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 to use it. The package \texttt{adabmDCA 2.0} is available in different programming languages (C++, Julia, Python) usable on different architectures (single-core and multi-core CPU, GPU) using a common front-end interface. In addition to several learning protocols for dense and sparse generative DCA models, it allows to directly address common downstream tasks like residue-residue contact prediction, mutational-effect prediction, scoring of sequence libraries and generation of artificial sequences for sequence design. It is readily applicable to protein and RNA sequence data.
@article{arxiv.2501.18456,
title = {adabmDCA 2.0 -- a flexible but easy-to-use package for Direct Coupling Analysis},
author = {Lorenzo Rosset and Roberto Netti and Anna Paola Muntoni and Martin Weigt and Francesco Zamponi},
journal= {arXiv preprint arXiv:2501.18456},
year = {2025}
}