论文标题
人工智能技术的内在无序蛋白质综合结构生物学
Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins
论文作者
论文摘要
我们概述了人工智能（AI）和机器学习（ML）技术的最新发展，用于固有无序蛋白质（IDP）合奏的整合结构生物学。 IDP通过对特定的结合伴侣的响应来调整其构象来挑战传统的蛋白质结构 - 功能范式，从而导致它们介导了多样化，并且通常是复杂的细胞功能，例如生物信号传导，自我组织和分隔。因此，对于传统的结构确定技术，获得对其功能的机理见解可能是具有挑战性的。通常，科学家必须依靠从多种实验技术中得出的零星证据来表征其功能机制。多尺度模拟可以帮助弥合有关IDP结构功能关系的关键知识差距 - 但是，这些技术在解决IDP构象合奏中的新兴现象方面也面临挑战。我们认为，可扩展的统计推断技术可以有效地整合从多种实验技术和模拟中收集的信息，从而提供对这些新兴现象的原子细节的访问。
We outline recent developments in artificial intelligence (AI) and machine learning (ML) techniques for integrative structural biology of intrinsically disordered proteins (IDP) ensembles. IDPs challenge the traditional protein structure-function paradigm by adapting their conformations in response to specific binding partners leading them to mediate diverse, and often complex cellular functions such as biological signaling, self organization and compartmentalization. Obtaining mechanistic insights into their function can therefore be challenging for traditional structural determination techniques. Often, scientists have to rely on piecemeal evidence drawn from diverse experimental techniques to characterize their functional mechanisms. Multiscale simulations can help bridge critical knowledge gaps about IDP structure function relationships - however, these techniques also face challenges in resolving emergent phenomena within IDP conformational ensembles. We posit that scalable statistical inference techniques can effectively integrate information gleaned from multiple experimental techniques as well as from simulations, thus providing access to atomistic details of these emergent phenomena.
