Towards protein folding pathways by reconstructing protein residue networks with a policy-driven model
This work addresses protein folding prediction for computational biology, but it is incremental as it extends an existing model with new policies.
The study tackled the problem of modeling protein folding pathways by reconstructing protein residue networks using a policy-driven model, achieving strong correlations (Pearson's coefficient < -0.83) with published folding rates for 73 proteins.
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.