Computational Approaches for Investigating Disease-causing Mutations in Membrane Proteins: Database Development, Analysis and Prediction

Abstract

Membrane proteins (MPs) play an essential role in a broad range of cellular functions, serving as transporters, enzymes, receptors, and communicators, and about ~60% of membrane proteins are primarily used as drug targets. These proteins adopt either α-helical or β-barrel structures in the lipid bilayer of a cell/organelle membrane. Mutations in membrane proteins alter their structure and function, and may lead to diseases. Data on disease-causing and neutral mutations in membrane proteins are available in MutHTP and TMSNP databases, which provide additional features based on sequence, structure, topology, and diseases. These databases have been effectively utilized for analysing sequence and structure-based features in disease-causing and neutral mutations in membrane proteins, exploring disease-causing mechanisms, elucidating the relationship between sequence/structural parameters and diseases, and developing computational tools. Further, machine learning-based tools have been developed for identifying disease-causing mutations using diverse features, such as evolutionary information, physicochemical properties, atomic contacts, contact potentials, and the contribution of different energetic terms. These membrane protein-specific tools are helpful in characterizing the effect of new variants in the whole human membrane proteome. In this review, we provide a discussion of the available databases for disease-causing mutations in membrane proteins, followed by a statistical analysis of membrane protein mutations using sequence and structural features. In addition, available prediction tools for identifying disease-causing and neutral mutations in membrane proteins will be described with their performances. This comprehensive review provides deep insights into designing mutation-specific strategies for different diseases.

Keywords: Databases; Disease-causing mutations; Function; Machine-learning; Membrane proteins; Neutral mutations; Structure; Topology; tools.