Classifying G-protein coupled receptors with support vector machines

R. Karchin, K. Karplus, D. Haussler
<span title="2002-01-01">2002</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="" style="color: black;">Bioinformatics</a> </i> &nbsp;
Motivation: The enormous amount of protein sequence data uncovered by genome research has increased the demand for computer software that can automate the recognition of new proteins. We discuss the relative merits of various automated methods for recognizing G-Protein Coupled Receptors (GPCRs), a superfamily of cell membrane proteins. GPCRs are found in a wide range of organisms and are central to a cellular signalling network that regulates many basic physiological processes. They are the
more &raquo; ... s of a significant amount of current pharmaceutical research because they play a key role in many diseases. However, their tertiary structures remain largely unsolved. The methods described in this paper use only primary sequence information to make their predictions. We compare a simple nearest neighbor approach (BLAST), methods based on multiple alignments generated by a statistical profile Hidden Markov Model (HMM), and methods, including Support Vector Machines (SVMs), that transform protein sequences into fixed-length feature vectors. Results: The last is the most computationally expensive method, but our experiments show that, for those interested in annotation-quality classification, the results are worth the effort. In two-fold cross-validation experiments testing recognition of GPCR subfamilies that bind a specific ligand (such as a histamine molecule), the errors per sequence at the Minimum Error Point (MEP) were 13.7% for multi-class SVMs, 17.1% for our SVMtree method of hierarchical multi-class SVM classification, 25.5% for BLAST, 30% for profile HMMs, and 49% for classification based on nearest neighbor feature vector Kernel Nearest Neighbor (kernNN). The percentage of true positives recognized before the first false positive was 65% for both SVM methods, 13% for BLAST, 5% for profile HMMs and 4% for kernNN. Availability: We have set up a web server for GPCR subfamily classification based on hierarchical multi-class * To whom correspondence should be addressed. SVMs at gpcr-subclass. By scanning predicted peptides found in the human genome with the SVMtree server, we have identified a large number of genes that encode GPCRs. A list of our predictions for human GPCRs is available at hg/ class results. We also provide suggested subfamily classification for 18 sequences previously identified as unclassified Class A (rhodopsin-like) GPCRs in GPCRDB (Horn et al.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1093/bioinformatics/18.1.147</a> <a target="_blank" rel="external noopener" href="">pmid:11836223</a> <a target="_blank" rel="external noopener" href="">fatcat:atr3aktehrd7ldwxv7chyohg7y</a> </span>
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