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用于膜蛋白拓扑结构和信号肽一致性预测的TOPCONS网络服务器。

The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides.

作者信息

Tsirigos Konstantinos D, Peters Christoph, Shu Nanjiang, Käll Lukas, Elofsson Arne

机构信息

Department of Biochemistry and Biophysics, Stockholm University, 10691 Stockholm, Sweden Science for Life Laboratory, Stockholm University, Box 1031, 17121 Solna, Sweden.

Department of Biochemistry and Biophysics, Stockholm University, 10691 Stockholm, Sweden Science for Life Laboratory, Stockholm University, Box 1031, 17121 Solna, Sweden Bioinformatics Infrastructure for Life Sciences (BILS), Stockholm University, Sweden.

出版信息

Nucleic Acids Res. 2015 Jul 1;43(W1):W401-7. doi: 10.1093/nar/gkv485. Epub 2015 May 12.

Abstract

TOPCONS (http://topcons.net/) is a widely used web server for consensus prediction of membrane protein topology. We hereby present a major update to the server, with some substantial improvements, including the following: (i) TOPCONS can now efficiently separate signal peptides from transmembrane regions. (ii) The server can now differentiate more successfully between globular and membrane proteins. (iii) The server now is even slightly faster, although a much larger database is used to generate the multiple sequence alignments. For most proteins, the final prediction is produced in a matter of seconds. (iv) The user-friendly interface is retained, with the additional feature of submitting batch files and accessing the server programmatically using standard interfaces, making it thus ideal for proteome-wide analyses. Indicatively, the user can now scan the entire human proteome in a few days. (v) For proteins with homology to a known 3D structure, the homology-inferred topology is also displayed. (vi) Finally, the combination of methods currently implemented achieves an overall increase in performance by 4% as compared to the currently available best-scoring methods and TOPCONS is the only method that can identify signal peptides and still maintain a state-of-the-art performance in topology predictions.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7026/4489233/17f32910eca1/gkv485fig1.jpg

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