Bell Eapen MD, PhD.

Bringing Digital health & Gen AI research to life!

The clinical context
Space-filling model of the chlorpromazine mole...
Space-filling model of the chlorpromazine molecule. X-ray diffraction data from H. S. Yathirajan, M. A. Ashok, B. Narayana Achar and M. Bolte (April 2007). “Chlorpromazinium picrate”. Acta. Cryst. E63 (4) : o1795-o1797. DOI:10.1107/S1600536807011555. (Photo credit: Wikipedia)

I recently read an article titled “Structural, phylogenetic and docking studies of D-amino acid oxidase activator (DAOA), a candidate schizophrenia gene”. (Open access, link to full text below).

This is a typical example of how over-zealous bioinformatics analysis without a clinical context can churn out garbage. The article after multiple analysis concludes that DAOA interacts with DAO. Well that is why DAOA is named DAO activator. The article also predicts that a predicted ligand of the predicted tertiary structure could be a schizophrenia drug.

If only it were all so simple! If Only….

Ref: http://www.tbiomed.com/content/10/1/3

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Protein-Protein Docking Problem.

I am looking for a solution to the following problem. Any insight will be greatly appreciated.

A membrane receptor (A) has an extracellular domain (AE), transmembrane domain (AT) and intracellular domain (AI).

A bacteria (B) binds to (AE) leading to dimerization of (A) at (AT) and subsequent downstream signaling through (AI).

(A) has no known natural ligands.

(A) has one known inhibitor (I) binding to (AE)

The structure of (AE) bound to (I) is available from PDB.

How do we identify which protein in (B) binds to (AE).

The obvious solution is to dock all proteins with known structures in (B) to all known pockets in AE. Any better solutions?

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