ArticleBioinformation2009
New tips for structure prediction by comparative modeling.
Article in Bioinformation, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
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- Comparative modeling of UDP-N-acetylmuramoyl-glycyl-D-glutamate-2, 6-diaminopimelate ligase from Mycobacterium leprae and analysis of its binding features through molecular docking studies.Journal of molecular modeling · 2012Article
- Systematic assessment of accuracy of comparative model of proteins belonging to different structural fold classes.Journal of molecular modeling · 2011Article
- Structure prediction of dihydroflavonol 4- reductase and anthocyanidin synthase from spinach.Bioinformation · 2010Article
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1 author.
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Abstract
Comparative modelling is utilized to predict the 3-dimensional conformation of a given protein (target) based on its sequence alignment to experimentally determined protein structure (template). The use of such technique is already rewarding and increasingly widespread in biological research and drug development. The accuracy of the predictions as commonly accepted depends on the score of sequence identity of the target protein to the template. To assess the relationship between sequence identity and model quality, we carried out an analysis of a set of 4753 sequence and structure alignments. Throughout this research, the model accuracy was measured by root mean square deviations of Calpha atoms of the target-template structures. Surprisingly, the results show that sequence identity of the target protein to the template is not a good descriptor to predict the accuracy of the 3-D structure model. However, in a large number of cases, comparative modelling with lower sequence identity of target to template proteins led to more accurate 3-D structure model. As a consequence of this study, we suggest new tips for improving the quality of omparative models, particularly for models whose target-template sequence identity is below 50%.
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