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利用小角X射线散射数据和基于知识的信息辅助,通过粗粒度UNRES力场预测蛋白质结构。

Prediction of protein structure with the coarse-grained UNRES force field assisted by small X-ray scattering data and knowledge-based information.

作者信息

Karczyńska Agnieszka S, Mozolewska Magdalena A, Krupa Paweł, Giełdoń Artur, Liwo Adam, Czaplewski Cezary

机构信息

Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk, 80-308, Poland.

Institute of Computer Science, Polish Academy of Sciences, ul. Jana Kazimierza 5, Warsaw, 01-248, Poland.

出版信息

Proteins. 2018 Mar;86 Suppl 1:228-239. doi: 10.1002/prot.25421. Epub 2017 Nov 29.

Abstract

A new approach to assisted protein-structure prediction has been proposed, which is based on running multiplexed replica exchange molecular dynamics simulations with the coarse-grained UNRES force field with restraints derived from knowledge-based models and distance distribution from small angle X-ray scattering (SAXS) measurements. The latter restraints are incorporated into the target function as a maximum-likelihood term that guides the shape of the simulated structures towards that defined by SAXS. The approach was first verified with the 1KOY protein, for which the distance distribution was calculated from the experimental structure, and subsequently used to predict the structures of 11 data-assisted targets in the CASP12 experiment. Major improvement of the GDT_TS was obtained for 2 targets, minor improvement for other 2 while, for 6 target GDT_TS deteriorated compared with that calculated for predictions without the SAXS data, partly because of assuming a wrong multimeric state (for Ts866) or because the crystal conformation was more compact than the solution conformation (for Ts942). Particularly good results were obtained for Ts909, in which use of SAXS data resulted in the selection of a correctly packed trimer and, subsequently, increased the GDT_TS of monomer prediction. It was found that running simulations with correct oligomeric state is essential for the success in SAXS-data-assisted prediction.

摘要

一种新的辅助蛋白质结构预测方法被提出来了,该方法基于使用粗粒度的UNRES力场运行多重副本交换分子动力学模拟,并结合基于知识的模型以及小角X射线散射(SAXS)测量得到的距离分布所导出的约束条件。后一种约束条件作为最大似然项被纳入目标函数,引导模拟结构的形状趋向于由SAXS定义的形状。该方法首先用1KOY蛋白进行了验证,其距离分布是根据实验结构计算得出的,随后在CASP12实验中用于预测11个数据辅助目标的结构。对于2个目标,GDT_TS有显著提高,另外2个目标有轻微提高,而对于6个目标,与没有SAXS数据时的预测结果相比,GDT_TS恶化了,部分原因是假设了错误的多聚体状态(对于Ts866),或者是因为晶体构象比溶液构象更紧凑(对于Ts942)。对于Ts909取得了特别好的结果,其中使用SAXS数据导致选择了正确堆积的三聚体,随后提高了单体预测的GDT_TS。研究发现,以正确的寡聚状态运行模拟对于SAXS数据辅助预测的成功至关重要。

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