A Transmembrane Topology Prediction Procedure for Identifying GPCR Sequences
نویسندگان
چکیده
SWISS-PROT release 42 [1] contains a total of 2,028 transmembrane (TM) protein sequences with seven TM helices (7-tms), out of which 1,762 sequences are G-protein-coupled receptors (GPCRs). This means that a TM protein predicted correctly as 7-tms should be a GPCR with a reliability of 87% even without using any other information. Then, the questions are how much degree TM topology prediction methods proposed so far can predict GPCR sequences correctly as 7-tms TM proteins, and which method has the best prediction performance. With this subject, there are two factors to be estimated: the outflow of 7-tms sequences to TM protein sequences with the number of TM helices other than seven (other-tms) and the inflow of other-tms sequences to 7-tms. The rates of both these factors should be reduced to the minimum for identifying as many genuine GPCR sequences as possible from the predicted 7-tms TM protein sequences. In this study, we assessed the ability of selected six TM topology prediction methods as with predicting 7-tms sequences properly as 7-tms and other-tms sequences as not 7-tms.
منابع مشابه
Identifying G-protein Coupled Receptors Using Weighted Levenshtein Distance and Nearest Neighbor Method
G-protein coupled receptors (GPCRs) are a class of seven-helix transmembrane proteins that have been used in bioinformatics as the targets to facilitate drug discovery for human diseases. Although thousands of GPCR sequences have been collected, the ligand specificity of many GPCRs are still unknown and only one crystal structure of the rhodopsin-like family has been solved. Therefore, identify...
متن کاملHigh-accuracy prediction of transmembrane inter-helix contacts and application to GPCR 3D structure modeling
MOTIVATION Residue-residue contacts across the transmembrane helices dictate the three-dimensional topology of alpha-helical membrane proteins. However, contact determination through experiments is difficult because most transmembrane proteins are hard to crystallize. RESULTS We present a novel method (MemBrain) to derive transmembrane inter-helix contacts from amino acid sequences by combini...
متن کاملHMM_RA: An Improved Method for Alpha-Helical Transmembrane Protein Topology Prediction
alpha-helical transmembrane (TM) proteins play important and diverse functional roles in cells. The ability to predict the topology of these proteins is important for identifying functional sites and inferring function of membrane proteins. This paper presents a Hidden Markov Model (referred to as HMM_RA) that can predict the topology of alpha-helical transmembrane proteins with improved perfor...
متن کاملSequence-based predictions of membrane-protein topology, homology and insertion
Membrane proteins comprise around 20-30% of a typical proteome and play crucial roles in a wide variety of biochemical pathways. Apart from their general biological significance, membrane proteins are of particular interest to the pharmaceutical industry, being targets for more than half of all available drugs. This thesis focuses on prediction methods for membrane proteins that ultimately rely...
متن کاملAn improved hidden Markov model for transmembrane protein detection and topology prediction and its applications to complete genomes
MOTIVATION Knowledge of the transmembrane helical topology can help identify binding sites and infer functions for membrane proteins. However, because membrane proteins are hard to solubilize and purify, only a very small amount of membrane proteins have structure and topology experimentally determined. This has motivated various computational methods for predicting the topology of membrane pro...
متن کامل