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

Jialin Wu1, Zhe Liu1, Xiaofeng Yang2

  • 1School of Biology and Biological Engineering, South China University of Technology, 382 East Outer Loop Road, University Park, Guangzhou, 510006, Guangdong, China.

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|December 16, 2022
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Summary
This summary is machine-generated.

This study introduces a new deep learning model for predicting compound-protein interactions and binding affinity. The model improves accuracy in interaction site and affinity predictions, aiding drug discovery.

Keywords:
Binding affinityCompound–protein interactionDeep learningSelf-supervised protein embedding

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Drug discovery

Background:

  • Compound-protein interaction site and binding affinity predictions are vital for drug discovery and design.
  • Deep learning methods are increasingly used for compound-protein interaction predictions.
  • Effective utilization of protein primary sequence and tertiary structure information is key for accurate predictions.

Purpose of the Study:

  • To propose a novel deep learning model for compound-protein interaction site and binding affinity prediction.
  • To enhance protein input representation using self-supervised embeddings.
  • To improve the accuracy of predicting both interaction sites and binding affinity.

Main Methods:

  • Developed a multi-objective neural network model.
  • Employed self-supervised protein embeddings to enrich protein input data.
  • Utilized convolutional neural networks for feature extraction from protein data.

Main Results:

  • The proposed model demonstrated improved performance in interaction site prediction compared to previous methods.
  • The model also showed enhanced accuracy in binding affinity prediction.
  • A case study confirmed the model's effectiveness in predicting binding sites.

Conclusions:

  • The developed deep learning model serves as a valuable tool for compound-protein interaction predictions.
  • The model's effectiveness was validated through improved prediction accuracy and a successful case study.
  • This approach contributes to advancing computational methods in drug discovery and design.