A method for measuring and modeling quantitative sequence-function relationships for possible applications in transcriptional misregulation, antibody optimization, and antigenic epitope development

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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.




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



Patent Information:
For Information, Contact:
Laurie Tzodikov
Licensing Associates
Princeton University
Justin b. Kinney
Edward Cox
Curtis Callan
Anand Murugan