Faculty of Chemistry, University of Gdansk, ul. Wita-Stwosza 63, 80-308 Gdańsk, Poland.
School of Computational Sciences, Korea Institute for Advanced Study, 85 Hoegiro, Dongdaemun-gu, Seoul 02455, Republic of Korea.
J Chem Theory Comput. 2024 Sep 10;20(17):7667-7681. doi: 10.1021/acs.jctc.4c00754. Epub 2024 Aug 22.
In this paper, we evaluated the ability of four coarse-grained methods to predict protein flexible regions with potential biological importance, UNRES-flex, UNRES-DSSP-flex (based on the united residue model of polypeptide chains without and with secondary structure restraints, respectively), CABS-flex (based on the C-α, C-β, and side chain model), and nonlinear rigid block normal mode analysis (NOLB) with a set of 100 protein structures determined by NMR spectroscopy or X-ray crystallography, with all secondary structure types. End regions with high fluctuations were excluded from analysis. The Pearson and Spearman correlation coefficients were used to quantify the conformity between the calculated and experimental fluctuation profiles, the latter determined from NMR ensembles and X-ray -factors, respectively. For X-ray structures (corresponding to proteins in a crowded environment), NOLB resulted in the best agreement between the predicted and experimental fluctuation profiles, while for NMR structures (corresponding to proteins in solution), the ranking of performance is CABS-flex > UNRES-DSSP-flex > UNRES-flex > NOLB; however, CABS-flex sometimes exaggerated the extent of small fluctuations, as opposed to UNRES-DSSP-flex.
在本文中,我们评估了四种粗粒化方法预测具有潜在生物学重要性的蛋白质柔性区域的能力,包括 UNRES-flex、UNRES-DSSP-flex(分别基于无二级结构约束和有二级结构约束的联合残基模型)、CABS-flex(基于 C-α、C-β 和侧链模型)以及非线性刚性块正则模态分析(NOLB)。我们使用了一组由 NMR 光谱或 X 射线晶体学确定的 100 种具有所有二级结构类型的蛋白质结构,排除了具有高波动的末端区域进行分析。Pearson 和 Spearman 相关系数用于量化计算和实验波动分布之间的一致性,后者分别通过 NMR 集合和 X 射线因子确定。对于 X 射线结构(对应于拥挤环境中的蛋白质),NOLB 导致预测和实验波动分布之间具有最佳的一致性,而对于 NMR 结构(对应于溶液中的蛋白质),性能的排序为 CABS-flex > UNRES-DSSP-flex > UNRES-flex > NOLB;然而,CABS-flex 有时会夸大小波动的程度,而 UNRES-DSSP-flex 则不会。