基于序列的蛋白-蛋白相互作用预测及其在药物发现中的应用
François Charih1,2,3, James R Green1,3, Kyle K Biggar2,3
1Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada.
Cells
|September 26, 2025
概括
本综述强调了基于序列的蛋白质-蛋白质相互作用 (PPI) 预测方法. 这些计算方法对于识别疾病点和推动药物发现至关重要.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 异常的蛋白质与蛋白质相互作用 (PPI) 涉及到许多人类疾病.
- 针对这些有害相互作用是一个有前途的治疗策略.
- 计算方法,特别是深度学习,正在推动PPI预测.
研究的目的:
- 审查最新的基于序列的PPI预测方法.
- 探索PPI预测对目标识别和药物发现的影响.
- 在PPI预测中强调严格的模型评估.
主要方法:
- 对PPI预测的数据源和策划技术的概述.
- 对传统基于相似性和基于深度学习的PPI预测指标的调查.
- 强调PPI预测的深度学习中的变压器架构.
主要成果:
- 基于序列的PPI预测为基于结构的方法提供了一个可行的替代方案.
- 提供了蛋白质组学中PPI预测,目标识别和治疗设计的例子.
- 审查强调了数据质量和模型评估的重要性.
结论:
- 基于序列的PPI预测是生物医学研究中广泛适用的工具.
- 这些方法对确定治疗点和设计新药作出了重大贡献.
- 计算方法的进步正在彻底改变疾病治疗策略.
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