ArticleBioinformatics (Oxford, England)2015
LoopIng: a template-based tool for predicting the structure of protein loops.
Article in Bioinformatics (Oxford, England), 2015. 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, 31 citations in OpenAlex.
- Highly Accurate and Efficient Deep Learning Paradigm for Full-Atom Protein Loop Modeling with KarmaLoop.Research (Washington, D.C.) · 2024Article
- Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency.Briefings in bioinformatics · 2023Article
- Benchmarking the Accuracy of AlphaFold 2 in Loop Structure Prediction.Biomolecules · 2022Article
- Current approaches to flexible loop modeling.Current research in structural biology · 2021Review
- ProMod3-A versatile homology modelling toolbox.PLoS computational biology · 2021Article
- Simple Selection Procedure to Distinguish between Static and Flexible Loops.International journal of molecular sciences · 2020Article
- DaReUS-Loop: a web server to model multiple loops in homology models.Nucleic acids research · 2019Article
- Computational design of structured loops for new protein functions.Biological chemistry · 2019Review
- DaReUS-Loop: accurate loop modeling using fragments from remote or unrelated proteins.Scientific reports · 2018Article
- High-resolution global peptide-protein docking using fragments-based PIPER-FlexPepDock.PLoS computational biology · 2017Article
- Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction.Bioinformatics (Oxford, England) · 2017Article
- Antibody H3 Structure Prediction.Computational and structural biotechnology journal · 2017Review
- SL2: an interactive webtool for modeling of missing segments in proteins.Nucleic acids research · 2016Article
- The effect of chirality and steric hindrance on intrinsic backbone conformational propensities: tools for protein design.Protein engineering, design & selection : PEDS · 2016Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationPredicting 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.
resultsWe 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). AVAILABILITY AND IMPLEMENTATION: www.biocomputing.it/looping. CONTACT: anna.tramontano@uniroma1.it. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.