FALCON@home: a high-throughput protein structure prediction server based on remote homologue recognition

• Published in: Bioinformatics (Oxford, England) England), 2016-02, Vol.32 (3), p.462-464
• Main Authors: Wang, Chao, Zhang, Haicang, Zheng, Wei-Mou, Xu, Dong, Zhu, Jianwei, Wang, Bing, Ning, Kang, Sun, Shiwei, Li, Shuai Cheng, Bu, Dongbo
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; Applications Notes; Computational Biology - methods; Databases, Protein; High-Throughput Screening Assays; Mice; Protein Conformation; Proteins - chemistry; Sequence Alignment - methods; Sequence Analysis, Protein - methods; Software
• ISSN: 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btv581

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@home-based 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. FALCON@home is freely available at http://protein.ict.ac.cn/FALCON/. [e-mail], [e-mail] Supplementary data are available at Bioinformatics online.