Modeling and prediction of mutation fitness on protein functionality with structural information using high-dimensional Potts model

TL;DR

This paper introduces a high-dimensional Potts model with structural information and sparse regularization to accurately predict mutation effects on protein function, supported by theoretical guarantees and empirical validation.

Contribution

It presents a novel high-dimensional Potts model framework with theoretical convergence guarantees and structural data integration for improved mutation effect prediction.

Findings

Outperforms existing methods in mutation fitness prediction.

Provides the first $ ext{l}_2$ convergence rate for high-dimensional Potts models.

Successfully incorporates structural data into the modeling process.

Abstract

Quantifying the effects of amino acid mutations in proteins presents a significant challenge due to the vast combinations of residue sites and amino acid types, making experimental approaches costly and time-consuming. The Potts model has been used to address this challenge, with parameters capturing evolutionary dependency between residue sites within a protein family. However, existing methods often use the mean-field approximation to reduce computational demands, which lacks provable guarantees and overlooks critical structural information for assessing mutation effects. We propose a new framework for analyzing protein sequences using the Potts model with node-wise high-dimensional multinomial regression. Our method identifies key residue interactions and important amino acids, quantifying mutation effects through evolutionary energy derived from model parameters. It encourages sparsity in both site-wise and amino acid-wise dependencies through element-wise and group sparsity. We have established, for the first time to our knowledge, the $\ell_2$ convergence rate for estimated parameters in the high-dimensional Potts model using sparse group Lasso, matching the existing minimax lower bound for high-dimensional linear models with a sparse group structure, up to a factor depending only on the multinomial nature of the Potts model. This theoretical guarantee enables accurate quantification of estimated energy changes. Additionally, we incorporate structural data into our model by applying penalty weights across site pairs. Our method outperforms others in predicting mutation fitness, as demonstrated by comparisons with high-throughput mutagenesis experiments across 12 protein families.