Braun Tatjana, Koehler Leman Julia, Lange Oliver F
Biomolecular NMR and Munich Center for Integrated Protein Science, Department Chemie, Technische Universität München, Garching, Germany.
Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.
PLoS Comput Biol. 2015 Dec 29;11(12):e1004661. doi: 10.1371/journal.pcbi.1004661. eCollection 2015 Dec.
Recent work has shown that the accuracy of ab initio structure prediction can be significantly improved by integrating evolutionary information in form of intra-protein residue-residue contacts. Following this seminal result, much effort is put into the improvement of contact predictions. However, there is also a substantial need to develop structure prediction protocols tailored to the type of restraints gained by contact predictions. Here, we present a structure prediction protocol that combines evolutionary information with the resolution-adapted structural recombination approach of Rosetta, called RASREC. Compared to the classic Rosetta ab initio protocol, RASREC achieves improved sampling, better convergence and higher robustness against incorrect distance restraints, making it the ideal sampling strategy for the stated problem. To demonstrate the accuracy of our protocol, we tested the approach on a diverse set of 28 globular proteins. Our method is able to converge for 26 out of the 28 targets and improves the average TM-score of the entire benchmark set from 0.55 to 0.72 when compared to the top ranked models obtained by the EVFold web server using identical contact predictions. Using a smaller benchmark, we furthermore show that the prediction accuracy of our method is only slightly reduced when the contact prediction accuracy is comparatively low. This observation is of special interest for protein sequences that only have a limited number of homologs.
最近的研究表明,通过整合蛋白质内部残基-残基接触形式的进化信息,从头预测结构的准确性可以得到显著提高。基于这一开创性成果,人们在改进接触预测方面投入了大量精力。然而,也迫切需要开发针对接触预测所获得的约束类型量身定制的结构预测方案。在此,我们提出一种结构预测方案,该方案将进化信息与Rosetta的分辨率适配结构重组方法相结合,称为RASREC。与经典的Rosetta从头预测方案相比,RASREC实现了更好的采样、更好的收敛性以及对错误距离约束更高的鲁棒性,使其成为解决上述问题的理想采样策略。为了证明我们方案的准确性,我们在一组包含28种球状蛋白的多样数据集上测试了该方法。我们的方法能够使28个目标中的26个收敛,并且与使用相同接触预测的EVFold网络服务器获得的排名最高的模型相比,将整个基准集的平均TM分数从0.55提高到了0.72。使用一个较小的基准集,我们还表明,当接触预测准确性相对较低时,我们方法的预测准确性仅略有降低。这一观察结果对于同源物数量有限的蛋白质序列尤为重要。