adabmDCA 2.0 -- a flexible but easy-to-use package for Direct Coupling Analysis
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914134826680320 |
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| author | Rosset, Lorenzo Netti, Roberto Muntoni, Anna Paola Weigt, Martin Zamponi, Francesco |
| author_facet | Rosset, Lorenzo Netti, Roberto Muntoni, Anna Paola Weigt, Martin Zamponi, Francesco |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_18456 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | adabmDCA 2.0 -- a flexible but easy-to-use package for Direct Coupling Analysis Rosset, Lorenzo Netti, Roberto Muntoni, Anna Paola Weigt, Martin Zamponi, Francesco Quantitative Methods Machine Learning Biological Physics 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. |
| title | adabmDCA 2.0 -- a flexible but easy-to-use package for Direct Coupling Analysis |
| topic | Quantitative Methods Machine Learning Biological Physics |
| url | https://arxiv.org/abs/2501.18456 |