DeepREAL: a deep learning powered multi-scale modeling framework for predicting out-of-distribution ligand-induced GPCR activity.
DeepREAL: a deep learning powered multi-scale modeling framework for predicting out-of-distribution ligand-induced GPCR activity.
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DOI：
10.1093/bioinformatics/btac154
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发表时间：
2022-04-28
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二十年来，药物发现见证了对药物与靶标物理相互作用的预测模型的深入探索。然而，需要填补一个关键的知识空白，将药物-靶标相互作用与临床结果相关联：预测全基因组受体活性或功能选择性，特别是由新型化学物质诱导的激动剂与拮抗剂。两个主要障碍加剧了这项任务的难度：已知的受体活动数据太稀缺，无法根据基因组规模的应用训练强大的模型，而现实世界的应用程序需要根据各种移位分布的数据部署模型。为了应对这些挑战，我们开发了一个端到端深度学习框架 DeepREAL，用于对全基因组配体诱导的受体活动进行多尺度建模。 DeepREAL利用对数千万个蛋白质序列的自监督学习和预训练的二元相互作用分类来解决数据分布偏移和数据稀缺问题。对 G 蛋白偶联受体 (GPCR) 的广泛基准研究模拟了真实场景，表明 DeepREAL 在分布外设置中实现了最先进的性能。 DeepREAL 可以扩展到 GPCR 之外的其他基因家族。所有使用的数据均从 Pfam、GLASS 和 IUPHAR/BPS 下载，数据来自参考文献。读者可前往其官方网站获取原始数据。代码可在 GitHub https://github.com/XieResearchGroup/DeepREAL 上获取。 补充数据可在生物信息学在线获取。
Drug discovery has witnessed intensive exploration of predictive modeling of drug–target physical interactions over two decades. However, a critical knowledge gap needs to be filled for correlating drug–target interactions with clinical outcomes: predicting genome-wide receptor activities or function selectivity, especially agonist versus antagonist, induced by novel chemicals. Two major obstacles compound the difficulty on this task: known data of receptor activity is far too scarce to train a robust model in light of genome-scale applications, and real-world applications need to deploy a model on data from various shifted distributions. To address these challenges, we have developed an end-to-end deep learning framework, DeepREAL, for multi-scale modeling of genome-wide ligand-induced receptor activities. DeepREAL utilizes self-supervised learning on tens of millions of protein sequences and pre-trained binary interaction classification to solve the data distribution shift and data scarcity problems. Extensive benchmark studies on G-protein coupled receptors (GPCRs), which simulate real-world scenarios, demonstrate that DeepREAL achieves state-of-the-art performances in out-of-distribution settings. DeepREAL can be extended to other gene families beyond GPCRs. All data used are downloaded from Pfam, GLASS and IUPHAR/BPS and the data from reference. Readers are directed to their official website for original data. Code is available on GitHub https://github.com/XieResearchGroup/DeepREAL. Supplementary data are available at Bioinformatics online.
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DOI：
10.3389/fbinf.2021.693177
发表时间：
2021
期刊：
FRONTIERS IN BIOINFORMATICS
影响因子：
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作者：
Liu, Yang;Wu, You;Shen, Xiaoke;Xie, Lei
通讯作者：
Xie, Lei
影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
影响因子：
4.3
作者：
Lim H;Poleksic A;Yao Y;Tong H;He D;Zhuang L;Meng P;Xie L
通讯作者：
Xie L
影响因子：
14.9
作者：
Mistry J;Chuguransky S;Williams L;Qureshi M;Salazar GA;Sonnhammer ELL;Tosatto SCE;Paladin L;Raj S;Richardson LJ;Finn RD;Bateman A
通讯作者：
Bateman A
DOI：
10.1109/tpami.2021.3095381
发表时间：
2022-10-01
影响因子：
23.6
作者：
Elnaggar, Ahmed;Heinzinger, Michael;Rost, Burkhard
通讯作者：
Rost, Burkhard