Dutagaci Bercem, Wittayanarakul Kitiyaporn, Mori Takaharu, Feig Michael
Department of Biochemistry and Molecular Biology, Michigan State University , East Lansing, Michigan, United States.
Department of Natural Resource and Environmental Management, Faculty of Applied Science and Engineering, Khon Kaen University , Nong Khai Campus, Nong Khai 43000, Thailand.
J Chem Theory Comput. 2017 Jun 13;13(6):3049-3059. doi: 10.1021/acs.jctc.7b00254. Epub 2017 May 11.
A scoring protocol based on implicit membrane-based scoring functions and a new protocol for optimizing the positioning of proteins inside the membrane was evaluated for its capacity to discriminate native-like states from misfolded decoys. A decoy set previously established by the Baker lab (Proteins: Struct., Funct., Genet. 2006, 62, 1010-1025) was used along with a second set that was generated to cover higher resolution models. The Implicit Membrane Model 1 (IMM1), IMM1 model with CHARMM 36 parameters (IMM1-p36), generalized Born with simple switching (GBSW), and heterogeneous dielectric generalized Born versions 2 (HDGBv2) and 3 (HDGBv3) were tested along with the new HDGB van der Waals (HDGBvdW) model that adds implicit van der Waals contributions to the solvation free energy. For comparison, scores were also calculated with the distance-scaled finite ideal-gas reference (DFIRE) scoring function. Z-scores for native state discrimination, energy vs root-mean-square deviation (RMSD) correlations, and the ability to select the most native-like structures as top-scoring decoys were evaluated to assess the performance of the scoring functions. Ranking of the decoys in the Baker set that were relatively far from the native state was challenging and dominated largely by packing interactions that were captured best by DFIRE with less benefit of the implicit membrane-based models. Accounting for the membrane environment was much more important in the second decoy set where especially the HDGB-based scoring functions performed very well in ranking decoys and providing significant correlations between scores and RMSD, which shows promise for improving membrane protein structure prediction and refinement applications. The new membrane structure scoring protocol was implemented in the MEMScore web server ( http://feiglab.org/memscore ).
基于隐式膜评分函数的评分方案以及一种优化蛋白质在膜内定位的新方案，被评估其区分天然态与错误折叠诱饵的能力。使用了贝克实验室先前建立的一组诱饵（《蛋白质：结构、功能、遗传学》，2006年，62卷，1010 - 1025页）以及另一组为涵盖更高分辨率模型而生成的诱饵。测试了隐式膜模型1（IMM1）、具有CHARMM 36参数的IMM1模型（IMM1 - p36）、简单切换广义玻恩（GBSW）、异构介电广义玻恩版本2（HDGBv2）和3（HDGBv3），以及新的HDGB范德华（HDGBvdW）模型，该模型将隐式范德华贡献添加到溶剂化自由能中。为作比较，还使用距离缩放有限理想气体参考（DFIRE）评分函数计算分数。评估了用于天然态区分的Z分数、能量与均方根偏差（RMSD）的相关性，以及选择最类似天然结构作为得分最高诱饵的能力，以评估评分函数的性能。对贝克组中离天然态相对较远的诱饵进行排名具有挑战性，并且在很大程度上由堆积相互作用主导，DFIRE能最好地捕捉这些相互作用，而基于隐式膜的模型受益较少。在第二组诱饵中，考虑膜环境更为重要，尤其是基于HDGB的评分函数在对诱饵进行排名以及提供分数与RMSD之间的显著相关性方面表现非常出色，这显示出在改进膜蛋白结构预测和优化应用方面的前景。新的膜结构评分方案已在MEMScore网络服务器（http://feiglab.org/memscore）中实现。
