Name
Title | ||
|---|---|---|
MUFold-Contact and TPCref: New Methods for Protein Structure Contact Prediction and Refinement |
Abstract | ||
|---|---|---|
When predicting proteins' 3-D structures from their primary sequences, many existing tools use predicted residue contact information, i.e. which residues are in contact with each other. In this paper, we propose two new methods: MUFold-Contact, a new two-stage multi-branch deep neural network for predicting structure contact from protein sequences, and TPCref for refining the result of a contact prediction tool using template information. MUFold-Contact uses four independently-trained deep neural networks to predict residue-residue distances in various ranges, followed by one deep neural network to predict residue contact. TPCref is a novel approach to use protein templates to refine contact prediction generated by a particular contact prediction method. It first finds multiple template sequences based on the target sequence, and use the templates' structures and the templates' predicted contact map generated by the contact-prediction method to form a target contact-map filter, which is then used to refine the predicted contact map of the target sequence. Experimental results using recently released PDB proteins show that the performance of MUFold-Contact was comparable with those of the state-of-the-art methods, while TPCref significantly improved the contact prediction results of existing methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BIBM47256.2019.8983080 | 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |
Keywords | Field | DocType |
Protein structure prediction,contact prediction,contact prediction refinement,deep neural network | Protein structure prediction,Contact map,Pattern recognition,Computer science,Artificial intelligence,Template,Artificial neural network,Protein Data Bank (RCSB PDB),Deep neural networks,Machine learning,Protein structure | Conference |
ISSN | ISBN | Citations |
2156-1125 | 978-1-7281-1868-0 | 0 |
PageRank | References | Authors |
0.34 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeffrey Ruffolo | 1 | 2 | 1.57 |
| Zhaoyu Li | 2 | 2 | 2.39 |
| Yi Shang | 3 | 1383 | 104.53 |