Hameduh Tareq, Haddad Yazan, Adam Vojtech, Heger Zbynek
Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic.
Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic.
Comput Struct Biotechnol J. 2020 Nov 14;18:3494-3506. doi: 10.1016/j.csbj.2020.11.007. eCollection 2020.
Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.
同源建模是一种利用蛋白质一级序列并借助与其他蛋白质结构相似性所获得的先验知识来构建蛋白质三维结构的方法。同源建模过程按顺序进行，先优化序列/结构比对，然后构建主链，随后添加侧链。一旦对低同源性环进行建模，就对整个三维结构进行优化和验证。在过去三十年中，一些集体和合作项目推动了同源建模在这两方面的持续发展。蛋白质结构预测关键评估（CASP）是一项全球范围内的社区实验，历来记录了该领域的进展。“在家折叠”（Folding@Home）和“在家罗塞塔”（Rosetta@Home）是众包项目的例子，社区在其中共享计算资源，而罗塞塔社区（RosettaCommons）是一个社区共享计算算法开发代码库的项目例子。“折叠它”（Foldit）是另一个项目，参与者在一款蛋白质折叠电子游戏中相互竞争以预测三维结构。在过去几年中，接触图深度机器学习被引入到三维结构预测过程中，增加了更多信息并显著提高了模型的准确性。在本综述中，我们将带领读者踏上一段探索之旅，从同源建模的起源到最近的转变，这些转变彻底改变了同源建模领域。此外，我们还将讨论这个快速发展领域中出现的新趋势。
