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Force fields for engineering applications are often parameterised using strategies based on quantum mechanicalab initiocalculations and thermodynamic properties from experiment. An automated procedure for adjusting molecular model parameters to experimental thermodynamic property data is introduced. The process accelerates the development of molecular models by an efficient use of parallel computing power and an autonomous progress of the model development without any user interaction. As a case study, the procedure is applied to the parameterisation of a molecular model for acetonitrile. The resulting model reproduces vapour–liquid equilibrium data of acetonitrile with an accuracy of 0.1% for the saturated liquid density, 4.9% for the vapour pressure and 3.7% for the enthalpy of vaporisation. These accuracies are superior to data obtained with previously published force fields for acetonitrile. [ABSTRACT FROM AUTHOR]

We have previously proposed a method for refining force-field parameters of protein systems, which consists of minimising the summation of the square of the force acting on each atom in the proteins with the structures from the protein data bank (PDB). The results showed that the modified force-field parameters for all-atom model gave structures more consistent with the experimental implications than the original force fields. In this work, we applied this method and a new method to the OPLS-UA force field. In the new method, we perform a minimisation of the average of the root-mean-square deviation of various protein structures from the native structure. We selected some torsion-energy parameters for this optimisation, and 100 molecules from the PDB were used. The results imply that the new force-field parameters gave structures of two peptides more consistent with the experimental implications for the secondary structure-forming tendencies than the original OPLS-UA force field. [ABSTRACT FROM AUTHOR]

We performed folding simulations of three proteins using four force fields, AMBER parm96, AMBER parm99, CHARMM 27 and OPLS-AA/L, in order to examine the features of these force fields. We studied three proteins, protein A (all α-helix), cold-shock protein (all β-strand) and protein G (α/β-structures), for the folding simulations. For the simulation, we used the simulated annealing molecular dynamics method, which was performed 50 times for each protein using the four force fields. The results showed that the secondary-structure-forming tendencies are largely different among the four force fields. AMBER parm96 favours β-bridge structures and extended β-strand structures, and AMBER parm99 favours α-helix structures and 310-helix structures. CHARMM 27 slightly favours α-helix structures, and there are also π-helix and β-bridge structures. OPLS-AA/L favours α-helix structures and 310-helix structures. [ABSTRACT FROM AUTHOR]

We propose a new method of optimisation of backbone torsion-energy parameters in the force field for molecular simulations of protein systems. This method is based on the idea of balancing the secondary-structure-forming tendencies, namely, those of α-helix and β-sheet structures. We perform a minimisation of the backbone dihedral angle-based root-mean-square deviation of the helix and β structure regions in many protein structures. As an example, we optimised the backbone torsion-energy parameters of AMBER parm96 force field using 100 protein molecules from the Protein Data Bank. We then performed folding simulations of α-helical and β-hairpin peptides, using the optimised force field. The results imply that the new force-field parameters give structures more consistent with the experimental implications than the original AMBER parm96 force field. [ABSTRACT FROM AUTHOR]