Computational design of substrate selective inhibition.

Area of Science:

• Enzymology and Drug Discovery
• Computational Chemistry
• Biochemistry

Background:

• Enzymes often process multiple substrates, necessitating selective inhibition to avoid blocking vital catalytic actions and causing side effects.
• Traditional enzyme inhibition methods targeting catalytic residues lack substrate selectivity.
• The need for substrate-selective inhibitors (SSI) is critical, but computational discovery methods have not yet been applied.

Purpose of the Study:

• To demonstrate a novel computational approach for discovering substrate-selective inhibitors (SSI).
• To identify SSI for Prolyl Oligopeptidase (POP), a serine protease with distinct substrate kinetics for Thyrotropin Releasing Hormone (TRH) and Angiotensin-III (Ang-III).

Main Methods:

• Utilized an in-house Iterative Stochastic Elimination (ISE) algorithm and a structure-based pharmacophore approach to develop two computational models.
• Screened a large dataset of approximately 1.8 million commercially available molecules.
• Reduced the dataset to a final set of 20 molecules for experimental validation, alongside five randomly selected controls.

Main Results:

• Two novel SSI candidates were identified and experimentally validated.
• One validated SSI acts as a competitive inhibitor for Ang-III (increasing Km) but a non-competitive inhibitor for TRH (decreasing Vmax).
• The computational models effectively prioritized molecules with desired substrate-selective inhibitory properties.

Conclusions:

• A successful computational strategy for discovering substrate-selective inhibitors (SSI) has been established.
• This approach enables the design of targeted enzyme inhibitors, offering a promising avenue for therapeutic development with reduced adverse effects.
• The identified SSI for POP demonstrates differential inhibition of substrate processing, validating the computational methodology.