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

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Huaqing Liu, Peiyi Chen, Xiaochen Zhai, Ku-Geng Huo, Shuxian Zhou, Lanqing Han, Guoxin Fan
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引用次数: 0

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.

PPB-Affinity:用于基于人工智能的蛋白质药物发现的蛋白质-蛋白质结合亲和力数据集。
蛋白-蛋白结合(PPB)亲和力预测在大分子药物发现中具有重要作用。深度学习(Deep learning, DL)已被用于预测PPB结合亲和力随突变的变化,但预测PPB亲和力本身的研究很少。主要原因是缺少PPB关联数据的开源数据集。为了解决这一差距,目前的研究引入了一个大型的综合PPB亲和力(PPB- affinity)数据集。PPB亲和力数据集包含蛋白质-蛋白质复合物的晶体结构(有或没有蛋白质突变模式)、PPB亲和力、受体蛋白链、配体蛋白链等关键信息。据我们所知,这是最大的公开可用的PPB亲和数据集,我们相信它将通过简化潜在大分子药物的筛选来显著推进药物发现。我们还利用该数据集开发了一个深度学习基准模型来预测PPB亲和力,为研究界提供基础比较。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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