Automated methods for force field parametrization have attracted renewed interest of the community, but the robustness issues associated with the often ill-conditioned nature of parameter optimization have been vastly underappreciated in the recent literature. For this reason, this article offers a detailed description of the origin and nature of these issues. This includes a discussion of the restrained electrostatic potential fit (RESP) charge model, which does contain explicit robustness-enhancing measures albeit not in the context of bonded parameters, and which forms an inspiration for the present work. It is also discussed how all the bonded parameters in a Class I force field can be simultaneously fit using the linear least squares (LLS) procedure, and a novel restraining strategy is presented that overcomes robustness issues in the LLS fitting of bonded parameters while minimally impacting the fitted values of well-behaved parameters. Two variants of this methodology are then validated through a number of case studies, including the fitting of bond-charge increments, which illustrates the method's potential for robustly solving general LLS problems beyond force field parametrization.
Automated methods for force field parametrization have attracted renewed interest of the community, but the robustness issues associated with the often ill-conditioned nature of parameter optimization have been vastly underappreciated in the recent literature. For this reason, this article offers a detailed description of the origin and nature of these issues. This includes a discussion of the restrained electrostatic potential fit (RESP) charge model, which does contain explicit robustness-enhancing measures albeit not in the context of bonded parameters, and which forms an inspiration for the present work. It is also discussed how all the bonded parameters in a Class I force field can be simultaneously fit using the linear least squares (LLS) procedure, and a novel restraining strategy is presented that overcomes robustness issues in the n class="Chemical">LLS fitting of bonded parameters while minimally impacting the fitted values of well-behaved parameters. Two variants of this methodology are then validated through a number of case studies, including the fitting of bond-charge increments, which illustrates the method's potential for robustly solving general LLS problems beyond force field parametrization.
Authors: B R Brooks; C L Brooks; A D Mackerell; L Nilsson; R J Petrella; B Roux; Y Won; G Archontis; C Bartels; S Boresch; A Caflisch; L Caves; Q Cui; A R Dinner; M Feig; S Fischer; J Gao; M Hodoscek; W Im; K Kuczera; T Lazaridis; J Ma; V Ovchinnikov; E Paci; R W Pastor; C B Post; J Z Pu; M Schaefer; B Tidor; R M Venable; H L Woodcock; X Wu; W Yang; D M York; M Karplus Journal: J Comput Chem Date: 2009-07-30 Impact factor: 3.376
Authors: Igor Vorobyov; Victor M Anisimov; Shannon Greene; Richard M Venable; Adam Moser; Richard W Pastor; Alexander D MacKerell Journal: J Chem Theory Comput Date: 2007-05 Impact factor: 6.006
Authors: K Vanommeslaeghe; E Hatcher; C Acharya; S Kundu; S Zhong; J Shim; E Darian; O Guvench; P Lopes; I Vorobyov; A D Mackerell Journal: J Comput Chem Date: 2010-03 Impact factor: 3.376
Authors: Olgun Guvench; Shannon N Greene; Ganesh Kamath; John W Brady; Richard M Venable; Richard W Pastor; Alexander D Mackerell Journal: J Comput Chem Date: 2008-11-30 Impact factor: 3.376
Authors: Camila Zanette; Caitlin C Bannan; Christopher I Bayly; Josh Fass; Michael K Gilson; Michael R Shirts; John D Chodera; David L Mobley Journal: J Chem Theory Comput Date: 2018-12-24 Impact factor: 6.006
Authors: Meagan C Small; Asaminew H Aytenfisu; Fang-Yu Lin; Xibing He; Alexander D MacKerell Journal: J Comput Aided Mol Des Date: 2017-02-11 Impact factor: 3.686
Authors: J V Vermaas; N Trebesch; C G Mayne; S Thangapandian; M Shekhar; P Mahinthichaichan; J L Baylon; T Jiang; Y Wang; M P Muller; E Shinn; Z Zhao; P-C Wen; E Tajkhorshid Journal: Methods Enzymol Date: 2016-07-11 Impact factor: 1.600