Towards protein folding pathways by reconstructing protein residue networks with a policy-driven model

TL;DR

This paper introduces a policy-driven extension of the ND model to reconstruct protein residue networks, achieving strong correlation with known folding rates and providing insights into protein folding pathways.

Contribution

The study extends the ND model with policies based on feature states, improving the modeling of protein folding pathways and correlating well with experimental folding rates.

Findings

Strong correlation (Pearson's < -0.83) with folding rates for diverse protein folders.

Policies and random seed significantly influence the success of the model.

Collected trajectory data enables analysis of plausible protein folding pathways.

Abstract

A method that reconstructs protein residue networks using suitable node selection and edge recovery policies produced numerical observations that correlate strongly (Pearson's correlation coefficient < -0.83) with published folding rates for 52 two-state folders and 21 multi-state folders; correlations are also strong at the fold-family level. These results were obtained serendipitously with the ND model, which was introduced previously, but is here extended with policies that dictate actions according to feature states. This result points to the importance of both the starting search point and the prevailing condition (random seed) for the quick success of policy search by a simple hill-climber. The two conditions, suitable policies and random seed, which (evidenced by the strong correlation statistic) setup a conducive environment for modelling protein folding within ND, could be compared to appropriate physiological conditions required by proteins to fold naturally. Of interest is an examination of the sequence of restored edges for potential as plausible protein folding pathways. Towards this end, trajectory data is collected for analysis and further model evaluation and development.