10 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…
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…
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…
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…
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…