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

Proteins. 2002 May 15;47(3):393-402. doi: 10.1002/prot.10104.


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

Publication types

  • Comparative Study

MeSH terms

  • Algorithms
  • Models, Molecular*
  • Monte Carlo Method
  • Nucleotides / chemistry
  • Protein Conformation
  • Proteins / chemistry*
  • Reproducibility of Results


  • Nucleotides
  • Proteins