# Context-Aware Prediction of Pathogenicity of Missense Mutations Involved in Human Disease

## TL;DR

This paper introduces an unsupervised, context-aware computational approach using direct-coupling analysis to predict the pathogenicity of missense mutations, improving accuracy by considering sequence context.

## Contribution

It presents a novel unsupervised method that incorporates sequence context to enhance mutation pathogenicity prediction, comparable to supervised state-of-the-art techniques.

## Key findings

- Context-aware analysis improves prediction accuracy.
- Method identifies functional and structural constraints.
- Comparable performance to supervised methods.

## Abstract

Amino-acid substitutions are implicated in a wide range of human diseases, many of which are lethal. Distinguishing such mutations from polymorphisms without significant effect on human health is a necessary step in understanding the etiology of such diseases. Computational methods can be used to select interesting mutations within a larger set, to corroborate experimental findings and to elucidate the cause of the deleterious effect. In this work, we show that taking into account the sequence context in which the mutation appears allows to improve the predictive and explanatory power of such methods. We present an unsupervised approach based on the direct-coupling analysis of homologous proteins. We show its capability to quantify mutations where methods without context dependence fail. We highlight cases where the context dependence is interpretable as functional or structural constraints and show that our simple and unsupervised method has an accuracy similar to state-of-the-art methods, including supervised ones.