PPB-Affinity: Protein-Protein Binding Affinity dataset for AI-based protein drug discovery

Prediction of protein-protein binding (PPB) affinity plays an important role in large-molecular drug discovery. Deep learning (DL) has been adopted to predict the changes of PPB binding affinities upon mutations, but there was a scarcity of studies predicting the PPB affinity itself. The major reason is the paucity of open-source dataset with PPB affinity data. To address this gap, the current study introduced a large comprehensive PPB affinity (PPB-Affinity) dataset. The PPB-Affinity dataset contains key information such as crystal structures of protein-protein complexes (with or without protein mutation patterns), PPB affinity, receptor protein chain, ligand protein chain, etc. To the best of our knowledge, this is the largest publicly available PPB affinity dataset, and we believe it will significantly advance drug discovery by streamlining the screening of potential large-molecule drugs. We also developed a deep-learning benchmark model with this dataset to predict the PPB affinity, providing a foundational comparison for the research community.

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Publication Details

Journal
Scientific Data
Published
2024-12-03
DOI
https://doi.org/10.1038/s41597-024-03997-4
Citations
9
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
3.25

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article

PPB-Affinity: Protein-Protein Binding Affinity dataset for AI-based protein drug discovery

Xiangjun Zhai, Guizhen Fan, Ku-Geng Huo, Peiyi Chen et al.
9 citations
Scientific Data
Computational Drug Discovery Methods
3.25
article

PPB-Affinity: Protein-Protein Binding Affinity dataset for AI-based protein drug discovery

Xiangjun Zhai, Guizhen Fan, Ku-Geng Huo, Peiyi Chen, Huaqing Liu, Shuxian Zhou, Lanqing Han
article en
9 citations

Abstract

Prediction of protein-protein binding (PPB) affinity plays an important role in large-molecular drug discovery. Deep learning (DL) has been adopted to predict the changes of PPB binding affinities upon mutations, but there was a scarcity of studies predicting the PPB affinity itself. The major reason is the paucity of open-source dataset with PPB affinity data. To address this gap, the current study introduced a large comprehensive PPB affinity (PPB-Affinity) dataset. The PPB-Affinity dataset contains key information such as crystal structures of protein-protein complexes (with or without protein mutation patterns), PPB affinity, receptor protein chain, ligand protein chain, etc. To the best of our knowledge, this is the largest publicly available PPB affinity dataset, and we believe it will significantly advance drug discovery by streamlining the screening of potential large-molecule drugs. We also developed a deep-learning benchmark model with this dataset to predict the PPB affinity, providing a foundational comparison for the research community.

Scientific DataVol. 11(1)
Bioscience (China) (CN), Key Laboratory of Guangdong Province (CN), ShenZhen People’s Hospital (CN), Pearl River Hydraulic Research Institute (CN)
National Natural Science Foundation of China, Guangdong Medical Research Foundation
Openalex Percentile: Top 7%
Computational Drug Discovery Methods
3.25
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