8 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…
Reconstructability of evolutionary intermediates in generative epistatic landscapes
Roberto Netti, Martin Weigt
Evolutionary intermediates connect observed proteins, but the sequence of steps that produced them is rarely recoverable from extant data alone. Here we ask what can, and cannot, b…
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…
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…