In silico identification and design of potent peptide inhibitors against PDZ-3 domain of Postsynaptic Density Protein (PSD-95)

Abstract

Unique intrinsic properties of peptides like low toxicity, high biological activity, and specificity make them attractive therapeutic agents. PDZ-binding peptide inhibitors have been demonstrated for curing of Alzheimer, Parkinson, Dementia, and other central nervous system ailments. In this article, we report the successful use of an integrated computational protocol to analyze the structural basis of how peptides bind to the shallow groove of the third PDZ domain (PDZ-3) from the postsynaptic density (PSD-95) protein. This protocol employs careful and precise computational techniques for design of new strategy for predicting novel and potent peptides against PDZ protein. We attempted to generate a pharmacophore model using crystal structure of peptide inhibitor bound to the PDZ-3. A highly specific and sensitive generated pharmacophore model was used for screening virtual database generated using different combination of amino acid substitutions as well as decoy peptide database for its sensitivity and specificity. Identified hit peptides were further analyzed by docking studies, and their stability analyzed using solvated molecular dynamics. Quantum Mechanics/Molecular Mechanics (QM/MM) interaction energy and GMX-PBSA scoring schemes were used for ranking of stable peptides. Computational approach applied here generated encouraging results for identifying peptides against PDZ interaction model. The workflow can be further exercised as a virtual screening technique for reducing the search space for candidate target peptides against PDZ domains.