Forming native shortcut networks to simulate protein folding

TL;DR

This paper introduces the Network Dynamics model to simulate protein folding by reconstructing native residue networks through specific edge addition recipes, revealing insights into folding rates and transition states.

Contribution

The study proposes a novel ND model and edge addition recipes that better replicate experimental folding behaviors and transition state properties.

Findings

Edge addition recipes favoring smaller sequence separation improve correlation with folding rates.

The model produces route-like trajectories indicating cooperative two-state folding.

Local centrality measures enhance the prediction of transition state phi-values.

Abstract

Native shortcut networks (SCN0) are sub-graphs of native protein residue networks (PRN0). In this paper, we propose the Network Dynamics (ND) model, which reconstructs a PRN0 by adding back its edges according to some recipe, while its nodes are fixed at native (PDB) locations. A PRN0 reconstruction will eventually reconstruct its SCN0, but only after exploring several non-native shortcut network (SCN) configurations. It is these other SCN configurations that are of interest to us, as they produce the statistics to evaluate the different recipes. The recipes vary from each other slightly to investigate the effect of different edge orderings on protein folding. An edge ordering is deemed more successful if it produces a stronger correlation with experimental folding rates. Over proteins of different chain fold types, this basic requirement is best satisfied when a recipe favours earlier restoration of edges with smaller sequence separation, and earlier restoration of edges incident on nodes with larger remaining degree within the 1-hop neighborhood of a previously selected node. Further, this recipe generated more route-like trajectories over SCN space and a wider range of simulated folding rates, both of which signal cooperative two-state folding behavior. It also produced better correspondence between ND calculated phi-values and experimental phi-values obtained from a set of ten transition state ensembles (TSE). Also introduced is the local centrality measure which uses centrality of initial fold substructures to yield calculated phi-values from PRN0s with even better overall correspondence than ND calculated phi-values. Preliminary investigation with mixed ND recipes by chain fold type suggests that concocting further context-sensitive recipes by chain fold type and combining them could improve ND. This search task may be better undertaken by machine learning.