AdabmDCA: adaptive Boltzmann machine learning for biological sequences.

ABSTRACT:

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 acc

SUBMITTER: Muntoni AP 

PROVIDER: S-EPMC8555268 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature