Advantages
· Novel platform for company creation
· Applicable to any protein receptor/ligand (protein or peptide) system
· Valuable as virtual screen of peptides containing expensive unnatural amino acids before experimental testing
Utility
This invention can be used for many important and timely applications in drug discovery. Most importantly the framework provided by this invention can be performed computationally as a screen before any expensive experiments are performed to construct analogs containing post-translational modifications and non-canonical amino acids.
The method utilizes in-house developed forcefields with partial charges derived to reproduce the quantum-mechanically calculated electrostatic potential of each molecule and a novel optimization design formulation for the introduction of these modified amino acids. The in-house developed forcefields are a library of over 70 post-translational modifications and noncanonical amino acids. Components of this technology have been used to aid in the design and screening of inhibitors under development of HIV gp41. The technology was tested on 63 non-redundant Compstatin analogs finding a correlation with experimental IC50 values and having high predictive ability with an area under the receiver operating characteristic curve of over 80%. For a peptide with 10 positions, there are 2010 (1.024x1013) combinations of possible designs in the design space. With this invention, with the over 70 non-canonical amino acids that are available to be designed into the peptide sequence, it leads to a design space of over 9010 (3.49x10191.) combinations, which is a combinatorial space many times larger. The sheer number of possibilities for combinatorial design is incredible and is an enormous improvement in space over the ability to design with only the 20 naturally occurring amino acids. The increased chemical space will allow for the design of new peptide and protein-based compositions of matter containing post-translational modifications and non-canonical amino acids.
The invention is a step to bridging the gap between natural amino-acid containing peptides and small molecules, affording the ability to design customized peptide analogs with targeted affinity, specificity, and bioavailability.
Inventors
Christodoulos A. Floudas is Stephen C. Macaleer '63 Professor in Engineering and Applied Science and Professor of Chemical and Biological Engineering at Princeton University. Professor Floudas is a world-renowned authority in mathematical modeling and optimization of complex systems at the macroscopic and microscopic level. He has authored over 280 publications with research interests that lie at the interface of chemical engineering, applied mathematics, and operations research, with principal areas of focus including chemical process synthesis and design, process control and operations, discrete-continuous nonlinear optimization, local and global optimization, computational chemistry, and molecular biology. Among Prof. Floudas' numerous honors and awards are Fellow of the Society for Industrial and Applied Mathematics (2013), Member of National Academy of Engineering (2011), Princeton University Graduate Mentoring Award (2007), AIChE Computing in Chemical Engineering Award (2006) and AIChE Professional Progress Award for Outstanding Progress in Chemical Engineering (2001).
George A. Khoury is a fourth-year Ph.D. Student and a National Science Foundation Graduate Research Fellow (NSFGRF) in the Department of Chemical and Biological Engineering. He graduated from the Pennsylvania State University Schreyer Honors College with a Bachelor of Science with High Distinction and Honors from the Department of Chemical Engineering (2009) and subsequently earned a Master of Science in Chemical Engineering (2010). George's research is driven by the development and application of forcefields and tools for post-translational modifications and non-canonical amino acids to de novo protein design for therapeutics, drug discovery, biomaterials, recognition, and protein folding. George has received a number of prestigious academic and leadership awards which include an NSF Fellowship (2010), Barry M. Goldwater Scholarship for Excellence in Math, Science, and Engineering (2008), a Gold Medal at the International Genetically Engineered Machines Competition at MIT (2007), and a Genentech Process Research & Development Outstanding Student Award (2007).
James Smadbeck is a fifth-year Ph.D. Student in the Department of Chemical and Biological Engineering. He graduated from the Massachusetts Institute of Technology with a B.S. in Chemical Engineering. His thesis work at Princeton focuses on developing computational tools for the design of peptides and proteins based on optimization principles. His research is driven by the goal of developing novel proteins for applications in therapeutics, biomaterials, and biocatalysis.