Improved compound-protein interaction site and binding affinity prediction using self-supervised protein embeddings.

Jialin Wu, Zhe Liu, Xiaofeng Yang, Zhanglin Lin

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Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Jialin WuSchool of Biology and Biological Engineering, South China University of Technology, 382 East Outer Loop Road, University Park, Guangzhou, 510006, Guangdong, China.

Zhe LiuSchool of Biology and Biological Engineering, South China University of Technology, 382 East Outer Loop Road, University Park, Guangzhou, 510006, Guangdong, China.

Xiaofeng YangSchool of Biology and Biological Engineering, South China University of Technology, 382 East Outer Loop Road, University Park, Guangzhou, 510006, Guangdong, China. biyangxf@scut.edu.cn.

Zhanglin LinSchool of Biology and Biological Engineering, South China University of Technology, 382 East Outer Loop Road, University Park, Guangzhou, 510006, Guangdong, China. zhanglinlin@scut.edu.cn.

Funding

National Key Research and Development Program of China 2018YFA0901000Program for Guangdong Introducing Innovative and Entrepreneurial Teams 2019ZT08Y318

6 · The paper itself

Abstract

backgroundCompound-protein interaction site and binding affinity predictions are crucial for drug discovery and drug design. In recent years, many deep learning-based methods have been proposed for predications related to compound-protein interaction. For protein inputs, how to make use of protein primary sequence and tertiary structure information has impact on prediction results.

resultsIn this study, we propose a deep learning model based on a multi-objective neural network, which involves a multi-objective neural network for compound-protein interaction site and binding affinity prediction. We used several kinds of self-supervised protein embeddings to enrich our protein inputs and used convolutional neural networks to extract features from them. Our results demonstrate that our model had improvements in terms of interaction site prediction and affinity prediction compared to previous models. In a case study, our model could better predict binding sites, which also showed its effectiveness.

conclusionThese results suggest that our model could be a helpful tool for compound-protein related predictions.

Indexed as

Neural Networks, ComputerProteinsAmino Acid SequenceBinding SitesDrug DiscoveryProteinsBinding affinityCompound–protein interactionDeep learningSelf-supervised protein embedding

Identifiers

PMID36526969

DOI10.1186/s12859-022-05107-w

PMCPMC9756525

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.