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Updated: Jan 12, 2026

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
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adabmDCA 2.0-A Flexible but Easy-to-Use Package for Direct Coupling Analysis.

Lorenzo Rosset1,2, Roberto Netti1, Anna Paola Muntoni3

  • 1Department of Computational, Quantitative and Synthetic Biology, Sorbonne Université, CNRS, Paris, France.

Methods in Molecular Biology (Clifton, N.J.)
|November 1, 2025
PubMed
Summary

We present adabmDCA 2.0, a flexible direct coupling analysis (DCA) tool using Boltzmann machine learning. This package aids in predicting protein and RNA sequence features, contact prediction, and sequence design.

Keywords:
Direct coupling analysisGenerative probabilistic modelsProtein-sequence modeling

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning

Background:

  • Direct Coupling Analysis (DCA) is a powerful method for inferring biological sequence information.
  • Existing DCA implementations can be complex and lack flexibility for diverse applications.
  • There is a need for user-friendly and versatile DCA tools applicable to various biological sequences and computational architectures.

Purpose of the Study:

  • To introduce adabmDCA 2.0, a novel implementation of direct coupling analysis (DCA).
  • To provide a flexible and easy-to-use tool for various downstream tasks in sequence analysis.
  • To support multiple programming languages and computational architectures.

Main Methods:

  • Implementation of DCA using Boltzmann machine learning.
  • Development of a common front-end interface for C++, Julia, and Python.
  • Inclusion of various learning protocols for dense and sparse generative DCA models.
  • Support for single-core, multicore CPU, and GPU architectures.

Main Results:

  • adabmDCA 2.0 offers a flexible and user-friendly implementation of DCA.
  • The package supports multiple programming languages and diverse computational hardware.
  • It enables direct application to residue-residue contact prediction, mutational-effect prediction, and sequence library scoring.
  • The tool facilitates the generation of artificial sequences for protein and RNA design.

Conclusions:

  • adabmDCA 2.0 provides a versatile and accessible platform for advanced sequence analysis using DCA.
  • The package empowers researchers in protein and RNA sequence data analysis and design.
  • This implementation simplifies complex DCA tasks, enhancing discoverability in biological sequence research.