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Yuan J, Zhao R, Xu J, Cheng M, Qin Z, Kou X, Fang X. Analyzing protein dynamics from fluorescence intensity traces using unsupervised deep learning network. Commun Biol 2020; 3:669. [PMID: 33184459 PMCID: PMC7665068 DOI: 10.1038/s42003-020-01389-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 10/13/2020] [Indexed: 11/09/2022] Open
Abstract
We propose an unsupervised deep learning network to analyze the dynamics of membrane proteins from the fluorescence intensity traces. This system was trained in an unsupervised manner with the raw experimental time traces and synthesized ones, so neither predefined state number nor pre-labelling were required. With the bidirectional Long Short-Term Memory (biLSTM) networks as the hidden layers, both the past and future context can be used fully to improve the prediction results and can even extract information from the noise distribution. The method was validated with the synthetic dataset and the experimental dataset of monomeric fluorophore Cy5, and then applied to extract the membrane protein interaction dynamics from experimental data successfully.
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Affiliation(s)
- Jinghe Yuan
- Key Laboratory of Molecular Nanostructure and Nanotechnology, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, 100190, Beijing, China.
| | - Rong Zhao
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, 100029, Beijing, China
| | - Jiachao Xu
- Key Laboratory of Molecular Nanostructure and Nanotechnology, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, 100190, Beijing, China
| | - Ming Cheng
- Key Laboratory of Molecular Nanostructure and Nanotechnology, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, 100190, Beijing, China
| | - Zidi Qin
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Xiaolong Kou
- Key Laboratory of Molecular Nanostructure and Nanotechnology, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, 100190, Beijing, China
| | - Xiaohong Fang
- Key Laboratory of Molecular Nanostructure and Nanotechnology, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, 100190, Beijing, China.
- University of Chinese Academy of Sciences, 100049, Beijing, China.
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Luo F, Qin G, Xia T, Fang X. Single-Molecule Imaging of Protein Interactions and Dynamics. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2020; 13:337-361. [PMID: 32228033 DOI: 10.1146/annurev-anchem-091619-094308] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Live-cell single-molecule fluorescence imaging has become a powerful analytical tool to investigate cellular processes that are not accessible to conventional biochemical approaches. This has greatly enriched our understanding of the behaviors of single biomolecules in their native environments and their roles in cellular events. Here, we review recent advances in fluorescence-based single-molecule bioimaging of proteins in living cells. We begin with practical considerations of the design of single-molecule fluorescence imaging experiments such as the choice of imaging modalities, fluorescent probes, and labeling methods. We then describe analytical observables from single-molecule data and the associated molecular parameters along with examples of live-cell single-molecule studies. Lastly, we discuss computational algorithms developed for single-molecule data analysis.
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Affiliation(s)
- Fang Luo
- Beijing National Research Center for Molecular Sciences, CAS Key Laboratory of Molecule Nanostructure and Nanotechnology, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China;
- Department of Chemistry, University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Gege Qin
- Beijing National Research Center for Molecular Sciences, CAS Key Laboratory of Molecule Nanostructure and Nanotechnology, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China;
- Department of Chemistry, University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Tie Xia
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Xiaohong Fang
- Beijing National Research Center for Molecular Sciences, CAS Key Laboratory of Molecule Nanostructure and Nanotechnology, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China;
- Department of Chemistry, University of the Chinese Academy of Sciences, Beijing 100049, China
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Xu J, Qin G, Luo F, Wang L, Zhao R, Li N, Yuan J, Fang X. Automated Stoichiometry Analysis of Single-Molecule Fluorescence Imaging Traces via Deep Learning. J Am Chem Soc 2019; 141:6976-6985. [DOI: 10.1021/jacs.9b00688] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Jiachao Xu
- Key Laboratory of Molecular Nanostructure and Nanotechnology, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Gege Qin
- Key Laboratory of Molecular Nanostructure and Nanotechnology, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fang Luo
- Key Laboratory of Molecular Nanostructure and Nanotechnology, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lina Wang
- Key Laboratory of Molecular Nanostructure and Nanotechnology, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Rong Zhao
- Key Laboratory of Molecular Nanostructure and Nanotechnology, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Nan Li
- Key Laboratory of Molecular Nanostructure and Nanotechnology, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jinghe Yuan
- Key Laboratory of Molecular Nanostructure and Nanotechnology, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaohong Fang
- Key Laboratory of Molecular Nanostructure and Nanotechnology, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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Hoffmann PM. How molecular motors extract order from chaos (a key issues review). REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2016; 79:032601. [PMID: 26863000 DOI: 10.1088/0034-4885/79/3/032601] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Molecular motors are the workhorses of living cells. Seemingly by 'magic', these molecules are able to complete purposeful tasks while being immersed in a sea of thermal chaos. Here, we review the current understanding of how these machines work, present simple models based on thermal ratchets, discuss implications for statistical physics, and provide an overview of ongoing research in this important and fascinating field of study.
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Affiliation(s)
- Peter M Hoffmann
- Department of Physics and Astronomy, Wayne State University, 666 W Hancock, Detroit, MI 48201, USA
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Chowdhury D. Michaelis-Menten at 100 and allosterism at 50: driving molecular motors in a hailstorm with noisy ATPase engines and allosteric transmission. FEBS J 2013; 281:601-11. [DOI: 10.1111/febs.12596] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2013] [Revised: 10/25/2013] [Accepted: 10/28/2013] [Indexed: 11/29/2022]
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Atzberger PJ, Peskin CS. A Brownian Dynamics model of kinesin in three dimensions incorporating the force-extension profile of the coiled-coil cargo tether. Bull Math Biol 2006; 68:131-60. [PMID: 16794924 DOI: 10.1007/s11538-005-9003-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2004] [Accepted: 03/15/2005] [Indexed: 10/25/2022]
Abstract
The kinesin family of motor proteins are involved in a variety of cellular processes that transport materials and generate force. With recent advances in experimental techniques, such as optical tweezers can probe individual molecules, there has been an increasing interest in understanding the mechanisms by which motor proteins convert chemical energy into mechanical work. Here we present a mathematical model for the chemistry and three dimensional mechanics of the kinesin motor protein which captures many of the force dependent features of the motor. For the elasticity of the tether that attaches cargo to the motor we develop a method for deriving the non-linear force-extension relationship from optical trap data. For the kinesin heads, cargo, and microscope stage we formulate a three dimensional Brownian Dynamics model that takes into account excluded volume interactions. To efficiently compute statistics from the model, an algorithm is proposed which uses a two step protocol that separates the simulation of the mechanical features of the model from the chemical kinetics of the model. Using this approach for a bead transported by the motor, the force dependent average velocity and randomness parameter are computed and compared with the experimental data.
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Affiliation(s)
- Paul J Atzberger
- Department of Mathematics, Rensselaer Polytechnic Institute, Amos Eaton Hall, Troy, NY 12180, USA.
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