Journal
FRONTIERS IN ENDOCRINOLOGY
Volume 13, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fendo.2022.825195
Keywords
GPCR; protein-protein interaction; membrane protein; disease-associated mutation; machine learning; web service; prediction; bioinformatics
Categories
Funding
- Ministry of Education, Culture, Sports, Science and Technology of Japan [25870764, 18K06199]
- Tokyo Denki University Science Promotion Fund [Q13L-03]
- Grants-in-Aid for Scientific Research [25870764, 18K06199] Funding Source: KAKEN
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The GGIP web server provides a web application for predicting GPCR-GPCR interaction pairs using a support vector machine. Users can input two sequences in FASTA format to receive predictions without the need for login. This server utilizes the GGIP software, which is currently the only method for predicting GPCR interaction pairs.
The GGIP web server (https://protein.b.dendai.ac.jp/GGIP/) provides a web application for GPCR-GPCR interaction pair prediction by a support vector machine. The server accepts two sequences in the FASTA format. It responds with a prediction that the input GPCR sequence pair either interacts or not. GPCRs predicted to interact with the monomers constituting the pair are also shown when query sequences are human GPCRs. The server is simple to use. A pair of amino acid sequences in the FASTA format is pasted into the text area, a PDB ID for a template structure is selected, and then the 'Execute' button is clicked. The server quickly responds with a prediction result. The major advantage of this server is that it employs the GGIP software, which is presently the only method for predicting GPCR-interaction pairs. Our web server is freely available with no login requirement. In this article, we introduce some application examples of GGIP for disease-associated mutation analysis.
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