29 citations · 46 across the 10 of their papers we have counts for
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…
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…
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…
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…
Integrating experimental feedback improves generative models for biological sequences
Francesco Calvanese, Giovanni Peinetti, Polina Pavlinova +2
Generative probabilistic models have shown promise in designing artificial RNA and protein sequences but often suffer from high rates of false positives, where sequences predicted…
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…