Bioinformatic Scaling of Allosteric Interactions in Biomedical Isozymes

TL;DR

This study uses bioinformatic scaling to analyze allosteric interactions in biomedical isozymes, revealing how remote mutations influence drug efficacy and enzyme function, with implications for drug design and enzyme optimization.

Contribution

It introduces a bioinformatic scaling approach to connect allosteric interactions with drug effects and enzyme function, providing precise insights into mutation impacts.

Findings

IDH results are accurate to 1%

Remote mutations can alter drug efficacy

Hydrophobic cutoff leveling may optimize enzyme function

Abstract

Allosteric (long-range) interactions can be surprisingly strong in proteins of biomedical interest. Here we use bioinformatic scaling to connect prior results on nonsteroidal anti-inflammatory drugs to promising new drugs that inhibit cancer cell metabolism. Many parallel features are apparent, which explain how even one amino acid mutation, remote from active sites, can alter medical results. The enzyme twins involved are cyclooxygenase (aspirin) and isocitrate dehydrogenase (IDH). The IDH results are accurate to 1% and are overdetermined by adjusting a single bioinformatic scaling parameter. It appears that the final stage in optimizing protein functionality may involve leveling of the hydrophobic cutoffs of the arms of conformational hydrophilic hinges.