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Grishin Lab

Howard Hughes Medical Institute
Department of Biophysics and
Department of Biochemistry
University of Texas
Southwestern Medical Center

5323 Harry Hines Blvd.
Dallas, Texas 75390-9050

Nick V. Grishin, Ph.D.
Phone: 214-645-5946
Fax: 214-645-5948
Email: grishin@chop.swmed.edu

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About
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We use theoretical methods to study proteins

Millions of species of living organisms on the planet possess billions of different proteins. This enormous diversity has evolved from a limited number of ancestral proteins, probably about a thousand. An expansion of more than 6 orders of magnitude in protein numbers has produced rich material for studying the laws of evolution.

From a pragmatic perspective, evolutionary links between proteins offer shortcuts to gain knowledge about homologs from a few experimentally characterized representatives. A homology link detected from sequences is the most powerful source of structure prediction, often leads to functional insights, and can guide experimental design.

From a theoretical perspective, we would like to understand how biological diversity is generated. Amazingly, the same themes and motifs are recurrently used, elaborately modified, and combined in evolution to produce functional entities. Our goal is to uncover these prevalent mechanisms of protein evolution.

Recent advances in obtaining sequence and structure information (~10,000,000 nonidentical sequences and ~60,000 different spatial structures) make for productive computational analysis. A grand step toward comprehending the protein universe would be the classification of sequence-structure data into an evolutionarily relevant hierarchical system. When two proteins display clear similarity, the task is straightforward. When similarity is low, however, discrimination between evolutionarily meaningful and spurious relationships becomes highly nontrivial. Since no available method deals well with this difficult problem, we develop new computational approaches to explore protein sequence-structure data, and since no single narrow approach is able to find remote homologs, we combine sequence and structure analyses with evolutionary considerations to facilitate discoveries of biological significance. For further information about our research see:

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