Current structure predictors are not learning the physics of protein folding.
Current structure predictors are not learning the physics of protein folding.
复制标题
目前的结构预测者并没有学习蛋白质折叠的物理原理。
DOI：
10.1093/bioinformatics/btab881
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发表时间：
2022-03-28
期刊：
影响因子：
--
通讯作者：
Deane CM
中科院分区：
文献类型：
--
作者：
Outeiral C;Nissley DA;Deane CM
动机预测蛋白质的天然状态一直被认为是理解蛋白质折叠的一个关键问题。由深度学习推动的结构建模的最新进展在预测蛋白质晶体结构方面取得了前所未有的成功，但尚不清楚这些模型是在学习蛋白质如何动态折叠成平衡结构的物理过程，还是仅仅是基于知识的最终状态的准确预测器。 结果在这项工作中，我们比较了由最先进的蛋白质结构预测方法产生的途径与有关蛋白质折叠途径的实验数据。所考虑的方法为AlphaFold 2、RoseTTAFold、trRosetta、RaptorX、DMPfold、EVfold、SAINT2和Rosetta。我们发现证据表明，它们的模拟动力学捕获了一些关于折叠途径的信息，但它们的预测能力不如使用序列不可知特征（如链长度）的平凡分类器。所产生的折叠轨迹也与实验可观测值如中间结构和折叠速率常数无关。这些结果表明，结构预测的最新进展还没有提供一个增强的理解蛋白质折叠。 空房的本文的基础数据可在GitHub上获得，网址为https://github.com/oxpig/structure-vs-folding/补充数据可在Bioinformatics在线获得。
Motivation. Predicting the native state of a protein has long been considered a gateway problem for understanding protein folding. Recent advances in structural modeling driven by deep learning have achieved unprecedented success at predicting a protein’s crystal structure, but it is not clear if these models are learning the physics of how proteins dynamically fold into their equilibrium structure or are just accurate knowledge-based predictors of the final state. Results. In this work, we compare the pathways generated by state-of-the-art protein structure prediction methods to experimental data about protein folding pathways. The methods considered were AlphaFold 2, RoseTTAFold, trRosetta, RaptorX, DMPfold, EVfold, SAINT2 and Rosetta. We find evidence that their simulated dynamics capture some information about the folding pathway, but their predictive ability is worse than a trivial classifier using sequence-agnostic features like chain length. The folding trajectories produced are also uncorrelated with experimental observables such as intermediate structures and the folding rate constant. These results suggest that recent advances in structure prediction do not yet provide an enhanced understanding of protein folding. Availability. The data underlying this article are available in GitHub at https://github.com/oxpig/structure-vs-folding/ Supplementary data are available at Bioinformatics online.
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影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
影响因子：
2.9
作者：
Del Alamo D;Govaerts C;Mchaourab HS
通讯作者：
Mchaourab HS
影响因子：
64.5
作者：
Hopf TA;Colwell LJ;Sheridan R;Rost B;Sander C;Marks DS
通讯作者：
Marks DS
DOI：
10.1002/prot.340230412
发表时间：
1995-12-01
期刊：
PROTEINS-STRUCTURE FUNCTION AND GENETICS
影响因子：
--
作者：
Frishman, D;Argos, P
通讯作者：
Argos, P
影响因子：
168.9
作者：
Kalia, Lorraine V.;Lang, Anthony E.
通讯作者：
Lang, Anthony E.