Workshop 4: Mathematical Challenges in Drug and Protein Design

(December 7,2015 - December 11,2015 )

Organizers


Eric Cances
CERMICS, Ecole des Ponts and INRIA
Michael Gilson
Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego
Martha (Marti) Head
Platform Technology and Science, GlaxoSmithKline Pharmaceuticals
Ridgway Scott
Computer Science and Mathematics, University of Chicago

Rational drug design and protein design have a profound impact to human health care. A fundamental goal is to predict whether a given molecule will bind to a biomolecule, such as a protein, so as to activate or inhibit its function, which in turn results in a therapeutic benefit to the patient. Typical drugs are small organic molecules, but biopolymer-based and protein-based drugs are becoming increasingly common. Computer-aided drug design and the design of protein containers for drug delivery have established a proven record of success, not only because of improved understanding of the basic science --- the molecular mechanism of drug and protein interactions, but also because of advances in mathematical models, geometric representations, computational algorithms, optimization procedure, and the availability of massive parallel and GPU computers. Indeed, mathematics plays an essential role in rational drug design and the development of new drug delivery systems, from consensus scoring, geometric analysis, cluster analysis, to global optimization. Moreover, mathematical approaches, such geometric analysis for high throughput drug screening, persistent homology for protein-drug binding detection, reduced manifold representation for discriminating false protein-protein and protein-drug interfaces, and machine learning techniques for protein-drug binding site analysis, have great potentials for drug design and drug discovery. Despite significant accomplishments, drug discovery rates seem to have reached a plateau, due to metabolism instability, side effects, and limitations in the understanding of fundamental drug-target interactions. An ideal drug should be acceptable to the human metabolic system, not to affect any other important ``off-target" molecules or antitargets that may be similar to the target molecule, and bind to a target sufficiently strongly. In fact, the molecular mechanism of drug design has its roots in another closely related field, the protein design, which tests the fundamental principles of protein-protein and protein-ligand interactions. Both protein-protein and protein-drug binding are subject to a large number of effects, from stereospecificity, polarization, hydrogen bond, electrostatic effect and solvation to allosteric modulation, to mention only a few. The application of molecular mechanism towards entire proteomes, enzyme pathways/families (e.g. catecholamine biosynthesis, botulinum neurotoxins), and high value drug targets, including G-protein coupled receptors (GPCRs) are now starting to emerge. Nano-bio technologies for drug transport and drug delivery have been a hot area of research. To design efficient drugs and functional protein, it takes collaborative efforts from biologists, biophysicists, biochemists, computer scientists and mathematicians to come up with better homology modeling, geometric models, molecular docking algorithms, molecular dynamics, quantum calculation, de novo design and statistical models. This workshop will bring together experts from both academia and industry that have an open mind to cross their line of defense to share their problems. We will create a forum for researchers to jointly find solutions and explore applications to the design of new drugs and delivery systems. This workshop will be of particular benefit to junior mathematicians who are looking for ways of applying their mathematical skills and tools also outside of academia and want to use their skills to make an impact in society via innovations benefiting the health sector. The interaction between mathematicians and pharmaceutical industry will be encouraged in this workshop.

Accepted Speakers

Nathan Baker
Computational and Statistical Analytics Division, Pacific Northwest National Laboratory
Ron Dror
Tom Kurtzman
Tony Lelievre
David Mobley
Ruth Nussinov
Pengyu Ren
Robert Rizzo
Tamar Schlick
Bio/Chem/Bio math, New York University
Christof Schütte
Jana Shen
Sandor Vajda
Wei Yang
Monday, December 7, 2015
Time Session
Tuesday, December 8, 2015
Time Session
Wednesday, December 9, 2015
Time Session
Thursday, December 10, 2015
Time Session
Friday, December 11, 2015
Time Session
Name Email Affiliation
Baker, Nathan nathan.baker@pnl.gov Computational and Statistical Analytics Division, Pacific Northwest National Laboratory
Cances, Eric cances@cermics.enpc.fr CERMICS, Ecole des Ponts and INRIA
Dror, Ron ron.dror@stanford.edu
Gilson, Michael mgilson@ucsd.edu Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego
Kurtzman, Tom thomas.kurtzman@lehman.cuny.edu
Lelievre, Tony lelievre@cermics.enpc.fr
Mobley, David dmobley@mobleylab.org
Nussinov, Ruth ruthnu@helix.nih.gov
Ren, Pengyu pren@mail.utexas.edu
Rizzo, Robert rizzorc@gmail.com
Sch�tte, Christof schuette@mi.fu-berlin.de
Schlick, Tamar Schlick@nyu.edu Bio/Chem/Bio math, New York University
Scott, Ridgway ridg@uchicago.edu Computer Science and Mathematics, University of Chicago
Shen, Jana jshen@rx.umaryland.edu
Vajda, Sandor vajda@bu.edu
Yang, Wei yang@sb.fsu.edu