3 papers
q-bio.PE2026
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…
q-bio.QM2026
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…
q-bio.QM2025
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…