Name
Abstract | ||
|---|---|---|
One approach to protein structure prediction is to first predict from sequence, a thresholded and binary 2D representation of a protein's topology known as a contact map. The predicted contact map can be used as distance constraints to construct a 3D structure. We focus on the latter half of the process for helix pairs and present an approach that aims to obtain a set of non-binary distance constraints from contacts maps. We extend the definition of ''in contact'' by incorporating fuzzy logic to construct fuzzy contact maps. Then, template-based retrieval and distance geometry bound smoothing were applied to obtain distance constraints in the form of a distance map. From the distance map, we can calculate the helix pair structure. Our experimental results indicate that distance constraints close to the true distance map could be predicted at various noise levels and the resulting structure was highly correlated to the predicted distance map. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.asoc.2013.10.009 | Appl. Soft Comput. |
Keywords | Field | DocType |
protein structure,helix | Protein structure prediction,Topology,Fuzzy logic,Quasi-open map,Distance transform,Smoothing,Helix,Artificial intelligence,Distance geometry,Machine learning,Mathematics,Binary number | Journal |
Volume | ISSN | Citations |
14, | 1568-4946 | 1 |
PageRank | References | Authors |
0.36 | 13 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tony Kuo | 1 | 20 | 1.87 |
| Janice I. Glasgow | 2 | 392 | 127.97 |