TsaiLab

Current research projects include:

Binding Interface Database (BID)
An overflow of information on the characterization of protein-protein interactions at the amino-acid level is continuing to develop with the goal of better understanding protein interfaces. For this reason it is necessary to acquire a protein-protein interaction database in which an enormous number of interactions can be easily accessed.

The Binding Interface Database (BID) is structured to organize vast amounts of protein interaction information into tabular form, graphical contact maps, and descriptive functional profiles. Detailed protein descriptions, interaction descriptions, bond formation, and the strength of each amino acid’s contribution to binding were systematically obtained by mining the primary literature containing alanine scanning and site-directed mutations.

Side-chain driven protein refinement
We propose to develop a more accurate refinement algorithm that addresses the major goals from the RFA on High Accuracy Protein Structure Modeling. Better refinement of the starting template towards the native structure is a primary step in improving the predictions from close as well as remote homologs. At this level of structure prediction, where the conformational space is limited to a single fold family, sequence specific differences in tertiary structure determine the perturbations necessary to refine a template towards its native structure. However, current refinement methods are dominated by random searches of local, backbone conformations and only consider tertiary structure (such as side-chain packing) indirectly as an outcome to these main-chain movements.
As supported by the data in the Preliminary Results section, this proposal is based on the hypothesis that a more accurate refinement method needs to be driven by tertiary structure. Therefore, the major goal of this proposal is to statistically model and apply more exact descriptions of the variation in tertiary structure to improve protein structure refinement in comparative modeling. In particular, our analysis will more clearly define the contributions to protein conformation from multi-bodied, tertiary interactions versus those determined by the linear, protein backbone.
As a new investigator, this proposal continues my group?s long-term objective of discovering the determinants of protein structure and function. I have assembled a collaborative, multi-disciplinary team of computational biochemists and statisticians with expertise in development of algorithms modeling protein structure, Bayesian nonparametric techniques, and high performance computing. With our computational resources and environment, we will complete the following objectives framed in the three stages of our refinement algorithm. First, we will create conformationally ?relaxed? starting structures that will have a higher likelihood of reaching the native state. Secondly, we will use our relative packing group construct to develop a side-chain centric, refinement move set. This move set will be incorporated into a structure build up routine based on distance geometry. Lastly, we will derive selection algorithms that will identify near native models. By emphasizing that sequence specific variation in tertiary structure determines a protein?s backbone, the proposed research represents a subtle but innovative shift in perspective to protein
refinement of comparative models.

Dynameomic Studies
We are trying to understand how proteins fold. Specifically, we would like to know the rules that drive a given sequence to form its native structure. As a first step, we have run molecular dynamics simulations over a variety of different protein folds in order sample as much conformational space as possible. These simulations of proteins in explicit water solvent form a knowledge base upon which we will base our investigations into protein folding: dyanmeomics.
We will approach this problem from two directions: outside and inside the protein. On the outside of the protein, water solvent drives the hydrophobic effect at the protein- solvent interface by maximizing solvent configurational entropy. Based on solvent arrangements from molecular dynamics simulations, we are trying to boot-strap our way to a better model for water?s role in the hydrophobic effect. On the inside of proteins, non-polar residues and structural elements dynamically interact or pack with each other as a result of the hydrophobic effect. In an analysis of molecular dynamics simulations, we want to discover which elements of structure are generally important in protein folding. Better knowledge of these phenomena will help us in protein structure prediction and design.