Name
Title | ||
|---|---|---|
FALCON@home: a high-throughput protein structure prediction server based on remote homologue recognition. |
Abstract | ||
|---|---|---|
The protein structure prediction approaches can be categorized into template-based modeling (including homology modeling and threading) and free modeling. However, the existing threading tools perform poorly on remote homologous proteins. Thus, improving fold recognition for remote homologous proteins remains a challenge. Besides, the proteome-wide structure prediction poses another challenge of increasing prediction throughput. In this study, we presented FALCON@home as a protein structure prediction server focusing on remote homologue identification. The design of FALCON@home is based on the observation that a structural template, especially for remote homologous proteins, consists of conserved regions interweaved with highly variable regions. The highly variable regions lead to vague alignments in threading approaches. Thus, FALCON@home first extracts conserved regions from each template and then aligns a query protein with conserved regions only rather than the full-length template directly. This helps avoid the vague alignments rooted in highly variable regions, improving remote homologue identification. We implemented FALCON@home using the Berkeley Open Infrastructure of Network Computing (BOINC) volunteer computing protocol. With computation power donated from over 20 000 volunteer CPUs, FALCON@home shows a throughput as high as processing of over 1000 proteins per day. In the Critical Assessment of protein Structure Prediction (CASP11), the FALCON@homebased prediction was ranked the 12th in the template-based modeling category. As an application, the structures of 880 mouse mitochondria proteins were predicted, which revealed the significant correlation between protein half-lives and protein structural factors. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1093/bioinformatics/btv581 | BIOINFORMATICS |
Field | DocType | Volume |
Sequence alignment,Data mining,Protein structure prediction,Computer science,Threading (protein sequence),Threading (manufacturing),Protein superfamily,Bioinformatics,Homology modeling,CASP,Protein structure | Journal | 32 |
Issue | ISSN | Citations |
3 | 1367-4803 | 3 |
PageRank | References | Authors |
0.40 | 2 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Wang | 1 | 3 | 0.74 |
| Haicang Zhang | 2 | 9 | 2.53 |
| Wei-Mou Zheng | 3 | 3 | 0.40 |
| Dong Xu | 4 | 73 | 13.93 |
| Jianwei Zhu | 5 | 11 | 3.22 |
| Bing Wang | 6 | 138 | 15.87 |
| Kang Ning | 7 | 14 | 4.19 |
| Shiwei Sun | 8 | 72 | 4.38 |
| Shuai Cheng Li | 9 | 184 | 30.25 |
| Dongbo Bu | 10 | 3 | 0.40 |