TMSNP: a web server to predict pathogenesis of missense mutations in the transmembrane region of membrane proteins

Identification of homologous GluN subunits variants accelerates GRIN variants stratification

The clinical spectrum of GRIN-related neurodevelopmental disorders (GRD) results from gene- and variant-dependent primary alterations of the NMDA receptor, disturbing glutamatergic neurotransmission. Despite GRIN gene variants' functional annotations being dually critical for stratification and precision medicine design, genetically diagnosed pathogenic GRIN variants currently outnumber their relative functional annotations. Based on high-resolution crystal 3D models and topological domains conservation between GluN1, GluN2A, and GluN2B subunits of the NMDAR, we have generated GluN1-GluN2A-GluN2B subunits structural superimposition model to find equivalent positions between GluN subunits. We have developed a GRIN structural algorithm that predicts functional changes in the equivalent structural positions in other GluN subunits. GRIN structural algorithm was computationally evaluated to the full GRIN missense variants repertoire, consisting of 4,525 variants. The analysis of this structure-based model revealed an absolute predictive power for GluN1, GluN2A, and GluN2B subunits, both in terms of pathogenicity-association (benign vs. pathogenic variants) and functional impact (loss-of-function, benign, gain-of-function). Further, we validated this computational algorithm experimentally, using an in silico library of GluN2B-equivalent GluN2A artificial variants, designed from pathogenic GluN2B variants. Thus, the implementation of the GRIN structural algorithm allows to computationally predict the pathogenicity and functional annotations of GRIN variants, resulting in the duplication of pathogenic GRIN variants assignment, reduction by 30% of GRIN variants with uncertain significance, and increase by 70% of functionally annotated GRIN variants. Finally, GRIN structural algorithm has been implemented into GRIN variants Database (http://lmc.uab.es/grindb), providing a computational tool that accelerates GRIN missense variants stratification, contributing to clinical therapeutic decisions for this neurodevelopmental disorder.

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

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