Improved inter-residue contact prediction via a hybrid generative model and dynamic loss function.

ABSTRACT: Protein contact maps represent spatial pairwise inter-residue interactions, providing a protein's translationally and rotationally invariant topological representation. Accurate contact map prediction has been a critical driving force for improving protein structure determination. Contact maps can also be used as a stand-alone tool for varied applications such as prediction of protein-protein interactions, structure-aware thermal stability or physicochemical properties. We develop a novel hybrid contact map prediction model, CGAN-Cmap, that uses a generative adversarial neural network embedded with a series of modified squeeze and excitation residual networks. To exploit features of different dimensions, we introduce two parallel modules. This architecture improves the prediction by increa

SUBMITTER: Madani M 

PROVIDER: S-EPMC9674885 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature