[2604.04677] Towards protein folding pathways by reconstructing protein residue networks with a policy-driven model

[2604.04677] Towards protein folding pathways by reconstructing protein residue networks with a policy-driven model

arXiv - Machine Learning 3 min read

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Abstract page for arXiv paper 2604.04677: Towards protein folding pathways by reconstructing protein residue networks with a policy-driven model

Quantitative Biology > Biomolecules arXiv:2604.04677 (q-bio) [Submitted on 6 Apr 2026] Title:Towards protein folding pathways by reconstructing protein residue networks with a policy-driven model Authors:Susan Khor View a PDF of the paper titled Towards protein folding pathways by reconstructing protein residue networks with a policy-driven model, by Susan Khor View PDF 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 e...

Originally published on April 07, 2026. Curated by AI News.

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