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

2026-01-22

AI Summary

Existing methods for analyzing coevolution in protein mutations struggle to quantify prediction uncertainty. This work introduces, for the first time, a model-agnostic statistical inference framework to this domain, reframing contact prediction as a hypothesis testing problem. By constructing categorical partial correlation graphs from one-hot encodings of multiple sequence alignments (MSAs), the authors propose a novel phylogeny-aware test statistic to assess whether pairs of sites exhibit coevolutionary associations and to identify critical amino acid combinations. The method rigorously controls Type I error while maintaining high statistical power. Empirical validation on real protein families demonstrates its effectiveness and practical utility in both coevolution analysis and the interpretation of mutational effects.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Evaluation and Analysis
• Game Theory and Economic Paradigms: Cooperative Game Theory

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Large-scale security measurements

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.

Problem

• protein mutations
• coevolution
• contact prediction
• statistical inference
• multiple sequence alignment

Innovation

• model-free inference
• partial correlation graph
• coevolutionary analysis
• contact prediction
• categorical MSA data