29 citations · 50 across the 12 of their papers we have counts for
12 papers
Modeling Protein Evolution with Generative Models: from Extant Sequence Data to Evolutionary Dynamics
Matteo Bisardi, Leonardo Di Bari, Saverio Rossi +3
Protein sequences carry a record of evolutionary history shaped by mutation, selection, drift, and epistasis. Recent generative models trained on homologous sequence families offer…
Towards coevolution-aware ancestral sequence reconstruction
Alya Zeinaty, Leonardo di Bari, Saverio Rossi +3
Ancestral sequence reconstruction (ASR) is a powerful approach for studying molecular evolution and the emergence of protein function. Yet most ASR methods assume that sites evolve…
Expanding functional protein sequence space using high entropy generative models
Roberto Netti, Emily Hinds, Francesco Calvanese +3
Boltzmann Machines trained on evolutionary sequence data have emerged as a powerful paradigm for the data-driven design of artificial proteins. However, the relationship between mo…
Modeling Protein Evolution via Generative Inference From Monte Carlo Chains to Population Genetics
Leonardo Di Bari, Thierry Mora, Andrea Pagnani +3
Generative models derived from large protein sequence alignments define complex fitness landscapes, but their utility for accurately modeling non-equilibrium evolutionary dynamics…
Demonstrating Real Advantage of Machine-Learning-Enhanced Monte Carlo for Combinatorial Optimization
Luca Maria Del Bono, Federico Ricci-Tersenghi, Francesco Zamponi
Combinatorial optimization problems are central to both practical applications and the development of optimization methods. While classical and quantum algorithms have been refined…
From Scarce Functional Labels to Label-Aware Generation in Homologous Protein Families
Lorenzo Rosset, Martin Weigt, Francesco Zamponi
Accurately annotating and controlling protein function from sequence data remains a major challenge in protein engineering, especially when functional labels are scarce within larg…
Performance of machine-learning-assisted Monte Carlo in sampling from simple statistical physics models
Luca Maria Del Bono, Federico Ricci-Tersenghi, Francesco Zamponi
Recent years have seen a rise in the application of machine learning techniques to aid the simulation of hard-to-sample systems that cannot be studied using traditional methods. De…
Functional bottlenecks can emerge from non-epistatic underlying traits
Anna Ottavia Schulte, Samar Alqatari, Saverio Rossi +1
Protein fitness landscapes frequently exhibit epistasis, where the effect of a mutation depends on the genetic context in which it occurs, i.e., the rest of the protein sequence. E…
adabmDCA 2.0 -- a flexible but easy-to-use package for Direct Coupling Analysis
Lorenzo Rosset, Roberto Netti, Anna Paola Muntoni +2
In this methods article, we provide a flexible but easy-to-use implementation of Direct Coupling Analysis (DCA) based on Boltzmann machine learning, together with a tutorial on how…
Fluctuations and the limit of predictability in protein evolution
Saverio Rossi, Leonardo Di Bari, Martin Weigt +1
Protein evolution involves mutations occurring across a wide range of time scales. In analogy with disordered systems in statistical physics, this dynamical heterogeneity suggests…
Emergent time scales of epistasis in protein evolution
Leonardo Di Bari, Matteo Bisardi, Sabrina Cotogno +2
We introduce a data-driven epistatic model of protein evolution, capable of generating evolutionary trajectories spanning very different time scales reaching from individual mutati…
Towards Parsimonious Generative Modeling of RNA Families
Francesco Calvanese, Camille N. Lambert, Philippe Nghe +2
Generative probabilistic models emerge as a new paradigm in data-driven, evolution-informed design of biomolecular sequences. This paper introduces a novel approach, called Edge Ac…