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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 recognitionImproved 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 marketComputational 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.  
The Rizzo Research Group employ computational techniques to drug discovery projects.  We are interested in both application and method development.
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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 methodsThe current major focuses of the laboratory are outlined as follows.
 
 
 
==HIV/AIDS==
 
====GP41 and viral membrane fusion inhibitor====
 
 
 
 
 
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|GP41 is a glycoprotein involved in viral membrane fusion.  Our laboratory is interested in developing inhibitors that target GP41 and prevent the fusion eventTo 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.   
 
 
 
  
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://rizzo.ams.sunysb.edu/~rizzo/StonyBrook/publications/rizzo015.pdf PDF]
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== '''DOCK Method Development:''' ==
  
McGillick, B. E.; Balius, T.E.; Mukherjee, S.; Rizzo, R. C. Origins of Resistance to the HIVgp41 Viral Entry Inhibitor T20. Biochemistry, 2010, 49 (17), 3575-3592 doi:10.1021/bi901915g PMID: 20230061 [http://pubs.acs.org/doi/abs/10.1021/bi901915g WEB] [http://rizzo.ams.sunysb.edu/~rizzo/StonyBrook/publications/rizzo020.pdf PDF]
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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:
|[[Image:GP41_research_image1.png|thumb|375px|alt=Example alt text|HIV GP41 N-terminal domain in complex with T20 ]]
 
|}
 
  
==Cancer==
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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
==== EGFR and ErbB family====
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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
{| border="0" cellpadding="8" cellspacing="0" style="background:white; text-align:left; width:90%"
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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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ErbB (epidermal growth factor receptor) family is a drug target for treating several types of cancers, including lung and breast cancers. ErbB family of receptor tyrosine kinases consists of EGFR, HER2, ErbB3, and ErbB4. Overexpression 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) extracellular 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.
 
  
  
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 WEB] [http://rizzo.ams.sunysb.edu/~rizzo/StonyBrook/publications/rizzo019.pdf PDF]
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== '''Scoring Functions for Virtual Screening and De novo Design:''' ==
| [[Image:ErbB_Kinases_research_image1.png|thumb|250px|alt=Example alt text| ErbB Kinases ]]
 
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==Method development==
 
====DOCK development====
 
 
 
 
 
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The Rizzo Group co-develops the DOCK program [http://dock.compbio.ucsf.edu/] 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.   
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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 moreExamples 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:
See [http://dock.compbio.ucsf.edu/DOCK_6/new_in_6.4.txt release notes]
 
  
The release v6.5 includes a new scoring function termed Footprint similarity score.  
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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 
See [http://dock.compbio.ucsf.edu/DOCK_6/new_in_6.5.txt release notes]
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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
[[Image:Dock_Development_research_image1.png|375px|alt=Example alt text|Dock development ]]
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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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=====Docking Testset Development=====
 
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| To facilitate the improvements to the DOCK codebase and docking protocols, our group has developed a docking testset for pose reproduction.
 
  
See the following paper:
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== '''Mechanisms of Specificity and Resistance:''' ==
  
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://rizzo.ams.sunysb.edu/~rizzo/StonyBrook/publications/rizzo021.pdf PDF]
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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:  
  
To obtain the testset visit [[Rizzo_Lab_Downloads]].
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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
| [[Image:Docking_Testset_research_image1.png|375px|alt=Example alt text| Docking Testset ]]
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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
  
=====Docking Scoring Functions=====
 
  
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== '''Inhibitor Discover:''' ==
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Docking has two tasks: sampling and scoring.  To Address scoring . . .
 
  
Receptor flexibility is important for docking to address this  . . . MultiGrid Docking
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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 HER2Importantly, 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 targetRepresentative publications include:
|[[Image:Dock_Scoring_research_image1.png|thumb|250px|alt=Example alt text|Dock Scoring  ]]
 
|-
 
|
 
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 moreWe are expanding this work on several levels including abstracting this scoring method to a grid-based methodSee 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. [http://rizzo.ams.sunysb.edu/~rizzo/StonyBrook/publications/rizzo022.pdf PDF]
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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 
| [[Image:FPS_Dock_Scoring_research_image1.png|thumb|250px|alt=Example alt text|Dock FPS Scoring  ]]
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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 14:50, 5 November 2020

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 Discover:

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