Date: Wednesday, 12 December 2007, 6:30 PM
Location: SAP LABS, Building D, 3410 Hillview Avenue,
Palo Alto, CA (Google
Maps | Yahoo!
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Cost: Free and open to all who wish to attend, but membership is
only $10/year.
Topic
ProteinGPS, the technology for navigation in protein space, addresses the shortcomings of existing protein engineering paradigms and takes advantage of the last 50 years of development in linear and nonlinear systems optimization. Protein engineering has classically been approached from two diametrically opposed directions: rational design and directed evolution. Rationalism attempts to understand protein structure and function at a complete mechanistic level so that the effect of any modification to the protein can be estimated by calculation from first principles. Directed evolution on the other hand follows the strict empirical tradition and attempts to find a desired solution by testing many many different solutions, typically using various evolution-based algorithms. ProteinGPS instead uses established machine learning and nonlinear systems optimization technologies to provide a standard convention for protein space navigation. The method calculates the specific location of a protein variant in multidimensional space and places unique information rich variants called infologs, at important crossroads within the space assessed. The resulting datasets are used to map the hyper space and calculate new protein variant sequences that fulfill the functional constraints needed. Application of technologies that the data mining society has established over the last 50 years, to Protein Engineering, results in far more functional protein improvement while needing far less samples to test.
References:
Curr. Opin. Chem. Biol. 2005 9:202-9. Predicting enzyme function from protein sequence. Minshull, Ness, Gustafsson, Govindarajan.
BMC Biotechnol. 2007 7:16. Engineering proteinase K using machine learning and synthetic genes. Liao, Warmuth, Govindarajan, Ness, Wang, Gustafsson, Minshull.
About the Speakers
Sridhar Govindarajan is a cofounder and VP of Informatics at DNA2.0. He has over 15 years of scientific computing experience and leads the DNA2.0 automation and protein engineering efforts. Prior to DNA2.0 Sridhar led the computational research at Maxygen in optimizing directed evolution technologies. He has also held the position of systems architect at EraGen Biosciences. He received his Ph.D. from the University of Michigan and holds an undergraduate degree from IIT Bombay, India.
Claes Gustafsson is a cofounder and has served as DNA2.0's VP of Sales and Marketing since its inception. Before founding DNA2.0, he headed the Bioinformatics group at Maxygen and was project leader for metabolic engineering of natural product synthesis. Prior to this, Claes was one of the first employees at Kosan Biosciences, developing technology for manipulating Streptomyces polyketide production. He was a post-doctoral fellow at UCSF and UCSC and received his Ph.D. in Umea, Sweden and is a co-author of one of the first publications applying nonlinear systems optimization to biopolymers.
