scholarly journals Reconstruction of protein structures from single-molecule time series

2020 ◽  
Vol 153 (19) ◽  
pp. 194102
Author(s):  
Maximilian Topel ◽  
Andrew L. Ferguson
Keyword(s):  
Time Series ◽  
Single Molecule ◽  
Protein Structures

scholarly journals Error-based Extraction of States and Energy Landscapes from Experimental Single-Molecule Time-Series

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
J. Nicholas Taylor ◽  
Chun-Biu Li ◽  
David R. Cooper ◽  
Christy F. Landes ◽  
Tamiki Komatsuzaki
Keyword(s):  
Time Series ◽  
Single Molecule ◽  
Energy Landscapes

scholarly journals Non-Markovian properties and multiscale hidden Markovian network buried in single molecule time series

2013 ◽  
Vol 139 (24) ◽  
pp. 245101 ◽  
Author(s):  
Tahmina Sultana ◽  
Hiroaki Takagi ◽  
Miki Morimatsu ◽  
Hiroshi Teramoto ◽  
Chun-Biu Li ◽  
...  
Keyword(s):  
Time Series ◽  
Single Molecule

scholarly journals Protein dynamics enables phosphorylation of buried residues in Cdk2/Cyclin A-bound p27

2020 ◽  
Author(s):  
João Henriques ◽  
Kresten Lindorff-Larsen
Keyword(s):  
Protein Phosphorylation ◽  
Protein Dynamics ◽  
Single Molecule ◽  
Protein Function ◽  
Cell Cycle Progression ◽  
Conformational Dynamics ◽  
Protein Structures ◽  
Cyclin A ◽  
General Role ◽  
Wide Range

AbstractProteins carry out a wide range of functions that are tightly regulated in space and time. Protein phosphorylation is the most common post-translation modification of proteins and plays key roles in the regulation of many biological processes. The finding that many phosphorylated residues are not solvent exposed in the unphosphorylated state opens several questions for understanding the mechanism that underlies phosphorylation and how phosphorylation may affect protein structures. First, since kinases need access to the phosphorylated residue, how do such buried residues become modified? Second, once phosphorylated, what are the structural effects of phosphorylation of buried residues and do they lead to changed conformational dynamics. We have used the ternary complex between p27, Cdk2 and Cyclin A to study these questions using enhanced sampling molecular dynamics simulations. In line with previous NMR and single-molecule fluorescence experiments we observe transient exposure of Tyr88 in p27, even in its unphosphorylated state. Once Tyr88 is phosphorylated, we observe a coupling to a second site, thus making Tyr74 more easily exposed, and thereby the target for a second phosphorylation step. Our observations provide atomic details on how protein dynamics plays a role in modulating multi-site phosphorylation in p27, thus supplementing previous experimental observations. More generally, we discuss how the observed phenomenon of transient exposure of buried residues may play a more general role in regulating protein function.Significance StatementProtein phosphorylation is a common post-translation modification and is carried out by kinases. While many phosphorylation sites are located in disordered regions of proteins or in loops, a surprisingly large number of modification sites are buried inside folded domains. This observation led us to ask the question of how kinases gain access to such buried residues. We used the complex between p27, a regulator of cell cycle progression, and Cyclin-dependent kinase 2/Cyclin A to study this problem. We hypothesized that transient exposure of buried tyrosines in p27 to the solvent would make them accessible to kinases, explaining how buried residues get modified. We provide an atomic-level description of these dynamic processes revealing how protein dynamics plays a role in regulation.

scholarly journals Unsupervised selection of optimal single-molecule time series idealization criterion

2021 ◽  
Author(s):  
Argha Bandyopadhyay ◽  
Marcel P. Goldschen-Ohm
Keyword(s):  
Time Series ◽  
Single Molecule ◽  
Time Windows ◽  
Underlying Mechanism ◽  
Rapid Analysis ◽  
Model Complexity ◽  
Decision Boundary ◽  
Signal To Noise ◽  
Objective Criterion ◽  
Selection Of

AbstractSingle-molecule (SM) approaches have provided valuable mechanistic information on many biophysical systems. As technological advances lead to ever-larger datasets, tools for rapid analysis and identification of molecules exhibiting the behavior of interest are increasingly important. In many cases the underlying mechanism is unknown, making unsupervised techniques desirable. The Divisive Segmentation and Clustering (DISC) algorithm is one such unsupervised method that idealizes noisy SM time series much faster than computationally intensive approaches without sacrificing accuracy. However, DISC relies on a user selected objective criterion (OC) to guide its estimation of the ideal time series. Here, we explore how different OCs affect DISC’s performance for data typical of SM fluorescence imaging experiments. We find that OCs differing in their penalty for model complexity each optimize DISC’s performance for time series with different properties such as signal-to-noise and number of sample points. Using a machine learning approach, we generate a decision boundary that allows unsupervised selection of OC based on the input time series to maximize performance for different types of data. This is particularly relevant for SM fluorescence datasets which often have signal-to-noise near the derived decision boundary and include time series of nonuniform length due to stochastic bleaching. Our approach allows unsupervised per-molecule optimization of DISC, which will substantially assist rapid analysis of high-throughput single-molecule datasets with noisy samples and nonuniform time windows.

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