CoinFold: a web server for protein contact prediction and contact-assisted protein folding

By Sheng Wang, Renyu Zhang, Shiwang Liu, Jinbo Xu. 2016

60 citations recorded by the source corpus.

CoinFold (http://raptorx2.uchicago.edu/ContactMap/) is a web server for protein contact prediction and contact-assisted de novo structure prediction. CoinFold predicts contacts by integrating joint multi-family evolutionary coupling (EC) analysis and supervised machine learning. This joint EC analysis is unique in that it not only uses residue coevolution information in the target protein family, but also that in the related families which may have divergent sequences but similar folds. The supervised learning further improves contact prediction accuracy by making use of sequence profile, contact (distance) potential and other information. Finally, this server predicts tertiary structure of a sequence by feeding its predicted contacts and secondary structure to the CNS suite. Tested on the CASP and CAMEO targets, this server shows significant advantages over existing ones of similar category in both contact and tertiary structure prediction.

Subfields: Molecular Biology

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In Molecular Biology

1. Sheng Wang
2. Jinbo Xu
3. Siqi Sun
4. Xiaogen Zhou
5. Gui-jun Zhang
6. Chunxiang Peng
7. Yizhou Yu
8. Z. Li

Citations in the corpus

• Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model (2016, background): “the two residues in a contact falls into [6, 11], [12, 23], and ≥24, respectively.”
• Predicting membrane protein contacts from non-membrane proteins by deep transfer learning (2017, methodology): “Although so many methods have been developed, for MPs with fewer sequence homologs, the predicted contacts by existing methods such as CCMpred, PSICOV, Evfold, MetaPSICOV and CoinDCA [18] are still of low quality and not very helpful to ab initio folding [19].”
• De novo Protein Structure Prediction by Coupling Contact With Distance Profile (2020, methodology): “In RaptorX [18], [26], [27], [28], predicted contacts are utilized as distance constraints to predict tertiary structural models.”