Kinase-inhibitor binding affinity prediction with pretrained graph encoder and language model.

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Xudong Guo, Zixu Ran, Fuyi Li
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引用次数: 0

Abstract

Accurately predicting inhibitor-kinase binding affinity is crucial in drug discovery and medical applications, especially in the treatment of diseases such as cancer. Existing methods for predicting inhibitor-kinase affinity still face challenges, including insufficient data expression, limited feature extraction, and low performance. Despite the progress made through artificial intelligence methods, particularly deep learning technology, many current methods fail to capture the intricate interactions between kinases and inhibitors. Therefore, it is necessary to develop more advanced methods to solve the existing problems in inhibitor-kinase binding prediction. This study proposed Kinhibit, a novel framework for inhibitor-kinase binding affinity prediction. Kinhibit integrates self-supervised graph contrastive learning with multiview molecular graph representation and structure-informed protein language model (ESM-S) to extract features effectively. Kinhibit also employed a feature fusion approach to optimize the fusion of inhibitor and kinase features. Experimental results demonstrate the superiority of this method, achieving an accuracy of 92.6% in inhibitor prediction tasks of three mitogen-activated protein kinase (MAPK) signalling pathway kinases: Raf protein kinase (RAF), Mitogen-activated protein kinase kinase (MEK), and Extracellular Signal-Regulated Kinase (ERK). Furthermore, the framework achieves an impressive accuracy of 92.9% on the MAPK-All dataset. This study provides promising and effective tools for drug screening and biological sciences.

基于预训练图编码器和语言模型的激酶抑制剂结合亲和力预测。
准确预测抑制剂-激酶结合亲和力在药物发现和医学应用中至关重要,特别是在治疗癌症等疾病方面。现有的预测抑制剂激酶亲和力的方法仍然面临挑战,包括数据表达不足,特征提取有限,性能低下。尽管人工智能方法,特别是深度学习技术取得了进展,但目前的许多方法都无法捕捉到激酶和抑制剂之间复杂的相互作用。因此,有必要开发更先进的方法来解决抑制剂-激酶结合预测中存在的问题。本研究提出了Kinhibit,一个新的框架来预测抑制剂-激酶结合亲和力。Kinhibit将自监督图对比学习与多视图分子图表示和结构信息蛋白语言模型(ESM-S)相结合,有效地提取特征。Kinhibit还采用了特征融合方法来优化抑制剂和激酶特征的融合。实验结果证明了该方法的优越性,在三种丝裂原活化蛋白激酶(MAPK)信号通路激酶:Raf蛋白激酶(Raf)、丝裂原活化蛋白激酶(MEK)和细胞外信号调节激酶(ERK)的抑制剂预测任务中,准确率达到92.6%。此外,该框架在MAPK-All数据集上达到了令人印象深刻的92.9%的准确率。该研究为药物筛选和生物科学提供了有前途的有效工具。
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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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