Rizzo Lab Research
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.
1. 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: a. 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 dx.doi.org/10.1021/bi901915g PMCID: PMC2867330 b. Holden, P. M.; Kaur, H.; Gochin, M.; Rizzo, R. C. Footprint-based Identification of HIVgp41 Inhibitors, Bioorg. Med. Chem. Lett., 2012, 22, 3011–3016 dx.doi.org/10.1016/j.bmcl.2012.02.017 PMCID: PMC3321075 c. Allen, W. J.; Rizzo, R. C. Computer-Aided Approaches for Targeting HIVgp41, Biology, 2012, 1, 311-338 dx.doi.org/10.3390/biology1020311 PMCID: PMC3666032 d. 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 dx.doi.org/10.1002/jcc.23245 PMCID: PMC4016043 e. 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 dx.doi.org/10.1016/j.bmc.2013.10.022 PMCID: PMC3913180
2. 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: a. Balius, T.E.; Rizzo, R. C. Quantitative Prediction of Fold Resistance for Inhibitors of EGFR. Biochemistry, 2009, 48, 8435-8448 dx.doi.org/10.1021/bi900729a PMCID: PMC2741091 b. Huang, Y.; Rizzo, R. C. A Water-based Mechanism of Specificity and Resistance for Lapatinib with ErbB Family Kinases, Biochemistry, 2012, 51, 2390-2406 dx.doi.org/10.1021/bi2016553 PMID: 22352796
3. 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: a. 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 dx.doi.org/10.1021/ci1001982 PMCID: PMC3058392 b. 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 dx.doi.org/10.1002/jcc.21814 PMCID: PMC3181325 c. 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 dx.doi.org/10.1007/s10822-012-9565-y PMCID: PMC3902891 d. 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 dx.doi.org/10.1021/ci400534h PMCID: PMC3958141 e. Jiang, L.; Rizzo, R. C. Pharmacophore-based Similarity Scoring for DOCK, J. Phys. Chem. B, 2014, in press, dx.doi.org/10.1021/jp506555w PMID: 25229837
Complete List of Published Work in MyBibliography: http://www.ncbi.nlm.nih.gov/myncbi/browse/collection/40578611/?sort=date&direction=descending
Complete List of Published Work in Google Scholar (H-index = 20 as of 01/05/2015): http://scholar.google.com/citations?hl=en&user=zSmJOOAAAAAJ&view_op=list_works&sortby=pubdate
Members of the Rizzo Group employ computational techniques to projects in drug discovery. We are interested in both application and method development. We use two primary tools: docking and molecular dynamics (MD). Types of studies we perform include MD used to probe the origins of activity (free energy calculations), virtual screening for lead identification, and testset development to evaluate our methods. The current major focuses of the laboratory are outlined as follows.
|HIV, which causes AIDS, is one of the most dangerous infectious diseases today. The WHO estimated 1.8 million HIV-related deaths and around 2.6 million new infections worldwide in 2009. As the world is entering the fourth decade in its battle against AIDS, a series of clinical drugs has been designed to target different steps of the HIV life cycle. The current anti-HIV inhibitors fall into five major categories: fusion and entry inhibitors, nucleotide reverse transcriptase inhibitors, non-nucleotide reverse transcriptase inhibitors, protease inhibitors, and other inhibitors such as integrase inhibitors.
HIV gp41 is a glycoprotein involved in viral membrane fusion. Our laboratory is interested in developing inhibitors that target gp41 and prevent the fusion event. To this end, we have constructed and all-atom model of T20 bound to gp41 and validated the model with all-atom molecular dynamics simulations. Virtual screening projects have also been performed to identify small molecule leads that target the hydrophobic pocket of gp41. Our collaborators have experimentally tested and identified molecules which exhibit strong activity.
EGFR and ErbB family
The ErbB family members are drug targets for treating several types of cancers, including lung and breast cancers. ErbB family of receptor tyrosine kinases consists of EGFR (epidermal growth factor receptor), HER2, ErbB3, and ErbB4. Over expression of EGFR is observed in 62% of NSCLC tumors (nonsmall cell lung cancer) and overexpression of EGFR and HER2 are important prognostic markers for breast cancer. Members of the ErbB family share a similar overall structural architecture comprising: (i) extra-cellular ligand binding domain, (ii) transmembrane domain, (iii) intracellular juxtamembrane domain, (iv) intracellular tyrosine kinase domain, and (v) C-terminal regulatory region where phosphorylation occurs. We are interested in targeting the tyrosine kinase domain (TKD). Approved small molecules of the TKD domain include erlotinib Tarceva, OSI Pharmaceuticals), gefitinib (Iressa, AstraZeneca), and lapatinib (Tykerb, Glaxo-SmithKline). A fourth compound called AEE788 (Novartis) is in development. Among them, erlotinib and gefitinib primarily target EGFR and lapatinib is a dual inhibitor of EGFR and ErbB2. Several cancer causing mutations or resistance mutations in EGFR and HER2 have been reported. We are interested in what is the driving force of binding and how these mutations affect binding. Through all-atom molecular dynamics simulations, water-mediated interactions seem to be especially important for understanding affinity and specificity for these systems.
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
Docking is a very useful tool in drug discovery efforts by predicting binding poses of molecules and by enriching databases in virtual screening applications. DOCK is the oldest widely used docking program. The Rizzo Group co-develops the DOCK program and contributed to the latest two releases. The release v6.4, greatly improved the sampling behavior with the inclusion of internal energy during growth and minimization. The release v6.5 includes a new scoring function termed Footprint similarity score described below. Our method development projects are motivated by the application projects pursued by group members.
Docking Testset Development
| Docking performs to tasks sampling and scoring. In pose reproduction experiments we ask can we generate the correct pose and can we rank it, among all the decoy poses, at the top of the list with our scoring function. To facilitate the development, in DOCK, of new scoring functions, sampling methods or improvement of current methods and docking protocols, our group has developed a large hand curated docking testset for pose reproduction termed SB2010. SB2010 consists of 780 protein-ligand systems processed from the PDB, this testset is partitioned in to subsets based on ligand flexibility and protein families. Family-based analysis and cross-docking experiments are facilitated by the inclusion of aligned structure in the testset distribution. To obtain the testset visit Rizzo_Lab_Downloads.
See the following paper:
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
Chemical Sampling Method
De novo design algorithms are useful for both drug discovery and lead optimization. In de novo design, candidate molecules are assembled or grown from fragment libraries in the binding site of a protein target. Then, the affinity of the molecule can be predicted, typically through molecular mechanics-based scoring functions. Presumably, those molecules that are predicted to have higher affinity to the target protein would make better drug candidates. By building molecules from fragments in this way, one is not limited by the size of publicly-available virtual screening databases (ca. 10^6-10^7 molecules), which are exceedingly small when compared to the predicted size of actual chemical space (ca. 10^65 molecules). However, de novo design can suffer from challenging obstacles including the inadvertent assembly of un-physical molecules, a combinatorial explosion in chemical space, and poor convergence. We have developed a novel de novo drug design method integrated into the infrastructure of the docking program DOCK6 which will be made available to the community in a future release.
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. WEB PMID: 24410429
Docking Scoring Functions
Receptor flexibility is important for docking because biomolecules, including drug targets (receptors), are always in motion and docking to a static structure is a crude (but often sufficient) approximation. Currently, DOCK accounts for receptor flexibility only in rescoring using AmberScore. In the Rizzo Group we are evaluating receptor flexibility using pre-generated ensembles from molecular dynamics simulations and from multiple crystallographic entries from the PDB. We can then dock to multiple grids where each grid represent a alternative receptor conformation.
Leveraging information from existing inhibitors is a useful paradigm for the discovery of new drugs. One scoring function developed in our group and implemented in DOCK 6.5, footprint similarity (FPS) score, uses an energetic profile or footprint of, for example, a known drug to identify a ligand which make similar interactions and is thus likely to bind. Other ideas for computationally generated references include molecular dynamics weighted ensembles, transition states and more. We are expanding this work on several levels including abstracting this scoring method to a grid-based method. See the following paper for more information on this topic.
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. WEB PMID: 21541962
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 WEB PMID: 23436713