UC Irvine

Studies of single biomolecules provide information that is buried in an ensemble-based measurement, including the evolution of an individual biomolecule’s behavior over time. Recent work showed that an electronic sensor composed of single-walled carbon nanotube field-effect transistors (SWCNT-FETs) can observe an individual biomolecule’s conformational motions over time and obtain accurate measurements of catalytic rates for a variety of enzymes (1-3).

This dissertation expands the scope of transistor-based biosensing techniques through several strategies. The first strategy extends previous work by investigating similar enzymes, such as other DNA polymerases, in order to identify unique characteristics of each enzyme. A second strategy focuses on investigating the dynamics of biomolecular interactions that have not been previously studied by this technique, such as ligand-binding interactions. A third strategy makes refinements to the measurement or analysis techniques to uncover additional, subtler dynamics and other information that was previously hidden in the acquired signal.

The dissertation is organized into two main parts. The first part (Chapters 1-3) discusses new measurements performed using the SWCNT-FET technique. Chapter 1 provides a brief introduction to the SWCNT-FET biosensing technique and details the methods and materials used in the experiments described in the following two chapters.

Chapter 2 studies the behavior of a weakly-interacting antibody-antigen system: antibody 3C6 in the presence of the antigen paclitaxel. SWCNT-FET recordings of antigen-antibody binding exhibited two conductance states corresponding to bound and unbound configurations, like the two-level dynamics previously recorded from enzymatic catalysis. The SWCNT-FET signal correlated with antigen concentration, remaining relatively static at concentrations far from the value of the dissociation constant K_D and fluctuating most actively near K_D. Analysis of the distribution of single-molecule bound and unbound times determined a value of K_D = 30 nM, a binding rate k_off = 10^4 s^-1, and a Hill coefficient of binding cooperativity of 1.8. Chapter 2 also compares antibody-antigen dynamics recorded in single-molecule, few-molecule, and many-molecule regimes of biofunctionalization.

Chapter 3 extends previous work on DNA polymerases by investigating an alternate polymerase, φ29 DNA polymerase, and characterizes its conformational motions and catalytic efficiency. Chapter 3 finds that the catalytic efficiency of φ29 DNA polymerase depends on the template composition. The enzyme continuously processed heteropolymer ssDNA templates and homopolymer templates containing thymine and cytosine at rates of ~50 s^-1 for 3-5 mins, but exhibited only 1-2 s bursts of conformational motion among 60 s of pauses when processing homopolymer templates containing adenine and guanine. Single-molecule recordings of the latter two templates showed the ability of the SWCNT-FET to measure enzyme motion when the enzyme’s activity in time was less than 2%, in contrast with ensemble-based observations which did not detect catalysis at such low activity. In addition, detailed analyses of the open and closed conformations of φ29 DNA polymerase suggested the presence of multiple operating modes during the catalytic cycle, including a closed conformation whose duration was 8 times longer than the typical catalytic event.

The second part of the dissertation (Chapters 4-5) expands the analysis toolkit with new methods. Chapter 4 introduces a wavelet-based denoising scheme and describes the optimization and application of the scheme to SWCNT-FET sensor signals. This chapter demonstrates the effectiveness of wavelet denoising in removing both low- and high-frequency noise from the SWCNT-FET sensor output and describes the artifacts introduced by the denoising process. The wavelet denoising scheme is also compared to other digital denoising methods.

Finally, Chapter 5 describes an automated analysis procedure for identifying conformational events and characterizing each event using a set of features. SWCNT-FET measurements from two variants of Taq DNA polymerase are compared to highlight features that are correlated to enzyme behavior, correlated to experimental noise, or completely uncorrelated. In addition, a preliminary analysis using principal component analysis (PCA) serves as an example of machine learning techniques that could be used in the future.