MutaCYP: Classification of missense mutations in human cytochromes P450

Polymorphisms in genes related to oxidative stress and inflammation: Emerging links with the pathogenesis and severity of Cerebral Cavernous Malformation disease

Cerebral Cavernous Malformation (CCM) is a cerebrovascular disease of genetic origin affecting 0.5% of the population and characterized by abnormally enlarged and leaky capillaries that predispose to seizures, neurological deficits, and intracerebral hemorrhage (ICH). CCM occurs sporadically or is inherited as dominant condition with incomplete penetrance and highly variable expressivity. Three disease genes have been identified: KRIT1 (CCM1), CCM2 and CCM3. Previous results demonstrated that loss-of-function mutations of CCM genes cause pleiotropic effects, including defective autophagy, altered reactive oxygen species (ROS) homeostasis, and enhanced sensitivity to oxidative stress and inflammatory events, suggesting a novel unifying pathogenetic mechanism, and raising the possibility that CCM disease onset and severity are influenced by the presence of susceptibility and modifier genes. Consistently, genome-wide association studies (GWAS) in large and homogeneous cohorts of patients sharing the familial form of CCM disease and identical mutations in CCM genes have led to the discovery of distinct genetic modifiers of major disease severity phenotypes, such as development of numerous and large CCM lesions, and susceptibility to ICH. This review deals with the identification of genetic modifiers with a significant impact on inter-individual variability in CCM disease onset and severity, including highly polymorphic genes involved in oxidative stress, inflammatory and immune responses, such as cytochrome P450 monooxygenases (CYP), matrix metalloproteinases (MMP), and Toll-like receptors (TLR), pointing to their emerging prognostic value, and opening up new perspectives for risk stratification and personalized medicine strategies.

IDRMutPred: predicting disease-associated germline nonsynonymous single nucleotide variants (nsSNVs) in intrinsically disordered regions

Motivation

Despite of the lack of folded structure, intrinsically disordered regions (IDRs) of proteins play versatile roles in various biological processes, and many nonsynonymous single nucleotide variants (nsSNVs) in IDRs are associated with human diseases. The continuous accumulation of nsSNVs resulted from the wide application of NGS has driven the development of disease-association prediction methods for decades. However, their performance on nsSNVs in IDRs remains inferior, possibly due to the domination of nsSNVs from structured regions in training data. Therefore, it is highly demanding to build a disease-association predictor specifically for nsSNVs in IDRs with better performance.

Results

We present IDRMutPred, a machine learning-based tool specifically for predicting disease-associated germline nsSNVs in IDRs. Based on 17 selected optimal features that are extracted from sequence alignments, protein annotations, hydrophobicity indices and disorder scores, IDRMutPred was trained using three ensemble learning algorithms on the training dataset containing only IDR nsSNVs. The evaluation on the two testing datasets shows that all the three prediction models outperform 17 other popular general predictors significantly, achieving the ACC between 0.856 and 0.868 and MCC between 0.713 and 0.737. IDRMutPred will prioritize disease-associated IDR germline nsSNVs more reliably than general predictors.

Availability and implementation

The software is freely available at http://www.wdspdb.com/IDRMutPred.

Supplementary information

Supplementary data are available at Bioinformatics online.

Variation benchmark datasets: update, criteria, quality and applications

Development of new computational methods and testing their performance has to be carried out using experimental data. Only in comparison to existing knowledge can method performance be assessed. For that purpose, benchmark datasets with known and verified outcome are needed. High-quality benchmark datasets are valuable and may be difficult, laborious and time consuming to generate. VariBench and VariSNP are the two existing databases for sharing variation benchmark datasets used mainly for variation interpretation. They have been used for training and benchmarking predictors for various types of variations and their effects. VariBench was updated with 419 new datasets from 109 papers containing altogether 329 014 152 variants; however, there is plenty of redundancy between the datasets. VariBench is freely available at http://structure.bmc.lu.se/VariBench/. The contents of the datasets vary depending on information in the original source. The available datasets have been categorized into 20 groups and subgroups. There are datasets for insertions and deletions, substitutions in coding and non-coding region, structure mapped, synonymous and benign variants. Effect-specific datasets include DNA regulatory elements, RNA splicing, and protein property for aggregation, binding free energy, disorder and stability. Then there are several datasets for molecule-specific and disease-specific applications, as well as one dataset for variation phenotype effects. Variants are often described at three molecular levels (DNA, RNA and protein) and sometimes also at the protein structural level including relevant cross references and variant descriptions. The updated VariBench facilitates development and testing of new methods and comparison of obtained performances to previously published methods. We compared the performance of the pathogenicity/tolerance predictor PON-P2 to several benchmark studies, and show that such comparisons are feasible and useful, however, there may be limitations due to lack of provided details and shared data.

Database URL: http://structure.bmc.lu.se/VariBench