School of Chemistry, Cardiff University, Park Place, Cardiff CF10 3AT, UK.
J Mol Graph Model. 2010 Sep;29(2):240-5. doi: 10.1016/j.jmgm.2010.05.010. Epub 2010 Jun 8.
Prediction of the binding energy of a peptide implicated in multipole sclerosis to its major histocompatibility complex (MHC) receptor is reported using numerous ab initio, density functional (DFT) and semi-empirical theoretical methods. Using the crystalline coordinates taken from the protein databank, two ab initio methods are shown to be in good agreement for pairwise interaction of amino acids. These data are then used to benchmark more approximate DFT and semi-empirical approaches, which are shown to have substantial errors. However, in some cases significant improvement is apparent on inclusion of an empirical correction to account for dispersion interactions. Most promising among these cases is RM1, a re-parameterization of the popular AM1 method for atoms typically found in organic and biological molecules. Together with the dispersion correction, this reproduces ab initio data with a mean unsigned error of 1.36 kcal/mol. This approach is used to predict binding for progressively larger model systems, up to binding of the peptide with the entire MHC receptor, and is then applied to multiple snapshots taken from molecular dynamics simulation.
使用多种从头算、密度泛函(DFT)和半经验理论方法,报道了与多发性硬化症相关的多肽与其主要组织相容性复合物(MHC)受体的结合能的预测。使用从蛋白质数据库中获取的晶体坐标,两种从头算方法对于氨基酸的成对相互作用表现出良好的一致性。然后,将这些数据用于基准测试更近似的 DFT 和半经验方法,这些方法显示出很大的误差。然而,在某些情况下,包括用于考虑色散相互作用的经验修正在内,明显会有显著的改进。在这些情况下,最有前途的是 RM1,这是一种针对通常存在于有机和生物分子中的原子的流行 AM1 方法的重新参数化。与色散修正一起,它以 1.36 kcal/mol 的平均无符号误差重现了从头算数据。该方法用于预测逐渐增大的模型系统的结合,直至与整个 MHC 受体的肽结合,然后应用于从分子动力学模拟中获取的多个快照。