arXiv subjects

Leonardo Di Bari

Publications and source records attributed to Leonardo Di Bari.

4 recordsLinked to original sources

arXiv2026

Modeling Protein Evolution with Generative Models: from Extant Sequence Data to Evolutionary Dynamics

Protein sequences carry a record of evolutionary history shaped by mutation, selection, drift, and epistasis. Recent generative models trained on homologous sequence families offer a new way to read this record: they define probabilistic landscapes that score sequences, generate viable variants, and capture constraints that are difficult to measure experimentally. In this review, we discuss how such landscapes can be used not only for protein design or mutation-effect prediction, but also for modeling evolutionary dynamics. We focus particularly on Direct Coupling Analysis as an interpretable and experimentally validated framework, while placing it in the broader context of generative sequence modeling. We first describe how generative sequence landscapes are inferred and assessed, then review how they can be coupled to population-genetic or substitution-model dynamics to simulate protein evolution across experimental and phylogenetic timescales. Applications include viral evolution, laboratory drift experiments, historical contingency, entrenchment, epistatic drift over time, and long-term sequence-space exploration. We conclude by discussing open challenges, including score-fitness calibration, phylogenetic structure, codon-level mutation biases, indels, and the integration of experimental data.

q-bio.PE↗

arXiv2026

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

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.

q-bio.PE↗

arXiv2025

Fluctuations and the limit of predictability in protein evolution

Protein evolution involves mutations occurring across a wide range of time scales. In analogy with disordered systems in statistical physics, this dynamical heterogeneity suggests strong correlations between mutations happening at distinct sites and times. To quantify these correlations, we examine the role of various fluctuation sources in protein evolution, simulated using a data-driven energy landscape as a proxy for protein fitness. By applying spatio-temporal correlation functions developed in the context of disordered physical systems, we disentangle fluctuations originating from the initial condition, i.e. the ancestral sequence from which the evolutionary process originated, from those driven by stochastic mutations along independent evolutionary paths. Our analysis shows that, in diverse protein families, fluctuations from the ancestral sequence predominate at shorter time scales. This allows us to identify a time scale over which ancestral sequence information persists, enabling its reconstruction. We link this persistence to the strength of epistatic interactions: ancestral sequences with stronger epistatic signatures impact evolutionary trajectories over extended periods. At longer time scales, however, ancestral influence fades as epistatically constrained sites evolve collectively. To confirm this idea, we apply a standard ancestral sequence reconstruction algorithm and verify that the time-dependent recovery error is influenced by the properties of the ancestor itself. Overall, our results reveal that the properties of ancestral sequences - particularly their epistatic constraints - influence the initial evolutionary dynamics and the performance of standard ancestral sequence reconstruction algorithms.

q-bio.BM↗

arXiv2024

Emergent time scales of epistasis in protein evolution

We introduce a data-driven epistatic model of protein evolution, capable of generating evolutionary trajectories spanning very different time scales reaching from individual mutations to diverged homologs. Our in silico evolution encompasses random nucleotide mutations, insertions and deletions, and models selection using a fitness landscape, which is inferred via a generative probabilistic model for protein families. We show that the proposed framework accurately reproduces the sequence statistics of both short-time (experimental) and long-time (natural) protein evolution, suggesting applicability also to relatively data-poor intermediate evolutionary time scales, which are currently inaccessible to evolution experiments. Our model uncovers a highly collective nature of epistasis, gradually changing the fitness effect of mutations in a diverging sequence context, rather than acting via strong interactions between individual mutations. This collective nature triggers the emergence of a long evolutionary time scale, separating fast mutational processes inside a given sequence context, from the slow evolution of the context itself. The model quantitatively reproduces epistatic phenomena such as contingency and entrenchment, as well as the loss of predictability in protein evolution observed in deep mutational scanning experiments of distant homologs. It thereby deepens our understanding of the interplay between mutation and selection in shaping protein diversity and novel functions, allows one to statistically forecast evolution, and challenges the prevailing independent-site models of protein evolution, which are unable to capture the fundamental importance of epistasis.

q-bio.BM↗