ClinVAP: a reporting strategy from variants to therapeutic options.

MOTIVATION: Next-generation sequencing has become routine in oncology and opens up new avenues of therapies, particularly in personalized oncology setting. An increasing number of cases also implies a need for a more robust, automated and reproducible processing of long lists of variants for cancer diagnosis and therapy. While solutions for the large-scale analysis of somatic variants have been implemented, existing solutions often have issues with reproducibility, scalability and interoperability. RESULTS: Clinical Variant Annotation Pipeline (ClinVAP) is an automated pipeline which annotates, filters and prioritizes somatic single nucleotide variants provided in variant call format. It augments the variant information with documented or predicted clinical effect. These annotated variants are prioritized based on driver gene status and druggability. ClinVAP is available as a fully containerized, self-contained pipeline maximizing reproducibility and scalability allowing the analysis of larger scale data. The resulting JSON-based report is suited for automated downstream processing, but ClinVAP can also automatically render the information into a user-defined template to yield a human-readable report. AVAILABILITY AND IMPLEMENTATION: ClinVAP is available at https://github.com/PersonalizedOncology/ClinVAP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The EVcouplings Python framework for coevolutionary sequence analysis.

SUMMARY: Coevolutionary sequence analysis has become a commonly used technique for de novo prediction of the structure and function of proteins, RNA, and protein complexes. We present the EVcouplings framework, a fully integrated open-source application and Python package for coevolutionary analysis. The framework enables generation of sequence alignments, calculation and evaluation of evolutionary couplings (ECs), and de novo prediction of structure and mutation effects. The combination of an easy to use, flexible command line interface and an underlying modular Python package makes the full power of coevolutionary analyses available to entry-level and advanced users. AVAILABILITY AND IMPLEMENTATION: https://github.com/debbiemarkslab/evcouplings.

Population-specific design of de-immunized protein biotherapeutics.

Immunogenicity is a major problem during the development of biotherapeutics since it can lead to rapid clearance of the drug and adverse reactions. The challenge for biotherapeutic design is therefore to identify mutants of the protein sequence that minimize immunogenicity in a target population whilst retaining pharmaceutical activity and protein function. Current approaches are moderately successful in designing sequences with reduced immunogenicity, but do not account for the varying frequencies of different human leucocyte antigen alleles in a specific population and in addition, since many designs are non-functional, require costly experimental post-screening. Here, we report a new method for de-immunization design using multi-objective combinatorial optimization. The method simultaneously optimizes the likelihood of a functional protein sequence at the same time as minimizing its immunogenicity tailored to a target population. We bypass the need for three-dimensional protein structure or molecular simulations to identify functional designs by automatically generating sequences using probabilistic models that have been used previously for mutation effect prediction and structure prediction. As proof-of-principle we designed sequences of the C2 domain of Factor VIII and tested them experimentally, resulting in a good correlation with the predicted immunogenicity of our model.

Genetic variation in human drug-related genes.

BACKGROUND: Variability in drug efficacy and adverse effects are observed in clinical practice. While the extent of genetic variability in classic pharmacokinetic genes is rather well understood, the role of genetic variation in drug targets is typically less studied. METHODS: Based on 60,706 human exomes from the ExAC dataset, we performed an in-depth computational analysis of the prevalence of functional variants in 806 drug-related genes, including 628 known drug targets. We further computed the likelihood of 1236 FDA-approved drugs to be affected by functional variants in their targets in the whole ExAC population as well as different geographic sub-populations. RESULTS: We find that most genetic variants in drug-related genes are very rare (f < 0.1%) and thus will likely not be observed in clinical trials. Furthermore, we show that patient risk varies for many drugs and with respect to geographic ancestry. A focused analysis of oncological drug targets indicates that the probability of a patient carrying germline variants in oncological drug targets is, at 44%, high enough to suggest that not only somatic alterations but also germline variants carried over into the tumor genome could affect the response to antineoplastic agents. CONCLUSIONS: This study indicates that even though many variants are very rare and thus likely not observed in clinical trials, four in five patients are likely to carry a variant with possibly functional effects in a target for commonly prescribed drugs. Such variants could potentially alter drug efficacy.

Mutation effects predicted from sequence co-variation.

Many high-throughput experimental technologies have been developed to assess the effects of large numbers of mutations (variation) on phenotypes. However, designing functional assays for these methods is challenging, and systematic testing of all combinations is impossible, so robust methods to predict the effects of genetic variation are needed. Most prediction methods exploit evolutionary sequence conservation but do not consider the interdependencies of residues or bases. We present EVmutation, an unsupervised statistical method for predicting the effects of mutations that explicitly captures residue dependencies between positions. We validate EVmutation by comparing its predictions with outcomes of high-throughput mutagenesis experiments and measurements of human disease mutations and show that it outperforms methods that do not account for epistasis. EVmutation can be used to assess the quantitative effects of mutations in genes of any organism. We provide pre-computed predictions for ∼7,000 human proteins at http://evmutation.org/.

Structural and Functional Characterization of the Bacterial Type III Secretion Export Apparatus.

Bacterial type III protein secretion systems inject effector proteins into eukaryotic host cells in order to promote survival and colonization of Gram-negative pathogens and symbionts. Secretion across the bacterial cell envelope and injection into host cells is facilitated by a so-called injectisome. Its small hydrophobic export apparatus components SpaP and SpaR were shown to nucleate assembly of the needle complex and to form the central "cup" substructure of a Salmonella Typhimurium secretion system. However, the in vivo placement of these components in the needle complex and their function during the secretion process remained poorly defined. Here we present evidence that a SpaP pentamer forms a 15 Å wide pore and provide a detailed map of SpaP interactions with the export apparatus components SpaQ, SpaR, and SpaS. We further refine the current view of export apparatus assembly, consolidate transmembrane topology models for SpaP and SpaR, and present intimate interactions of the periplasmic domains of SpaP and SpaR with the inner rod protein PrgJ, indicating how export apparatus and needle filament are connected to create a continuous conduit for substrate translocation.

Antiparallel protocadherin homodimers use distinct affinity- and specificity-mediating regions in cadherin repeats 1-4.

Protocadherins (Pcdhs) are cell adhesion and signaling proteins used by neurons to develop and maintain neuronal networks, relying on trans homophilic interactions between their extracellular cadherin (EC) repeat domains. We present the structure of the antiparallel EC1-4 homodimer of human PcdhγB3, a member of the γ subfamily of clustered Pcdhs. Structure and sequence comparisons of α, ß, and γ clustered Pcdh isoforms illustrate that subfamilies encode specificity in distinct ways through diversification of loop region structure and composition in EC2 and EC3, which contains isoform-specific conservation of primarily polar residues. In contrast, the EC1/EC4 interface comprises hydrophobic interactions that provide non-selective dimerization affinity. Using sequence coevolution analysis, we found evidence for a similar antiparallel EC1-4 interaction in non-clustered Pcdh families. We thus deduce that the EC1-4 antiparallel homodimer is a general interaction strategy that evolved before the divergence of these distinct protocadherin families.

Structure and Sequence Analyses of Clustered Protocadherins Reveal Antiparallel Interactions that Mediate Homophilic Specificity.

Clustered protocadherin (Pcdh) proteins mediate dendritic self-avoidance in neurons via specific homophilic interactions in their extracellular cadherin (EC) domains. We determined crystal structures of EC1-EC3, containing the homophilic specificity-determining region, of two mouse clustered Pcdh isoforms (PcdhγA1 and PcdhγC3) to investigate the nature of the homophilic interaction. Within the crystal lattices, we observe antiparallel interfaces consistent with a role in trans cell-cell contact. Antiparallel dimerization is supported by evolutionary correlations. Two interfaces, located primarily on EC2-EC3, involve distinctive clustered Pcdh structure and sequence motifs, lack predicted glycosylation sites, and contain residues highly conserved in orthologs but not paralogs, pointing toward their biological significance as homophilic interaction interfaces. These two interfaces are similar yet distinct, reflecting a possible difference in interaction architecture between clustered Pcdh subfamilies. These structures initiate a molecular understanding of clustered Pcdh assemblies that are required to produce functional neuronal networks.

ballaxy: web services for structural bioinformatics.

MOTIVATION: Web-based workflow systems have gained considerable momentum in sequence-oriented bioinformatics. In structural bioinformatics, however, such systems are still relatively rare; while commercial stand-alone workflow applications are common in the pharmaceutical industry, academic researchers often still rely on command-line scripting to glue individual tools together. RESULTS: In this work, we address the problem of building a web-based system for workflows in structural bioinformatics. For the underlying molecular modelling engine, we opted for the BALL framework because of its extensive and well-tested functionality in the field of structural bioinformatics. The large number of molecular data structures and algorithms implemented in BALL allows for elegant and sophisticated development of new approaches in the field. We hence connected the versatile BALL library and its visualization and editing front end BALLView with the Galaxy workflow framework. The result, which we call ballaxy, enables the user to simply and intuitively create sophisticated pipelines for applications in structure-based computational biology, integrated into a standard tool for molecular modelling. AVAILABILITY AND IMPLEMENTATION: ballaxy consists of three parts: some minor modifications to the Galaxy system, a collection of tools and an integration into the BALL framework and the BALLView application for molecular modelling. Modifications to Galaxy will be submitted to the Galaxy project, and the BALL and BALLView integrations will be integrated in the next major BALL release. After acceptance of the modifications into the Galaxy project, we will publish all ballaxy tools via the Galaxy toolshed. In the meantime, all three components are available from http://www.ball-project.org/ballaxy. Also, docker images for ballaxy are available at https://registry.hub.docker.com/u/anhi/ballaxy/dockerfile/. ballaxy is licensed under the terms of the GPL.

Structure, dynamics and implied gating mechanism of a human cyclic nucleotide-gated channel.

Cyclic nucleotide-gated (CNG) ion channels are nonselective cation channels, essential for visual and olfactory sensory transduction. Although the channels include voltage-sensor domains (VSDs), their conductance is thought to be independent of the membrane potential, and their gating regulated by cytosolic cyclic nucleotide-binding domains. Mutations in these channels result in severe, degenerative retinal diseases, which remain untreatable. The lack of structural information on CNG channels has prevented mechanistic understanding of disease-causing mutations, precluded structure-based drug design, and hampered in silico investigation of the gating mechanism. To address this, we built a 3D model of the cone tetrameric CNG channel, based on homology to two distinct templates with known structures: the transmembrane (TM) domain of a bacterial channel, and the cyclic nucleotide-binding domain of the mouse HCN2 channel. Since the TM-domain template had low sequence-similarity to the TM domains of the CNG channels, and to reconcile conflicts between the two templates, we developed a novel, hybrid approach, combining homology modeling with evolutionary coupling constraints. Next, we used elastic network analysis of the model structure to investigate global motions of the channel and to elucidate its gating mechanism. We found the following: (i) In the main mode of motion, the TM and cytosolic domains counter-rotated around the membrane normal. We related this motion to gating, a proposition that is supported by previous experimental data, and by comparison to the known gating mechanism of the bacterial KirBac channel. (ii) The VSDs could facilitate gating (supplementing the pore gate), explaining their presence in such 'voltage-insensitive' channels. (iii) Our elastic network model analysis of the CNGA3 channel supports a modular model of allosteric gating, according to which protein domains are quasi-independent: they can move independently, but are coupled to each other allosterically.

Activation of lymphoma-associated MyD88 mutations via allostery-induced TIR-domain oligomerization.

Myeloid differentiation 88 (MyD88) is the key signaling adapter of Toll-like and interleukin-1 receptors. Recurrent lymphoma-associated mutations, particularly Leu265Pro (L265P), within the MyD88 Toll/interleukin-1 receptor (TIR) domain sustain lymphoma cell survival due to constitutive nuclear factor κB signaling. We found that mutated TIR domains displayed an intrinsic propensity for augmented oligomerization and spontaneous formation of cytosolic Myddosome aggregates in lymphoma cell lines, mimicking the effect of dimerized TIR domains. Blocking of MyD88 oligomerization induced apoptosis. The L265P TIR domain can recruit the endogenous wild-type MyD88 for oligomer formation and hyperactivity. Molecular dynamics simulations and analysis of additional mutations suggest that constitutive activity is caused by allosteric oligomerization.

Sequence co-evolution gives 3D contacts and structures of protein complexes.

Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.

The MoSGrid Science Gateway - A Complete Solution for Molecular Simulations.

The MoSGrid portal offers an approach to carry out high-quality molecular simulations on distributed compute infrastructures to scientists with all kinds of background and experience levels. A user-friendly Web interface guarantees the ease-of-use of modern chemical simulation applications well established in the field. The usage of well-defined workflows annotated with metadata largely improves the reproducibility of simulations in the sense of good lab practice. The MoSGrid science gateway supports applications in the domains quantum chemistry (QC), molecular dynamics (MD), and docking. This paper presents the open-source MoSGrid architecture as well as lessons learned from its design.