The interpretation of single-molecule measurements is greatly complicated by the presence of multiple fluorescent labels. However, many molecular systems of interest consist of multiple interacting components. We investigate this issue using multiply labeled dextran polymers that we intentionally photobleach to the background on a single-molecule basis. Hidden Markov models allow for unsupervised analysis of the data to determine the number of fluorescent subunits involved in the fluorescence intermittency of the 6-carboxy-tetramethylrhodamine labels by counting the discrete steps in fluorescence intensity. The Bayes information criterion allows us to distinguish between hidden Markov models that differ by the number of states, that is, the number of fluorescent molecules. We determine information-theoretical limits and show via Monte Carlo simulations that the hidden Markov model analysis approaches these theoretical limits. This technique has resolving power of one fluorescing unit up to as many as 30 fluorescent dyes with the appropriate choice of dye and adequate detection capability. We discuss the general utility of this method for determining aggregation-state distributions as could appear in many biologically important systems and its adaptability to general photometric experiments.

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Glucose/galactose binding protein (GGBP) functions in two different larger systems of proteins used by enteric bacteria for molecular recognition and signaling. Here we report on the thermodynamics of conformational equilibrium distributions of GGBP. Three fluorescence components appear at zero glucose concentration and systematically transition to three components at high glucose concentration. Fluorescence anisotropy correlations, fluorescent lifetimes, thermodynamics, computational structure minimization, and literature work were used to assign the three components as open, closed, and twisted conformations of the protein. The existence of three states at all glucose concentrations indicates that the protein continuously fluctuates about its conformational state space via thermally driven state transitions; glucose biases the populations by reorganizing the free energy profile. These results and their implications are discussed in terms of the two types of specific and nonspecific interactions GGBP has with cytoplasmic membrane proteins.

Over the past 20 years, the biological sciences have increasingly incorporated chemistry, physics, computer science, and mathematics to aid in the development and use of mathematical models. Such combined approaches have been used to address problems from protein structure-function relationships to the workings of complex biological systems. Computer simulations of molecular events can now be accomplished quickly and with standard computer technology. Also, simulation software is freely available for most computing platforms, and online support for the novice user is ample. We have therefore created a molecular dynamics laboratory module to enhance undergraduate student understanding of molecular events underlying organismal phenotype. This module builds on a previously described project in which students use site-directed mutagenesis to investigate functions of conserved sequence features in members of a eukaryotic protein kinase family. In this report, we detail the laboratory activities of a MD module that provide a complement to phenotypic outcomes by providing a hypothesis-driven and quantifiable measure of predicted structural changes caused by targeted mutations. We also present examples of analyses students may perform. These laboratory activities can be integrated with genetics or biochemistry experiments as described, but could also be used independently in any course that would benefit from a quantitative approach to protein structurefunction relationships.

A method is described to empower students to efficiently perform general and literature searches using online resources. The method was tested on undergraduate and graduate students with varying backgrounds with scientific literature. Students involved in this study showed marked improvement in their awareness of how and where to find accurate scientific information.

Vision and Change [AAAS, 2011] outlines a blueprint for modernizing biology education by addressing conceptual understanding of key concepts, such as the relationship between structure and function. The document also highlights skills necessary for student success in 21st century Biology, such as the use of modeling and simulation. Here we describe a laboratory activity that allows students to investigate the dynamic nature of protein structure and function through the use of a modeling technique known as molecular dynamics (MD). The activity takes place over two lab periods that are 3 hr each. The first lab period unpacks the basic approach behind MD simulations, beginning with the kinematic equations that all bioscience students learn in an introductory physics course. During this period students are taught rudimentary programming skills in Python while guided through simple modeling exercises that lead up to the simulation of the motion of a single atom. In the second lab period students extend concepts learned in the first period to develop skills in the use of expert MD software. Here students simulate and analyze changes in protein conformation resulting from temperature change, solvation, and phosphorylation. The article will describe how these activities can be carried out using free software packages, including Abalone and VMD/NAMD.

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