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  1. Article ; Online: Correction to Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex.

    Zhang, Ivy / Rufa, Dominic A / Pulido, Iván / Henry, Michael M / Rosen, Laura E / Hauser, Kevin / Singh, Sukrit / Chodera, John D

    Journal of chemical theory and computation

    2024  Volume 20, Issue 2, Page(s) 990–991

    Language English
    Publishing date 2024-01-02
    Publishing country United States
    Document type Published Erratum
    ISSN 1549-9626
    ISSN (online) 1549-9626
    DOI 10.1021/acs.jctc.3c01298
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex.

    Zhang, Ivy / Rufa, Dominic A / Pulido, Iván / Henry, Michael M / Rosen, Laura E / Hauser, Kevin / Singh, Sukrit / Chodera, John D

    Journal of chemical theory and computation

    2023  Volume 19, Issue 15, Page(s) 4863–4882

    Abstract: Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on ... ...

    Abstract Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a graphics processing unit (GPU)-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches─alchemical replica exchange and alchemical replica exchange with solute tempering─for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and is available at https://github.com/choderalab/perses.
    MeSH term(s) Thermodynamics ; Molecular Dynamics Simulation ; Entropy ; Protein Binding ; Amino Acids
    Chemical Substances Amino Acids
    Language English
    Publishing date 2023-07-14
    Publishing country United States
    Document type Journal Article
    ISSN 1549-9626
    ISSN (online) 1549-9626
    DOI 10.1021/acs.jctc.3c00333
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Identifying and overcoming the sampling challenges in relative binding free energy calculations of a model protein:protein complex.

    Zhang, Ivy / Rufa, Dominic A / Pulido, Iván / Henry, Michael M / Rosen, Laura E / Hauser, Kevin / Singh, Sukrit / Chodera, John D

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on ... ...

    Abstract Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a GPU-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches-alchemical replica exchange and alchemical replica exchange with solute tempering-for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally-determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and available at https://github.com/choderalab/perses .
    Language English
    Publishing date 2023-06-21
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.03.07.530278
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors.

    Boby, Melissa L / Fearon, Daren / Ferla, Matteo / Filep, Mihajlo / Koekemoer, Lizbé / Robinson, Matthew C / Chodera, John D / Lee, Alpha A / London, Nir / von Delft, Annette / von Delft, Frank / Achdout, Hagit / Aimon, Anthony / Alonzi, Dominic S / Arbon, Robert / Aschenbrenner, Jasmin C / Balcomb, Blake H / Bar-David, Elad / Barr, Haim /
    Ben-Shmuel, Amir / Bennett, James / Bilenko, Vitaliy A / Borden, Bruce / Boulet, Pascale / Bowman, Gregory R / Brewitz, Lennart / Brun, Juliane / Bvnbs, Sarma / Calmiano, Mark / Carbery, Anna / Carney, Daniel W / Cattermole, Emma / Chang, Edcon / Chernyshenko, Eugene / Clyde, Austin / Coffland, Joseph E / Cohen, Galit / Cole, Jason C / Contini, Alessandro / Cox, Lisa / Croll, Tristan Ian / Cvitkovic, Milan / De Jonghe, Steven / Dias, Alex / Donckers, Kim / Dotson, David L / Douangamath, Alice / Duberstein, Shirly / Dudgeon, Tim / Dunnett, Louise E / Eastman, Peter / Erez, Noam / Eyermann, Charles J / Fairhead, Michael / Fate, Gwen / Fedorov, Oleg / Fernandes, Rafaela S / Ferrins, Lori / Foster, Richard / Foster, Holly / Fraisse, Laurent / Gabizon, Ronen / García-Sastre, Adolfo / Gawriljuk, Victor O / Gehrtz, Paul / Gileadi, Carina / Giroud, Charline / Glass, William G / Glen, Robert C / Glinert, Itai / Godoy, Andre S / Gorichko, Marian / Gorrie-Stone, Tyler / Griffen, Ed J / Haneef, Amna / Hassell Hart, Storm / Heer, Jag / Henry, Michael / Hill, Michelle / Horrell, Sam / Huang, Qiu Yu Judy / Huliak, Victor D / Hurley, Matthew F D / Israely, Tomer / Jajack, Andrew / Jansen, Jitske / Jnoff, Eric / Jochmans, Dirk / John, Tobias / Kaminow, Benjamin / Kang, Lulu / Kantsadi, Anastassia L / Kenny, Peter W / Kiappes, J L / Kinakh, Serhii O / Kovar, Boris / Krojer, Tobias / La, Van Ngoc Thuy / Laghnimi-Hahn, Sophie / Lefker, Bruce A / Levy, Haim / Lithgo, Ryan M / Logvinenko, Ivan G / Lukacik, Petra / Macdonald, Hannah Bruce / MacLean, Elizabeth M / Makower, Laetitia L / Malla, Tika R / Marples, Peter G / Matviiuk, Tatiana / McCorkindale, Willam / McGovern, Briana L / Melamed, Sharon / Melnykov, Kostiantyn P / Michurin, Oleg / Miesen, Pascal / Mikolajek, Halina / Milne, Bruce F / Minh, David / Morris, Aaron / Morris, Garrett M / Morwitzer, Melody Jane / Moustakas, Demetri / Mowbray, Charles E / Nakamura, Aline M / Neto, Jose Brandao / Neyts, Johan / Nguyen, Luong / Noske, Gabriela D / Oleinikovas, Vladas / Oliva, Glaucius / Overheul, Gijs J / Owen, C David / Pai, Ruby / Pan, Jin / Paran, Nir / Payne, Alexander Matthew / Perry, Benjamin / Pingle, Maneesh / Pinjari, Jakir / Politi, Boaz / Powell, Ailsa / Pšenák, Vladimír / Pulido, Iván / Puni, Reut / Rangel, Victor L / Reddi, Rambabu N / Rees, Paul / Reid, St Patrick / Reid, Lauren / Resnick, Efrat / Ripka, Emily Grace / Robinson, Ralph P / Rodriguez-Guerra, Jaime / Rosales, Romel / Rufa, Dominic A / Saar, Kadi / Saikatendu, Kumar Singh / Salah, Eidarus / Schaller, David / Scheen, Jenke / Schiffer, Celia A / Schofield, Christopher J / Shafeev, Mikhail / Shaikh, Aarif / Shaqra, Ala M / Shi, Jiye / Shurrush, Khriesto / Singh, Sukrit / Sittner, Assa / Sjö, Peter / Skyner, Rachael / Smalley, Adam / Smeets, Bart / Smilova, Mihaela D / Solmesky, Leonardo J / Spencer, John / Strain-Damerell, Claire / Swamy, Vishwanath / Tamir, Hadas / Taylor, Jenny C / Tennant, Rachael E / Thompson, Warren / Thompson, Andrew / Tomásio, Susana / Tomlinson, Charles W E / Tsurupa, Igor S / Tumber, Anthony / Vakonakis, Ioannis / van Rij, Ronald P / Vangeel, Laura / Varghese, Finny S / Vaschetto, Mariana / Vitner, Einat B / Voelz, Vincent / Volkamer, Andrea / Walsh, Martin A / Ward, Walter / Weatherall, Charlie / Weiss, Shay / White, Kris M / Wild, Conor Francis / Witt, Karolina D / Wittmann, Matthew / Wright, Nathan / Yahalom-Ronen, Yfat / Yilmaz, Nese Kurt / Zaidmann, Daniel / Zhang, Ivy / Zidane, Hadeer / Zitzmann, Nicole / Zvornicanin, Sarah N

    Science (New York, N.Y.)

    2023  Volume 382, Issue 6671, Page(s) eabo7201

    Abstract: We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, ... ...

    Abstract We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property-free knowledge base for future anticoronavirus drug discovery.
    MeSH term(s) Humans ; Coronavirus 3C Proteases/antagonists & inhibitors ; Coronavirus 3C Proteases/chemistry ; Molecular Docking Simulation ; SARS-CoV-2 ; Drug Discovery ; Coronavirus Protease Inhibitors/chemical synthesis ; Coronavirus Protease Inhibitors/chemistry ; Coronavirus Protease Inhibitors/pharmacology ; Structure-Activity Relationship ; COVID-19 Drug Treatment ; Crystallography, X-Ray
    Chemical Substances 3C-like proteinase, SARS-CoV-2 (EC 3.4.22.-) ; Coronavirus 3C Proteases (EC 3.4.22.28) ; Coronavirus Protease Inhibitors
    Language English
    Publishing date 2023-11-10
    Publishing country United States
    Document type Journal Article
    ZDB-ID 128410-1
    ISSN 1095-9203 ; 0036-8075
    ISSN (online) 1095-9203
    ISSN 0036-8075
    DOI 10.1126/science.abo7201
    Database MEDical Literature Analysis and Retrieval System OnLINE

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