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CRITICAL ASSESSMENT OF COMPUTATIONAL HIT-FINDING EXPERIMENTS

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Challenge #4

Hit Identification
Hybrid of the above
High-throughput docking coupled with reevaluation of top hits & docked poses
Description of your approach (min 200 and max 800 words)

The hit identification and drug discovery strategy consist in high-throughput docking for the identification of binder to the TKB domain of CBL-B that (1) bind to the same pocket and complete with the co-crystallized compound, (2) represent novel chemical templates and (3) bind with a KD below 30 uM.
Large library of commercially available compounds (Enamine) will be downloaded and prepared using the LigPrep preparation workflow from Maestro (Schrödinger, Inc). Briefly, the main tautomers from each compound will be generated and only compounds with reasonable physico-chemical properties will be considered (e.g., molecular weight, HBAcc/HBDon, number of rings, log P/D, PSA, ligand flexibility, as well as number of undefined chiral centers. We will also use our proprietary filters to eliminate reactive and chemically unstable compounds, compounds with undesirable functional groups and Pan-Assay INterfeering Structures (PAINS). The docking simulations will be performed on the PDB structure 8GCY. 
The virtual screening stage will consist in a several-step molecular docking workflow using the popular Glide (Schrödinger, Inc) docking tool. First, all the compounds will be docked using the fast HTVS scoring function. Then, the top 500K compounds will be docked using the SP function. The top 50K compounds will then be docking using the more accurate XP function. For each considered PDB structure, the top 20K poses from SP function and 5K poses from XP scoring functions will be finally merged before the refinement stage.
The extracted poses will be subsequently re-evaluated using the independent SeeSAR (BioSolveIT) tool using the HYDE scoring function with key structural waters added. This structure-based software will be mainly used to discard putative false positives from virtual screening (e.g., poses with either geometry or energy warnings).
We will explore application of a MM/ML approach that approximate high level ab initio binding energies with a fast machine learning approach to rescore and prioritize the final list of virtual hits for ordering.
Finally, a thorough visual inspection by experienced computational and medicinal chemists over all kept poses with the aforementioned recommendations from a variety of methods (Glide SP & XP, HYDE scores and MM/ML score that make into account structural waters) will be conducted to select the most promising compounds to be experimentally tested.
 

What makes your approach stand out from the community? (<100 words)

This approach combines high throughput docking with the re-evaluation of the top potential hit compounds from the VS as a post-processing stage. Besides, a visual analysis stage by molecular modeler and chemists will add some value to the workflow. Finally, only reasonable compounds from both physicochemical and medicinal chemistry point of views will be considered, thanks to the multi-stage ligand preparation workflow. This workflow has previously proven instrumental for in house drug discovery projects (that led to $US1.4 billion partnerships with Janssen and Celgene/BMS) projects and Challenge 1 LRRK2 in identification of quality hits.

Commercial software packages used

Schrodinger Drug Discovery suite, BIOVIA Pipeline Pilot, BioSolvIT, MolSoft ICM

Relevant publications of previous uses by your group of this software/method

Publications

  1. Li, Alice; Kimani, Serah; Wilson, Brian; Noureldin, Mahmoud; González-Álvarez, Héctor; Mamai, Ahmed; Hoffer, Laurent; Guilinger, John; Zhang, Ying ; von Rechenberg, Moritz; Disch, Jeremy; Mulhern, Christopher; Slakman, Belinda; Cuozzo, John; Dong, Aiping; Poda, Gennady; Mohammed, Mohammed; Saraon, Punit; Mittal, Manish; Modh, Pratik; Rathod, Vaibhavi; Patel, Bhashant; Ackloo, Suzanne; Santhakumar, Vijayaratnam; Szewczyk, Magdalena ; Barsyte-Lovejoy, Dalia; Arrowsmith, Cheryl; Marcellus, Richard; Guié, Marie-Aude ; Keefe, Anthony; Brown, Peter; Halabelian, Levon; Al-awar, Rima; Vedadi, Masoud. “Discovery of Nanomolar DCAF1 Small Molecule Ligands.” J. Med. Chem. 2023, accepted.
  2. Peccati T.N.F., Alonso M., Arias-Olivares D., Bohorquez H., Contreras-Garcia J. “New Developments in the Non-Covalent Interactions (NCI) Index”. In: Comprehensive Computational Chemistry, 1st Edition, Editors: Russel Boyd, Manuel Yanez, Sept. 11, 2023.
  3. Mamai, Ahmed; Chau, Anh M.; Wilson, Brian J.; Watson, Iain D.; Joseph, Babu B.; Subramanian, Pandiaraju R.; Morshed, Monzur M.; Morin, Justin A.; Prakesch, Michael A.; Lu, Tianbao; Connolly, Pete; Kuntz, Douglas A.; Pomroy, Neil C.; Poda, Gennady; Nguyen, Kong; Marcellus, Richard; Strathdee, Graig; Theriault, Brigitte; Subramaniam, Ratheesh; Mohammed, Mohammed; Abibi, Ayome; Chan, Manuel; Winston, Jeffrey; Kiyota, Taira; Undzys, Elijus; Aman, Ahmed; Austin, Nigel; Du Jardin, Marc; Packman, Kathryn; Phillippar, Ulrike; Attar, Riccardo; Edwards, James; O’Meara, Jeff; Uehling, David E.; Al-Awar, Rima; Privé, Gilbert G.; Isaac Methvin B. “Discovery of OICR12694: A Novel, Potent, Selective and Orally Bioavailable BCL6 BTB Inhibitor.” ACS Med. Chem. Lett. 2023, 14(2), 199-210.
  4. Saraon, Punit; Snider, Jamie; Kalaidzidis, Yannis; Wybenga-Groot, Leanne E.; Weiss, Konstantin; Rai, Ankit; Radulovich, Nikolina; Drecun, Luka; Vuckovic, Nika; Vucetic, Adriana; Wong, Victoria; Theriault, Brigitte; Pham, Nh-An; Park, Jin; Datti, Alessandro; Wang, Jenny; Pathmananthan, Shivanthy; Aboualizadeh, Farzaneh; Lyakisheva, Anna; Yao, Zhong; Wang, Yuhui; Aman, Ahmed; Moran, Michael F.; Poda, Gennady; Marcellus, Richard; Uehling, David; Samarzija, Miroslav; Jakopovic, Marko; Tsao, Ming-Sound; Shepherd, Frances A.; Sacher, Adrian; Leighl, Natasha; Akhmatova, Anna; Al-awar, Rima; Zerial, Marino; Stagljar, Igor. “A live-cell drug discovery platform for detection of compounds targeting receptor tyrosine kinases identifies inhibitors of osimertinib-resistant EGFR triple mutant.” Nature Chem. Biol. 2020, 16, 577-586.
  5. Tan, Joanne; Grouleff, Julie J.; Jitkova, Yulia; Diaz, Diego B.; Griffith Elizabeth C.; Shao, Wenje; Bogdanchikova Anastasia F.; Poda, Gennady; Schimmer, Aaron D.; Lee, Richard E.; Yudin Andrei. “De novo design of boron-based peptidomimetics as potent inhibitors of human ClpP in the presence of human ClpX.” J. Med. Chem. 2019, 62(13), 6377-6390.
  6. Moustakim, Moses; Christott, Thomas; Monteiro, Octovia P.; Bennett, James; Giroud, Charline; Ward, Jennifer; Rogers, Catherine M.; Smith, Paul; Panagakou, Ioanna; Díaz-Sáez, Laura; Felce, Suet L.; Gamble, Vicki; Gileadi, Carina; Halidi, Nadia; Heidenreich, David; Chaikuad, Apirat; Knapp, Stefan; Huber, Kilian V.M.; Farnie, Gillian; Heer, Jag; Manevski, Nenad; Poda, Gennady; Al-Awar, Rima; Dixon, Darren J.; Brennan, Paul E.; Fedorov, Oleg. “Discovery of an MLLT1/3 YEATS Domain Chemical Probe.” Angew. Chem. Int. Ed. Engl. 2018, 57(50), 16302-16307.

Patents

  1. Al-awar, Rima; Isaac, Methvin; Chau, Anh M.; Mamai, Ahmed; Watson, Iain; Poda, Gennady; Subramanian, Pandiaraju; Wilson, Brian; Uehling, David; Prakesch, Michael, Babu, Joseph; Morin, Justin-Alexander. “Substituted Heteroaryls as Inhibitors of the BCL6 BTB Domain Protein-Protein Interaction.” US Patent App. 11/518,764, 2022.
  2. Al-awar, Rima; Zepeda-Velazquez, Carlos; Poda, Gennady; Isaac, Methvin; Uehling, David; Wilson, Brian; Joseph, Babu; Liu, Yong; Subramanian, Pandiaraju; Mamai, Ahmed. “Substituted Carboxamides as Inhibitors of WDR5 Protein-Protein Binding”, US Patent App. 11/319,299, 2022.
  3. Sicheri, Frank; Posternak, Ganna; Poda, Gennady. “Amide-based Proteolysis Modulators of B Rapidly Accelerated Fibrosarcoma (BRaf) and Associated Uses” (Ontario Institute for Cancer Research). US Patent App. 17/310,837, 2022.
  4. Al-awar, Rima; Isaac, Methvin; Chau, Anh M.; Mamai, Ahmed; Watson, Iain; Poda, Gennady; Subramanian, Pandiaraju; Wilson, Brian; Uehling, David. “Tricyclic Inhibitors of the BCL6 BTB Domain Protein-Protein Interaction and Uses Thereof.” US Patent App. 11/242,351, 2022.
  5. Al-awar, Rima; Zepeda-Velazquez, Carlos; Poda, Gennady; Isaac, Methvin; Uehling, David; Wilson, Brian; Joseph, Babu; Liu, Yong; Subramanian, Pandiaraju; Mamai, Ahmed. “Substituted Carboxamides as Inhibitors of WDR5 Protein-Protein Interactions”, US Patent App. 11/174,250, 2021.
  6. Al-awar, Rima; Isaac, Methvin; Chau, Anh M.; Mamai, Ahmed; Watson, Iain; Poda, Gennady; Subramanian, Pandiaraju; Wilson, Brian; Uehling, David “Tricyclic Inhibitors of the BCL6 BTB Domain Protein-Protein Interaction and Uses Thereof” (Ontario Institute for Cancer Research). US Patent App. 16/955,975, 2021.
  7. Al-awar, Rima; Isaac, Methvin; Chau, Anh M.; Mamai, Ahmed; Watson, Iain; Poda, Gennady; Subramanian, Pandiaraju; Wilson, Brian; Uehling, David; Prakesch, Michael; Babu, Joseph; Morin, Justin-Alexander “Inhibitors of the BCL6 BTB Domain Protein-Protein Interaction and Uses Thereof” (Ontario Institute for Cancer Research). US Patent App. 16/690,924, 2020.
  8. Al-awar, Rima; Isaac, Methvin; Joseph, Babu; Liu, Yong; Mamai, Ahmed; Poda, Gennady; Subramanian, Pandiaraju; Uehling, David; Wilson, Brian; Zepeda-Velazquez, Carlos “Inhibitors of WDR5 protein-protein binding” (Ontario Institute for Cancer Research), US Patent App. 16/643,633, 2020.
  9. Al-awar, Rima; Zepeda-Velazquez, Carlos; Poda, Gennady; Isaac, Methvin; Uehling, David; Wilson, Brian; Joseph, Babu; Liu, Yong; Subramanian, Pandiaraju; Mamai, Ahmed; Prakesch, Michael; Stille, Julia K. “Inhibitors of WDR5 protein-protein binding” (Ontario Institute for Cancer Research), US Patent App. 16/080,866, 2019.
  10.  Al-awar, Rima; Zepeda-Velazquez, Carlos; Poda, Gennady; Isaac, Methvin; Uehling, David; Wilson, Brian; Joseph, Babu; Liu, Yong; Subramanian, Pandiaraju; Mamai, Ahmed “Inhibitors of WDR5 protein-protein binding” (Ontario Institute for Cancer Research), US Patent App. 16/080,851, 2019.
     
Hit Optimization Methods
Method type (check all that applies)
Deep learning
High-throughput docking
Physics-based
Description of your approach (min 200 and max 800 words)

The hit-2-lead stage will consist in the large exploration of the chemical space around the validated hits of interest.
A dictionary of modifications will be plugged-in at different positions of the hits to explore the surrounding chemical space. In parallel, theoretically accessible compounds around the hits will be generated using commercially available building blocks and encode reaction schemes. The same kind of filters will be used to discard compounds with poor physico-chemical properties and undesired functions by medicinal chemists. 
All the designed molecules will be directly docked in SeeSAR using the template-based docking mode, followed by their re-evaluation using the HYDE scoring function and an MM/ML approach to estimate interaction energies at a very high ab initio/DFT level with key structural waters included.
Compounds with better predicted energy, no torsion constraints and no intra/inter-clashes will be subjected to visual analysis for the prioritization stage.
In silico evaluation of synthetic accessibility of the kept compounds will allow to generate the final subset of molecules to be synthesized and tested for the hit optimization stage.
 

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