ConEVA: a toolbox for comprehensive assessment of protein contacts

By Badri Adhikari, Jackson Nowotny, Debswapna Bhattacharya, Jie Hou, Jianlin Cheng. 2016

23 citations recorded by the source corpus.

In 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 (Xd). We 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/. ConEVA 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.

Subfields: Cell Biology, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cell Biology

1. Jianlin Cheng
2. Badri Adhikari

In Molecular Biology

1. Badri Adhikari
2. Jianlin Cheng
3. Jinbo Xu
4. Jie Hou
5. Yang Zhang
6. Sheng Wang
7. Björn Wallner
8. Renzhi Cao

Citations in the corpus

• DNCON2: improved protein contact prediction using two-level deep convolutional neural networks (2017, neutral): “The metric has also been the main measure in the recent CASP evaluations (Monastyrskyy et al. , 2011, 2014, 2016) and some recent studies (Adhikari et al. , 2016).”
• CONFOLD2: improved contact-driven ab initio protein structure modeling (2017, methodology): “The rationale for using top L/5 or L/2 contacts (instead of L or more) is that these subsets are found to best reflect the accuracy of the predicted contacts [12].”