Estimating probabilistic context-free grammars for proteins using contact map constraints.

ABSTRACT: Interactions between amino acids that are close in the spatial structure, but not necessarily in the sequence, play important structural and functional roles in proteins. These non-local interactions ought to be taken into account when modeling collections of proteins. Yet the most popular representations of sets of related protein sequences remain the profile Hidden Markov Models. By modeling independently the distributions of the conserved columns from an underlying multiple sequence alignment of the proteins, these models are unable to capture dependencies between the protein residues. Non-local interactions can be represented by using more expressive grammatical models. However, learning such grammars is difficult. In this work, we propose to use information on protein contacts to faci

SUBMITTER: Dyrka W 

PROVIDER: S-EPMC6428041 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature