Model-Free Inference for Characterizing Protein Mutations through a Coevolutionary Lens

TL;DR

This paper introduces a model-free statistical inference framework for protein contact prediction from MSA data, enabling uncertainty quantification and identification of amino acid contributions, advancing coevolutionary analysis.

Contribution

It develops a novel partial correlation-based testing framework for contact prediction that does not rely on traditional model assumptions.

Findings

Validates control of Type I errors in simulations

Demonstrates high power in numerical experiments

Successfully applied to real protein data

Abstract

Multiple sequence alignment (MSA) data play a crucial role in the study of protein mutations, with contact prediction being a notable application. Existing methods are often model-based or algorithmic and typically do not incorporate statistical inference to quantify the uncertainty of the prediction outcomes. To address this, we propose a novel framework that transforms the task of contact prediction into a statistical testing problem. Our approach is motivated by the partial correlation for continuous random variables. With one-hot encoding of MSA data, we are able to construct a partial correlation graph for multivariate categorical variables. In this framework, two connected nodes in the graph indicate that the corresponding positions on the protein form a contact. A new spectrum-based test statistic is introduced to test whether two positions are partially correlated. Moreover, the new framework enables the identification of amino acid combinations that contribute to the correlation within the identified contacts, an important but largely unexplored aspect of protein mutations. Numerical experiments demonstrate that our proposed method is valid in terms of controlling Type I errors and powerful in general. Real data applications on various protein families further validate the practical utility of our approach in coevolution and mutation analysis.