Manchester Institute of Biotechnology (MIB), 131 Princess Street, Manchester, M1 7DN, Great Britain.
School of Chemistry, University of Manchester, Oxford Road, Manchester, M13 9PL, Great Britain.
J Comput Chem. 2017 May 15;38(13):1005-1014. doi: 10.1002/jcc.24775. Epub 2017 Mar 10.
The fully polarizable, multipolar, and atomistic force field protein FFLUX is being built from machine learning (i.e., kriging) models, each of which predicts an atomic property. Each atom of a given protein geometry needs to be assigned such a kriging model. Such a knowledgeable atom needs to be informed about a sufficiently large environment around it. The resulting complexity can be tackled by collecting the 20 natural amino acids into a few groups. Using substituted deca-alanines, we present the proof-of-concept that a given atom's charge can be modeled by a few kriging models only. © 2017 Wiley Periodicals, Inc.
正在使用机器学习（即克里金法）模型构建完全可极化、多极和原子力场蛋白质 FFLUX，每个模型都预测一个原子特性。需要为给定蛋白质几何形状的每个原子分配这样的克里金模型。这样一个有见识的原子需要了解其周围足够大的环境。通过将 20 种天然氨基酸分为几类，可以解决由此产生的复杂性问题。我们使用取代的十肽来证明，仅用几个克里金模型就可以对给定原子的电荷进行建模。 © 2017 威立父子出版公司
