Increasing the precision of comparative models with YASARA NOVA—a self‐parameterizing force field

TLDR

The CASP4 meeting concluded that applying various force fields during refinement of template‑based models tends to move predictions away from experimentally determined coordinates. The authors developed NOVA, an all‑atom force field designed to avoid these detrimental effects, with the goals of preserving high‑resolution X‑ray structures and improving homology models, and they propose it for general modeling, X‑ray and NMR refinement while introducing a new tree‑based parameter‑assignment method. NOVA’s parameters were optimized by random Monte Carlo sampling in parameter space, each evaluated by simulated annealing on a 50‑protein set, allowing non‑physically correct parameters to compensate for force‑field equation errors, and a new tree‑based assignment method was introduced. The force field performed equally well on an independent validation set and moved models closer to experimental reality. A free NOVA server is available at http://www.yasara.com/servers, © 2002 Wiley‑Liss, Inc.

Abstract

Abstract One of the conclusions drawn at the CASP4 meeting in Asilomar was that applying various force fields during refinement of template‐based models tends to move predictions in the wrong direction, away from the experimentally determined coordinates. We have derived an all‐atom force field aimed at protein and nucleotide optimization in vacuo (NOVA), which has been specifically designed to avoid this problem. NOVA resembles common molecular dynamics force fields but has been automatically parameterized with two major goals: (i) not to make high resolution X‐ray structures worse and (ii) to improve homology models built by WHAT IF. Force‐field parameters were not required to be physically correct; instead, they were optimized with random Monte Carlo moves in force‐field parameter space, each one evaluated by simulated annealing runs of a 50‐protein optimization set. Errors inherent to the approximate force‐field equation could thus be canceled by errors in force‐field parameters. Compared with the optimization set, the force field did equally well on an independent validation set and is shown to move in silico models closer to reality. It can be applied to modeling applications as well as X‐ray and NMR structure refinement. A new method to assign force‐field parameters based on molecular trees is also presented. A NOVA server is freely accessible at http://www.yasara.com/servers Proteins 2002;47:393–402. © 2002 Wiley‐Liss, Inc.