Research

September 2000

September 1, 2000

Protein Conformational Dynamics Background

Posted by David Talaga

During their life cycle proteins undergo many types ofconformational changes. After folding, the activity and regulation of proteins are also mediated by conformational changes. Misfolded and damaged proteins are usually degraded. However, irreversible aggregation into amyloid is accompanied by yet another conformational change.

During their life cycle proteins undergo many types of conformational changes.  The first type of conformational change is the process of protein folding.  The polypeptide must fold into its active three-dimensional structure before it is degraded by cellular proteases or aggregates. In fact this process is so important that organisms have developed systems such as Chaperonins that consume energy to assist in the folding process. Once the protein has obtained its proper three-dimensional structure it may undergo further structural changes that are associated with its function. Many enzymes and ligand-binding proteins must undergo large conformational changes during their function. The activity of a protein can be modulated by allosteric interactions with other molecules. These interactions result in a dynamic structural change that changes the protein's activity. Finally, most proteins only survive a short time in the cell without becoming damaged.  Damage often will change the structure of the protein and allow proteases to degrade it allowing the cycle to begin anew.  If the degradation process is faulty then aggregation can occur.  Thus, during their life cycle proteins can undergo five basic classes of conformational change.
  • Folding
  • Activity
  • Regulation
  • Degradation
  • Aggregation
Defects in these processes can result in diseases.

September 1, 2000

Single molecule protein/peptide conformational dynamics

Posted by David Talaga

We are interested in understanding the mechanisms of protein and peptide conformational dynamics including structural changes that occur upon ligand binding as well as protein folding and misfolding.

September 1, 2000

Information theory and hidden Markov models

Posted by David Talaga

Coding

We are using information theory and hidden Markov models to improve the design and interpretation of single molecule fluorescence measurements. Single molecule (SM) measurements are rapidly becoming commonplace in research laboratories around the world and are contributing to many areas of investigation because of their ability to provide insight into phenomena that were previously intractable because of the ensemble averaging present in bulk measurements. In particular the dynamics of conformationally heterogeneous systems are benefiting from single-molecule studies. Protein folding and conformational dynamics, enzymology, ribozyme function, bacterial light harvesting, and protein-nucleic acid interactions are just a few examples of complex systems that have benefited from the application of SM techniques. However, the impact of SM results has been mitigated by the lack of uniform data analysis and interpretation. This research focuses on SM fluorescence measurements and how to place the experimental design, analysis, and expectations onto solid statistical and theoretical ground.

We use information theory to determine the fundamental limits of SM experiments. This provides a theoretical framework that can be used for experimental design as it provides the limit of the measurement’s ability to make inferences about the properties of the system. It will also provide the benchmark by which to judge data reduction methods.

We are developing statistically rigorous analysis methods based on hidden Markov models develops the algorithms and core codes to implement statistically rigorous methods of data analysis that allow unbiased estimation of system parameters with accuracy approaching the information theory limit including meaningful uncertainty estimates.

We are implementing these methods as user-oriented additions to common data analysis packages so as to provide useable tools for experimental design and analysis to allow other investigators to exploit these methods for their own research.

September 1, 2000

Amyloid aggregation mechanisms

Posted by David Talaga

The study of amyloid structure and growth has been motivated by their implication in many human diseases. There are ~20 diseases associated with excessive deposits of amyloid plaques in the affected tissue or organ including Alzheimer?s disease (AD), Parkinson?s disease (PD), type II diabetes, and spongiform encephalopathies. In these disease states, proteins that are normally soluble undergo aggregation to form various intermediates and amyloidogenic species. These species subsequently assemble to generate insoluble fibrils that accumulate in the affected tissues or organs. A detailed understanding of amyloid growth mechanisms will allow new approaches to the prevention of amyloid formation and better diagnostics for early detection of amyloidogenic diseases.

A molecular-level mechanism of how the different amyloid species interconvert is the goal of this project. There are many species of amyloid particles present physiologically. Our single molecule studies aim to classify the species involved in amyloid formation according to size, shape, kinetic reactivity, and monomer 2° and 3&degree; structural information. A molecular-level mechanism of amyloid growth must include details as to when the protein misfold occurs and how it is influenced by the dynamics of protein structure. To determine the physical interactions and structural changes involved in the amyloid assembly mechanism, we study effect of environmental variables such as temperature, pH, helix promoting solvents, denaturants, and reducing agents. The environmental effect on aggregation is expected to be species-dependent reflecting a possible hierarchy of structural interactions.

September 1, 2000

Single molecule Methods development

Posted by David Talaga

We are developing new approaches to single molecule measurements to enhance our ability to extract useful information from them. Innovations include data acquisition and instrument automation, incorporation of microfluidics, development of new labeling methods, and investigation of various immobilization schemes.