对于ATP结合位预测的残留级多视图深度学习以及对激酶抑制剂的应用
Jaechan Lee1,2, Dongmin Bang2,3, Sun Kim1,2,3,4
1Department of Computer Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea.
Journal of chemical information and modeling
|December 18, 2024
概括
我们开发了Multiview-ATPBind和ResiBoost,以准确识别腺三酸盐 (ATP) 的结合部位. 这种深度学习方法通过精确预测相互作用和增强激酶抑制剂模拟来改善药物发现.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 结构生物信息学 结构生物信息学
背景情况:
- 准确识别腺三酸盐 (ATP) 结合部位对于理解细胞机制和开发向疗法至关重要,特别是在癌症治疗中用于激酶抑制剂.
- 目前用于识别ATP结合位点的方法往往受到冗长的预计算特征要求和显著的数据不平衡的阻碍,限制了它们在药物发现中的直接适用性.
- 现有预测模型在实际药物发现场景中的实用性,特别是用于增强抑制剂设计,仍然未得到充分探索.
研究的目的:
- 引入新的计算方法,Multiview-ATPBind和ResiBoost,以准确有效地预测ATP结合部位.
- 通过整合不同的数据类型和减轻数据不平衡问题,解决现有方法的局限性.
- 证明拟议方法在药物发现中的实际实用性,特别是对于激酶抑制剂.
主要方法:
- 开发了Multiview-ATPBind,这是一个端到端的深度学习模型,集成了1D序列和3D结构数据,用于预测口袋-连接体相互作用的残留水平.
- 引入了ResiBoost,一种残留水平提升算法,以抵消数据不平衡,并改善罕见的正结合残留物的预测.
- 在使用实验和人工智能预测的蛋白质结构的基准数据集上验证了模型的性能.
主要成果:
- 拟议的方法,Multiview-ATPBind与ResiBoost相结合,在基准数据集上显著超过了最先进的模型.
- 在平衡的绩效指标方面取得了实质性的改进,这表明在各种数据集中具有强大的预测能力.
- 在预测像伊马替尼和达沙替尼这样的激酶抑制剂的结合部位以及增强分子对接模拟方面证明了成功的应用.
结论:
- 多视图ATPBind和ResiBoost提供了一种快速,精确和强大的方法来识别ATP结合部位,克服了以前方法的关键局限性.
- 集成的深度学习和提升策略有效地处理复杂的绑定站点数据,并提高预测准确性.
- 在激酶抑制剂药物发现中取得的成功表明了这种方法在推进治疗开发方面的巨大潜力.
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