Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes.

ABSTRACT: Membrane proteins are encoded by approximately a quarter of human genes. Inter-chain residue-residue contact information is important for structure prediction of membrane protein complexes and valuable for understanding their molecular mechanism. Although many deep learning methods have been proposed to predict the intra-protein contacts or helix-helix interactions in membrane proteins, it is still challenging to accurately predict their inter-chain contacts due to the limited number of transmembrane proteins. Addressing the challenge, here we develop a deep transfer learning method for predicting inter-chain contacts of transmembrane protein complexes, named DeepTMP, by taking advantage of the knowledge pre-trained from a large data set of non-transmembrane proteins. DeepTMP utilizes a ge

SUBMITTER: Lin P 

PROVIDER: S-EPMC10427616 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature