Difference between revisions of "Rizzo Lab Research"

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'''OVERVIEW:'''  
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== '''OVERVIEW''' ==
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Under the broad umbrella of Computational Structural Biology, research in the Rizzo lab involves Development, Validation, and Application of improved atomic-level computational modeling procedures and protocols for ranking and prioritizing compounds complexed with a biological target in order to better quantify, understand, and predict molecular recognition.  Improved computational methods have great potential to save billions of dollars in drug development costs and reduce the time associated with bringing clinically useful medicines to market.  Computational techniques for which we have expertise include docking (virtual screening), de novo design, molecular dynamics simulations, free energy calculations, SAR and associated analysis (energy decomposition, molecular footprinting, fold-resistance characterization). As outlined below, our Contributions to Science can be arranged into the following groups.  
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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.  
 
 
   
 
   
  
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'''1. COMPUTATIONAL DESIGN OF MEMBRANE FUSION INHIBITORS TARGETING DRUG RESISTANT HIVGP41.'''
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== '''DOCK Method Development''' ==
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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:
 
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* 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
 
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* 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
 
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* 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
 
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* 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
 
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* 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
 
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'''2. STRUCTURE-BASED DESIGN OF KINASE INHIBITORS.'''
 
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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:
 
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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
 
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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
 
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'''3. DOCK METHOD DEVELOPMENT.'''
 
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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:
 
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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
 
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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 
 
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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
 
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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
 
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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
 
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'''Complete List of Published Work in MyBibliography:'''
 
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http://www.ncbi.nlm.nih.gov/myncbi/browse/collection/40578611/?sort=date&direction=descending
 
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'''Complete List of Published Work in Google Scholar:'''
 
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http://scholar.google.com/citations?hl=en&user=zSmJOOAAAAAJ&view_op=list_works&sortby=pubdate
 
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Members of the Rizzo Group employ computational techniques to projects in drug discovery.  We are interested in both application and method development.  
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A long-term goal of our group involves development and validation of improved computational procedures for predicting molecular recognition.  Many of our application projects have a virtual screening component, thus a substantial effort has been undertaken to evaluate and improve sampling and scoring procedures in the program DOCK6, for which we are key developers. Among our accomplishments, we have: (i) spearheaded the last six DOCK6 releases (version 6.4, 6.5, 6.6, 6.7, 6.8, 6.9) graciously assisted by S. Brozell, D. Case group Rutgers, (ii) provided numerous code enhancements including growth trees, bug fixes, improved ligand internal energy, RMSD tether, torsion pre-minimizer, database filter, footprint similarity scoring, pharmacophore matching, multi-grid options, anchor selection options, SASA code, symmetry-corrected RMSD (Hungarian algorithm), (iii) constructed large validation databases, which allows us and others to develop and optimize new docking protocols, and (iv) provided educational resources for the community including a suite of online tutorials, fielding questions posted by the online community to the DOCK-fans listserv, and teaching a hands-on computer lab on molecule modeling, docking, and molecular dynamics.  Websites reflecting some of our research contributions to the community include: (a) the DOCK6 Website, dock.compbio.ucsf.edu/DOCK_6/index.htm, (b) Rizzo Group Online Validation Databases, ringo.ams.sunysb.edu/index.php/Rizzo_Lab_Downloads, and (c) Rizzo Group Online Tutorials, ringo.ams.sunysb.edu/index.php/DOCK_Tutorials.  Importantly, our software is free for academics and users receive all source code which, together with the educational activities outlined above, facilitates transparency, rigor, and reproducibility of the calculations. Representative publications include:
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We use two primary tools: [http://en.wikipedia.org/wiki/Docking_%28molecular%29 docking] and [http://en.wikipedia.org/wiki/Molecular_dynamics molecular dynamics] (MD).  Types of studies we perform include MD used to probe the origins of activity (free energy calculations), [http://en.wikipedia.org/wiki/Virtual_screening virtual screening] for lead identification, and testset development to evaluate our methods.  The current major focuses of the laboratory are outlined as follows.  
 
  
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==HIV/AIDS==
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*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 PMCID: PMC3058392
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====GP41 and viral membrane fusion inhibitor====
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*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 PMCID: PMC3902891
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*Allen, W.J; Balius, T. E.; Mukherjee, S.; Brozell, S. R.; Moustakas, D. T.; Lang, P. T.; Case, D. A.; Kuntz, I. D.; Rizzo, R. C. DOCK 6: Impact of New Features and Current Docking Performance, J. Comput. Chem., 2015, 36, 1132–1156 PMCID: PMC4469538
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*Allen, W. J.; Fochtman, B. C.; Balius, T. E.; Rizzo, R. C. Customizable de novo Design Strategies for DOCK: Application to HIVgp41 and Other Therapeutic Targets, J. Comput. Chem., 2017, 38, 2641–2663 PMCID: PMC5659719
  
  
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== '''Scoring Functions for Virtual Screening and De novo Design''' ==
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|[http://en.wikipedia.org/wiki/HIV HIV], which causes [http://en.wikipedia.org/wiki/AIDS 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.
 
  
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[http://en.wikipedia.org/wiki/Gp41 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.
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In addition to sampling, a critical component of molecular modeling software involves scoring functions.  In particular, scoring functions are used for clustering, pruning, and rank-ordering of partially and fully-grown conformers (virtual screening) or newly constructed molecules (de novo design, genetic algorithms) so that the most promising candidates can be prioritized.  We have developed, validated, and used in real word applications powerful new scoring functions, implemented into DOCK6, that enables users to search for candidates related to a known reference inhibitor, substrate, collection of protein hotspot residues, and more.   Examples include footprint similarity (FPS) score, Hungarian matching similarity (HMS) score, and pharmacophore matching similarity (FMS) score, all of which can be used alone, or in combination, with or without standard energy-based terms. Representative publications include:
  
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*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 PMCID: PMC3181325 
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*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 PMCID:  PMC4016043
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*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 PMCID: PMC3958141
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*Jiang, L.; Rizzo, R. C. Pharmacophore-based Similarity Scoring for DOCK, J. Phys. Chem. B, 2015, 119, 1083-1102 PMCID: PMC4306494
  
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* Strockbine, B.; Rizzo, R. C. Binding of Anti-Fusion Peptides with HIVgp41 from Molecular Dynamics Simulations: Quantitative Correlation with Experiment. ''Proteins: Struct. Func. Bioinformatics'', '''2007''', ''67'', 630-642. [http://dx.doi.org/10.1002/prot.21301 DOI]  PMID: 17335007
 
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* 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. [http://pubs.acs.org/doi/abs/10.1021/bi901915g DOI] PMID: 20230061
 
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* Holden, P. M.; Kaur, H.; Gochin, M.; Rizzo, R. C. Footprint-based Identification of HIVgp41 Inhibitors, ''Bioorg. Med. Chem. Lett.'', '''2012''', ''22'', 3011–3016. [http://dx.doi.org/10.1016/j.bmcl.2012.02.017 DOI] PMID: 22425565
 
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* Allen, W. J.; Rizzo, R. C. Computer-Aided Approaches for Targeting HIVgp41, ''Biology'', '''2012''', ''1'', 311-338. [http://dx.doi.org/10.3390/biology1020311 DOI] PMID: 23730525
 
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* 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. [http://dx.doi.org/10.1016/j.bmc.2013.10.022 DOI] PMID: 24315195
 
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|[[Image:GP41_research_image1.png|thumb|375px|alt=Example alt text|HIV gp41 N-terminal domain in complex with T20 ]]
 
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==Cancer==
 
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==== EGFR and ErbB family====
 
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The [http://en.wikipedia.org/wiki/ErbB ErbB family] members are drug targets for treating several types of cancers, including lung and breast cancers.  ErbB family of receptor [http://en.wikipedia.org/wiki/Tyrosine_kinase tyrosine kinases] consists of EGFR (epidermal growth factor receptor), HER2, ErbB3, and ErbB4.  Over expression of EGFR is observed in 62% of [http://en.wikipedia.org/wiki/Non-small-cell_lung_carcinoma NSCLC] tumors (nonsmall cell lung cancer) and overexpression of EGFR and HER2 are important prognostic markers for [http://en.wikipedia.org/wiki/Breast_cancer 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. 
 
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Balius, T. E.; Rizzo, R. C. Quantitative Prediction of Fold Resistance for Inhibitors of EGFR. ''Biochemistry'', '''2009''', ''48'', 8435-8448. [http://pubs.acs.org/doi/abs/10.1021/bi900729a DOI], PMID: 19627157
 
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Huang, Y.; Rizzo, R. C. A Water-Based Mechanism of Specificity and Resistance for Lapatinib with ErbB Family Kinases. ''Biochemistry'', '''2012''', ''51'', 2390–2406. [http://pubs.acs.org/doi/abs/10.1021/bi2016553 DOI], PMID: 22352796
 
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| [[Image:ErbB_Kinases_research_image1.png|thumb|250px|alt=Example alt text| ErbB Kinases ]]
 
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==Method development==
 
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====DOCK development====
 
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Docking is a very useful tool in drug discovery efforts by predicting binding poses of molecules and by enriching databases in virtual screening applications. [http://dock.compbio.ucsf.edu/ DOCK] is the oldest widely used docking program.  The Rizzo Group co-develops the  DOCK program and contributed to the latest two releases.  The [http://dock.compbio.ucsf.edu/DOCK_6/new_in_6.4.txt release v6.4], greatly improved the sampling behavior with the inclusion of internal energy during growth and minimization.  The [http://dock.compbio.ucsf.edu/DOCK_6/new_in_6.5.txt 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. 
 
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=====Docking Testset Development=====
 
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| 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]]. 
 
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See the following paper:
 
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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. [http://dx.doi.org/10.1021/ci1001982 DOI] PMID: 21033739
 
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| [[Image:Docking_Testset_research_image1.png|thumb|300px|alt=Example alt text| Docking Testset ]]
 
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=====Chemical Sampling Method=====
 
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''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.
 
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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. [http://dx.doi.org/10.1021/ci400534h WEB] PMID: 24410429
 
  
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== '''Mechanisms of Specificity and Resistance:''' ==
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[[Image:Chemical_Sampling_Method_image1.png|thumb|200px|alt=Example alt text|Chemical Sampling using ''de novo'' disign]]
 
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=====Docking Scoring Functions=====
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In addition to inhibitor discovery we also employ atomic-level modeling to characterize mechanisms of binding, specificity, and in particular "resistance".  As examples, 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.  And, we 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).  Another study quantified the specific ligand interactions which govern molecular recognition and resistance for the protein neuraminidase from influenza.  Water mediated interactions are increasingly being appreciated as important for molecular recognition.  Importantly, our work has elucidated the role of water in cancer-causing and acquired resistance mutations affecting specificity for several FDA-approved compounds and experimental inhibitors affecting ErbB-family receptor tyrosine kinases.  Other breakthroughs, made in collaboration with experimental groups, include identification of residues most likely to be involved in substrate binding and catalysis for thioesterases (Wilson lab), and uncovering the molecular determinates of ceramide specificity and sphingolipid discriminations (Hannun lab).  Representative publications include:
  
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*Chachra, R.; Rizzo, R. C. Origins of Resistance Conferred by the R292K Neuraminidase Mutation via Molecular Dynamics and Free Energy Calculations. J. Chem. Theory Comput., 2008, 4, 1526-1540 PMID: 26621436
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*Balius, T.E.; Rizzo, R. C. Quantitative Prediction of Fold Resistance for Inhibitors of EGFR. Biochemistry, 2009, 48, 8435-8448 PMCID: PMC2741091
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*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 PMCID: PMC2867330
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*Huang, Y.; Rizzo, R. C. A Water-based Mechanism of Specificity and Resistance for Lapatinib with ErbB Family Kinases, Biochemistry, 2012, 51, 2390-2406 PMID: 22352796
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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 [http://www.rcsb.org PDB]. We can then dock to multiple grids where each grid represent a alternative receptor conformation.   
 
  
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|[[Image:Dock_Scoring_research_image1.png|thumb|250px|alt=Example alt text|Dock Scoring  ]]
 
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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. 
 
  
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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. [http://dx.doi.org/10.1002/jcc.21814 WEB] PMID: 21541962
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== '''Inhibitor Discovery''' ==
  
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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 [http://dx.doi.org/10.1002/jcc.23245 WEB] PMID: 23436713
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Application projects in our group, in collaboration with several experimental labs (Jacobs, Deutsch, Ojima, Subramanyam, Miller), have identified experimentally-verified inhibitors targeting Zika glycoprotein E, Ebola GP2, HIVgp41, botulinum neurotoxin subtypes E and A, fatty acid binding protein (FABP), and HER2. Importantly, these successes are a direct result of our extensive method development efforts that have led to several innovative screening approaches, including those that leverage the wealth of energetic and structural information inherent to atomic-level molecular footprints – per-residue interaction patterns (maps) within targetable pockets on proteins – to rationally identify small molecules for compatibility with each target. Representative publications include:
  
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| [[Image:FPS_Dock_Scoring_research_image1.png|thumb|250px|alt=Example alt text|Dock FPS Scoring  ]]
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*Zhou, Y.; McGillick, B. E.; Teng, Y. H.; Haranahalli, K; Ojima, I; Subramanyam, S.; Rizzo, R. C. Identification of Small Molecule Inhibitors of Botulinum Neurotoxin Serotype E via Footprint Similarity, Bioorg. Med. Chem., 2016, 24, 4875–4889 PMID: 27543389 
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*Guo, J.; Collins, S.; Miller, W.T.; Rizzo, R. C. Identification of a water-coordinating HER2 inhibitor by virtual screening using similarity-based scoring, Biochemistry, 2018, 57, 4934-4951, PMCID: PMC6110523
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*Zhou, Y; Elmes, M. W.; Sweeney, J. M.; Joseph, O. M.; Che, J.; Hsu, H. C.; Li, H.; Deutsch, D. G.; Ojima, I; Kaczocha, M; Rizzo, R. C. Identification of Fatty Acid Binding Protein 5 Inhibitors Through Similarity-based Screening, Biochemistry, 2019, 58, 4304−4316, PMCID: PMC6812325
 +
*Singleton, C. S.; Humbly, M.S.; Yi, H. A.; Rizzo, R. C.; Jacobs, A. Identification of Ebola Virus Inhibitors Targeting GP2 using Principles of Molecular Mimicry, Journal of Virology, 2019, 93, e00676-19 PMID: 31092576

Latest revision as of 16:26, 17 December 2024

OVERVIEW

Under the broad umbrella of Computational Structural Biology, research in the Rizzo lab involves Development, Validation, and Application of improved atomic-level computational modeling procedures and protocols for ranking and prioritizing compounds complexed with a biological target in order to better quantify, understand, and predict molecular recognition. Improved computational methods have great potential to save billions of dollars in drug development costs and reduce the time associated with bringing clinically useful medicines to market. Computational techniques for which we have expertise include docking (virtual screening), de novo design, molecular dynamics simulations, free energy calculations, SAR and associated analysis (energy decomposition, molecular footprinting, fold-resistance characterization). As outlined below, our Contributions to Science can be arranged into the following groups.


DOCK Method Development

A long-term goal of our group involves development and validation of improved computational procedures for predicting molecular recognition. Many of our application projects have a virtual screening component, thus a substantial effort has been undertaken to evaluate and improve sampling and scoring procedures in the program DOCK6, for which we are key developers. Among our accomplishments, we have: (i) spearheaded the last six DOCK6 releases (version 6.4, 6.5, 6.6, 6.7, 6.8, 6.9) graciously assisted by S. Brozell, D. Case group Rutgers, (ii) provided numerous code enhancements including growth trees, bug fixes, improved ligand internal energy, RMSD tether, torsion pre-minimizer, database filter, footprint similarity scoring, pharmacophore matching, multi-grid options, anchor selection options, SASA code, symmetry-corrected RMSD (Hungarian algorithm), (iii) constructed large validation databases, which allows us and others to develop and optimize new docking protocols, and (iv) provided educational resources for the community including a suite of online tutorials, fielding questions posted by the online community to the DOCK-fans listserv, and teaching a hands-on computer lab on molecule modeling, docking, and molecular dynamics. Websites reflecting some of our research contributions to the community include: (a) the DOCK6 Website, dock.compbio.ucsf.edu/DOCK_6/index.htm, (b) Rizzo Group Online Validation Databases, ringo.ams.sunysb.edu/index.php/Rizzo_Lab_Downloads, and (c) Rizzo Group Online Tutorials, ringo.ams.sunysb.edu/index.php/DOCK_Tutorials. Importantly, our software is free for academics and users receive all source code which, together with the educational activities outlined above, facilitates transparency, rigor, and reproducibility of the calculations. 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 PMCID: PMC3058392
  • 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 PMCID: PMC3902891
  • Allen, W.J; Balius, T. E.; Mukherjee, S.; Brozell, S. R.; Moustakas, D. T.; Lang, P. T.; Case, D. A.; Kuntz, I. D.; Rizzo, R. C. DOCK 6: Impact of New Features and Current Docking Performance, J. Comput. Chem., 2015, 36, 1132–1156 PMCID: PMC4469538
  • Allen, W. J.; Fochtman, B. C.; Balius, T. E.; Rizzo, R. C. Customizable de novo Design Strategies for DOCK: Application to HIVgp41 and Other Therapeutic Targets, J. Comput. Chem., 2017, 38, 2641–2663 PMCID: PMC5659719


Scoring Functions for Virtual Screening and De novo Design

In addition to sampling, a critical component of molecular modeling software involves scoring functions. In particular, scoring functions are used for clustering, pruning, and rank-ordering of partially and fully-grown conformers (virtual screening) or newly constructed molecules (de novo design, genetic algorithms) so that the most promising candidates can be prioritized. We have developed, validated, and used in real word applications powerful new scoring functions, implemented into DOCK6, that enables users to search for candidates related to a known reference inhibitor, substrate, collection of protein hotspot residues, and more. Examples include footprint similarity (FPS) score, Hungarian matching similarity (HMS) score, and pharmacophore matching similarity (FMS) score, all of which can be used alone, or in combination, with or without standard energy-based terms. Representative publications include:

  • 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 PMCID: PMC3181325
  • 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 PMCID: PMC4016043
  • 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 PMCID: PMC3958141
  • Jiang, L.; Rizzo, R. C. Pharmacophore-based Similarity Scoring for DOCK, J. Phys. Chem. B, 2015, 119, 1083-1102 PMCID: PMC4306494


Mechanisms of Specificity and Resistance:

In addition to inhibitor discovery we also employ atomic-level modeling to characterize mechanisms of binding, specificity, and in particular "resistance". As examples, 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. And, we 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). Another study quantified the specific ligand interactions which govern molecular recognition and resistance for the protein neuraminidase from influenza. Water mediated interactions are increasingly being appreciated as important for molecular recognition. Importantly, our work has elucidated the role of water in cancer-causing and acquired resistance mutations affecting specificity for several FDA-approved compounds and experimental inhibitors affecting ErbB-family receptor tyrosine kinases. Other breakthroughs, made in collaboration with experimental groups, include identification of residues most likely to be involved in substrate binding and catalysis for thioesterases (Wilson lab), and uncovering the molecular determinates of ceramide specificity and sphingolipid discriminations (Hannun lab). Representative publications include:

  • Chachra, R.; Rizzo, R. C. Origins of Resistance Conferred by the R292K Neuraminidase Mutation via Molecular Dynamics and Free Energy Calculations. J. Chem. Theory Comput., 2008, 4, 1526-1540 PMID: 26621436
  • Balius, T.E.; Rizzo, R. C. Quantitative Prediction of Fold Resistance for Inhibitors of EGFR. Biochemistry, 2009, 48, 8435-8448 PMCID: PMC2741091
  • 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 PMCID: PMC2867330
  • Huang, Y.; Rizzo, R. C. A Water-based Mechanism of Specificity and Resistance for Lapatinib with ErbB Family Kinases, Biochemistry, 2012, 51, 2390-2406 PMID: 22352796


Inhibitor Discovery

Application projects in our group, in collaboration with several experimental labs (Jacobs, Deutsch, Ojima, Subramanyam, Miller), have identified experimentally-verified inhibitors targeting Zika glycoprotein E, Ebola GP2, HIVgp41, botulinum neurotoxin subtypes E and A, fatty acid binding protein (FABP), and HER2. Importantly, these successes are a direct result of our extensive method development efforts that have led to several innovative screening approaches, including those that leverage the wealth of energetic and structural information inherent to atomic-level molecular footprints – per-residue interaction patterns (maps) within targetable pockets on proteins – to rationally identify small molecules for compatibility with each target. Representative publications include:

  • Zhou, Y.; McGillick, B. E.; Teng, Y. H.; Haranahalli, K; Ojima, I; Subramanyam, S.; Rizzo, R. C. Identification of Small Molecule Inhibitors of Botulinum Neurotoxin Serotype E via Footprint Similarity, Bioorg. Med. Chem., 2016, 24, 4875–4889 PMID: 27543389
  • Guo, J.; Collins, S.; Miller, W.T.; Rizzo, R. C. Identification of a water-coordinating HER2 inhibitor by virtual screening using similarity-based scoring, Biochemistry, 2018, 57, 4934-4951, PMCID: PMC6110523
  • Zhou, Y; Elmes, M. W.; Sweeney, J. M.; Joseph, O. M.; Che, J.; Hsu, H. C.; Li, H.; Deutsch, D. G.; Ojima, I; Kaczocha, M; Rizzo, R. C. Identification of Fatty Acid Binding Protein 5 Inhibitors Through Similarity-based Screening, Biochemistry, 2019, 58, 4304−4316, PMCID: PMC6812325
  • Singleton, C. S.; Humbly, M.S.; Yi, H. A.; Rizzo, R. C.; Jacobs, A. Identification of Ebola Virus Inhibitors Targeting GP2 using Principles of Molecular Mimicry, Journal of Virology, 2019, 93, e00676-19 PMID: 31092576
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