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. 2003 Jul;12(7):1547-55.
doi: 10.1110/ps.0305103.

Predicting the topology of transmembrane helical proteins using mean burial propensity and a hidden-Markov-model-based method

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Predicting the topology of transmembrane helical proteins using mean burial propensity and a hidden-Markov-model-based method

Hongyi Zhou et al. Protein Sci. 2003 Jul.

Abstract

Helices in membrane spanning regions are more tightly packed than the helices in soluble proteins. Thus, we introduce a method that uses a simple scale of burial propensity and a new algorithm to predict transmembrane helical (TMH) segments and a positive-inside rule to predict amino-terminal orientation. The method (the topology predictor of transmembrane helical proteins using mean burial propensity [THUMBUP]) correctly predicted the topology of 55 of 73 proteins (or 75%) with known three-dimensional structures (the 3D helix database). This level of accuracy can be reached by MEMSAT 1.8 (a 200-parameter model-recognition method) and a new HMM-based method (a 111-parameter hidden Markov model, UMDHMM(TMHP)) if they were retrained with the 73-protein database. Thus, a method based on a physiochemical property can provide topology prediction as accurate as those methods based on more complicated statistical models and learning algorithms for the proteins with accurately known structures. Commonly used HMM-based methods and MEMSAT 1.8 were trained with a combination of the partial 3D helix database and a 1D helix database of TMH proteins in which topology information were obtained by gene fusion and other experimental techniques. These methods provide a significantly poorer prediction for the topology of TMH proteins in the 3D helix database. This suggests that the 1D helix database, because of its inaccuracy, should be avoided as either a training or testing database. A Web server of THUMBUP and UMDHMM(TMHP) is established for academic users at http://www.smbs.buffalo.edu/phys_bio/service.htm. The 3D helix database is also available from the same Web site.

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Figures

Figure 1.
Figure 1.
The profile of the mean burial propensity and predicted seven TM segments of bacteriorhodopsin. The upper and lower bars indicate the locations of predicted and observed TM segments, respectively. The predicted segments all overlap with the corresponding observed ones for at least five residues long.

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References

    1. Argos, P., Rao, J.K., and Hargrave, P.A. 1982. Structural prediction of membrane-bound proteins. Eur. J. Biochem. 128 565–575. - PubMed
    1. Bairoch, A. and Apweiler, R. 2000. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28 45–48. - PMC - PubMed
    1. Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., and Bourne, P. 2000. The protein data bank. Nucleic Acids Res. 28 235–242. - PMC - PubMed
    1. Boyd, D., Schierle, C., and Beckwith, J. 1998. How many membrane proteins are there? Protein Sci. 7 201–205. - PMC - PubMed
    1. Chen, C.P., Kernytsky, A., and Rost, B. 2002. Transmembrane helix predictions revisited. Protein Sci. 11 2774–2791. - PMC - PubMed

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