Zhu K, Su H, Peng Z, Yang J. A unified approach to protein domain parsing with inter-residue distance matrix.
Bioinformatics 2023;
39:7025502. [PMID:
36734597 PMCID:
PMC9919455 DOI:
10.1093/bioinformatics/btad070]
[Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 01/02/2023] [Accepted: 02/01/2023] [Indexed: 02/04/2023] Open
Abstract
MOTIVATION
It is fundamental to cut multi-domain proteins into individual domains, for precise domain-based structural and functional studies. In the past, sequence-based and structure-based domain parsing was carried out independently with different methodologies. The recent progress in deep learning-based protein structure prediction provides the opportunity to unify sequence-based and structure-based domain parsing.
RESULTS
Based on the inter-residue distance matrix, which can be either derived from the input structure or predicted by trRosettaX, we can decode the domain boundaries under a unified framework. We name the proposed method UniDoc. The principle of UniDoc is based on the well-accepted physical concept of maximizing intra-domain interaction while minimizing inter-domain interaction. Comprehensive tests on five benchmark datasets indicate that UniDoc outperforms other state-of-the-art methods in terms of both accuracy and speed, for both sequence-based and structure-based domain parsing. The major contribution of UniDoc is providing a unified framework for structure-based and sequence-based domain parsing. We hope that UniDoc would be a convenient tool for protein domain analysis.
AVAILABILITY AND IMPLEMENTATION
https://yanglab.nankai.edu.cn/UniDoc/.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
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