Avner Schlessinger, PhD
- ASSISTANT PROFESSOR | Pharmacological Sciences
Research Topics:Cancer, Computational Biology, Drug Design and Discovery, Membrane Proteins/Channels, Protein Structure/Function, Transporters
Dr. Schlessinger is an Assistant Professor in the Department of Pharmacology and Systems Therapeutics, and is a member of the Tisch Cancer Institute, at the Icahn School of Medicine at Mount Sinai in New York City. The overall goal of Dr. Schlessinger’s lab is to improve and automate the structure-based discovery process by developing and applying novel computational approaches, and to collaborate with experimental labs to characterize pharmacologically important proteins, with a long-term goal of developing cancer drugs. His lab publishes in the areas of structural biology, bioinformatics, and drug discovery, as well as in personalized medicine and pharmacogenetics. Dr. Schlessinger graduated from Tel Aviv University with a B.Sc. in Chemistry and Biology, and completed his Ph.D. from Columbia University in the Department of Biochemistry and Molecular Biophysics. Following his graduate studies, Dr. Schlessinger was an NIH NRSA postdoctoral fellow at the Department of Bioengineering and Therapeutic Sciences, University of California San Francisco (UCSF), where he developed methods for protein structure prediction and structure-based drug design. Dr. Schlessinger is an Associate Editor of PLOS Computational Biology. Dr. Schlessinger joined the faculty at the Icahn School of Medicine at Mount Sinai in January, 2013.
Multi-Disciplinary Training AreasBiophysics and Systems Pharmacology [BSP], Cancer Biology [CAB]
BSc, Tel Aviv University
PhD, Columbia University
Postdoctoral Training, University of California, San Francisco
Our lab focuses on the development and application of computational tools to annotate the functions of proteins. The two major research areas of our group include:
1. Structure-based drug design for membrane transporters. Our group characterizes cancer-related membrane transporter proteins, using a structure-based discovery approach, including homology modeling and virtual ligand screening, in collaboration with experimental labs. We rationally design novel chemical tools to study transporters’ role in cancer metabolism pathways, with a long-term goal of developing drugs against these potential cancer drug targets.
2. Structural bioinformatics. The lab works on developing and applying sequence-based and structure-based methods to predict different features of proteins using various machine-learning techniques. We analyze the predicted features of proteins in the context of networks and proteomes, to characterize protein functions.