Pred‐MutHTP: Prediction of disease‐causing and neutral mutations in human transmembrane proteins

Abstract

Membrane proteins are unique in that segments thereof concurrently reside in vastly different physicochemical environments: the extracellular space, the lipid bilayer, and the cytoplasm. Accordingly, the effects of missense variants disrupting their sequence depend greatly on the characteristics of the environment of the protein segment affected as well as the function it performs. Because membrane proteins have many crucial roles (transport, signal transduction, cell adhesion, etc.), compromising their functionality often leads to diseases including cancers, diabetes mellitus or cystic fibrosis. Here, we report a suite of sequence-based computational methods "Pred-MutHTP" for discriminating between disease-causing and neutral alterations in their sequence. With a data set of 11,846 disease-causing and 9,533 neutral mutations, we obtained an accuracy of 74% and 78% with 10-fold group-wise crossvalidation and test set, respectively. The features used in the models include evolutionary information, physiochemical properties, neighboring residue information, and specialized membrane protein attributes incorporating the number of transmembrane segments, substitution matrices specific to membrane proteins as well as residue distributions occurring in specific topological regions. Across 11 disease classes, the method achieved accuracies in the range of 75-85%. The model designed specifically for the transmembrane segments achieved an accuracy of 85% on the test set with a sensitivity and specificity of 86% and 83%, respectively. This renders our method the current state-of-the-art with regard to predicting the effects of variants in the transmembrane protein segments. Pred-MutHTP allows predicting the effect of any variant occurring in a membrane protein-available at

ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning

“…Top prediction methods in computational biology [15], [16], [17], [18], [19], [20] combine machine learning (ML) and evolutionary information (EI), first established as the winning strategy to predict protein secondary structure [21], [22] in two steps. First, search for a family of related proteins summarized as multiple sequence alignment (MSA) and extract the evolutionary information contained in this align-ment.…”

ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Learning

“…Top prediction methods in computational biology [15], [16], [17], [18], [19], [20] combine machine learning (ML) and evolutionary information (EI), first established as the winning strategy to predict protein secondary structure [21], [22] in two steps. First, search for a family of related proteins summarized as multiple sequence alignment (MSA) and extract the evolutionary information contained in this alignment.…”

Distribution of disease-causing germline mutations in coiled-coils implies an important role of their N-terminal region

“…A handful of popular methods are available to predict the effect of variations 45,46 or to highlight vulnerable regions in proteins 47,48 , yet most of these are based on purely statistical approaches. Methods incorporating structural information are largely limited to general features of PDB structures, or prediction of transmembrane domains or disordered segments, although no currently available methods incorporate features of coiled-coils.…”