Structural Biology:
The language used by
molecules in cells to communicate with each other is diverse. Molecular
communication is achieved, in many cases, by the conformational
complementarities between communicating molecules along the chains of signaling
pathways. To understand the molecular communication we study the structures of
proteins involved in the signal transduction pathways associated with cell
growth, cell cycle, sensory perception and chemotaxis. We are also interested
in discovering and designing drugs that inhibit these proteins for therapeutic
purposes.
Structural Genomics/Proteomics:
An analysis of the genomic
sequences of many organisms indicates that a large fraction of the encoded
proteins cannot be assigned a particular molecular and/or cellular function
based on the gene or protein sequence alone. The molecular (biochemical and biophysical)
function of a protein is tightly coupled to its three-dimensional structure,
and the three-dimensional structure, in combination with sequence information,
may provide important insight into its molecular function. Thus, the structural
study of the proteins encoded by an entire genome or a cellular process—an
approach often called “Structural Genomics” or “Structural Proteomics”—can
provide an important foundation for the understanding of the biological
processes in the whole organism. As one of the NIH supported centers of the
Protein Structure Initiative, Berkeley Structural Genomics Center is involved
in an effort to determine a near complete structural complement of the
proteomes of “minimal organisms,” Mycoplasma pneumoniae and Mycoplasma genitalium,
which have fewer than 500 and 700 genes, respectively. Two of the objectives
are to discover the “basis set” of the protein architecture that is required to
sustain Life, and to understand how protein structures may have evolved from
having simple to complex architecture to accomplish various tasks essential for
a living cell.
Computational Genomics/Proteomics:
As a computational
counterpart of the Structural Genomics described above, five aspects of
computational biology are being pursued: (1) Knowledge-based protein fold
prediction, where we apply rapid text searching algorithms, developed by
computer scientists, to protein structures to discover similarities and
differences between two protein structures, (2) Global mapping of conformations
of all proteins and nucleic acids to understand the conformational “landscapes”
of these two classes of molecules, (3) “Global mapping of the protein universe”
to classify all proteins into protein fold families and to discover their
evolutionary relationship among the families, (4) “Remote homologue” detection
to discover how a pair of proteins with no sequence similarities can have the
same or very similar structures and the same or related molecular functions. We
are developing computational methods to predict such remote homologues by
combining several powerful computational algorithms for text searching on
protein structural features: treating the protein structure (a “text”) as a
collection of local structural features (“words”). If successful, this method
will dramatically change the way functional annotation is done for all genes
and proteins, and (5) Functional mapping of protein structure universe to map
the molecular function of the proteins on to the protein universe map to
generate a “dictionary” of protein structure vs. function.
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Sung-Hou Kim's homepage.