MESSA - MEta Server for Sequence Analysis

To facilitate the need of larger scale analysis of protein sequences in the era of genomics, we updated our server to speed up the analysis. We are still testing and adding the new functions to the web server. Please let us know if you encounter any problems or have any suggestions.

MESSA: MEta Server for Sequence Analysis

MESSA provides predictions of local sequence features, spatial structure,
domain architecture and function for a given protein sequence. MESSA
and its application are described here. Details about how to use MESSA
and interpretation of the results are available here.

DATA INPUT

Enter your Protein Sequence in FASTA format or as plain-text:

Or upload your Protein Sequence as a file
Organism Type (required)

needed for signal peptide prediction and calculation of conservation indices

Organism Name (required)

input organism name (it will be autocompleted if it matches one in our database)

DATA SUBMIT

Input your Email Address (required):
Input a Job Name (optional):

Please cite:

1. Cong Q, Grishin NV. 2012. MESSA: MEta Server for Sequence Analysis. BMC Biol. 10:82.
[PMID: 23031578]

2. Altschul SF, Gish W, Miller W, Myers EW, and Lipman DJ. 1990. Basic local alignment search tool. J Mol Biol 215:403-410.

3. Bendtsen JD, Nielsen H, von Heijne G, and Brunak S. 2004. Improved prediction of signal peptides: SignalP 3.0. J Mol Biol 340(4):783-795.

4. Jones DT. 1999. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292:195-202.

5. Jones DT. 2007. Improving the accuracy of transmembrane protein topology prediction using evolutionary information. Bioinformatics 23:538-544.

6. Kall L, Krogh A, and Sonnhammer EL. 2004. A combined transmembrane topology and signal peptide prediction method. J Mol Biol 338:1027-1036.

7. Krogh A, Larsson B, von Heijne G, and Sonnhammer EL. 2001. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305:567-580.

8. Linding R, Jensen LJ, Diella F, Bork P, Gibson TJ and Russell RB. 2003. Protein disorder prediction: implications for structural proteomics. Structure 11:1453-1459.

9. Lupas A, Van Dyke M, and Stock J. 1991. Predicting coiled coils from protein sequences. Science 252:1162-1164.

10. Marchler-Bauer A, and Bryant SH. 2004. CD-Search: protein domain annotations on the fly. Nucleic Acids Res 32:W327-331.

11. Pollastri G, Przybylski D, Rost B, and Baldi P. 2002. Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins 47(2):228-235.

12. Tusnady GE, and Simon I. 1998. Principles governing amino acid composition of integral membrane proteins: application to topology prediction. J Mol Biol 283:489-506.

13. von Heijne G. 1992. Membrane protein structure prediction. Hydrophobicity analysis and the positive-inside rule. J Mol Biol 225:487-494.

14. Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF and Jones DT. 2004. Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J Mol Biol 337:635-645.

15. Wootton JC. 1994. Non-globular domains in protein sequences: automated segmentation using complexity measures. Comput Chem 18:269-285.

16. Eswar N, Eramian D, Webb B, Shen MY, Sali A. 2008. Protein structure modeling with MODELLER. Methods Mol Biol 426:145-159.

17. Lobanov MY, Galzitskaya OV. 2011. The Ising model for prediction of disordered residues from protein sequence alone. Phys Biol 8(3):035004.

Comments, suggestions and bug reports to: Qian Cong