NMR assignment of the SH2 domain from the human feline sarcoma oncogene FES.
- Journal of Biomolecular NMR 05/2005; 31(4):357-61. DOI:10.1007/s10858-005-0946-6 · 3.31 Impact Factor
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ABSTRACT: The recently introduced fully automated protein NMR structure determination algorithm (FLYA) yields, without any human intervention, a three-dimensional (3D) protein structure starting from a set of two- and three-dimensional NMR spectra. This paper investigates the influence of reduced sets of experimental spectra on the quality of NMR structures obtained with FLYA. In a case study using the Src homology domain 2 from the human feline sarcoma oncogene Fes (Fes SH2), five reduced data sets selected from the full set of 13 three-dimensional spectra of the previously determined conventional structure were used to calculate the protein structure. Three reduced data sets utilized only CBCA(CO)NH and CBCANH for the backbone assignments and two data sets used only CBCA(CO)NH. All, some, or none of the five original side-chain assignment spectra were used. Results were compared with those of a FLYA calculation for the complete set of spectra and those of the conventionally determined structure. In four of the five cases tested, the three-dimensional structures deviated by less than 1.3 A in backbone RMSD from the conventionally determined Fes SH2 reference structure, showing that the FLYA algorithm is remarkably stable and accurate when used with reduced sets of input spectra.Magnetic Resonance in Chemistry 07/2006; 44 Spec No(S1):S83-8. DOI:10.1002/mrc.1813 · 1.56 Impact Factor
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ABSTRACT: The automation of objectively selecting amino acid residue ranges for structure superpositions is important for meaningful and consistent protein structure analyses. So far there is no widely-used standard for choosing these residue ranges for experimentally determined protein structures, where the manual selection of residue ranges or the use of suboptimal criteria remain commonplace. We present an automated and objective method for finding amino acid residue ranges for the superposition and analysis of protein structures, in particular for structure bundles resulting from NMR structure calculations. The method is implemented in an algorithm, CYRANGE, that yields, without protein-specific parameter adjustment, appropriate residue ranges in most commonly occurring situations, including low-precision structure bundles, multi-domain proteins, symmetric multimers, and protein complexes. Residue ranges are chosen to comprise as many residues of a protein domain that increasing their number would lead to a steep rise in the RMSD value. Residue ranges are determined by first clustering residues into domains based on the distance variance matrix, and then refining for each domain the initial choice of residues by excluding residues one by one until the relative decrease of the RMSD value becomes insignificant. A penalty for the opening of gaps favours contiguous residue ranges in order to obtain a result that is as simple as possible, but not simpler. Results are given for a set of 37 proteins and compared with those of commonly used protein structure validation packages. We also provide residue ranges for 6351 NMR structures in the Protein Data Bank. The CYRANGE method is capable of automatically determining residue ranges for the superposition of protein structure bundles for a large variety of protein structures. The method correctly identifies ordered regions. Global structure superpositions based on the CYRANGE residue ranges allow a clear presentation of the structure, and unnecessary small gaps within the selected ranges are absent. In the majority of cases, the residue ranges from CYRANGE contain fewer gaps and cover considerably larger parts of the sequence than those from other methods without significantly increasing the RMSD values. CYRANGE thus provides an objective and automatic method for standardizing the choice of residue ranges for the superposition of protein structures.BMC Bioinformatics 05/2011; 12(1):170. DOI:10.1186/1471-2105-12-170 · 2.67 Impact Factor