组合方法用于预测蛋白质 - 连接物结合亲和力
Jiffriya Mohamed Abdul Cader1,2, M A Hakim Newton3,4, Julia Rahman5,6
1School of Information and Communication Technology, Griffith University, Nathan Campus, Australia. jiffriya.cader@griffithuni.edu.au.
集结绑定亲和力 (EBA) 通过使用多种深度学习模型和输入功能,改善了蛋白质 - 连接体结合的预测. 这种方法提高了准确性和概括性,加速了药物发现.
科学领域:
- 计算化学和化学信息学
- 人工智能在药物发现中的作用
背景情况:
- 准确的蛋白质 - 配体结合亲和力预测对于计算机辅助药物发现至关重要.
- 由于依赖单个模型,现有的深度学习方法往往缺乏准确性和概括性.
研究的目的:
- 开发一种新的深度学习方法,即集束结合亲和力 (EBA),用于准确的蛋白质-连接体结合亲和力预测.
- 通过组合多个深度学习模型,训练各种输入特征来提高预测准确性和概括性.
主要方法:
- 训练了13个深度学习模型,使用5个输入特征的组合,结合交叉注意力和自我注意力层.
- 探索了所有可能的模型组合,以确定用于结合亲和力预测的最佳组合.
- 利用蛋白质 - 配体复合体的1D序列和结构特征,避免复杂的3D结构数据.
主要成果:
- 在CASF2016基准数据集上获得了0.914的皮尔森相关系数 (R) 和0.957的根平均平方误差 (RMSE).
- 与CAPLA预测器相比,CSAR-HiQ数据集的R值有超过15%的改善,RMSE有19%的改善.
- 与最先进的方法相比,在五个基准数据集上的所有指标上表现出卓越的表现.
结论:
- 该EBA方法显著提高了蛋白质 - 配体结合亲和力预测的准确性和稳定性.
- 整体方法有效地利用各种模型和特征来增强预测能力.
- 这项工作有助于通过提高潜在药物候选者的成功率来加速药物开发.
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