Spring 2020 Schedule
Office: HAC 317A
Office Hours: Mon 13:00–15:30; Wed 13:00–15:30; Fri 09:00–10:00; and by appointment
Despite its historical longevity, the “central dogma” of molecular biology is not sufficient to account for the flow of all information in the “language of life”. Methylation of DNA (epigenetics), the existence of inteins and prions, and post-translational modifications (PTMs) of proteins are all examples of information flow within biology that do not fit cleanly into the simple scheme where DNA leads to RNA, which leads to protein. A complete understanding of biology will require a deciphering of what factors control these alternative forms of information flow. These factors involve an understanding chemistry at a three-dimensional level.
The overarching goal of my undergraduate research group is to develop computational tools to decipher the language of the cell at the chemical level, focusing on interactions with carbohydrates and other PTMs. Our work involves a three-stemmed approach: protocol development, data mining and analysis, and experimental conformation of predictions.
Students in my lab will learn skills in organic chemistry, biochemistry, and computational modeling, with a potential to learn programming skills applicable both in and out of scientific careers. In addition to those with majors in the biological and chemical sciences, my lab welcomes students in the computer sciences.
Project 1: Computational Glycoengineering
Goal: Develop a computational tool to design and predict glycan residues and glycosylation patterns as favorable candidates for experimentation.
The plan for this project involves three phases, including five steps. The first step, within the development phase, is to build a database of chemical moieties for modified sugars. The second step is to adapt Rosetta code to design saccharide residues.
The predictive phase of the project begins with step 3, to use our new algorithms to design saccharide residues within a structural context. Because of the combinatorial way in which Rosetta can build residues from “base residues” and “patches” of chemical moieties, we will be able to virtually screen thousands of glycan forms to identify structures that are compatible within the context of the network of interactions leading to enzyme activity. Step 4 is to sort and cluster our resulting models and to rank them based on predicted activity or function.
The final step is to measure enzyme activities during an experimental phase to confirm our predictions.
Project 2: Virtual Post-Translational Modification of Proteins
Goal: Develop a tool to read consensus sequences from a database and use this information to build models for a wide range of PTMs, including glycosylation, phosphorylation, and methylation.
The planned workflow for this project is outlined below. In a bioinformatic research phase, we will mine structural patterns by making comparisons of structures in the Protein Data Bank (PDB) and searching for measureable features (steps 1 and 2).
In a predictive phase, after virtually post-translationally modifying a structure (step 3), we will refine and score to rate the feasibility of such a modification (step 4). For example, we will be able to evaluate whether such a modification is energetically favorable or might lead to clashes or instability of a local area of protein structure.
Finally, in an experimental phase, we will perform simple in vitro modifications of proteins using, for example, glycosyltransferases or kinases (step 5). In our final step, we will use mass spectrometry (MS) to confirm that a PTM has in fact occurred where we predicted.
Professor Labonte grew up in Maine, where he was homeschooled. He attended Grove City College in Pennsylvania to study biochemistry. He then moved to Baltimore, where he has now lived for more than 15 years, to earn his Ph.D. in bioorganic chemistry at Johns Hopkins University, studying “mega-synthases”. As a postdoctoral fellow in the Chemical & Biomolecular Engineering Department at Hopkins, Dr. Labonte learned to code in C++ and Python and built a framework for computationally modeling carbohydrate structures.
Professor Labonte is a want-to-be “Renaissance man” who enjoys learning about just about everything. He especially loves to learn new languages. For activities, he enjoys complicated board games, building LEGOs®, and going on crazy outdoor adventures, such as climbing four-mile-high active volcanoes or exploring vast cave complexes.