LoopIng: a template-based tool for predicting the structure of protein loops.
Type
ArticleKAUST Grant Number
KUK-I1-012-43Date
2015-08-06Permanent link to this record
http://hdl.handle.net/10754/596798
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Predicting the structure of protein loops is very challenging, mainly because they are not necessarily subject to strong evolutionary pressure. This implies that, unlike the rest of the protein, standard homology modeling techniques are not very effective in modeling their structure. However, loops are often involved in protein function, hence inferring their structure is important for predicting protein structure as well as function.We describe a method, LoopIng, based on the Random Forest automated learning technique, which, given a target loop, selects a structural template for it from a database of loop candidates. Compared to the most recently available methods, LoopIng is able to achieve similar accuracy for short loops (4-10 residues) and significant enhancements for long loops (11-20 residues). The quality of the predictions is robust to errors that unavoidably affect the stem regions when these are modeled. The method returns a confidence score for the predicted template loops and has the advantage of being very fast (on average: 1 min/loop).www.biocomputing.it/loopinganna.tramontano@uniroma1.itSupplementary data are available at Bioinformatics online.Citation
Messih MA, Lepore R, Tramontano A (2015) LoopIng: a template-based tool for predicting the structure of protein loops. Bioinformatics: btv438. Available: http://dx.doi.org/10.1093/bioinformatics/btv438.Sponsors
KAUST Award No. KUK-I1-012-43 made by King Abdullah University of Science and Technology (KAUST) and PRIN No. 20108XYHJS.Publisher
Oxford University Press (OUP)Journal
BioinformaticsPubMed ID
26249814PubMed Central ID
PMC4653384ae974a485f413a2113503eed53cd6c53
10.1093/bioinformatics/btv438
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Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
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