Residue-Level Multiview Deep Learning for ATP Binding Site Prediction and Applications in Kinase Inhibitors.

IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Journal of Chemical Information and Modeling Pub Date : 2025-01-13 Epub Date: 2024-12-17 DOI:10.1021/acs.jcim.4c01255
Jaechan Lee, Dongmin Bang, Sun Kim
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Abstract

Accurate identification of adenosine triphosphate (ATP) binding sites is crucial for understanding cellular functions and advancing drug discovery, particularly in targeting kinases for cancer treatment. Existing methods face significant challenges due to their reliance on time-consuming precomputed features and the heavily imbalanced nature of binding site data without further investigations on their utility in drug discovery. To address these limitations, we introduced Multiview-ATPBind and ResiBoost. Multiview-ATPBind is an end-to-end deep learning model that integrates one-dimensional (1D) sequence and three-dimensional (3D) structural information for rapid and precise residue-level pocket-ligand interaction predictions. Additionally, ResiBoost is a novel residue-level boosting algorithm designed to mitigate data imbalance by enhancing the prediction of rare positive binding residues. Our approach outperforms state-of-the-art models on benchmark data sets, showing significant improvements in balanced metrics with both experimental and AI-predicted structures. Furthermore, our model seamlessly transfers to predicting binding sites and enhancing docking simulations for kinase inhibitors, including imatinib and dasatinib, underscoring the potential of our method in drug discovery applications.

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残差水平多视图深度学习用于ATP结合位点预测及其在激酶抑制剂中的应用。
准确识别三磷酸腺苷(ATP)结合位点对于理解细胞功能和推进药物发现至关重要,特别是在靶向激酶治疗癌症方面。现有的方法面临着巨大的挑战,因为它们依赖于耗时的预计算特征和结合位点数据的严重不平衡性质,而没有进一步研究它们在药物发现中的效用。为了解决这些限制,我们引入了Multiview-ATPBind和ResiBoost。Multiview-ATPBind是一种端到端深度学习模型,集成了一维(1D)序列和三维(3D)结构信息,用于快速精确的残基级口袋配体相互作用预测。此外,ResiBoost是一种新的残基水平增强算法,旨在通过增强对罕见正结合残基的预测来缓解数据不平衡。我们的方法在基准数据集上优于最先进的模型,在实验和人工智能预测结构的平衡指标上都有显着改善。此外,我们的模型无缝地转移到预测结合位点和增强激酶抑制剂的对接模拟,包括伊马替尼和达沙替尼,强调了我们的方法在药物发现应用中的潜力。
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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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