Crucial stages of protein folding through a solvable model: predicting target sites for enzyme-inhibiting drugs

TL;DR

This paper introduces an exactly solvable model based on protein topology to identify folding bottlenecks and key sites in HIV-1 Protease, aiding drug resistance understanding and potential drug target identification.

Contribution

The study presents a novel, deterministic model that predicts folding bottlenecks and mutation sites correlating with drug resistance, enhancing traditional methods.

Findings

Predicted key sites match clinical resistance data.

Mutations induced by therapy align with folding bottlenecks.

Model shows high statistical significance in correlations.

Abstract

An exactly solvable model based on the topology of a protein native state is applied to identify bottlenecks and key-sites for the folding of HIV-1 Protease. The predicted sites are found to correlate well with clinical data on resistance to FDA-approved drugs. It has been observed that the effects of drug therapy are to induce multiple mutations on the protease. The sites where such mutations occur correlate well with those involved in folding bottlenecks identified through the deterministic procedure proposed in this study. The high statistical significance of the observed correlations suggests that the approach may be promisingly used in conjunction with traditional techniques to identify candidate locations for drug attacks.