Predicting Protein Interaction

Pred_PPI is a web-based system that serves for predicting PPIs from different organisms. This server is freely available to any researcher wishing to use it for non-commercial purposes. Based on auto covariance (AC) and support vector machine (SVM), this tool is capable of predicting PPIs for any target protein pair only using their primary sequences, and assigning an interaction probability to each SVM prediction as well. So the user can use this tool to predict novel PPIs with high confidence.

Protein-protein interactions (PPIs) are essential for almost all cellular processes, such as metabolic cycles, DNA transcription and replication, different signaling cascades and so on. However the biochemical methods are all time-consuming and expensive, so current PPI pairs elucidated by experiments are absolutely insufficient compared to the complete PPI networks. Consequently it is increasing important to develop computational tools for effectively identifying PPIs.

Check out this one too - PREDICTING PROTEIN INTERACTION FROM THE GENOME

In Silico Transfer of Neurotransmitter Transporter Motif Between Structurally Analogous Protein (Catechol-O-methyltransferase)

Here is a abstract that i had submitted at the 3D sig:Structral Bioinformatics at the 1st Structural Bioinformatics Meeting at ISMB 29-30 July 2004, Glasgow, Scotland, UK. I do not know how relevant is the issue to this day. I am interested in working further in this area and i am interested in collaborating. Do let me know your comments also pour your suggestions on how to further this study.

In silico methods can be used to design protein, based on stability and functionality using computational methods rather than laboratory procedures (Comet et al., 2000). The changes taking place due to the transfer of motif region from human Catechol-O-methyltansferase to rat Catechol-O-methyltransferase can be studied effectively using computational methods. This will provide insight for further development of the study about the function of neurotransmitter region of catechol-O-methyltransferase and its involvement in the Parkinson’s disease.

Catechol-O-methyltransferase (COMT) catalyzes the transfer of the active methyl group from S-adenosyl-L-methionine to the two ring hydroxyl groups of catechols (Garnier et al., 1978). The main function of COMT involves the elimination of biologically active or toxic catechols and their metabolites. COMT may modulate the neurotransmitter function of dopamine and norepinephrine in various mental processes through altering the rate of their metabolic inactivation in different parts of the brain.

COMT is found both in membrane bound form in post synaptic neurons and in cytoplasmic soluble form in the extraneuronal tissue like glial cells.

The sequence of the Human COMT was retrieved from Human Protein Reference Database (HPRD). The BLAST and ClustalW was performed to confirm the homologous sequence for human COMT as rat COMT seq. The motif sequence was identified from the Blocks searcher. PDB BLAST confirmed rat COMT as the structural homology for modeling the structure of human COMT using SWISSMODEL. The human sequence contained VLLELGAYCGYSAVRMA in the neurotransmitter motif sequence. But some organism and rat posses neurotransmitter sequence with the change in the two amino acid val and leu (LVLELGAYCGYSAVRMA)

An initial model of COMT of human was generated through the on-line automated comparative protein modeling server, SWISSMODEL (Guex,N., Peitsch, M.C.1997). and was verified to evaluate the quality of the structure.

SAVS, a Structure Analysis and Verification Server was used to study the quality of the model structure generated by SWISSMODEL (Van G.W. 1996). The overall quality factor of the model was 96.429* and the 98.31* of the residues had an average 3D-ID score >0.2.
The in silico transfer was done with the help of SwissPDB viewer mutation tool. The structure of the mutated rat sequence was compared with original rat COMT structure. This may give us information about changes involved after the mutation and these changes may in turn affect the functions of the animal.

The results from both Expasy tools and Predict Protein states that there is very less difference in the % composition of the secondary structures of both the sequences. These results infer that the mutated rat with the human motif sequences may also poses the same functionality and stability as that of the original rat COMT sequence.

References:
Combet C., Blanchet C., Geourjon C. and Deléage G., 2000 March, TIBS Vol. 25, No 3 [291]:147-150

Garnier J, Osguthorpe DJ, Robson B., 1978, Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. J Mol Biol. Vol. 120, pp. no 97-120.

Guex,N., Peitsch,M.C. (1997). SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis 18, 2714-2723.

Van Gunsteren, W., 1996, Biomolecular Simulations: The GROMOS96 Manual and User Guide. VdF Hochschulverlag ETHZ.

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A price tag of around US$1 billion to map human proteome

Ambitious plans to catalogue and characterize all proteins in the human body — a Human Proteome Project — are being drawn up by a small group of researchers. But with a price tag of around US$1 billion, some question whether the organizers can raise enough money or momentum for such an undertaking.

Project aims to characterize all human proteins. Talking about characterizing human proteins; there are several organizations working around the globe towards this initiative, spanning across both the public and commercial domains. Having missed out on HGMP, this time the industry in India is all geared up towards the mapping of human proteome.

Molecular Connections collaborates with Plasma Proteome Institute and with Max Planck Institute of Psychiatry, Proteomics & Biomarkers Research Group that would significantly aid them in the development of their new knowledgebase CliPro.