Modeling Protein Evolution via Generative Inference From Monte Carlo Chains to Population Genetics

TL;DR

This paper compares three simulation methods for modeling protein evolution using generative models, finding that population genetics schemes best capture evolutionary dynamics and phylogenetic correlations.

Contribution

It introduces a comparative analysis of simulation schemes for protein evolution, highlighting the effectiveness of population genetics models in capturing complex evolutionary features.

Findings

Population genetics models accurately reproduce phylogenetic correlations.

Standard Monte Carlo fails to capture realistic evolutionary trajectories.

Tree-based Monte Carlo improves phylogenetic fidelity.

Abstract

Generative models derived from large protein sequence alignments define complex fitness landscapes, but their utility for accurately modeling non-equilibrium evolutionary dynamics remains unclear. In this work, we perform a rigorous comparative analysis of three simulation schemes, designed to mimic evolution in silico by local sampling of the probability distribution defined by a generative model. We compare standard independent Markov Chain Monte Carlo, Monte Carlo on a phylogenetic tree, and a population genetics dynamics, benchmarking their outputs against deep sequencing data from four distinct in vitro evolution experiments. We find that standard Monte Carlo fails to reproduce the correct phylogenetic structure and generates unrealistic, gradual mutational sweeps. Performing Monte Carlo on a tree inferred from data improves phylogenetic fidelity and historical accuracy. The population genetics scheme successfully captures phylogenetic correlations, mutational abundances, and selective sweeps as emergent properties, without the need to infer additional information from data. However, the latter choice come at the price of not sampling the proper generative model distribution at long times. Our findings highlight the crucial role of phylogenetic correlations and finite-population effects in shaping evolutionary trajectories on fitness landscapes. These models therefore provide powerful tools for predicting complex adaptive paths and for reliably extrapolating evolutionary dynamics beyond current experimental limitations.