Hello and welcome to Parag's research page
History : Graduate
I am a graduate student at
The University of California Los Angeles with the
Department of Chemistry and
Biochemistry. My advisor Dr. David Eisenberg
is a member of the
UCLA-DOE Lab of Structural Biology and Molecular Medicine. We are both affiliated with and helping develop the New UCLA Bioinformatics Program. We are also both involved in the Mycobacterium tuberculosis Structural Genomics Consortium.
History : Undergraduate
I graduated a few years ago from the Department of Computer Science in the
School of Engineering and Applied
Science and the Department
of Chemistry in the College of Arts &
Sciences of Washington University in
St. Louis. While at Washington University, I worked with Dr. Jay Ponder of the Department of Biochemistry and
Molecular Biophysics. In a separate project, I worked with Keith Bennett of the Department of Computer Science.
What I study and enjoy to study
My main interests lie in the application of computational methods to the study of biological problems.
Here are a few problems that I am interested in:
Protein Folding.
The 20 amino acids are fundamental building blocks of life. Large chains of amino acids,
known as polypeptides or proteins are responsible for a diversity of biological
processes. Although most people have heard the words "protein" and amino acid, usually
as related to the importance of eating enough, most people don't realize that proteins
are complex and beautiful molecules with intricate structures.
It is these structures (often called "folds") that govern what a protein does, and how
it does it. Decades ago, it was observed that different proteins were composed of
different combinations of amino acids and that the different combinations produced
different protein structures. Moreover, it was discovered, that many proteins would
assume these structures on their own, without any help. The
discovery implied all of the information a protein needed to assume a particular
structure, was contained within its sequence of amino acids. It is generally
believed, if a protein can use its amino acid sequence to "figure out" what
structure to assume then it should be possible for scientists to predict that
structure using just the sequence alone. Scientists have yet to be successful doing
this, but a huge community (of which I am a part) is working on it.
Exobiology/Origin of Life/Evolution
If life existed on another planet millions of years ago - how would you know? Walking
about the surface today, what would you look for as "chemical signatures" for life?
Looking at the random things you found, could you tell anything about where the
organisms came from and how they evolved? We can ask many of these questions of our own
planet. What is the relationship (if any) between the billions of organisms on the
planet? We know there must be some, but finding it is fun and challenging!
Protein Functional Networks/Proteomics
It is true that proteins have beautiful structures that contribute to what they do and
how they do it. One question often neglected is - "who do
they do it with?" It is known that proteins interact with a wide variety of
molecules in the cell (and outside the cell - more on this later!) but not much is
yet known about how these interactions occur, or how to tell if one protein is
compatible with another protein. All of these questions are of great importance in
understanding biology and are also great fun. For you hard core computer
scientists out there - think of it as a grand problem in graph theory - perhaps
even greater in scale than even the internet!
Cell Signaling
Cells are little cities. Like cities they have phone lines running throughout them to
pass messages around. Usually this is done via cascades of proteins interacting with
other proteins and with small molecules. However, little is known about how proteins
communicate with each other to pass messages around - however, understanding biology on
a larger scale will require an understanding of the mechanism and infrastructure to
propagate conversations across large distances.
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