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Learning dynamical information from static protein and sequencing data

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

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Authors

P. Pearce

F.G. Woodhouse

A. Forrow

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Ashley Kelly a.j.kelly@durham.ac.uk
PGR Student Doctor of Philosophy

J. Dunkel



Abstract

Many complex processes, from protein folding to neuronal network dynamics, can be described as stochastic exploration of a high-dimensional energy landscape. While 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-electronmicroscopy data.

Citation

Pearce, P., Woodhouse, F., Forrow, A., Kelly, A., Kusumaatmaja, H., & Dunkel, J. (2019). Learning dynamical information from static protein and sequencing data. Nature Communications, 10, Article 5368. https://doi.org/10.1038/s41467-019-13307-x

Journal Article Type Article
Acceptance Date Oct 24, 2019
Online Publication Date Nov 26, 2019
Publication Date Nov 26, 2019
Deposit Date Oct 28, 2019
Publicly Available Date Nov 26, 2019
Journal Nature Communications
Publisher Nature Research
Peer Reviewed Peer Reviewed
Volume 10
Article Number 5368
DOI https://doi.org/10.1038/s41467-019-13307-x

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