Description:
Princeton University Invention #
10-2580
Researchers in
the Physics and Molecular Biology Departments, Princeton University, have
developed a method that uses a simple mutational assay and information theory to
decipher the molecular mechanisms by which a biological sequence functions,
either in vitro or in living cells. This technology was demonstrated as a
powerful tool for studying transcriptional regulation, and may thus be useful
for identifying therapeutic targets for a variety of diseases. The method is
very general, though, and should be applicable to a wide variety of problems in
molecular biology and bioengineering. In particular, it may provide a
qualitatively new way of developing antibody-based pharmaceuticals as well as
antigen epitopes for vaccines.
The Method in Brief and Proof of
Concept:
Applied to
transcriptional regulation, this method allows investigators, in one assay, to
identify all the DNA regulatory proteins that bind a specific promoter or
enhancer, characterize what role these different proteins play in regulating
gene expression, and build predictive models of how mutations within this
promoter or enhancer might disrupt biological function. This capability has been
demonstrated in E. coli using the well studied lac promoter: data
from a single experiment revealed all regulatory protein binding sites, enabled
the precise characterization of the sequence specificities of each DNA-binding
protein, and allowed the interaction energy between two DNA-bound proteins to be
measured in their native configuration in living cells.
This simple
technique provides a major advance over existing technologies for determining
how regulatory sequences function. Applied to mammalian transcriptional
regulation, this technique may prove to be a powerful way of identifying causal
factors and possible therapeutic targets for diseases resulting from the
misregulation of gene expression.
Other Potential
Applications:
Current methods
for optimizing the affinity of antibodies to a specific molecular target require
the screening of large antibody libraries. While such methods have had much
success, they ultimately boil down to blindly searching sequence space for an
optimal antibody, and the number of possible antibody sequences is much larger
than can be screened experimentally. This method may provide a way, using simple
experiments coupled to deep sequencing and computational analysis, to
quantitatively characterize the landscape of antibody-antigen affinity in
sequence space. Knowing this quantitative affinity landscape may allow one to
predict, computationally, which antibody sequences have optimal binding energy
to a specific antigen. Experiments to test this hypothesis will soon be
performed.
Alternatively,
knowing how the affinity of human antibodies against a given virus depend on the
sequences of that virus's coat proteins may allow one to predict which viral
mutations are most likely to cause epidemics in the future. It may also allow
one to intelligently design epitopes for use in vaccines.
Princeton is
currently seeking commercial partners for the further development and
commercialization of this opportunity. Patent protection is
pending.
Publications:
Kinney JB, Murugan A, Callan CG, Cox
EC, Using Deep Sequencing to Characterize the biophysical mechanism of a
Transcriptional Regulatory Sequence, PNAS, May 18th 2010, Vol
107, # 20, 9158-9163.
Kinney JB, Tkačik G, Calan CG, Precise
Physical Models of protein-DNA interaction form High-throughput Data,
PNAS, January 9, 2007, Vol 104, # 2, 501-506.
For more information on Princeton
University invention # 10-2580 please contact:
Laurie Tzodikov
Office of Technology Licensing and Intellectual Property
Princeton University
(609) 258-7256
tzodikov@princeton.edu