LoopIng: a template-based tool for predicting the structure of protein loops.
KAUST Grant NumberKUK-I1-012-43
Permanent link to this recordhttp://hdl.handle.net/10754/596798
MetadataShow full item record
AbstractPredicting 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).email@example.comSupplementary data are available at Bioinformatics online.
CitationMessih 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.
SponsorsKAUST Award No. KUK-I1-012-43 made by King Abdullah University of Science and Technology (KAUST) and PRIN No. 20108XYHJS.
PublisherOxford University Press (OUP)
PubMed Central IDPMC4653384
CollectionsPublications Acknowledging KAUST Support
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 firstname.lastname@example.org
- Conformational sampling and structure prediction of multiple interacting loops in soluble and β-barrel membrane proteins using multi-loop distance-guided chain-growth Monte Carlo method.
- Authors: Tang K, Wong SW, Liu JS, Zhang J, Liang J
- Issue date: 2015 Aug 15
- Modeling of loops in proteins: a multi-method approach.
- Authors: Jamroz M, Kolinski A
- Issue date: 2010 Feb 11
- Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies.
- Authors: Messih MA, Lepore R, Marcatili P, Tramontano A
- Issue date: 2014 Oct
- PDB-based protein loop prediction: parameters for selection and methods for optimization.
- Authors: van Vlijmen HW, Karplus M
- Issue date: 1997 Apr 11
- Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model.
- Authors: Wang S, Sun S, Li Z, Zhang R, Xu J
- Issue date: 2017 Jan