aims to predict a patients disease risk and best therapeutic options by using that individuals 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: Crohns disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohns 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.

When loss-of-function is loss of function: assessing mutational signatures and impact of loss-of-function genetic variants

Motivation: Loss‐of‐function genetic variants are frequently associated with severe clinical phenotypes, yet many are present in the genomes of healthy individuals. The available methods to assess the impact of these variants rely primarily upon evolutionary conservation with little to no consideration of the structural and functional implications for the protein. They further do not provide information to the user regarding specific molecular alterations potentially causative of disease. Results: To address this, we investigate protein features underlying loss‐of‐function genetic variation and develop a machine learning method, MutPred‐LOF, for the discrimination of pathogenic and tolerated variants that can also generate hypotheses on specific molecular events disrupted by the variant. We investigate a large set of human variants derived from the Human Gene Mutation Database, ClinVar and the Exome Aggregation Consortium. Our prediction method shows an area under the Receiver Operating Characteristic curve of 0.85 for all loss‐of‐function variants and 0.75 for proteins in which both pathogenic and neutral variants have been observed. We applied MutPred‐LOF to a set of 1142 de novo vari3ants from neurodevelopmental disorders and find enrichment of pathogenic variants in affected individuals. Overall, our results highlight the potential of computational tools to elucidate causal mechanisms underlying loss of protein function in loss‐of‐function variants. Availability and Implementation: http://mutpred.mutdb.org Contact: predrag@indiana.edu

Draft Genome Sequence of Caedibacter varicaedens, a Kappa Killer Endosymbiont Bacterium of the Ciliate Paramecium biaurelia

ABSTRACT Caedibacter varicaedens is a kappa killer endosymbiont bacterium of the ciliate Paramecium biaurelia. Here, we present the draft genome sequence of C. varicaedens.