Learning dynamical information from static protein and sequencing data

Author: 

Pearce, P
Woodhouse, F
Forrow, A
Kelly, A
Kusumaatmaja, H
Dunkel, J

Publication Date: 

26 November 2019

Journal: 

Nature Communications

Last Updated: 

2020-09-27T23:31:25.093+01:00

Issue: 

1

Volume: 

10

DOI: 

10.1038/s41467-019-13307-x

page: 

5368-

abstract: 

Many complex processes, from protein folding to neuronal network dynamics, can be described as stochastic exploration of a high-dimensional energy landscape. Although efficient algorithms for cluster detection in high-dimensional spaces have been developed over the last two decades, considerably less is known about the reliable inference of state transition dynamics in such settings. Here we introduce a flexible and robust numerical framework to infer Markovian transition networks directly from time-independent data sampled from stationary equilibrium distributions. We demonstrate the practical potential of the inference scheme by reconstructing the network dynamics for several protein-folding transitions, gene-regulatory network motifs, and HIV evolution pathways. The predicted network topologies and relative transition time scales agree well with direct estimates from time-dependent molecular dynamics data, stochastic simulations, and phylogenetic trees, respectively. Owing to its generic structure, the framework introduced here will be applicable to high-throughput RNA and protein-sequencing datasets, and future cryo-electron microscopy (cryo-EM) data.

Symplectic id: 

1070559

Submitted to ORA: 

Submitted

Publication Type: 

Journal Article