ArticleBMC bioinformatics2011
Objective identification of residue ranges for the superposition of protein structures.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed.
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- Integrative Modeling of Protein-Polypeptide Complexes by Bayesian Model Selection using AlphaFold and NMR Chemical Shift Perturbation Data.bioRxiv : the preprint server for biology · 2024Article
- Restraint validation of biomolecular structures determined by NMR in the Protein Data Bank.Structure (London, England : 1993) · 2024Article
- The 100-protein NMR spectra dataset: A resource for biomolecular NMR data analysis.Scientific data · 2024Article
- Representing structures of the multiple conformational states of proteins.Current opinion in structural biology · 2023Review
- Time-optimized protein NMR assignment with an integrative deep learning approach using AlphaFold and chemical shift prediction.Science advances · 2023Article
- Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2023Article
- Blind Assessment of Monomeric AlphaFold2 Protein Structure Models with Experimental NMR Data.bioRxiv : the preprint server for biology · 2023Article
- Rapid protein assignments and structures from raw NMR spectra with the deep learning technique ARTINA.Nature communications · 2022Article
- The accuracy of protein structures in solution determined by AlphaFold and NMR.Structure (London, England : 1993) · 2022Article
- Identification of resistance gene analogs of the NBS-LRR family through transcriptome probing andFrontiers in genetics · 2022Article
- AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures.Frontiers in molecular biosciences · 2022Article
- Assessment of prediction methods for protein structures determined by NMR in CASP14: Impact of AlphaFold2.Proteins · 2021Article
- Patterns in Protein Flexibility: A Comparison of NMR "Ensembles", MD Trajectories, and Crystallographic B-Factors.Molecules (Basel, Switzerland) · 2021Article
- Mutations of Intrinsically Disordered Protein Regions Can Drive Cancer but Lack Therapeutic Strategies.Biomolecules · 2021Article
- Federating Structural Models and Data: Outcomes from A Workshop on Archiving Integrative Structures.Structure (London, England : 1993) · 2019Article
- Structure and evolution of the 4-helix bundle domain of Zuotin, a J-domain protein co-chaperone of Hsp70.PloS one · 2019Article
- Structural and functional dissection of the DH and PH domains of oncogenic Bcr-Abl tyrosine kinase.Nature communications · 2017Article
- Validation of Structures in the Protein Data Bank.Structure (London, England : 1993) · 2017Article
- Broadening the functionality of a J-protein/Hsp70 molecular chaperone system.PLoS genetics · 2017Article
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2 authors.
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Abstract
backgroundThe 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.
resultsWe 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.
conclusionsThe 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.
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