Difference between revisions of "Rizzo Lab Research"

Revision as of 11:46, 29 April 2015

OVERVIEW:

Our labs research consists of three primary projects (HIV, cancer, method development) as described below. Although each project is distinct, there is considerable cohesiveness in terms of desired outcome (quantitative understanding of molecular recognition), methodology (molecular dynamics and docking), and fundamental approach (atomic level structure activity relationships) thus the projects form a synergistic group.

COMPUTATIONAL DESIGN OF MEMBRANE FUSION INHIBITORS TARGETING DRUG RESISTANT HIVGP41.

The long-term goal of this research project is to develop critically-needed small molecule drugs to help treat the approximately 34 million people worldwide living with HIV. The emergence of deleterious drug-resistance mutations against currently-approved therapies, such as the peptide drug T20, necessitates new strategies for targeting HIV life-cycle events that include complementary inhibition mechanisms and exploitation of regions with high sequence conservation. An innovative approach, recently developed in our lab, aims to leverage the wealth of energetic and structural information inherent to atomic-level molecular footprints – defined as per-residue interaction patterns within targetable pockets on proteins – to rationally identify, develop, and design novel small molecule inhibitors against the viral protein gp41. Our objectives in this project are to exploit the information contained in footprints to rationally design small molecules that specifically bind to gp41, inhibit membrane fusion, and arrest viral entry. We were the first lab to provide quantitative evidence that van der Waals interactions drive C-peptide binding to HIVgp41, supporting the hypothesis that a conserved hydrophobic pocket on gp41 is an important drug target site. We also constructed and validated the first complete structural binding model for the fusion inhibitor T20 (Fuzeon) with gp41, subsequently verified by experiment (Buzeon et al, PLoS Pathog 2010). Other notable results include discovery of seven promising gp41 leads identified through our novel footprint-based virtual screening methodology. Representative publications include:

• McGillick, B. E.; Balius, T.E.; Mukherjee, S.; Rizzo, R. C. Origins of Resistance to the HIVgp41 Viral Entry Inhibitor T20. Biochemistry, 2010, 49, 3575-3592 DOI PMID: 20230061
• Holden, P. M.; Kaur, H.; Gochin, M.; Rizzo, R. C. Footprint-based identification of HIVgp41 inhibitors, Bioorg. Med. Chem. Lett., 2012, 22, 3011–3016 DOI PMID: 22425565
• Allen, W. J.; Rizzo, R. C. Computer-Aided Approaches for Targeting HIVgp41, Biology, 2012, 1, 311-338 DOI PMID: 23730525
• Balius, T. E.; Allen, W. J.; Mukherjee, S.; Rizzo, R. C. Grid-based Molecular Footprint Comparison Method for Docking and De Novo Design: Application to HIVgp41, J. Comput. Chem., 2013, 34, 1226-1240 DOI PMID: 23436713
• Holden, P. M.; Allen, W. J.; Gochin, M.; Rizzo, R. C. Strategies for Lead Discovery: Application of Footprint Similarity Targeting HIVgp41, Bioorg. Med. Chem., 2014, 22, 651-661 DOI PMID: 24315195
• Allen, W. J.; Yi, H. A.; Gochin, M.; Jacobs, A.; Rizzo, R. C. Small Molecule Inhibitors of HIVgp41 N-heptad Repeat Trimer Formation, Bioorg. Med. Chem. Lett., 2015, In Press. DOI

STRUCTURE-BASED DESIGN OF KINASE INHIBITORS.

The long term goal of this research project is identification and optimization of novel anti-cancer drugs targeting kinases (wildtype and mutant). The overall objective is to develop computational models to characterize binding of ligands of the "molecular targeted therapeutics" class to members of the ErbB family of receptor tyrosine kinases (EGFR, HER2, ErB4) and related proteins. The project goals are to: (1) elucidate mechanisms by which cancer-causing mutations and acquired drug resistance mutations affect inhibitor binding, (2) determine origins of specificity for FDA-approved drugs and experimental inhibitors, and (3) identify new drug leads through collaborations with experimentalists. Representative publications include:

• Balius, T.E.; Rizzo, R. C. Quantitative Prediction of Fold Resistance for Inhibitors of EGFR. Biochemistry, 2009, 48, 8435-8448 DOI PMID: 19627157
• Huang, Y.; Rizzo, R. C. A Water-based Mechanism of Specificity and Resistance for Lapatinib with ErbB Family Kinases, Biochemistry, 2012, 51, 2390-2406 DOI PMID: 22352796

DOCK METHOD DEVELOPMENT.

The long-term goal of this research project involves development of improved computational procedures for predicting molecular recognition. As most of our lab projects have a virtual screening component, a substantial effort has been undertaken to evaluate and improve sampling and scoring procedures in the program DOCK, for which we are co-developers, to increase the accuracy and robustness of virtual screening. Among our accomplishments, we have: (1) spearheaded the recent DOCK 6.4, 6.5, and 6.6 releases (assisted by S. Brozell, D. Case group Rutgers), (2) provided numerous code enhancements including growth trees (movies and forensics), bug fixes, ligand internal energy, RMSD tether (energy minimization), torsion pre-minimizer, database filter, footprint similarity scoring (FPS), multi-grid options (FPS or multiple receptors), anchor selection options, SASA code, symmetry-corrected RMSD (Hungarian algorithm), and (3) constructed a large docking validation database (currently 1043 systems), which allows us to develop and optimize new docking protocols (see rizzolab.org/downloads). Representative publications include:

• Mukherjee, S.; Balius, T.E.; Rizzo, R. C. Docking Validation Resources: Protein Family and Ligand Flexibility Experiments. J. Chem. Inf. Model, 2010, 50, 1986-2000 DOI PMID: 21033739
• Balius, T. E.; Mukherjee, S.; Rizzo, R. C. Implementation and Evaluation of a Docking-rescoring Method using Molecular Footprint Comparisons. J. Comput. Chem., 2011, 32, 2273-2289 DOI PMID: 21541962
• Brozell, S. R.; Mukherjee, S.; Balius, T. E.; Roe, D. R.; Case, D. A.; Rizzo, R. C. Evaluation of DOCK 6 as a Pose Generation and Database Enrichment Tool, J. Comput-Aided Mol. Des., 2012, 26, 749-773 DOI PMID: 22569593
• Allen, W. J.; Rizzo, R. C. Implementation of the Hungarian Algorithm to Account for Ligand Symmetry and Similarity in Structure-Based Design, J. Chem. Inf. Model., 2014, 54, 518-529 DOI PMID: 24410429
• Jiang, L.; Rizzo, R. C. Pharmacophore-Based Similarity Scoring for DOCK, J. Phys. Chem. B., 2015, 119, 1083-1102 DOI PMID:25229837