deepBBQ: A Deep Learning Approach to the Protein Backbone Reconstruction

deepBBQ: A Deep Learning Approach to the Protein Backbone Reconstruction

Coarse-grained models have provided researchers with greatly improved computational efficiency in modeling structures and dynamics of biomacromolecules, but, to be practically useful, they need fast and accurate conversion methods back to the all-atom representation. Reconstruction of atomic details...

Full description

Saved in:
Bibliographic Details
Main Authors: Justyna D. Kryś, Maksymilian Głowacki , Piotr Śmieja , Dominik Gront
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Biomolecules
Subjects:
atomic reconstruction
structural bioinformatics
deep learning
coarse-grained modeling
convolutional neural network
Online Access:https://www.mdpi.com/2218-273X/14/11/1448
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Coarse-grained models have provided researchers with greatly improved computational efficiency in modeling structures and dynamics of biomacromolecules, but, to be practically useful, they need fast and accurate conversion methods back to the all-atom representation. Reconstruction of atomic details may also be required in the case of some experimental methods, like electron microscopy, which may provide C<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>-only structures. In this contribution, we present a new method for recovery of all backbone atom positions from just the C<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> coordinates. Our approach, called deepBBQ, uses a deep convolutional neural network to predict a single internal coordinate per peptide plate, based on C<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> trace geometric features, and then proceeds to recalculate the cartesian coordinates based on the assumption that the peptide plate atoms lie in the same plane. Extensive comparison with similar programs shows that our solution is accurate and cost-efficient. The deepBBQ program is available as part of the open-source bioinformatics toolkit Bioshell and is free for download and the documentation is available online.
ISSN:2218-273X

Similar Items