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浅析无模板蛋白质结构预测方法的发展历程。

A glance into the evolution of template-free protein structure prediction methodologies.

机构信息

Université de Nantes, CNRS, UFIP, UMR6286, F-44000, Nantes, France.

Computational Approaches to Protein Science (CAPS), National Centre for Biological Sciences (NCBS), Tata Institute for Fundamental Research (TIFR), Bangalore, 560-065, India.

出版信息

Biochimie. 2020 Aug;175:85-92. doi: 10.1016/j.biochi.2020.04.026. Epub 2020 May 15.

Abstract

Prediction of protein structures using computational approaches has been explored for over two decades, paving a way for more focused research and development of algorithms in comparative modelling, ab intio modelling and structure refinement protocols. A tremendous success has been witnessed in template-based modelling protocols, whereas strategies that involve template-free modelling still lag behind, specifically for larger proteins (>150 a.a.). Various improvements have been observed in ab initio protein structure prediction methodologies overtime, with recent ones attributed to the usage of deep learning approaches to construct protein backbone structure from its amino acid sequence. This review highlights the major strategies undertaken for template-free modelling of protein structures while discussing few tools developed under each strategy. It will also briefly comment on the progress observed in the field of ab initio modelling of proteins over the course of time as seen through the evolution of CASP platform.

摘要

利用计算方法预测蛋白质结构已经探索了二十多年,为比较建模、从头建模和结构精修协议中的算法的更有针对性的研究和开发铺平了道路。基于模板的建模协议取得了巨大的成功,而涉及无模板建模的策略仍然落后,特别是对于较大的蛋白质(>150 个氨基酸)。随着时间的推移,从头蛋白质结构预测方法学中观察到了各种改进,最近的改进归因于使用深度学习方法根据其氨基酸序列构建蛋白质骨架结构。本文综述了无模板建模蛋白质结构的主要策略,同时讨论了每个策略下开发的几个工具。还将简要评论通过 CASP 平台的发展,从时间上观察到的蛋白质从头建模领域的进展。

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