Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data

We address the problem of analyzing sets of noisy time-varying signals that all report on the same process but confound straightforward analyses due to complex inter-signal heterogeneities and measurement artifacts. In particular we consider single-molecule experiments which indirectly measure the distinct steps in a biomolecular process via observations of noisy time-dependent signals such as a fluorescence intensity or bead position. Straightforward hidden Markov model (HMM) analyses attempt to characterize such processes in terms of a set of conformational states, the transitions that can occur between these states, and the associated rates at which those transitions occur; but require ad-hoc post-processing steps to combine multiple signals. Here we develop a hierarchically coupled HMM that allows experimentalists to deal with inter-signal variability in a principled and automatic way. Our approach is a generalized expectation maximization hyperparameter point estimation procedure with variational Bayes at the level of individual time series that learns an single interpretable representation of the overall data generating process.Comment: 9 pages, 5 figure

Hierarchical dynamics of individual RNA helix base pair formation and disruption

This thesis explores the RNA folding problem using single-molecule field effect transistors (smFETs) to measure the lifetimes of individual RNA base-pairing rearrangements. In the course of this research, considerable computational, chemical, and engineering contributions were developed so that the single-molecule measurements could be conducted and quantified. These advancements have allowed, on the basis of the smFET data collected herein, the quantification of a kinetic model for RNA stem-loop structures which has been generalized to quantitatively explore the phenomenological observation that an RNA found in the bacillus subtilis strain acts as a metabolite-sensing switch, allowing RNA polymerase to transcribe the messenger RNA when the metabolite is present and preventing transcription when the metabolite is absent. Together, the data presented quantify a simple model for the base pairing rearrangements that underlie RNA folding

Multiple Lac-mediated loops revealed by Bayesian statistics and tethered particle motion

The bacterial transcription factor LacI loops DNA by binding to two separate locations on the DNA simultaneously. Despite being one of the best-studied model systems for transcriptional regulation, the number and conformations of loop structures accessible to LacI remain unclear, though the importance of multiple co-existing loops has been implicated in interactions between LacI and other cellular regulators of gene expression. To probe this issue, we have developed a new analysis method for tethered particle motion, a versatile and commonly-used in vitro single-molecule technique. Our method, vbTPM, performs variational Bayesian inference in hidden Markov models. It learns the number of distinct states (i.e., DNA-protein conformations) directly from tethered particle motion data with better resolution than existing methods, while easily correcting for common experimental artifacts. Studying short (roughly 100 bp) LacI-mediated loops, we provide evidence for three distinct loop structures, more than previously reported in single-molecule studies. Moreover, our results confirm that changes in LacI conformation and DNA binding topology both contribute to the repertoire of LacI-mediated loops formed in vitro, and provide qualitatively new input for models of looping and transcriptional regulation. We expect vbTPM to be broadly useful for probing complex protein-nucleic acid interactions.Comment: 34 pages, 25 figures, including Supporting information. To appear in Nucleic Acids Research. Accompanying open-source software: http://sourceforge.net/projects/vbtpm

Bayesian approaches for modeling protein biophysics

textProteins are the fundamental unit of computation and signal processing in biological systems. A quantitative understanding of protein biophysics is of paramount importance, since even slight malfunction of proteins can lead to diverse and severe disease states. However, developing accurate and useful mechanistic models of protein function can be strikingly elusive. I demonstrate that the adoption of Bayesian statistical methods can greatly aid in modeling protein systems. I first discuss the pitfall of parameter non-identifiability and how a Bayesian approach to modeling can yield reliable and meaningful models of molecular systems. I then delve into a particular case of non-identifiability within the context of an emerging experimental technique called single molecule photobleaching. I show that the interpretation of this data is non-trivial and provide a rigorous inference model for the analysis of this pervasive experimental tool. Finally, I introduce the use of nonparametric Bayesian inference for the analysis of single molecule time series. These methods aim to circumvent problems of model selection and parameter identifiability and are demonstrated with diverse applications in single molecule biophysics. The adoption of sophisticated inference methods will lead to a more detailed understanding of biophysical systems.Neuroscienc

The dynamic landscape of transcription initiation in yeast mitochondria

Controlling efficiency and fidelity in the early stage of mitochondrial DNA transcription is crucial for regulating cellular energy metabolism. Conformational transitions of the transcription initiation complex must be central for such control, but how the conformational dynamics progress throughout transcription initiation remains unknown. Here, we use single-molecule fluorescence resonance energy transfer techniques to examine the conformational dynamics of the transcriptional system of yeast mitochondria with single-base resolution. We show that the yeast mitochondrial transcriptional complex dynamically transitions among closed, open, and scrunched states throughout the initiation stage. Then abruptly at position +8, the dynamic states of initiation make a sharp irreversible transition to an unbent conformation with associated promoter release. Remarkably, stalled initiation complexes remain in dynamic scrunching and unscrunching states without dissociating the RNA transcript, implying the existence of backtracking transitions with possible regulatory roles. The dynamic landscape of transcription initiation suggests a kinetically driven regulation of mitochondrial transcription. Conformational dynamics during the early stage of transcription is crucial to understanding the regulation of transcription efficiency and fidelity. Here the authors, by single-molecule fluorescence resonance energy transfer approaches, examine the conformational dynamics of the two-component transcription system of yeast mitochondria with single-base resolution

Single-molecule studies of nucleic acid dynamics using carbon nanotube-based field-effect transistors

This thesis describes the development and implementation of single-molecule carbon nanotube-based field-effect transistors (smFETs) for studies of nucleic acid dynamics. Single-molecule techniques, most notably fluorescence resonance energy transfer (smFRET) and single-molecule force spectroscopy, have been employed to investigate biomolecular dynamics due to their ability to directly observe discrete, rare events, as well as to characterize structural motions in a diverse ensemble. However, these techniques are hampered by difficulties in measuring millisecond-scale dynamics, such as base pair rearrangements, as well as the inability to observe unperturbed individual molecules for long times. Alternatively, smFETs allow observation of the dynamics of charged biomolecules, such as charged amino acids in proteins or the phosphate groups of nucleic acid backbones, with microsecond temporal resolution. Structural rearrangements of a single charged molecule on the surface of a single-walled carbon nanotube (CNT) transistor can lead to measureable fluctuations in conductance through the CNT. Thus, this technique allows for simultaneous characterization of fast events and, due to the label-free and minimally-invasive nature of smFET technology, the quantification of how the frequency of these events change over long time-scales. A portion of this work describes smFET fabrication, focusing on improvements to the functionalization method, a critical step to reliably generate individual attachment sites on the CNT for subsequent single-molecule studies. A new synthetic chemistry approach is performed in ultraminiaturized, nanofabricated reaction chambers; using lithographically-defined nanowells, single-point attachments are achieved on hundreds of individual carbon nanotube transistors, providing robust statistics and unprecedented spatial control in adduct positioning. Each device acts as a sensor to detect, in real-time and through quantized changes in conductance, single-point functionalization of the nanotube, as well as consecutive chemical reactions and subsequent molecular interactions molecular conformational changes. In particular, this thesis is focused on studying the dynamics of nucleic acids using smFET technology. First, the smFET technique presented is verified by studying the thermodynamics and kinetics of DNA hybridization, the results of which compare favorably both with predicted values and previous smFET studies using alternative device architectures. Next, the reversible folding of a single-stranded telomeric DNA sequence known to form a G-quadruplex structure is studied, revealing the characteristic increased stability of the G-quadruplex structure in the presence of potassium ions relative to sodium ions. Finally, smFET studies of the dynamics of the adenine-sensing pbuE riboswitch aptamer found in Bacillus subtilis are discussed. These results demonstrate how long-lived, ligand-dependent intermediates form at a base-pair level and suggest that these intermediates have consequences for riboswitch-regulation by adenine binding to the aptamer. With the increased time resolution of smFET technology, this work has achieved the first observation of RNA zipping and unzipping at the single-molecule level, as well as label-free observations of the effects of a three-way junction motif on helix zipping and unzipping