Francesco Zamponi

This paper introduces a coevolution-aware ancestral sequence reconstruction framework that improves reconstruction by incorporating residue-residue constraints from extant protein families.

This study compares three simulation schemes for modeling protein evolution, finding that population genetics dynamics best capture phylogenetic correlations and selective sweeps without requiring additional data.

The paper uses probabilistic generative models to estimate the number of self-reproducing RNAs and finds that at least 10^39 such ribozymes may exist.

The paper develops a two-stage strategy for functional annotation and label-aware sequence generation in protein families using limited labels and phylogeny-aware evaluation.

The paper quantifies fluctuations in protein evolution by analyzing correlations between mutations and shows ancestral sequence properties influence evolutionary dynamics and reconstruction accuracy.

The paper shows functional bottlenecks can arise from global epistasis with an additive trait, not requiring complex network interactions.

The paper uses coevolutionary analysis and deep mutational scanning to study epistatic networks in B1-lactamases, revealing both conserved and specific interactions across homologs.

This paper introduces a data-driven epistatic model of protein evolution that reproduces sequence statistics across different time scales and uncovers the collective nature of epistasis.

This paper introduces eaDCA, a sparse generative model for RNA families that efficiently predicts functional sequences and mutation effects, showing comparable performance to complex methods with fewer parameters.

The paper introduces simple autoregressive models for efficient generative modeling of protein sequences, showing comparable performance to complex models at lower computational cost.

The paper introduces DCAlign, a method for sequence alignment that incorporates coevolution and second-order interactions, improving upon profile models for proteins and RNA.

adabmDCA 2.0 provides a flexible and multi-language implementation of Direct Coupling Analysis for protein and RNA sequence data, enabling tasks like contact prediction and sequence design.