Learning dynamical information from static protein and sequencing data

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

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
• Electronic ISSN: 2041-1723
• Publisher: Nature Research
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Article Number: 5368
• DOI: https://doi.org/10.1038/s41467-019-13307-x
• Public URL: https://durham-repository.worktribe.com/output/1286575