Abstract
By monitoring the processes of individual, immobilized molecules in real time, it is possible to capture transient and stochastic events that cannot be detected using conventional ensemble-averaged methods. Such rare events on the molecular level are believed to have significant consequences in biological functions. The single-molecule approach therefore offers promising new routes to uncovering the physical and chemical transformations underlying cellular responses. Dynamics and distribution are two unique pieces of information provided by time-dependent single-molecule spectroscopy. However, to extract these pieces of information from the noisy single-molecule time series in an unbiased way is very challenging because single-molecule signals are qualitatively different from ensemble-averaged experiments. With an overarching goal of formulating a predictive understanding of protein molecular machines, this chapter outlines a framework that affords a quantitative and objective analysis of single-molecule signals, with an emphasis on Förster-type energy transfer. Both computer simulations and experimental results are used to illustrate the ideas and practical protocols.
Original language | English (US) |
---|---|
Title of host publication | Cell Signaling Reactions |
Subtitle of host publication | Single-Molecular Kinetic Analysis |
Publisher | Springer Netherlands |
Pages | 199-219 |
Number of pages | 21 |
ISBN (Print) | 9789048198634 |
DOIs | |
State | Published - Dec 1 2011 |
All Science Journal Classification (ASJC) codes
- Biochemistry, Genetics and Molecular Biology(all)
Keywords
- Conformation distribution
- Correlation function
- Cramér-Rao bound
- Dynamical depolarization
- Dynamically induced fit
- Emergence
- Ergodic
- Fisher information
- Förster-Type Resonance Energy Transfer (FRET)
- Induced fit
- Local unfolding
- Maximum Entropy
- Maximum Likelihood Estimate (MLE)
- Maximum-information algorithm
- Molecular machine
- Orientation factor, (κ)