Name
Title | ||
|---|---|---|
Identifying protein-protein interface via a novel multi-scale local sequence and structural representation. |
Abstract | ||
|---|---|---|
Protein-protein interaction plays a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. Gaining insights of various binding abilities can deepen our understanding of the interaction. It is of great interest to understand how proteins in a complex interact with each other. Many efficient methods have been developed for identifying protein-protein interface. In this paper, we obtain the local information on protein-protein interface, through multi-scale local average block and hexagon structure construction. Given a pair of proteins, we use a trained support vector regression (SVR) model to select best configurations. On Benchmark v4.0, our method achieves average Irmsd value of 3.28Å and overall Fnat value of 63%, which improves upon Irmsd of 3.89Å and Fnat of 49% for ZRANK, and Irmsd of 3.99Å and Fnat of 46% for ClusPro. On CAPRI targets, our method achieves average Irmsd value of 3.45Å and overall Fnat value of 46%, which improves upon Irmsd of 4.18Å and Fnat of 40% for ZRANK, and Irmsd of 5.12Å and Fnat of 32% for ClusPro. The success rates by our method, FRODOCK 2.0, InterEvDock and SnapDock on Benchmark v4.0 are 41.5%, 29.0%, 29.4% and 37.0%, respectively. Experiments show that our method performs better than some state-of-the-art methods, based on the prediction quality improved in terms of CAPRI evaluation criteria. All these results demonstrate that our method is a valuable technological tool for identifying protein-protein interface. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1186/s12859-019-3048-2 | BMC Bioinformatics |
Keywords | Field | DocType |
Protein-protein interface, Multi-scale local average block, Hexagon structure construction | Local sequence,Biology,Support vector machine,Structural representation,Local average,Protein protein,Artificial intelligence,Genetics,Machine learning | Journal |
Volume | Issue | ISSN |
15 | suppl | 1471-2105 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Guo | 1 | 16 | 5.31 |
| quan zou | 2 | 558 | 67.61 |
| Guang Yang | 3 | 0 | 0.34 |
| Dan Wang | 4 | 0 | 0.34 |
| Jijun Tang | 5 | 370 | 48.23 |
| Junhai Xu | 6 | 3 | 2.78 |