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MSE Seminar - Professor Jeffrey Skolnick - Georgia Tech
Monday, October 24, 2016 - 4:00pm
GTMI - Callaway/MARC Auditorium
PROGNOSTIX: A pipeline for personalized diagnostics and drug treatments
Professor Jeffrey Skolnick
Center for the Study of Systems Biology
Georgia Institute of Technology
How can one convert the plethora of information provided by Next Generation Sequencing into clinically actionable suggestions for diagnostics and drug treatments. We describe the key tools in the PROGNOSTIX methodology that begins to address these issues. We first describe and validate the ENTPRISE algorithm for predicting the likely disease association of missense variations. Compared to existing algorithms such as FATHMM whose false positive rate is 12.9%, the false positive rate of ENTPRISE is 5.4%. Moreover, unlikely many other approaches it does not assign variations based on the identity of the protein rather than the variations within the protein. We then describe a comprehensive proteome scale approach that predicts human protein targets and side effect of drugs. For drug-protein interaction prediction, FINDSITEcomb, whose average precision is ~30% and recall ~27%, is employed. Successful applications of the methodology to treat Chronic Fatigue Syndrome, to identify novel antibiotic leads and promising anti-seizure drugs are described. For side effect prediction, a new method is developed with a precision of ~57% and a recall of ~24%. Our predictions show that drugs are quite promiscuous, with the average (median) number of human targets per drug of 329 (38), while a given protein interacts with 57 drugs. The result implies that drug side effects are inevitable and existing drugs may be useful for repurposing, with only ~1,000 human proteins likely causing serious side effects. A killing index derived from serious side effects has a strong correlation with FDA approved drugs being withdrawn. Therefore, it provides a pre-filter for new drug development. The methodology is free to the academic community on the DR. PRODIS (DRugome, PROteome, and DISeasome) webserver at http://cssb.biology.gatech.edu/dr.prodis/. DR. PRODIS provides protein targets of drugs, drugs for a given protein target, associated diseases and side effects of drugs, as well as an interface for the virtual target screening of new compounds.
Jeffrey Skolnick is the Director of the Center for the Study of Systems Biology in the School of Biology at the Georgia Institute of Technology. He is also the Mary and Maisie Gibson Chair in Computational Systems Biology and a Georgia Research Alliance Eminent Scholar in Computational Systems Biology. He attended graduate school in Chemistry at Yale University, receiving a Ph.D. in Chemistry in polymer statistical mechanics. He then held a postdoctoral fellowship at Bell Laboratories. Next, he joined the faculty of the Chemistry Department at Louisiana State University, Baton Rouge. Then, he moved to Washington University, where he was subsequently appointed Professor of Chemistry. There he was also Director of the Institute of Macromolecular Chemistry at Washington University. He joined the Department of Molecular Biology of the Scripps Research Institute, where he held the rank of Professor. Among his awards is Southeastern Universities Research Association (SURA), Distinguished Scientist Award an Alfred P. Sloan Research Fellowship and he is a Fellow of the American Association for the Advancement of Science, the Biophysical Society, and the St. Louis Academy of Science. He is the author of over 360 publications and has served on numerous editorial boards including the Israel Journal of Chemistry, Peer J, Biology Direct, Biophysical Journal, Biopolymers, Proteins, and the Journal of Chemical Physics. He is also a cofounder of an early stage structural proteomics company, GeneFormatics, and his software has been commercialized by Intellimedix and Tripos.
Dr. Skolnick’s current research interests are in the area of computational biology and bioinformatics. He has developed and applied approaches to proteomes for the prediction of protein structure and function, the prediction of small molecule ligand-protein interactions with applications to drug discovery and the prediction of off-target uses of existing drugs, fundamental studies on the nature and completeness of protein structure space and the exploration of the interplay between protein physics and evolution in determining protein structure and function, prediction of protein-protein and protein-DNA interactions, cancer metabolomics and molecular simulations of cellular processes.