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.

使用计算方法预测蛋白质结构已探索了二十多年，为比较建模，从头建模和结构优化协议中算法的更集中研究与开发铺平了道路。在基于模板的建模协议中已见证了巨大的成功，而涉及无模板建模的策略仍然滞后，特别是对于较大的蛋白质（> 150 aa）。从头开始观察到各种改进蛋白质结构预测方法随着时间的推移而发展，最近的方法归因于使用深度学习方法从其氨基酸序列构建蛋白质骨架结构。这篇综述重点介绍了蛋白质结构无模板建模的主要策略，同时讨论了每种策略下开发的工具很少。通过CASP平台的演变，它还将简要评论在一段时间内从头开始进行蛋白质建模的过程中所观察到的进展。