Learning dynamical information from static protein and sequencing data

Philip Pearce, Francis G. Woodhouse, Aden Forrow, Ashley Kelly, Halim Kusumaatmaja () and Jörn Dunkel ()
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Philip Pearce: Massachusetts Institute of Technology
Francis G. Woodhouse: University of Oxford
Aden Forrow: Massachusetts Institute of Technology
Ashley Kelly: Durham University
Halim Kusumaatmaja: Durham University
Jörn Dunkel: Massachusetts Institute of Technology

Nature Communications, 2019, vol. 10, issue 1, 1-8

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-13307-x

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