We present a CPU efficient protocol for refinement of protein structures in a thin layer of explicit solvent and energy parameters with completely revised dihedral angle terms. Our approach is suitable for protein structures determined by theoretical (e.g., homology modeling or threading) or experimental methods (e.g., NMR). In contrast to other recently proposed refinement protocols, we put a strong emphasis on consistency with widely accepted covalent parameters and computational efficiency. We illustrate the method for NMR structure calculations of three proteins: interleukin-4, ubiquitin, and crambin. We show a comparison of their structure ensembles before and after refinement in water with and without a force field energy term for the dihedral angles; crambin was also refined in DMSO. Our results demonstrate the significant improvement of structure quality by a short refinement in a thin layer of solvent. Further, they show that a dihedral angle energy term in the force field is beneficial for structure calculation and refinement. We discuss the optimal weight for the energy constant for the backbone angle omega and include an extensive discussion of meaning and relevance of the calculated validation criteria, in particular root mean square Z scores for covalent parameters such as bond lengths.

XML DTDs (for chemical shifts and NOE crosspeaks), Python scripts for the conversion of various NMR data formats and the results of example calculations using data from the S. cerevisiae HRDC domain are available from: http://www.pasteur.fr/recherche/unites/Binfs/aria/

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The HRDC domain represents a structural scaffold that resembles auxiliary domains in proteins that are involved in nucleic acid metabolism. In Sgs1p, the HRDC domain could modulate the helicase function via auxiliary contacts to DNA. However, in the Werner and Bloom syndrome helicases the HRDC domain may have a role in their functional differences by mediating diverse molecular interactions.

A recent workshop discusses the progress toward integrating NMR data into a unifying data model.