用AI预测蛋白质构象动态，突破传统结构预测瓶颈
AI-based Methods for Simulating, Sampling, and Predicting Protein Ensembles
- 融合生成模型与多序列比对扰动，构建蛋白质动态构象集
- 提出闭环训练框架，缓解数据稀缺导致的模型偏差
- 适合结构生物学与药物设计领域研究者参考
深度学习推动了蛋白质结构预测的快速发展，但在蛋白质构象集合（protein ensembles）方面进展缓慢。本文综述了基于AI的蛋白质构象集合模拟、采样与预测的最新方向，包括粗粒度力场、生成模型、多序列比对扰动方法以及构象描述符建模。重点评估了当前方法的技术成熟度、各类技术的优劣，并探讨了处于早期发展阶段的有前景机器学习框架。文章倡导建立模型训练、模拟与推断之间的闭环反馈机制，以应对训练数据不足的问题，推动下一代模型的发展。
原文摘要 · Abstract (English)
Advances in deep learning have opened an era of abundant and accurate predicted protein structures; however, similar progress in protein ensembles has remained elusive. This review highlights several recent research directions towards AI-based predictions of protein ensembles, including coarse-grained force fields, generative models, multiple sequence alignment perturbation methods, and modeling of ensemble descriptors. An emphasis is placed on realistic assessments of the technological maturity of current methods, the strengths and weaknesses of broad families of techniques, and promising machine learning frameworks at an early stage of development. We advocate for "closing the loop" between model training, simulation, and inference to overcome challenges in training data availability and to enable the next generation of models.
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