ArticleBMC bioinformatics2016
ConEVA: a toolbox for comprehensive assessment of protein contacts.
Article in BMC bioinformatics, 2016. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
14 citing papers in PubMed, 29 citations in OpenAlex.
- Improving deep learning-based protein distance prediction in CASP14.Bioinformatics (Oxford, England) · 2021Article
- DNCON2_Inter: predicting interchain contacts for homodimeric and homomultimeric protein complexes using multiple sequence alignments of monomers and deep learning.Scientific reports · 2021Article
- Deep Learning-Based Advances in Protein Structure Prediction.International journal of molecular sciences · 2021Review
- Study on cocoonase, sericin, and degumming of silk cocoon: computational and experimental.Journal, genetic engineering & biotechnology · 2021Article
- Evaluating the significance of contact maps in low-homology protein modeling using contact-assisted threading.Scientific reports · 2020Article
- Analysis of several key factors influencing deep learning-based inter-residue contact prediction.Bioinformatics (Oxford, England) · 2020Article
- StructureDistiller: Structural relevance scoring identifies the most informative entries of a contact map.Scientific reports · 2019Article
- Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13.Proteins · 2019Article
- DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.Bioinformatics (Oxford, England) · 2018Article
- Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning.Proteins · 2018Article
- Article
- CONFOLD2: improved contact-driven ab initio protein structure modeling.BMC bioinformatics · 2018Article
- Ordering Protein Contact Matrices.Computational and structural biotechnology journal · 2018Article
- Review
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundIn recent years, successful contact prediction methods and contact-guided ab initio protein structure prediction methods have highlighted the importance of incorporating contact information into protein structure prediction methods. It is also observed that for almost all globular proteins, the quality of contact prediction dictates the accuracy of structure prediction. Hence, like many existing evaluation measures for evaluating 3D protein models, various measures are currently used to evaluate predicted contacts, with the most popular ones being precision, coverage and distance distribution score (X
resultsWe have built a web application and a downloadable tool, ConEVA, for comprehensive assessment and detailed comparison of predicted contacts. Besides implementing existing measures for contact evaluation we have implemented new and useful methods of contact visualization using chord diagrams and comparison using Jaccard similarity computations. For a set (or sets) of predicted contacts, the web application runs even when a native structure is not available, visualizing the contact coverage and similarity between predicted contacts. We applied the tool on various contact prediction data sets and present our findings and insights we obtained from the evaluation of effective contact assessments. ConEVA is publicly available at http://cactus.rnet.missouri.edu/coneva/ .
conclusionConEVA is useful for a range of contact related analysis and evaluations including predicted contact comparison, investigation of individual protein folding using predicted contacts, and analysis of contacts in a structure of interest.
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Registered trials
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.