adabmDCA: adaptive Boltzmann machine learning for biological sequences

Background Boltzmann machines are energy-based models that have been shown to provide an accurate statistical description of domains of evolutionary-related protein and RNA families. They are parametrized in terms of local biases accounting for residue conservation, and pairwise terms to model epist...

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Bibliographic Details
Published in:BMC bioinformatics 2021-10, Vol.22 (1), p.1-528, Article 528
Main Authors: Muntoni, Anna Paola, Pagnani, Andrea, Weigt, Martin, Zamponi, Francesco
Format: Article
Language:English
Subjects:
Boltzmann machine learning
Coevolution
Computer applications
Computing time
Condensed Matter
Epistasis
Inverse problems
Learning algorithms
Learning set
Machine learning
Mathematical models
Model accuracy
Parameters
Physics
Protein modelling
Proteins
Residues
Ribonucleic acid
RNA
RNA modelling
Software
Statistical analysis
Statistical inference
Statistical Mechanics
User requirements
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Summary:Background Boltzmann machines are energy-based models that have been shown to provide an accurate statistical description of domains of evolutionary-related protein and RNA families. They are parametrized in terms of local biases accounting for residue conservation, and pairwise terms to model epistatic coevolution between residues. From the model parameters, it is possible to extract an accurate prediction of the three-dimensional contact map of the target domain. More recently, the accuracy of these models has been also assessed in terms of their ability in predicting mutational effects and generating in silico functional sequences. Results Our adaptive implementation of Boltzmann machine learning, adabmDCA, can be generally applied to both protein and RNA families and accomplishes several learning set-ups, depending on the complexity of the input data and on the user requirements. The code is fully available at https://github.com/anna-pa-m/adabmDCA. As an example, we have performed the learning of three Boltzmann machines modeling the Kunitz and Beta-lactamase2 protein domains and TPP-riboswitch RNA domain. Conclusions The models learned by adabmDCA are comparable to those obtained by state-of-the-art techniques for this task, in terms of the quality of the inferred contact map as well as of the synthetically generated sequences. In addition, the code implements both equilibrium and out-of-equilibrium learning, which allows for an accurate and lossless training when the equilibrium one is prohibitive in terms of computational time, and allows for pruning irrelevant parameters using an information-based criterion.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-021-04441-9