3 papers
q-bio.BM2025
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…
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…
q-bio.BM2024
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…