Near-native structure refinement using in vacuo energy minimization

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 104; no. 9; pp. 3177 - 3182
• Main Authors: Summa, Christopher M, Levitt, Michael
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 27.02.2007; National Acad Sciences
• Subjects: Atomic interactions; Atoms; Biological Sciences; Biophysics; Biophysics - methods; Computational Biology - methods; Crystal structure; Crystallography; Deformation; Energy; Energy Transfer - physiology; Force field; Harmonic functions; Mathematical vectors; Modeling; Models, Molecular; Molecular biology; Molecular structure; Protein Conformation; Proteins; Proteins - chemistry
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.0611593104

One of the greatest shortcomings of macromolecular energy minimization and molecular dynamics techniques is that they generally do not preserve the native structure of proteins as observed by x-ray crystallography. This deformation of the native structure means that these methods are not generally used to refine structures produced by homology-modeling techniques. Here, we use a database of 75 proteins to test the ability of a variety of popular molecular mechanics force fields to maintain the native structure. Minimization from the native structure is a weak test of potential energy functions: It is complemented by a much stronger test in which the same methods are compared for their ability to attract a near-native decoy protein structure toward the native structure. We use a powerfully convergent energy-minimization method and show that, of the traditional molecular mechanics potentials tested, only one showed a modest net improvement over a large data set of structurally diverse proteins. A smooth, differentiable knowledge-based pairwise atomic potential performs better on this test than traditional potential functions. This work is expected to have important implications for protein structure refinement, homology modeling, and structure prediction.