SCOPmap: Automated assignment of protein structures to evolutionary superfamilies
By S. Cheek, Yuan Qi, S. Krishna, Lisa N. Kinch, Nick V. Grishin. 2004
53 citations recorded by the source corpus.
BackgroundInference of remote homology between proteins is very challenging and remains a prerogative of an expert. Thus a significant drawback to the use of evolutionary-based protein structure classifications is the difficulty in assigning new proteins to unique positions in the classification scheme with automatic methods. To address this issue, we have developed an algorithm to map protein domains to an existing structural classification scheme and have applied it to the SCOP database.ResultsThe general strategy employed by this algorithm is to combine the results of several existing sequence and structure comparison tools applied to a query protein of known structure in order to find the homologs already classified in SCOP database and thus determine classification assignments. The algorithm is able to map domains within newly solved structures to the appropriate SCOP superfamily level with ~95% accuracy. Examples of correctly mapped remote homologs are discussed. The algorithm is also capable of identifying potential evolutionary relationships not specified in the SCOP database, thus helping to make it better. The strategy of the mapping algorithm is not limited to SCOP and can be applied to any other evolutionary-based classification scheme as well. SCOPmap is available for download.ConclusionThe SCOPmap program is useful for assigning domains in newly solved structures to appropriate superfamilies and for identifying evolutionary links between different superfamilies.
Subfields: Computational Theory and Mathematics, Molecular Biology
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- John-Marc Chandonia
- Naomi Fox
- Steven E. Brenner
- Antonina Andreeva
- Alexey G. Murzin
- Cyrus Chothia
- Eugene Kulesha
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Citations in the corpus
- SCOPe: Structural Classification of Proteins—extended, integrating SCOP and ASTRAL data and classification of new structures (2013, methodology): “For example, SCOPmap reports a 5% error rate in benchmarking experiments.”
- SCOPe: Manual Curation and Artifact Removal in the Structural Classification of Proteins – extended Database (2016, methodology): “For these users, manual curation with SCOP standards is necessary in order to include newly structurally characterized protein families in the classification without compromising the utility of SCOPe-derived benchmark To date, computational methods alone have not been able to classify structures wit”
- SCOP2 prototype: a new approach to protein structure mining (2013, methodology): “SCOP also prompted the development of automated classifications such as SCOPmap (19) and QSCOP (20).”