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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 adabmDCA 2.0 is available in different programming languages (C \( ++ \) , Julia, Python) usable on different architectures (single-core and multicore 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.

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adabmDCA 2.0—A Flexible but Easy-to-Use Package for Direct Coupling Analysis

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  • 发表日期：

    2026年

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  • Lorenzo Rosset,
  • Roberto Netti,
  • Anna Paola Muntoni,
  • Martin Weigt,
  • Francesco Zamponi

摘要

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 adabmDCA 2.0 is available in different programming languages (C \( ++ \) , Julia, Python) usable on different architectures (single-core and multicore 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.