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

By Konstantinos D. Tsirigos, Christoph Peters, Nanjiang Shu, Lukas Käll, Arne Elofsson. 2015

894 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

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In Molecular Biology

1. Jinbo Xu
2. Sheng Wang
3. Yizhou Yu
4. Sai Pooja Mahajan
5. Z. Li
6. Bonnie Berger
7. Jeffrey J. Gray
8. Zhen Li

Citations in the corpus

• Learning protein sequence embeddings using information from structure (2019, methodology): “Remarkably, this is by simply replacing the potential function in the CRF with a function of our embedding vectors, the hidden state grammar is the same as that of TOPCONS.”
• Folding membrane proteins by deep transfer learning (2017, methodology): “By adding MP-specific features such as lipid accessibility (Phatak et al., 2011) and topology structure (Tsirigos et al., 2015) to our deep learning models, we can improve contact accuracy by about 1%.”
• Deep Learning in Protein Structural Modeling and Design (2020, background): “Other efforts include classification of the trans-membrane topology.(174) Since experimental biophysical data are sparse for MPs, Alford and Gray(175) compiled a collection of 12 diverse benchmark sets for membrane protein prediction and design for testing and learning of implicit membrane energy mo”
• Predicting membrane protein contacts from non-membrane proteins by deep transfer learning (2017, background): “We have also studied if MP-specific features such as lipid accessibility [44] and topology structure [45] are useful for MP contact prediction or not.”