Background: G protein-coupled receptors (GPCRs) are a large superfamily of membrane
proteins and because of the difficulties in experimentally determining their structures, computational
approaches are essential.
Objective: GPCRTOP v.1.0 is an HMM-based web server which has been developed for predicting
helical transmembrane (TM) segments and identifying GPCRs based on amino acid distribution
patterns. The performance of the method was evaluated in comparison to other general TM prediction
Methods: 49093 unannotated human protein sequences were retrieved from TrEMBL-SwissProt. The
InterPro database was used for finding the GPCR sequences in common with those predicted by
GPCRTOP v.1.0. For those which were not in common, ten well-known TM predictors were utilized to
analyse these sequences.
Results: The results showed that 199 sequences were predicted as GPCRs by GPCRTOP v.1.0 whereas,
there were 182 GPCR sequences in InterPro database. Among these sequences, 104 sequences were
identified as GPCR by both GPCRTOP v.1.0 and InterPro database. The remaining sequences were then
predicted by general TM predictors and their results showed 11.1% more agreement to that of
GPCRTOP v.1.0 than InterPro database.
Conclusion: GPCRTOP v.1.0 is useful for identifying GPCRs and determining their topologies with
overall accuracy of ~99%. Here, we also announce the web availability of GPCRTOP v.1.0
(http://gpcrtop.tbzmed.ac.ir/services.aspx) and also describe its prediction features, which include
protein type (i.e., GPCR or non-GPCR), number of TM segments, as well as the topology of the