The Gene and Linda Voiland School of Chemical Engineering and Bioengineering is hosting a seminar presented by Dr. Anton Persikov, Associate Research Scholar at the Lewis-Sigler Institute for Integrative Genomics of Princeton University, Oct. 23, at 4:10 p.m. in ADBF 1002/FLOYD 256 (Tri-Cities).
Anton Persikov received his B.S. and M.S. in Physics from the Faculty of Physics, Moscow State University in 1994 and a Ph.D. in Biophysics and Physiology at Institute of Developmental Biology, Russian Academy of Sciences in 1999.
Dr. Persikov worked as a Lab Technician in the Biomagnetism Lab, Department of Magnetism, Faculty of Physics, Moscow State University, Russia from 1990-1993. Starting in 1994, he served as a Senior Lab Technician / Junior Research Associate, Biophysics Lab, Institute of Developmental Biology, Russian Academy of Sciences, Russia. In 1999, Dr. Persikov was appointed as a Postdoctoral Fellow, in the Department of Biochemistry at the Robert-Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey, later becoming in 2002 a Research Teaching Specialist III in the Department of Biochemistry and in 2004, an Adjunct Assistant Professor of Biochemistry. From 2006 to the present, he has held the position of Associate Research Scholar at the Lewis-Sigler Institute for Integrative Genomics of Princeton University.
Machine learning techniques for predicting protein-DNA binding specificity
Proteins with sequence-specific DNA binding function are important for a wide range of biological activities. Cys2His2 zinc fingers (C2H2-ZFs) comprise the largest class of metazoan DNA-binding domains. Despite a well-defined interaction interface, much remains unknown about the DNA-binding landscape of this domain. We have screened large synthetic libraries of C2H2-ZFs for members able to bind each possible three base pair target, thereby providing one of the most comprehensive in-vestigations of C2H2-ZF DNA-binding interactions to date. An integrated computa-tional analysis of these independent screens yielded DNA-binding profiles for tens of thousands of domains, and led the development of machine learning techniques for prediction of C2H2-ZF DNA-binding specificities.
