Unknown

Dataset Information

0

Accurate prediction of inter-protein residue-residue contacts for homo-oligomeric protein complexes.


ABSTRACT: Protein-protein interactions play a fundamental role in all cellular processes. Therefore, determining the structure of protein-protein complexes is crucial to understand their molecular mechanisms and develop drugs targeting the protein-protein interactions. Recently, deep learning has led to a breakthrough in intra-protein contact prediction, achieving an unusual high accuracy in recent Critical Assessment of protein Structure Prediction (CASP) structure prediction challenges. However, due to the limited number of known homologous protein-protein interactions and the challenge to generate joint multiple sequence alignments of two interacting proteins, the advances in inter-protein contact prediction remain limited. Here, we have proposed a deep learning model to predict inter-protein residue-residue contacts across homo-oligomeric protein interfaces, named as DeepHomo. Unlike previous deep learning approaches, we integrated intra-protein distance map and inter-protein docking pattern, in addition to evolutionary coupling, sequence conservation, and physico-chemical information of monomers. DeepHomo was extensively tested on both experimentally determined structures and realistic CASP-Critical Assessment of Predicted Interaction (CAPRI) targets. It was shown that DeepHomo achieved a high precision of >60% for the top predicted contact and outperformed state-of-the-art direct-coupling analysis and machine learning-based approaches. Integrating predicted inter-chain contacts into protein-protein docking significantly improved the docking accuracy on the benchmark dataset of realistic homo-dimeric targets from CASP-CAPRI experiments. DeepHomo is available at http://huanglab.phys.hust.edu.cn/DeepHomo/.

SUBMITTER: Yan Y 

PROVIDER: S-EPMC8425427 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC6909155 | biostudies-literature
| S-EPMC4894841 | biostudies-literature
| S-EPMC4221654 | biostudies-literature
| S-EPMC3534397 | biostudies-literature
| S-EPMC6332208 | biostudies-literature
| S-EPMC6821021 | biostudies-literature
| S-EPMC4201158 | biostudies-literature
| S-EPMC3498326 | biostudies-literature
| S-EPMC4523921 | biostudies-literature
| S-EPMC6247404 | biostudies-literature