ArticleBMC bioinformatics2019
Identifying protein-protein interface via a novel multi-scale local sequence and structural representation.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Protein engineering in the deep learning era.mLife · 2024Review
- A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries.Angewandte Chemie (International ed. in English) · 2024Article
- HormoNet: a deep learning approach for hormone-drug interaction prediction.BMC bioinformatics · 2024Article
- Sifting Through the Noise: A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries.bioRxiv : the preprint server for biology · 2024Article
- AptaNet as a deep learning approach for aptamer-protein interaction prediction.Scientific reports · 2021Article
- Toward complete rational control over protein structure and function through computational design.Current opinion in structural biology · 2021Review
- A Cancer Gene Module Mining Method Based on Bio-Network of Multi-Omics Gene Groups.Frontiers in oncology · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
backgroundProtein-protein interaction plays a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. Gaining insights of various binding abilities can deepen our understanding of the interaction. It is of great interest to understand how proteins in a complex interact with each other. Many efficient methods have been developed for identifying protein-protein interface.
resultsIn this paper, we obtain the local information on protein-protein interface, through multi-scale local average block and hexagon structure construction. Given a pair of proteins, we use a trained support vector regression (SVR) model to select best configurations. On Benchmark v4.0, our method achieves average I
conclusionExperiments show that our method performs better than some state-of-the-art methods, based on the prediction quality improved in terms of CAPRI evaluation criteria. All these results demonstrate that our method is a valuable technological tool for identifying protein-protein interface.
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