adabmDCA 2.0 -- a flexible but easy-to-use package for Direct Coupling Analysis

TL;DR

adabmDCA 2.0 offers a versatile, user-friendly package for Direct Coupling Analysis applicable to protein and RNA sequences, supporting various programming languages and architectures with multiple downstream applications.

Contribution

It introduces a flexible, easy-to-use implementation of DCA with tutorials, supporting multiple languages, architectures, and downstream tasks, enhancing accessibility and functionality.

Findings

Supports multiple programming languages and architectures.

Enables various downstream tasks like contact prediction and sequence generation.

Applicable to protein and RNA sequence data.

Abstract

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.