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Therapeutic Target Space:
Methods for Druggability Assessment
 


Short Course


Wednesday, June 8, 2011  6:00pm-9:00 pm

 

Identification of Druggable Sites for Protein-Protein Interaction Targets* 

 

Despite the growing number of examples of small molecule inhibitors that disrupt protein-protein interactions (PPIs), the origin of druggability in this target class is poorly understood. This course is designed to demonstrate the use of computational methods to determine the most likely structure of the complex formed by interacting proteins, identify potentially druggable sites in the interface, determine whether the target is druggable, and provide information on potential ligands. Participants are urged to bring laptop computers, as the course will include demonstration sessions where they can study a number of targets (time permitting their targets of choice) using web-based software.

INSTRUCTORS: 

Dima Kozakov, Ph.D., Research Assistant Professor, Departments of Biomedical Engineering, Boston University
Dmitri Beglov, Ph.D., Senior Research Scientist, Structural Bioinformatics Laboratory, Boston University
Ryan Brenke, Ph.D., Postdoctoral Research Associate, Structural Bioinformatics Laboratory and Department of Chemistry, Boston University
David R. Hall, Ph.D. student, Department of Biomedical Engineering, Boston University
 

AGENDA 

6:00 PM Organizer’s Welcome and Chair’s Opening Comments
6:05 PM DINNER
 

6:35 PM Overview of protein-protein interactions as potential drug targetsDima KozakovReversible protein-protein interactions are involved at multiple points in virtually all biological pathways, including disease pathways where therapeutic intervention could bring widespread benefit. Although many PPI interfaces are biologically compelling targets for drug discovery, and a number of systems are known for which small molecules inhibit the interactions of two proteins with moderate to high potency, determining the druggability of targets and identifying druggable sites are challenging tasks. Based on the analysis of ~20 well studied targets, we introduce a classification of protein-protein interactions in terms of their expected druggability and the difficulty of finding druggable sites.


7:05 PM Predicting protein-protein interactions from the unbound structures of the two proteinsRyan Brenke and Dima KozakovAlthough the atom-level structure of interacting proteins provides information which is generally very useful for developing small molecular modulators (inhibitors or allosteric activators), it many cases it is difficult to determine the structure of the complex by X-ray crystallography or NMR. Here we describe the computational approaches that can be used, either on their own or in conjunction with a variety of low resolution experimental approaches, for predicting the structure of protein complexes. We also suggest a classification of protein complexes in terms of the docking difficulty and discuss the relationships between docking and potential druggability.

7:35 PM DEMONSTRATION: Protein docking by PIPER and ClusProRyan Brenke and Dima KozakovClusPro is a web-based server that performs the docking of selected proteins using the global docking program PIPER, which generates billions of conformations, clusters the 1000 lowest energy structures, and returns the centers of the largest clusters as putative models of the complex. ClusPro was judged to be the best performing automated server in the latest rounds of the CAPRI (Critical Assessment of Predicted Interactions) worldwide protein docking competition, whereas the team that developed PIPER was the best participating “human”predictor group. This demonstration will provide the opportunity of using the programs, including the more sophisticated use in “expert” mode.

8:00 PM Predicting the druggability of protein-protein interaction targets by computational solvent mappingDavid R. Hall and Dmitri BeglovTo identify druggable sites in protein-protein interfaces we combine computational solvent mapping, which explores the protein surface using a variety of small “probe” molecules, with a conformer generator to account for side chain flexibility. Applications to unliganded structures of PPI target proteins show that the druggable sites comprise a cluster of binding hot spots, distinguishable from other regions of the protein due to their concave topology combined with a pattern of hydrophobic and polar functionality. Results also highlight the importance of conformational adaptivity at the binding site to allow the hot spots to expand to accommodate a ligand of druglike dimensions. The critical components of this adaptivity are largely local, involving primarily low energy side chain motions within 6 Å of a hot spot. The structural and physicochemical signature of druggable sites at PPI interfaces is sufficiently robust to be detectable from the structure of the unliganded protein, even when substantial conformational adaptation is required for optimal ligand binding. This information could potentially allow those PPI targets most likely to be druggable to be identified from structural data alone, without expanding resources on exploratory lead finding efforts against intractable targets.

8:30 PM DEMONSTRATION: Predicting druggability by protein mappingDavid R. Hall and Dmitri BeglovWe first demonstrate the use of the FTMap protein mapping server. FTMap docks small organic molecules, used as molecular “probes”, on a protein surface, finds favorable binding positions, clusters the conformations of all prediction, and ranks the clusters on the basis of their average free energy. The low energy clusters are grouped into consensus sites and the largest consensus sites are able to identify hot spots and ligand binding sites. The docked fragments can also serve as building blocks for fragment-based drug design. We also demonstrate a four-step algorithm, which extends computational solvent mapping to PPI targets. The method finds the main hot spot by FTMap, uses a set of rules to select the potentially important side chains nearby, generates their energetically accessible conformers, maps all alternative structures, and selects the one with the highest number of probe clusters in the binding site.

Instructor Biographies 

 

Dima Kozakov Dima Kozakov received an M.S. in Applied Mathematics and Physics in Moscow Institute of Physics and Technology, and PhD in Biomedical Engineering at Boston University. Currently he is Research Assistant Professor of Biomedical Engineering at Boston University.  Dr. Kozakov has been active in method development for modeling of biological macromolecules, with emphasis on molecular interactions and drug design. Dr. Kozakov (with Dr. Vajda) has developed protein docking methods that were ranked the best in the latest evaluation of the worldwide blind protein docking  experiment CAPRI.  His research has been funded by the National Institute of General Medical Sciences.

David Hall David Hall is a doctoral student in the Structural Bioinformatics Lab at Boston University and Scientist at Acpharis, a start-up company in Boston, MA.  He received his Masters in Biomedical Engineering from Boston University in 2010 and Bachelors in Chemical Engineering from Washington University in St. Louis in 2007.  He has applied computational solvent mapping to a wide variety of both traditional and protein interaction targets and is a member of the best assessed team in the latest rounds of CAPRI, an international blind protein docking competition.

Dmitri Beglov Dmitri Beglov is the President of Acpharis Inc., and a senior research scientist in the Department of Biomedical Engineering at Boston University. Prior to joining Boston University, he acquired eight years of industrial experience as a developer of commercial molecular simulation software such as CHARMM and MBO(N)D. Working with Dr. Vajda, Dr. Beglov was one of the key developers of the computational fragment mapping program FTMap, and of the protein docking program Piper.

Ryan Brenke Ryan Brenke is Secretary and Treasurer of Acpharis Inc., and a Postdoctoral Research Associate in the Department of Biomedical Engineering and the Department of Chemistry at Boston University. As a graduate student in Dr. Vajda's lab, Ryan was a lead developer of the computational solvent mapping algorithm, FTMap, and the internationally recognized protein-protein docking algorithm, Piper. As a Postdoctoral Associate, Dr. Brenke is applying the computational methods developed as a graduate student along with X-ray crystallography to the interesting case of protein-protein interactions.

*Separate Registration Required 


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