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.
翻译:我们概述了人工智能(AI)和机器学习(ML)技术的近期发展情况,这些技术有助于内在障碍蛋白综合结构生物学(IDP)组合。 境内流离失所者挑战传统的蛋白结构功能模式,办法是调整其符合性,以适应特定的约束性伙伴,促使它们调解生物信号、自我组织和条块化等多种而且往往是复杂的细胞功能。因此,获取对其功能的机械性洞察力对于传统结构确定技术来说可能具有挑战性。科学家往往不得不依靠从各种实验技术中提取的零碎证据来描述其功能机制。多尺度模拟可以帮助弥合关于境内流离失所者结构功能关系的重大知识差距 — — 然而,这些技术在解决境内流离失所者结构功能组合内部的突发现象方面也面临着挑战。 我们假设,可扩展的统计推论技术能够有效地整合从多种实验技术和模拟中提取的信息,从而提供这些新兴现象的共性细节。