1
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Meller A, Kelly D, Smith LG, Bowman GR. Toward physics-based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants. Protein Sci 2024; 33:e4902. [PMID: 38358129 PMCID: PMC10868452 DOI: 10.1002/pro.4902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 12/01/2023] [Accepted: 01/04/2024] [Indexed: 02/16/2024]
Abstract
The goal of precision medicine is to utilize our knowledge of the molecular causes of disease to better diagnose and treat patients. However, there is a substantial mismatch between the small number of food and drug administration (FDA)-approved drugs and annotated coding variants compared to the needs of precision medicine. This review introduces the concept of physics-based precision medicine, a scalable framework that promises to improve our understanding of sequence-function relationships and accelerate drug discovery. We show that accounting for the ensemble of structures a protein adopts in solution with computer simulations overcomes many of the limitations imposed by assuming a single protein structure. We highlight studies of protein dynamics and recent methods for the analysis of structural ensembles. These studies demonstrate that differences in conformational distributions predict functional differences within protein families and between variants. Thanks to new computational tools that are providing unprecedented access to protein structural ensembles, this insight may enable accurate predictions of variant pathogenicity for entire libraries of variants. We further show that explicitly accounting for protein ensembles, with methods like alchemical free energy calculations or docking to Markov state models, can uncover novel lead compounds. To conclude, we demonstrate that cryptic pockets, or cavities absent in experimental structures, provide an avenue to target proteins that are currently considered undruggable. Taken together, our review provides a roadmap for the field of protein science to accelerate precision medicine.
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Affiliation(s)
- Artur Meller
- Department of Biochemistry and Molecular BiophysicsWashington University in St. LouisSt. LouisMissouriUSA
- Medical Scientist Training ProgramWashington University in St. LouisSt. LouisMissouriUSA
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Devin Kelly
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Louis G. Smith
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Gregory R. Bowman
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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2
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Grasso G, Di Gregorio A, Mavkov B, Piga D, Labate GFD, Danani A, Deriu MA. Fragmented blind docking: a novel protein-ligand binding prediction protocol. J Biomol Struct Dyn 2022; 40:13472-13481. [PMID: 34641761 DOI: 10.1080/07391102.2021.1988709] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
In the present paper we propose a novel blind docking protocol based on Autodock-Vina. The developed docking protocol can provide binding site identification and binding pose prediction at the same time, by a systematical exploration of the protein volume performed with several preliminary docking calculations. In our opinion, this protocol can be successfully applied during the first steps of the virtual screening pipeline, because it provides binding site identification and binding pose prediction at the same time without visual evaluation of the binding site. After the binding pose prediction, MM/GBSA re-scoring rescoring procedures has been applied to improve the accuracy of the protein-ligand bound state. The FRAD protocol has been tested on 116 protein-ligand complexes of the Heat Shock Protein 90 - alpha, on 176 of Human Immunodeficiency virus protease 1, and on more than 100 protein-ligand system taken from the PDBbind dataset. Overall, the FRAD approach combined to MM/GBSA re-scoring can be considered as a powerful tool to increase the accuracy and efficiency with respect to other standard docking approaches when the ligand-binding site is unknown.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Gianvito Grasso
- Dalle Molle Institute for Artificial Intelligence, IDSIA - USI/SUPSI, Lugano-Viganello, Switzerland
| | - Arianna Di Gregorio
- Dalle Molle Institute for Artificial Intelligence, IDSIA - USI/SUPSI, Lugano-Viganello, Switzerland.,PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Italy
| | - Bojan Mavkov
- Dalle Molle Institute for Artificial Intelligence, IDSIA - USI/SUPSI, Lugano-Viganello, Switzerland
| | - Dario Piga
- Dalle Molle Institute for Artificial Intelligence, IDSIA - USI/SUPSI, Lugano-Viganello, Switzerland
| | | | - Andrea Danani
- Dalle Molle Institute for Artificial Intelligence, IDSIA - USI/SUPSI, Lugano-Viganello, Switzerland
| | - Marco A Deriu
- PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Italy
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3
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Iype E, Pillai U J, Kumar I, Gaastra-Nedea SV, Subramanian R, Saha RN, Dutta M. In silico and in vitro assays reveal potential inhibitors against 3CL pro main protease of SARS-CoV-2. J Biomol Struct Dyn 2022; 40:12800-12811. [PMID: 34550861 DOI: 10.1080/07391102.2021.1977181] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The COVID-19 pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is not showing any sign of slowing down even after the ongoing efforts of vaccination. The threats of new strains are concerning, as some of them are more infectious than the original one. A therapeutic against the disease is, therefore, of urgent need. Here, we use the DrugBank database to screen for potential inhibitors against the 3CLpro main protease of SARS-CoV-2. Instead of using the traditional approach of computational screening by docking, we developed a kernel ridge regressor (using a part of the docking data) to predict the binding energy of ligands. We used this model to screen the DrugBank database and shortlist two lead candidates (bromocriptine and avoralstat) for in vitro enzymatic study. Our results show that the 3CLpro enzyme activity in presence of 100 μM concentration of bromocriptine and avoralstat is 9.9% and 15.9%, respectively. Remarkably, bromocriptine exhibited submicromolar IC50 of 130 nM (0.13 μM). Avoralstat showed an IC50 of 2.16 μM. Further, the interactions of both drugs with 3CLpro were analyzed using molecular dynamics simulations of 100 ns. Results indicate that both ligands are stable in the binding pocket of the 3CLpro receptor. In addition, the MM-PBSA analysis revealed that bromocriptine (-29.37 kcal/mol) has a lower binding free energy compared to avoralstat (-6.91 kcal/mol). Further, hydrogen bond analysis also showed that bromocriptine interacts with the two catalytic residues, His41 and Cys145, more frequently than avoralstat.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Eldhose Iype
- Department of Chemical Engineering, BITS Pilani, Dubai Campus, Dubai, United Arab Emirates
| | - Jisha Pillai U
- Department of Biotechnology, BITS Pilani, Dubai Campus, Dubai, United Arab Emirates
| | - Indresh Kumar
- Department of Chemistry, BITS Pilani, Pilani Campus, Pilani, India
| | - Silvia V Gaastra-Nedea
- Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | | | - Mainak Dutta
- Department of Biotechnology, BITS Pilani, Dubai Campus, Dubai, United Arab Emirates
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4
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Amezcua M, Setiadi J, Ge Y, Mobley DL. An overview of the SAMPL8 host-guest binding challenge. J Comput Aided Mol Des 2022; 36:707-734. [PMID: 36229622 PMCID: PMC9596595 DOI: 10.1007/s10822-022-00462-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 06/21/2022] [Indexed: 11/23/2022]
Abstract
The SAMPL series of challenges aim to focus the community on specific modeling challenges, while testing and hopefully driving progress of computational methods to help guide pharmaceutical drug discovery. In this study, we report on the results of the SAMPL8 host–guest blind challenge for predicting absolute binding affinities. SAMPL8 focused on two host–guest datasets, one involving the cucurbituril CB8 (with a series of common drugs of abuse) and another involving two different Gibb deep-cavity cavitands. The latter dataset involved a previously featured deep cavity cavitand (TEMOA) as well as a new variant (TEETOA), both binding to a series of relatively rigid fragment-like guests. Challenge participants employed a reasonably wide variety of methods, though many of these were based on molecular simulations, and predictive accuracy was mixed. As in some previous SAMPL iterations (SAMPL6 and SAMPL7), we found that one approach to achieve greater accuracy was to apply empirical corrections to the binding free energy predictions, taking advantage of prior data on binding to these hosts. Another approach which performed well was a hybrid MD-based approach with reweighting to a force matched QM potential. In the cavitand challenge, an alchemical method using the AMOEBA-polarizable force field achieved the best success with RMSE less than 1 kcal/mol, while another alchemical approach (ATM/GAFF2-AM1BCC/TIP3P/HREM) had RMSE less than 1.75 kcal/mol. The work discussed here also highlights several important lessons; for example, retrospective studies of reference calculations demonstrate the sensitivity of predicted binding free energies to ethyl group sampling and/or guest starting pose, providing guidance to help improve future studies on these systems.
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Affiliation(s)
- Martin Amezcua
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA
| | - Jeffry Setiadi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Yunhui Ge
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA. .,Department of Chemistry, University of California, Irvine, CA, 92697, USA.
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5
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Grosjean H, Işık M, Aimon A, Mobley D, Chodera J, von Delft F, Biggin PC. SAMPL7 protein-ligand challenge: A community-wide evaluation of computational methods against fragment screening and pose-prediction. J Comput Aided Mol Des 2022; 36:291-311. [PMID: 35426591 PMCID: PMC9010448 DOI: 10.1007/s10822-022-00452-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 03/22/2022] [Indexed: 11/01/2022]
Abstract
A novel crystallographic fragment screening data set was generated and used in the SAMPL7 challenge for protein-ligands. The SAMPL challenges prospectively assess the predictive power of methods involved in computer-aided drug design. Application of various methods to fragment molecules are now widely used in the search for new drugs. However, there is little in the way of systematic validation specifically for fragment-based approaches. We have performed a large crystallographic high-throughput fragment screen against the therapeutically relevant second bromodomain of the Pleckstrin-homology domain interacting protein (PHIP2) that revealed 52 different fragments bound across 4 distinct sites, 47 of which were bound to the pharmacologically relevant acetylated lysine (Kac) binding site. These data were used to assess computational screening, binding pose prediction and follow-up enumeration. All submissions performed randomly for screening. Pose prediction success rates (defined as less than 2 Å root mean squared deviation against heavy atom crystal positions) ranged between 0 and 25% and only a very few follow-up compounds were deemed viable candidates from a medicinal-chemistry perspective based on a common molecular descriptors analysis. The tight deadlines imposed during the challenge led to a small number of submissions suggesting that the accuracy of rapidly responsive workflows remains limited. In addition, the application of these methods to reproduce crystallographic fragment data still appears to be very challenging. The results show that there is room for improvement in the development of computational tools particularly when applied to fragment-based drug design.
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Affiliation(s)
- Harold Grosjean
- Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, South Parks Road, OX1 3QU, Oxford, UK
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, OX11 0QX, Didcot, UK
| | - Mehtap Işık
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, 10065, New York, NY, USA
| | - Anthony Aimon
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, OX11 0QX, Didcot, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, OX11 0FA, Didcot, UK
| | - David Mobley
- Department of Pharmaceutical Sciences, Department of Chemistry, University of California, 92617, Irvine, California, USA
| | - John Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, 10065, New York, NY, USA
| | - Frank von Delft
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, OX11 0QX, Didcot, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, OX11 0FA, Didcot, UK
- Centre for Medicines Discovery, University of Oxford, Old Road Campus, Roosevelt Drive, OX3 7DQ, Headington, UK
- Structural Genomics Consortium, University of Oxford, Old Road Campus, Roosevelt Drive, OX3 7DQ, Headington, UK
| | - Philip C Biggin
- Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, South Parks Road, OX1 3QU, Oxford, UK.
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6
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Sun Q, Biswas A, Vijayan RSK, Craveur P, Forli S, Olson AJ, Castaner AE, Kirby KA, Sarafianos SG, Deng N, Levy R. Structure-based virtual screening workflow to identify antivirals targeting HIV-1 capsid. J Comput Aided Mol Des 2022; 36:193-203. [PMID: 35262811 PMCID: PMC8904208 DOI: 10.1007/s10822-022-00446-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/24/2022] [Indexed: 02/07/2023]
Abstract
We have identified novel HIV-1 capsid inhibitors targeting the PF74 binding site. Acting as the building block of the HIV-1 capsid core, the HIV-1 capsid protein plays an important role in the viral life cycle and is an attractive target for antiviral development. A structure-based virtual screening workflow for hit identification was employed, which includes docking 1.6 million commercially-available drug-like compounds from the ZINC database to the capsid dimer, followed by applying two absolute binding free energy (ABFE) filters on the 500 top-ranked molecules from docking. The first employs the Binding Energy Distribution Analysis Method (BEDAM) in implicit solvent. The top-ranked compounds are then refined using the Double Decoupling method in explicit solvent. Both docking and BEDAM refinement were carried out on the IBM World Community Grid as part of the FightAIDS@Home project. Using this virtual screening workflow, we identified 24 molecules with calculated binding free energies between − 6 and − 12 kcal/mol. We performed thermal shift assays on these molecules to examine their potential effects on the stability of HIV-1 capsid hexamer and found that two compounds, ZINC520357473 and ZINC4119064 increased the melting point of the latter by 14.8 °C and 33 °C, respectively. These results support the conclusion that the two ZINC compounds are primary hits targeting the capsid dimer interface. Our simulations also suggest that the two hit molecules may bind at the capsid dimer interface by occupying a new sub-pocket that has not been exploited by existing CA inhibitors. The possible causes for why other top-scored compounds suggested by ABFE filters failed to show measurable activity are discussed.
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Affiliation(s)
- Qinfang Sun
- Center for Biophysics and Computational Biology and Department of Chemistry, Temple University, Philadelphia, PA, 19122, USA
| | - Avik Biswas
- Center for Biophysics and Computational Biology and Department of Chemistry, Temple University, Philadelphia, PA, 19122, USA
| | - R S K Vijayan
- Institute for Applied Cancer Science, MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Pierrick Craveur
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - Andres Emanuelli Castaner
- Laboratory of Biochemical Pharmacology, Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, 30322, USA.,Children's Healthcare of Atlanta, Atlanta, GA, 30322, USA
| | - Karen A Kirby
- Laboratory of Biochemical Pharmacology, Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, 30322, USA.,Children's Healthcare of Atlanta, Atlanta, GA, 30322, USA
| | - Stefan G Sarafianos
- Laboratory of Biochemical Pharmacology, Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, 30322, USA.,Children's Healthcare of Atlanta, Atlanta, GA, 30322, USA
| | - Nanjie Deng
- Department of Chemistry and Physical Sciences, Pace University, New York, NY, 10038, USA.
| | - Ronald Levy
- Center for Biophysics and Computational Biology and Department of Chemistry, Temple University, Philadelphia, PA, 19122, USA
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7
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Deng CL, Cheng M, Zavalij PY, Isaacs L. Thermodynamics of Pillararene•Guest Complexation: Blinded Dataset for the SAMPL9 Challenge. NEW J CHEM 2022; 46:995-1002. [PMID: 35250257 PMCID: PMC8896905 DOI: 10.1039/d1nj05209h] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
We report an investigation of the complexation between a water soluble pillararene host (WP6) and a panel of hydrophobic cationic guests (G1 - G20) by a combination of 1H NMR spectroscopy and isothermal titration calorimetry in phosphate buffered saline. We find that WP6 forms 1:1 complexes with Ka values in the 104 - 109 M-1 range driven by favorable enthalpic contributions. This thermodynamic dataset serves as blinded data for the SAMPL9 challenge.
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Affiliation(s)
- Chun-Lin Deng
- Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland 20742, USA
| | - Ming Cheng
- Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland 20742, USA
| | - Peter Y. Zavalij
- Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland 20742, USA
| | - Lyle Isaacs
- Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland 20742, USA
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8
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Azimi S, Wu JZ, Khuttan S, Kurtzman T, Deng N, Gallicchio E. Application of the alchemical transfer and potential of mean force methods to the SAMPL8 host-guest blinded challenge. J Comput Aided Mol Des 2022; 36:63-76. [PMID: 35059940 PMCID: PMC8982563 DOI: 10.1007/s10822-021-00437-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 12/01/2021] [Indexed: 01/23/2023]
Abstract
We report the results of our participation in the SAMPL8 GDCC Blind Challenge for host-guest binding affinity predictions. Absolute binding affinity prediction is of central importance to the biophysics of molecular association and pharmaceutical discovery. The blinded SAMPL series have provided an important forum for assessing the reliability of binding free energy methods in an objective way. In this challenge, we employed two binding free energy methods, the newly developed alchemical transfer method (ATM) and the well-established potential of mean force (PMF) physical pathway method, using the same setup and force field model. The calculated binding free energies from the two methods are in excellent quantitative agreement. Importantly, the results from the two methods were also found to agree well with the experimental binding affinities released subsequently, with R values of 0.89 (ATM) and 0.83 (PMF). These results were ranked among the best of the SAMPL8 GDCC challenge and second only to those obtained with the more accurate AMOEBA force field. Interestingly, the two host molecules included in the challenge (TEMOA and TEETOA) displayed distinct binding mechanisms, with TEMOA undergoing a dehydration transition whereas guest binding to TEETOA resulted in the opening of the binding cavity that remains essentially dry during the process. The coupled reorganization and hydration equilibria observed in these systems is a useful prototype for the study of these phenomena often observed in the formation of protein-ligand complexes. Given that the two free energy methods employed here are based on entirely different thermodynamic pathways, the close agreement between the two and their general agreement with the experimental binding free energies are a testament to the high quality and precision achieved by theory and methods. The study provides further validation of the novel ATM binding free energy estimation protocol and paves the way to further extensions of the method to more complex systems.
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Affiliation(s)
- Solmaz Azimi
- Department of Chemistry, Brooklyn College of the City University of New York,PhD Program in Biochemistry, Graduate Center of the City University of New York
| | - Joe Z. Wu
- Department of Chemistry, Brooklyn College of the City University of New York,PhD Program in Chemistry, Graduate Center of the City University of New York
| | - Sheenam Khuttan
- Department of Chemistry, Brooklyn College of the City University of New York,PhD Program in Biochemistry, Graduate Center of the City University of New York
| | - Tom Kurtzman
- Department of Chemistry, Lehman College of the City University of New York,PhD Program in Chemistry, Graduate Center of the City University of New York,PhD Program in Biochemistry, Graduate Center of the City University of New York
| | - Nanjie Deng
- Department of Chemistry and Physical Sciences, Pace University, New York, New York
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York,PhD Program in Chemistry, Graduate Center of the City University of New York,PhD Program in Biochemistry, Graduate Center of the City University of New York
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9
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MUHAMMED MT, AKI-YALCIN E. Pharmacophore Modeling in Drug Discovery: Methodology and Current Status. JOURNAL OF THE TURKISH CHEMICAL SOCIETY, SECTION A: CHEMISTRY 2021. [DOI: 10.18596/jotcsa.927426] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
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10
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Bergazin TD, Tielker N, Zhang Y, Mao J, Gunner MR, Francisco K, Ballatore C, Kast SM, Mobley DL. Evaluation of log P, pK a, and log D predictions from the SAMPL7 blind challenge. J Comput Aided Mol Des 2021; 35:771-802. [PMID: 34169394 PMCID: PMC8224998 DOI: 10.1007/s10822-021-00397-3] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 06/05/2021] [Indexed: 12/16/2022]
Abstract
The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pKa for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pKa challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pKa prediction methods.
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Affiliation(s)
| | - Nicolas Tielker
- Physikalische Chemie III, Technische Universität Dortmund, Otto-Hahn-Str. 4a, 44227, Dortmund, Germany
| | - Yingying Zhang
- Department of Physics, The Graduate Center, City University of New York, New York, 10016, USA
| | - Junjun Mao
- Department of Physics, City College of New York, New York, 10031, USA
| | - M R Gunner
- Department of Physics, The Graduate Center, City University of New York, New York, 10016, USA.,Department of Physics, City College of New York, New York, 10031, USA
| | - Karol Francisco
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, Ja Jolla, CA, 92093-0756, USA
| | - Carlo Ballatore
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, Ja Jolla, CA, 92093-0756, USA
| | - Stefan M Kast
- Physikalische Chemie III, Technische Universität Dortmund, Otto-Hahn-Str. 4a, 44227, Dortmund, Germany
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA. .,Department of Chemistry, University of California, Irvine, Irvine, CA, 92697, USA.
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11
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Fischer A, Smieško M, Sellner M, Lill MA. Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results. J Med Chem 2021; 64:2489-2500. [PMID: 33617246 DOI: 10.1021/acs.jmedchem.0c02227] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Molecular docking is a computational method widely used in drug discovery. Due to the inherent inaccuracies of molecular docking, visual inspection of binding modes is a crucial routine in the decision making process of computational medicinal chemists. Despite its apparent importance for medicinal chemistry projects, guidelines for the visual docking pose assessment have been hardly discussed in the literature. Here, we review the medicinal chemistry literature with the aim of identifying consistent principles for visual inspection, highlighting cases of its successful application, and discussing its limitations. In this context, we conducted a survey reaching experts in both academia and the pharmaceutical industry, which also included a challenge to distinguish native from incorrect poses. We were able to collect 93 expert opinions that offer valuable insights into visually supported decision-making processes. This perspective shall motivate discussions among experienced computational medicinal chemists and guide young scientists new to the field to stratify their compounds.
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Affiliation(s)
- André Fischer
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland
| | - Martin Smieško
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland
| | - Manuel Sellner
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland
| | - Markus A Lill
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland
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12
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Abstract
Every protein has a story-how it folds, what it binds, its biological actions, and how it misbehaves in aging or disease. Stories are often inferred from a protein's shape (i.e., its structure). But increasingly, stories are told using computational molecular physics (CMP). CMP is rooted in the principled physics of driving forces and reveals granular detail of conformational populations in space and time. Recent advances are accessing longer time scales, larger actions, and blind testing, enabling more of biology's stories to be told in the language of atomistic physics.
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Affiliation(s)
- Emiliano Brini
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Carlos Simmerling
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA.,Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA
| | - Ken Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA. .,Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA.,Department of Physics and Astronomy, Stony Brook University, Stony Brook, New NY 11794, USA
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13
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Gill SC, Mobley DL. Reversibly Sampling Conformations and Binding Modes Using Molecular Darting. J Chem Theory Comput 2021; 17:302-314. [PMID: 33289558 DOI: 10.1021/acs.jctc.0c00752] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Sampling multiple binding modes of a ligand in a single molecular dynamics simulation is difficult. A given ligand may have many internal degrees of freedom, along with many different ways it might orient itself in a binding site or across several binding sites, all of which might be separated by large energy barriers. We have developed a novel Monte Carlo move called molecular darting (MolDarting) to reversibly sample between predefined binding modes of a ligand. Here, we couple this with nonequilibrium candidate Monte Carlo (NCMC) to improve acceptance of moves. We apply this technique to a simple dipeptide system, a ligand binding to T4 lysozyme L99A, and ligand binding to HIV integrase to test this new method. We observe significant increases in acceptance compared to uniformly sampling the internal and rotational/translational degrees of freedom in these systems.
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Affiliation(s)
- Samuel C Gill
- Department of Chemistry, University of California, Irvine, California 92617, United States
| | - David L Mobley
- Department of Chemistry, University of California, Irvine, California 92617, United States.,Department of Pharmaceutical Sciences, University of California, Irvine, California 92617, United States
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14
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Khalak Y, Tresadern G, de Groot BL, Gapsys V. Non-equilibrium approach for binding free energies in cyclodextrins in SAMPL7: force fields and software. J Comput Aided Mol Des 2020; 35:49-61. [PMID: 33230742 PMCID: PMC7862541 DOI: 10.1007/s10822-020-00359-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 11/07/2020] [Indexed: 11/24/2022]
Abstract
In the current work we report on our participation in the SAMPL7 challenge calculating absolute free energies of the host–guest systems, where 2 guest molecules were probed against 9 hosts-cyclodextrin and its derivatives. Our submission was based on the non-equilibrium free energy calculation protocol utilizing an averaged consensus result from two force fields (GAFF and CGenFF). The submitted prediction achieved accuracy of \documentclass[12pt]{minimal}
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\begin{document}$${1.38}\,\hbox {kcal}/\hbox {mol}$$\end{document}1.38kcal/mol in terms of the unsigned error averaged over the whole dataset. Subsequently, we further report on the underlying reasons for discrepancies between our calculations and another submission to the SAMPL7 challenge which employed a similar methodology, but disparate ligand and water force fields. As a result we have uncovered a number of issues in the dihedral parameter definition of the GAFF 2 force field. In addition, we identified particular cases in the molecular topologies where different software packages had a different interpretation of the same force field. This latter observation might be of particular relevance for systematic comparisons of molecular simulation software packages. The aforementioned factors have an influence on the final free energy estimates and need to be considered when performing alchemical calculations.
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Affiliation(s)
- Yuriy Khalak
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, 37077, Göttingen, Germany
| | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Bert L de Groot
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, 37077, Göttingen, Germany
| | - Vytautas Gapsys
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, 37077, Göttingen, Germany.
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15
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Oshima H, Re S, Sugita Y. Prediction of Protein–Ligand Binding Pose and Affinity Using the gREST+FEP Method. J Chem Inf Model 2020; 60:5382-5394. [DOI: 10.1021/acs.jcim.0c00338] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Hiraku Oshima
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Integrated Innovation Building 7F, 6-7-1 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Suyong Re
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Integrated Innovation Building 7F, 6-7-1 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Yuji Sugita
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Integrated Innovation Building 7F, 6-7-1 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Integrated Innovation Building 7F, 6-7-1 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
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16
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Mey ASJS, Allen BK, Macdonald HEB, Chodera JD, Hahn DF, Kuhn M, Michel J, Mobley DL, Naden LN, Prasad S, Rizzi A, Scheen J, Shirts MR, Tresadern G, Xu H. Best Practices for Alchemical Free Energy Calculations [Article v1.0]. LIVING JOURNAL OF COMPUTATIONAL MOLECULAR SCIENCE 2020; 2:18378. [PMID: 34458687 PMCID: PMC8388617 DOI: 10.33011/livecoms.2.1.18378] [Citation(s) in RCA: 114] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Alchemical free energy calculations are a useful tool for predicting free energy differences associated with the transfer of molecules from one environment to another. The hallmark of these methods is the use of "bridging" potential energy functions representing alchemical intermediate states that cannot exist as real chemical species. The data collected from these bridging alchemical thermodynamic states allows the efficient computation of transfer free energies (or differences in transfer free energies) with orders of magnitude less simulation time than simulating the transfer process directly. While these methods are highly flexible, care must be taken in avoiding common pitfalls to ensure that computed free energy differences can be robust and reproducible for the chosen force field, and that appropriate corrections are included to permit direct comparison with experimental data. In this paper, we review current best practices for several popular application domains of alchemical free energy calculations performed with equilibrium simulations, in particular relative and absolute small molecule binding free energy calculations to biomolecular targets.
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Affiliation(s)
- Antonia S. J. S. Mey
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | | | - Hannah E. Bruce Macdonald
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY, USA
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY, USA
| | - David F. Hahn
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Maximilian Kuhn
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
- Cresset, Cambridgeshire, UK
| | - Julien Michel
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | - David L. Mobley
- Departments of Pharmaceutical Sciences and Chemistry, University of California, Irvine, Irvine, USA
| | - Levi N. Naden
- Molecular Sciences Software Institute, Blacksburg VA, USA
| | | | - Andrea Rizzi
- Silicon Therapeutics, Boston, MA, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA
| | - Jenke Scheen
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | | | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
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17
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Işık M, Levorse D, Mobley DL, Rhodes T, Chodera JD. Octanol-water partition coefficient measurements for the SAMPL6 blind prediction challenge. J Comput Aided Mol Des 2019; 34:405-420. [PMID: 31858363 DOI: 10.1007/s10822-019-00271-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Accepted: 12/07/2019] [Indexed: 11/24/2022]
Abstract
Partition coefficients describe the equilibrium partitioning of a single, defined charge state of a solute between two liquid phases in contact, typically a neutral solute. Octanol-water partition coefficients ([Formula: see text]), or their logarithms (log P), are frequently used as a measure of lipophilicity in drug discovery. The partition coefficient is a physicochemical property that captures the thermodynamics of relative solvation between aqueous and nonpolar phases, and therefore provides an excellent test for physics-based computational models that predict properties of pharmaceutical relevance such as protein-ligand binding affinities or hydration/solvation free energies. The SAMPL6 Part II octanol-water partition coefficient prediction challenge used a subset of kinase inhibitor fragment-like compounds from the SAMPL6 [Formula: see text] prediction challenge in a blind experimental benchmark. Following experimental data collection, the partition coefficient dataset was kept blinded until all predictions were collected from participating computational chemistry groups. A total of 91 submissions were received from 27 participating research groups. This paper presents the octanol-water log P dataset for this SAMPL6 Part II partition coefficient challenge, which consisted of 11 compounds (six 4-aminoquinazolines, two benzimidazole, one pyrazolo[3,4-d]pyrimidine, one pyridine, one 2-oxoquinoline substructure containing compounds) with log P values in the range of 1.95-4.09. We describe the potentiometric log P measurement protocol used to collect this dataset using a Sirius T3, discuss the limitations of this experimental approach, and share suggestions for future log P data collection efforts for the evaluation of computational methods.
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Affiliation(s)
- Mehtap Işık
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY, 10065, USA
| | - Dorothy Levorse
- Pharmaceutical Sciences, MRL, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, NJ, 07065, USA
| | - David L Mobley
- Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, Irvine, CA, 92697, USA
| | - Timothy Rhodes
- Pharmaceutical Sciences, MRL, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, NJ, 07065, USA.
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
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18
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Xia J, Flynn W, Gallicchio E, Uplinger K, Armstrong JD, Forli S, Olson AJ, Levy RM. Massive-Scale Binding Free Energy Simulations of HIV Integrase Complexes Using Asynchronous Replica Exchange Framework Implemented on the IBM WCG Distributed Network. J Chem Inf Model 2019; 59:1382-1397. [PMID: 30758197 DOI: 10.1021/acs.jcim.8b00817] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
To perform massive-scale replica exchange molecular dynamics (REMD) simulations for calculating binding free energies of protein-ligand complexes, we implemented the asynchronous replica exchange (AsyncRE) framework of the binding energy distribution analysis method (BEDAM) in implicit solvent on the IBM World Community Grid (WCG) and optimized the simulation parameters to reduce the overhead and improve the prediction power of the WCG AsyncRE simulations. We also performed the first massive-scale binding free energy calculations using the WCG distributed computing grid and 301 ligands from the SAMPL4 challenge for large-scale binding free energy predictions of HIV-1 integrase complexes. In total there are ∼10000 simulated complexes, ∼1 million replicas, and ∼2000 μs of aggregated MD simulations. Running AsyncRE MD simulations on the WCG requires accepting a trade-off between the number of replicas that can be run (breadth) and the number of full RE cycles that can be completed per replica (depth). As compared with synchronous Replica Exchange (SyncRE) running on tightly coupled clusters like XSEDE, on the WCG many more replicas can be launched simultaneously on heterogeneous distributed hardware, but each full RE cycle requires more overhead. We compared the WCG results with that from AutoDock and more advanced RE simulations including the use of flattening potentials to accelerate sampling of selected degrees of freedom of ligands and/or receptors related to slow dynamics due to high energy barriers. We propose a suitable strategy of RE simulations to refine high throughput docking results which can be matched to corresponding computing resources: from HPC clusters, to small or medium-size distributed campus grids, and finally to massive-scale computing networks including millions of CPUs like the resources available on the WCG.
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Affiliation(s)
- Junchao Xia
- Center for Biophysics and Computational Biology and Department of Physics , Temple University , Philadelphia , Pennsylvania 19122 , United States
| | - William Flynn
- Center for Biophysics and Computational Biology and Department of Chemistry , Temple University , Philadelphia , Pennsylvania 19122 , United States
| | - Emilio Gallicchio
- Department of Chemistry , CUNY Brooklyn College , Brooklyn , New York 11210 , United States
| | - Keith Uplinger
- IBM WCG Team, 1177 South Belt Line Road , Coppell , Texas 75019 , United States
| | - Jonathan D Armstrong
- IBM WCG Team, 11400 Burnet Road , 0453B129, Austin , Texas 78758 , United States
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology , The Scripps Research Institute , La Jolla , California 92037-1000 , United States
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology , The Scripps Research Institute , La Jolla , California 92037-1000 , United States
| | - Ronald M Levy
- Center for Biophysics and Computational Biology and Department of Chemistry , Temple University , Philadelphia , Pennsylvania 19122 , United States
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19
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pK a measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments. J Comput Aided Mol Des 2018; 32:1117-1138. [PMID: 30406372 DOI: 10.1007/s10822-018-0168-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2018] [Accepted: 09/26/2018] [Indexed: 12/14/2022]
Abstract
Determining the net charge and protonation states populated by a small molecule in an environment of interest or the cost of altering those protonation states upon transfer to another environment is a prerequisite for predicting its physicochemical and pharmaceutical properties. The environment of interest can be aqueous, an organic solvent, a protein binding site, or a lipid bilayer. Predicting the protonation state of a small molecule is essential to predicting its interactions with biological macromolecules using computational models. Incorrectly modeling the dominant protonation state, shifts in dominant protonation state, or the population of significant mixtures of protonation states can lead to large modeling errors that degrade the accuracy of physical modeling. Low accuracy hinders the use of physical modeling approaches for molecular design. For small molecules, the acid dissociation constant (pKa) is the primary quantity needed to determine the ionic states populated by a molecule in an aqueous solution at a given pH. As a part of SAMPL6 community challenge, we organized a blind pKa prediction component to assess the accuracy with which contemporary pKa prediction methods can predict this quantity, with the ultimate aim of assessing the expected impact on modeling errors this would induce. While a multitude of approaches for predicting pKa values currently exist, predicting the pKas of drug-like molecules can be difficult due to challenging properties such as multiple titratable sites, heterocycles, and tautomerization. For this challenge, we focused on set of 24 small molecules selected to resemble selective kinase inhibitors-an important class of therapeutics replete with titratable moieties. Using a Sirius T3 instrument that performs automated acid-base titrations, we used UV absorbance-based pKa measurements to construct a high-quality experimental reference dataset of macroscopic pKas for the evaluation of computational pKa prediction methodologies that was utilized in the SAMPL6 pKa challenge. For several compounds in which the microscopic protonation states associated with macroscopic pKas were ambiguous, we performed follow-up NMR experiments to disambiguate the microstates involved in the transition. This dataset provides a useful standard benchmark dataset for the evaluation of pKa prediction methodologies on kinase inhibitor-like compounds.
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20
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Rizzi A, Murkli S, McNeill JN, Yao W, Sullivan M, Gilson MK, Chiu MW, Isaacs L, Gibb BC, Mobley DL, Chodera JD. Overview of the SAMPL6 host-guest binding affinity prediction challenge. J Comput Aided Mol Des 2018; 32:937-963. [PMID: 30415285 PMCID: PMC6301044 DOI: 10.1007/s10822-018-0170-6] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 10/07/2018] [Indexed: 10/27/2022]
Abstract
Accurately predicting the binding affinities of small organic molecules to biological macromolecules can greatly accelerate drug discovery by reducing the number of compounds that must be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the accuracy of current computational approaches to affinity prediction against binding data to biological macromolecules is frustrated by several challenges, such as slow conformational dynamics, multiple titratable groups, and the lack of high-quality blinded datasets. Over the last several SAMPL blind challenge exercises, host-guest systems have emerged as a practical and effective way to circumvent these challenges in assessing the predictive performance of current-generation quantitative modeling tools, while still providing systems capable of possessing tight binding affinities. Here, we present an overview of the SAMPL6 host-guest binding affinity prediction challenge, which featured three supramolecular hosts: octa-acid (OA), the closely related tetra-endo-methyl-octa-acid (TEMOA), and cucurbit[8]uril (CB8), along with 21 small organic guest molecules. A total of 119 entries were received from ten participating groups employing a variety of methods that spanned from electronic structure and movable type calculations in implicit solvent to alchemical and potential of mean force strategies using empirical force fields with explicit solvent models. While empirical models tended to obtain better performance than first-principle methods, it was not possible to identify a single approach that consistently provided superior results across all host-guest systems and statistical metrics. Moreover, the accuracy of the methodologies generally displayed a substantial dependence on the system considered, emphasizing the need for host diversity in blind evaluations. Several entries exploited previous experimental measurements of similar host-guest systems in an effort to improve their physical-based predictions via some manner of rudimentary machine learning; while this strategy succeeded in reducing systematic errors, it did not correspond to an improvement in statistical correlation. Comparison to previous rounds of the host-guest binding free energy challenge highlights an overall improvement in the correlation obtained by the affinity predictions for OA and TEMOA systems, but a surprising lack of improvement regarding root mean square error over the past several challenge rounds. The data suggests that further refinement of force field parameters, as well as improved treatment of chemical effects (e.g., buffer salt conditions, protonation states), may be required to further enhance predictive accuracy.
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Affiliation(s)
- Andrea Rizzi
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, 10065, USA
| | - Steven Murkli
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, 20742, USA
| | - John N McNeill
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, 20742, USA
| | - Wei Yao
- Department of Chemistry, Tulane University, Louisiana, LA, 70118, USA
| | - Matthew Sullivan
- Department of Chemistry, Tulane University, Louisiana, LA, 70118, USA
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Michael W Chiu
- Qualcomm Institute, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Lyle Isaacs
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, 20742, USA
| | - Bruce C Gibb
- Department of Chemistry, Tulane University, Louisiana, LA, 70118, USA
| | - David L Mobley
- Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, California, 92697, USA.
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
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21
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Xia J, Flynn W, Levy RM. Improving Prediction Accuracy of Binding Free Energies and Poses of HIV Integrase Complexes Using the Binding Energy Distribution Analysis Method with Flattening Potentials. J Chem Inf Model 2018; 58:1356-1371. [PMID: 29927237 PMCID: PMC6287956 DOI: 10.1021/acs.jcim.8b00194] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
To accelerate conformation sampling of slow dynamics from receptor or ligand, we introduced flattening potentials on selected bonded and nonbonded intramolecular interactions to the binding energy distribution analysis method (BEDAM) for calculating absolute binding free energies of protein-ligand complexes using an implicit solvent model and implemented flattening BEDAM using the asynchronous replica exchange (AsyncRE) framework for performing large scale replica exchange molecular dynamics (REMD) simulations. The advantage of using the flattening feature to reduce high energy barriers was exhibited first by the p-xylene-T4 lysozyme complex, where the intramolecular interactions of a protein side chain on the binding site were flattened to accelerate the conformational transition of the side chain from the trans to the gauche state when the p-xylene ligand is present in the binding site. Much more extensive flattening BEDAM simulations were performed for 53 experimental binders and 248 nonbinders of HIV-1 integrase which formed the SAMPL4 challenge, with the total simulation time of 24.3 μs. We demonstrated that the flattening BEDAM simulations not only substantially increase the number of true positives (and reduce false negatives) but also improve the prediction accuracy of binding poses of experimental binders. Furthermore, the values of area under the curve (AUC) of receiver operating characteristic (ROC) and the enrichment factors at 20% cutoff calculated from the flattening BEDAM simulations were improved significantly in comparison with that of simulations without flattening as we previously reported for the whole SAMPL4 database. Detailed analysis found that the improved ability to discriminate the binding free energies between the binders and nonbinders is due to the fact that the flattening simulations reduce the reorganization free energy penalties of binders and decrease the overlap of binding free energy distributions of binders relative to that of nonbinders. This happens because the conformational ensemble distributions for both the ligand and protein in solution match those at the fully coupled (complex) state more closely when the systems are more fully sampled after the flattening potentials are applied to the intermediate states.
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Affiliation(s)
- Junchao Xia
- Center for Biophysics and Computational Biology and Department of Physics , Temple University , Philadelphia , Pennsylvania 19122 , United States
| | - William Flynn
- Center for Biophysics and Computational Biology and Department of Chemistry , Temple University , Philadelphia , Pennsylvania 19122 , United States
| | - Ronald M Levy
- Center for Biophysics and Computational Biology and Department of Chemistry , Temple University , Philadelphia , Pennsylvania 19122 , United States
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22
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Gill SC, Lim NM, Grinaway PB, Rustenburg AS, Fass J, Ross GA, Chodera JD, Mobley DL. Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo. J Phys Chem B 2018; 122:5579-5598. [PMID: 29486559 PMCID: PMC5980761 DOI: 10.1021/acs.jpcb.7b11820] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Accurately predicting protein-ligand binding affinities and binding modes is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation time scales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes. In this technique, the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over 2 orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step toward applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding modes of ligands using enhanced sampling (BLUES) package which is freely available on GitHub.
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Affiliation(s)
- Samuel C. Gill
- Department of Chemistry, University of California, Irvine
| | - Nathan M. Lim
- Department of Pharmaceutical Sciences, University of California, Irvine
| | - Patrick B. Grinaway
- Graduate Program in Physiology, Biophysics, and Systems Biology, Weill Cornell Medical College, New York, NY 10065
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Ariën S. Rustenburg
- Graduate Program in Physiology, Biophysics, and Systems Biology, Weill Cornell Medical College, New York, NY 10065
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Josh Fass
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Tri-Institutional PhD Program in Computational Biology and Medicine, New York, NY 10065
| | - Gregory A. Ross
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | | | - David L. Mobley
- Department of Chemistry, University of California, Irvine
- Department of Pharmaceutical Sciences, University of California, Irvine
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23
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Cournia Z, Allen B, Sherman W. Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations. J Chem Inf Model 2017; 57:2911-2937. [PMID: 29243483 DOI: 10.1021/acs.jcim.7b00564] [Citation(s) in RCA: 383] [Impact Index Per Article: 54.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, technical, and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calculations, which rely on physics-based molecular simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, we present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. We focus specifically on relative binding free energies because the calculations are less computationally intensive than absolute binding free energy (ABFE) calculations and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a reference molecule and new ideas (virtual molecules) can be used to prioritize molecules for synthesis. We describe the critical aspects of running RBFE calculations, from both theoretical and applied perspectives, using a combination of retrospective literature examples and prospective studies from drug discovery projects. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative binding free energy simulations, with a focus on real-world drug discovery applications. We offer guidelines for improving the accuracy of RBFE simulations, especially for challenging cases, and emphasize unresolved issues that could be improved by further research in the field.
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Affiliation(s)
- Zoe Cournia
- Biomedical Research Foundation, Academy of Athens , 4 Soranou Ephessiou, 11527 Athens, Greece
| | - Bryce Allen
- Silicon Therapeutics , 300 A Street, Boston, Massachusetts 02210, United States
| | - Woody Sherman
- Silicon Therapeutics , 300 A Street, Boston, Massachusetts 02210, United States
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24
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Gathiaka S, Liu S, Chiu M, Yang H, Stuckey JA, Kang YN, Delproposto J, Kubish G, Dunbar JB, Carlson HA, Burley SK, Walters WP, Amaro RE, Feher VA, Gilson MK. D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions. J Comput Aided Mol Des 2016; 30:651-668. [PMID: 27696240 DOI: 10.1007/s10822-016-9946-8] [Citation(s) in RCA: 149] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2016] [Accepted: 08/18/2016] [Indexed: 11/26/2022]
Abstract
The Drug Design Data Resource (D3R) ran Grand Challenge 2015 between September 2015 and February 2016. Two targets served as the framework to test community docking and scoring methods: (1) HSP90, donated by AbbVie and the Community Structure Activity Resource (CSAR), and (2) MAP4K4, donated by Genentech. The challenges for both target datasets were conducted in two stages, with the first stage testing pose predictions and the capacity to rank compounds by affinity with minimal structural data; and the second stage testing methods for ranking compounds with knowledge of at least a subset of the ligand-protein poses. An additional sub-challenge provided small groups of chemically similar HSP90 compounds amenable to alchemical calculations of relative binding free energy. Unlike previous blinded Challenges, we did not provide cognate receptors or receptors prepared with hydrogens and likewise did not require a specified crystal structure to be used for pose or affinity prediction in Stage 1. Given the freedom to select from over 200 crystal structures of HSP90 in the PDB, participants employed workflows that tested not only core docking and scoring technologies, but also methods for addressing water-mediated ligand-protein interactions, binding pocket flexibility, and the optimal selection of protein structures for use in docking calculations. Nearly 40 participating groups submitted over 350 prediction sets for Grand Challenge 2015. This overview describes the datasets and the organization of the challenge components, summarizes the results across all submitted predictions, and considers broad conclusions that may be drawn from this collaborative community endeavor.
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Affiliation(s)
- Symon Gathiaka
- Drug Design Data Resource, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Shuai Liu
- Drug Design Data Resource, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Michael Chiu
- Drug Design Data Resource, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Huanwang Yang
- RCSB Protein Data Bank, Center for Integrative Proteomics Research, Institute for Quantitative Biomedicine, Department Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ, 08854, USA
| | - Jeanne A Stuckey
- Center for Structural Biology, Life Sciences Institute, University of Michigan, 210 Washtenaw Avenue, Ann Arbor, MI, 48109-2216, USA
| | - You Na Kang
- Center for Structural Biology, Life Sciences Institute, University of Michigan, 210 Washtenaw Avenue, Ann Arbor, MI, 48109-2216, USA
| | - Jim Delproposto
- Center for Structural Biology, Life Sciences Institute, University of Michigan, 210 Washtenaw Avenue, Ann Arbor, MI, 48109-2216, USA
| | - Ginger Kubish
- Center for Structural Biology, Life Sciences Institute, University of Michigan, 210 Washtenaw Avenue, Ann Arbor, MI, 48109-2216, USA
| | - James B Dunbar
- Department of Medicinal Chemistry, University of Michigan, 428 Church St., Ann Arbor, MI, 48109-1065, USA
| | - Heather A Carlson
- Department of Medicinal Chemistry, University of Michigan, 428 Church St., Ann Arbor, MI, 48109-1065, USA
| | - Stephen K Burley
- RCSB Protein Data Bank, Center for Integrative Proteomics Research, Institute for Quantitative Biomedicine, Department Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ, 08854, USA
- Department of Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
- San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | | | - Rommie E Amaro
- Drug Design Data Resource, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
- Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
| | - Victoria A Feher
- Drug Design Data Resource, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
- Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
- Schrodinger, Inc., New York, NY, USA.
| | - Michael K Gilson
- Drug Design Data Resource, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
- Department of Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
- Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
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Absolute binding free energy calculations of CBClip host-guest systems in the SAMPL5 blind challenge. J Comput Aided Mol Des 2016; 31:71-85. [PMID: 27677749 DOI: 10.1007/s10822-016-9968-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Accepted: 09/08/2016] [Indexed: 12/11/2022]
Abstract
Herein, we report the absolute binding free energy calculations of CBClip complexes in the SAMPL5 blind challenge. Initial conformations of CBClip complexes were obtained using docking and molecular dynamics simulations. Free energy calculations were performed using thermodynamic integration (TI) with soft-core potentials and Bennett's acceptance ratio (BAR) method based on a serial insertion scheme. We compared the results obtained with TI simulations with soft-core potentials and Hamiltonian replica exchange simulations with the serial insertion method combined with the BAR method. The results show that the difference between the two methods can be mainly attributed to the van der Waals free energies, suggesting that either the simulations used for TI or the simulations used for BAR, or both are not fully converged and the two sets of simulations may have sampled difference phase space regions. The penalty scores of force field parameters of the 10 guest molecules provided by CHARMM Generalized Force Field can be an indicator of the accuracy of binding free energy calculations. Among our submissions, the combination of docking and TI performed best, which yielded the root mean square deviation of 2.94 kcal/mol and an average unsigned error of 3.41 kcal/mol for the ten guest molecules. These values were best overall among all participants. However, our submissions had little correlation with experiments.
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Bhakat S, Söderhjelm P. Resolving the problem of trapped water in binding cavities: prediction of host-guest binding free energies in the SAMPL5 challenge by funnel metadynamics. J Comput Aided Mol Des 2016; 31:119-132. [PMID: 27573983 PMCID: PMC5239820 DOI: 10.1007/s10822-016-9948-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Accepted: 08/20/2016] [Indexed: 12/15/2022]
Abstract
The funnel metadynamics method enables rigorous calculation of the potential of mean force along an arbitrary binding path and thereby evaluation of the absolute binding free energy. A problem of such physical paths is that the mechanism characterizing the binding process is not always obvious. In particular, it might involve reorganization of the solvent in the binding site, which is not easily captured with a few geometrically defined collective variables that can be used for biasing. In this paper, we propose and test a simple method to resolve this trapped-water problem by dividing the process into an artificial host-desolvation step and an actual binding step. We show that, under certain circumstances, the contribution from the desolvation step can be calculated without introducing further statistical errors. We apply the method to the problem of predicting host–guest binding free energies in the SAMPL5 blind challenge, using two octa-acid hosts and six guest molecules. For one of the hosts, well-converged results are obtained and the prediction of relative binding free energies is the best among all the SAMPL5 submissions. For the other host, which has a narrower binding pocket, the statistical uncertainties are slightly higher; longer simulations would therefore be needed to obtain conclusive results.
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Affiliation(s)
- Soumendranath Bhakat
- Division of Biophysical Chemistry, Chemical Center, Lund University, P.O.B. 124, 22100, Lund, Sweden
| | - Pär Söderhjelm
- Division of Biophysical Chemistry, Chemical Center, Lund University, P.O.B. 124, 22100, Lund, Sweden.
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27
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Misini Ignjatović M, Caldararu O, Dong G, Muñoz-Gutierrez C, Adasme-Carreño F, Ryde U. Binding-affinity predictions of HSP90 in the D3R Grand Challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations. J Comput Aided Mol Des 2016; 30:707-730. [PMID: 27565797 PMCID: PMC5078160 DOI: 10.1007/s10822-016-9942-z] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Accepted: 08/17/2016] [Indexed: 11/25/2022]
Abstract
We have estimated the binding affinity of three sets of ligands of the heat-shock protein 90 in the D3R grand challenge blind test competition. We have employed four different methods, based on five different crystal structures: first, we docked the ligands to the proteins with induced-fit docking with the Glide software and calculated binding affinities with three energy functions. Second, the docked structures were minimised in a continuum solvent and binding affinities were calculated with the MM/GBSA method (molecular mechanics combined with generalised Born and solvent-accessible surface area solvation). Third, the docked structures were re-optimised by combined quantum mechanics and molecular mechanics (QM/MM) calculations. Then, interaction energies were calculated with quantum mechanical calculations employing 970-1160 atoms in a continuum solvent, combined with energy corrections for dispersion, zero-point energy and entropy, ligand distortion, ligand solvation, and an increase of the basis set to quadruple-zeta quality. Fourth, relative binding affinities were estimated by free-energy simulations, using the multi-state Bennett acceptance-ratio approach. Unfortunately, the results were varying and rather poor, with only one calculation giving a correlation to the experimental affinities larger than 0.7, and with no consistent difference in the quality of the predictions from the various methods. For one set of ligands, the results could be strongly improved (after experimental data were revealed) if it was recognised that one of the ligands displaced one or two water molecules. For the other two sets, the problem is probably that the ligands bind in different modes than in the crystal structures employed or that the conformation of the ligand-binding site or the whole protein changes.
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Affiliation(s)
- Majda Misini Ignjatović
- Department of Theoretical Chemistry, Lund University, Chemical Centre, P. O. Box 124, 221 00, Lund, Sweden
| | - Octav Caldararu
- Department of Theoretical Chemistry, Lund University, Chemical Centre, P. O. Box 124, 221 00, Lund, Sweden
| | - Geng Dong
- Department of Theoretical Chemistry, Lund University, Chemical Centre, P. O. Box 124, 221 00, Lund, Sweden
| | - Camila Muñoz-Gutierrez
- Centro de Bioinformática y Simulación Molecular, Facultad de Ingeniería, Universidad de Talca, 2 Norte 685, Talca, Chile
| | - Francisco Adasme-Carreño
- Centro de Bioinformática y Simulación Molecular, Facultad de Ingeniería, Universidad de Talca, 2 Norte 685, Talca, Chile
| | - Ulf Ryde
- Department of Theoretical Chemistry, Lund University, Chemical Centre, P. O. Box 124, 221 00, Lund, Sweden.
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28
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Large scale free energy calculations for blind predictions of protein-ligand binding: the D3R Grand Challenge 2015. J Comput Aided Mol Des 2016; 30:743-751. [PMID: 27562018 DOI: 10.1007/s10822-016-9952-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Accepted: 08/23/2016] [Indexed: 01/11/2023]
Abstract
We describe binding free energy calculations in the D3R Grand Challenge 2015 for blind prediction of the binding affinities of 180 ligands to Hsp90. The present D3R challenge was built around experimental datasets involving Heat shock protein (Hsp) 90, an ATP-dependent molecular chaperone which is an important anticancer drug target. The Hsp90 ATP binding site is known to be a challenging target for accurate calculations of ligand binding affinities because of the ligand-dependent conformational changes in the binding site, the presence of ordered waters and the broad chemical diversity of ligands that can bind at this site. Our primary focus here is to distinguish binders from nonbinders. Large scale absolute binding free energy calculations that cover over 3000 protein-ligand complexes were performed using the BEDAM method starting from docked structures generated by Glide docking. Although the ligand dataset in this study resembles an intermediate to late stage lead optimization project while the BEDAM method is mainly developed for early stage virtual screening of hit molecules, the BEDAM binding free energy scoring has resulted in a moderate enrichment of ligand screening against this challenging drug target. Results show that, using a statistical mechanics based free energy method like BEDAM starting from docked poses offers better enrichment than classical docking scoring functions and rescoring methods like Prime MM-GBSA for the Hsp90 data set in this blind challenge. Importantly, among the three methods tested here, only the mean value of the BEDAM binding free energy scores is able to separate the large group of binders from the small group of nonbinders with a gap of 2.4 kcal/mol. None of the three methods that we have tested provided accurate ranking of the affinities of the 147 active compounds. We discuss the possible sources of errors in the binding free energy calculations. The study suggests that BEDAM can be used strategically to discriminate binders from nonbinders in virtual screening and to more accurately predict the ligand binding modes prior to the more computationally expensive FEP calculations of binding affinity.
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29
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Kumar A, Zhang KYJ. Prospective evaluation of shape similarity based pose prediction method in D3R Grand Challenge 2015. J Comput Aided Mol Des 2016; 30:685-693. [DOI: 10.1007/s10822-016-9931-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2016] [Accepted: 07/25/2016] [Indexed: 01/23/2023]
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30
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Mentes A, Deng NJ, Vijayan RSK, Xia J, Gallicchio E, Levy RM. Binding Energy Distribution Analysis Method: Hamiltonian Replica Exchange with Torsional Flattening for Binding Mode Prediction and Binding Free Energy Estimation. J Chem Theory Comput 2016; 12:2459-70. [PMID: 27070865 PMCID: PMC4862910 DOI: 10.1021/acs.jctc.6b00134] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Molecular dynamics modeling of complex biological systems is limited by finite simulation time. The simulations are often trapped close to local energy minima separated by high energy barriers. Here, we introduce Hamiltonian replica exchange (H-REMD) with torsional flattening in the Binding Energy Distribution Analysis Method (BEDAM), to reduce energy barriers along torsional degrees of freedom and accelerate sampling of intramolecular degrees of freedom relevant to protein-ligand binding. The method is tested on a standard benchmark (T4 Lysozyme/L99A/p-xylene complex) and on a library of HIV-1 integrase complexes derived from the SAMPL4 blind challenge. We applied the torsional flattening strategy to 26 of the 53 known binders to the HIV Integrase LEDGF site found to have a binding energy landscape funneled toward the crystal structure. We show that our approach samples the conformational space more efficiently than the original method without flattening when starting from a poorly docked pose with incorrect ligand dihedral angle conformations. In these unfavorable cases convergence to a binding pose within 2-3 Å from the crystallographic pose is obtained within a few nanoseconds of the Hamiltonian replica exchange simulation. We found that torsional flattening is insufficient in cases where trapping is due to factors other than torsional energy, such as the formation of incorrect intramolecular hydrogen bonds and stacking. Work is in progress to generalize the approach to handle these cases and thereby make it more widely applicable.
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Affiliation(s)
- Ahmet Mentes
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
- Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Nan-Jie Deng
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
- Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - R. S. K. Vijayan
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
- Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Junchao Xia
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
- Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College, The City University of New York, Brooklyn, New York 11210, United States
| | - Ronald M. Levy
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
- Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
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31
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Gallicchio E, Xia J, Flynn WF, Zhang B, Samlalsingh S, Mentes A, Levy RM. Asynchronous Replica Exchange Software for Grid and Heterogeneous Computing. COMPUTER PHYSICS COMMUNICATIONS 2015; 196:236-246. [PMID: 27103749 PMCID: PMC4834714 DOI: 10.1016/j.cpc.2015.06.010] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Parallel replica exchange sampling is an extended ensemble technique often used to accelerate the exploration of the conformational ensemble of atomistic molecular simulations of chemical systems. Inter-process communication and coordination requirements have historically discouraged the deployment of replica exchange on distributed and heterogeneous resources. Here we describe the architecture of a software (named ASyncRE) for performing asynchronous replica exchange molecular simulations on volunteered computing grids and heterogeneous high performance clusters. The asynchronous replica exchange algorithm on which the software is based avoids centralized synchronization steps and the need for direct communication between remote processes. It allows molecular dynamics threads to progress at different rates and enables parameter exchanges among arbitrary sets of replicas independently from other replicas. ASyncRE is written in Python following a modular design conducive to extensions to various replica exchange schemes and molecular dynamics engines. Applications of the software for the modeling of association equilibria of supramolecular and macromolecular complexes on BOINC campus computational grids and on the CPU/MIC heterogeneous hardware of the XSEDE Stampede supercomputer are illustrated. They show the ability of ASyncRE to utilize large grids of desktop computers running the Windows, MacOS, and/or Linux operating systems as well as collections of high performance heterogeneous hardware devices.
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Affiliation(s)
- Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York, Brooklyn, NY
| | - Junchao Xia
- Center for Biophysics and Computational Biology, Institute of Computational Molecular Science and Department of Chemistry, Temple University, Philadelphia PA
| | - William F. Flynn
- Center for Biophysics and Computational Biology, Institute of Computational Molecular Science and Department of Chemistry, Temple University, Philadelphia PA
| | - Baofeng Zhang
- Department of Chemistry, Brooklyn College of the City University of New York, Brooklyn, NY
| | - Sade Samlalsingh
- Department of Chemistry, Brooklyn College of the City University of New York, Brooklyn, NY
| | - Ahmet Mentes
- Center for Biophysics and Computational Biology, Institute of Computational Molecular Science and Department of Chemistry, Temple University, Philadelphia PA
| | - Ronald M. Levy
- Center for Biophysics and Computational Biology, Institute of Computational Molecular Science and Department of Chemistry, Temple University, Philadelphia PA
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32
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Wickstrom L, Deng N, He P, Mentes A, Nguyen C, Gilson MK, Kurtzman T, Gallicchio E, Levy RM. Parameterization of an effective potential for protein-ligand binding from host-guest affinity data. J Mol Recognit 2015; 29:10-21. [PMID: 26256816 DOI: 10.1002/jmr.2489] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2015] [Revised: 06/06/2015] [Accepted: 06/07/2015] [Indexed: 12/13/2022]
Abstract
Force field accuracy is still one of the "stalemates" in biomolecular modeling. Model systems with high quality experimental data are valuable instruments for the validation and improvement of effective potentials. With respect to protein-ligand binding, organic host-guest complexes have long served as models for both experimental and computational studies because of the abundance of binding affinity data available for such systems. Binding affinity data collected for cyclodextrin (CD) inclusion complexes, a popular model for molecular recognition, is potentially a more reliable resource for tuning energy parameters than hydration free energy measurements. Convergence of binding free energy calculations on CD host-guest systems can also be obtained rapidly, thus offering the opportunity to assess the robustness of these parameters. In this work, we demonstrate how implicit solvent parameters can be developed using binding affinity experimental data and the binding energy distribution analysis method (BEDAM) and validated using the Grid Inhomogeneous Solvation Theory analysis. These new solvation parameters were used to study protein-ligand binding in two drug targets against the HIV-1 virus and improved the agreement between the calculated and the experimental binding affinities. This work illustrates how benchmark sets of high quality experimental binding affinity data and physics-based binding free energy models can be used to evaluate and optimize force fields for protein-ligand systems. Copyright © 2015 John Wiley & Sons, Ltd.
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Affiliation(s)
- Lauren Wickstrom
- Borough of Manhattan Community College, Department of Science, The City University of New York, New York, NY, 10007, USA
| | - Nanjie Deng
- Center for Biophysics and Computational Biology/ICMS, Department of Chemistry, Temple University, Philadelphia, PA, 19122, USA
| | - Peng He
- Center for Biophysics and Computational Biology/ICMS, Department of Chemistry, Temple University, Philadelphia, PA, 19122, USA
| | - Ahmet Mentes
- Center for Biophysics and Computational Biology/ICMS, Department of Chemistry, Temple University, Philadelphia, PA, 19122, USA
| | - Crystal Nguyen
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093-0736, USA
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093-0736, USA
| | - Tom Kurtzman
- Department of Chemistry, Lehman College, The City University of New York, Bronx, NY, 10468, USA
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College, The City University of New York, Brooklyn, NY, 11210, USA
| | - Ronald M Levy
- Center for Biophysics and Computational Biology/ICMS, Department of Chemistry, Temple University, Philadelphia, PA, 19122, USA
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BEDAM binding free energy predictions for the SAMPL4 octa-acid host challenge. J Comput Aided Mol Des 2015; 29:315-25. [PMID: 25726024 DOI: 10.1007/s10822-014-9795-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2014] [Accepted: 09/05/2014] [Indexed: 12/14/2022]
Abstract
The binding energy distribution analysis method (BEDAM) protocol has been employed as part of the SAMPL4 blind challenge to predict the binding free energies of a set of octa-acid host-guest complexes. The resulting predictions were consistently judged as some of the most accurate predictions in this category of the SAMPL4 challenge in terms of quantitative accuracy and statistical correlation relative to the experimental values, which were not known at the time the predictions were made. The work has been conducted as part of a hands-on graduate class laboratory session. Collectively the students, aided by automated setup and analysis tools, performed the bulk of the calculations and the numerical and structural analysis. The success of the experiment confirms the reliability of the BEDAM methodology and it shows that physics-based atomistic binding free energy estimation models, when properly streamlined and automated, can be successfully employed by non-specialists.
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Perryman AL, Yu W, Wang X, Ekins S, Forli S, Li SG, Freundlich JS, Tonge PJ, Olson AJ. A virtual screen discovers novel, fragment-sized inhibitors of Mycobacterium tuberculosis InhA. J Chem Inf Model 2015; 55:645-59. [PMID: 25636146 DOI: 10.1021/ci500672v] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Isoniazid (INH) is usually administered to treat latent Mycobacterium tuberculosis (Mtb) infections and is used in combination therapy to treat active tuberculosis (TB). Unfortunately, resistance to this drug is hampering its clinical effectiveness. INH is a prodrug that must be activated by Mtb catalase-peroxidase (KatG) before it can inhibit InhA (Mtb enoyl-acyl-carrier-protein reductase). Isoniazid-resistant cases of TB found in clinical settings usually involve mutations in or deletion of katG, which abrogate INH activation. Compounds that inhibit InhA without requiring prior activation by KatG would not be affected by this resistance mechanism and hence would display continued potency against these drug-resistant isolates of Mtb. Virtual screening experiments versus InhA in the GO Fight Against Malaria (GO FAM) project were designed to discover new scaffolds that display base-stacking interactions with the NAD cofactor. GO FAM experiments included targets from other pathogens, including Mtb, when they had structural similarity to a malaria target. Eight of the 16 soluble compounds identified by docking against InhA plus visual inspection were modest inhibitors and did not require prior activation by KatG. The best two inhibitors discovered are both fragment-sized compounds and displayed Ki values of 54 and 59 μM, respectively. Importantly, the novel inhibitors discovered have low structural similarity to known InhA inhibitors and thus help expand the number of chemotypes on which future medicinal chemistry efforts can be focused. These new fragment hits could eventually help advance the fight against INH-resistant Mtb strains, which pose a significant global health threat.
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Affiliation(s)
- Alexander L Perryman
- †Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
| | | | | | - Sean Ekins
- ⊥Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States.,#Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States
| | - Stefano Forli
- †Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
| | | | | | | | - Arthur J Olson
- †Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
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Forli S, Olson AJ. Computational challenges of structure-based approaches applied to HIV. Curr Top Microbiol Immunol 2015; 389:31-51. [PMID: 25711462 DOI: 10.1007/82_2015_432] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Here, we review some of the opportunities and challenges that we face in computational modeling of HIV therapeutic targets and structural biology, both in terms of methodology development and structure-based drug design (SBDD). Computational methods have provided fundamental support to HIV research since the initial structural studies, helping to unravel details of HIV biology. Computational models have proved to be a powerful tool to analyze and understand the impact of mutations and to overcome their structural and functional influence in drug resistance. With the availability of structural data, in silico experiments have been instrumental in exploiting and improving interactions between drugs and viral targets, such as HIV protease, reverse transcriptase, and integrase. Issues such as viral target dynamics and mutational variability, as well as the role of water and estimates of binding free energy in characterizing ligand interactions, are areas of active computational research. Ever-increasing computational resources and theoretical and algorithmic advances have played a significant role in progress to date, and we envision a continually expanding role for computational methods in our understanding of HIV biology and SBDD in the future.
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Affiliation(s)
- Stefano Forli
- MGL, Department of Integrative Structural and Computational Biology and HIV Interaction and Viral Evolution Center, The Scripps Research Institute, 10550 N. Torrey Pines Rd., La Jolla, CA, 92037, USA
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36
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Deng N, Forli S, He P, Perryman A, Wickstrom L, Vijayan RSK, Tiefenbrunn T, Stout D, Gallicchio E, Olson AJ, Levy RM. Distinguishing binders from false positives by free energy calculations: fragment screening against the flap site of HIV protease. J Phys Chem B 2014; 119:976-88. [PMID: 25189630 PMCID: PMC4306491 DOI: 10.1021/jp506376z] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
![]()
Molecular docking is a powerful tool
used in drug discovery and
structural biology for predicting the structures of ligand–receptor
complexes. However, the accuracy of docking calculations can be limited
by factors such as the neglect of protein reorganization in the scoring
function; as a result, ligand screening can produce a high rate of
false positive hits. Although absolute binding free energy methods
still have difficulty in accurately rank-ordering binders, we believe
that they can be fruitfully employed to distinguish binders from nonbinders
and reduce the false positive rate. Here we study a set of ligands
that dock favorably to a newly discovered, potentially allosteric
site on the flap of HIV-1 protease. Fragment binding to this site
stabilizes a closed form of protease, which could be exploited for
the design of allosteric inhibitors. Twenty-three top-ranked protein–ligand
complexes from AutoDock were subject to the free energy screening
using two methods, the recently developed binding energy analysis
method (BEDAM) and the standard double decoupling method (DDM). Free
energy calculations correctly identified most of the false positives
(≥83%) and recovered all the confirmed binders. The results
show a gap averaging ≥3.7 kcal/mol, separating the binders
and the false positives. We present a formula that decomposes the
binding free energy into contributions from the receptor conformational
macrostates, which provides insights into the roles of different binding
modes. Our binding free energy component analysis further suggests
that improving the treatment for the desolvation penalty associated
with the unfulfilled polar groups could reduce the rate of false positive
hits in docking. The current study demonstrates that the combination
of docking with free energy methods can be very useful for more accurate
ligand screening against valuable drug targets.
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Affiliation(s)
- Nanjie Deng
- Center for Biophysics & Computational Biology/ICMS, ‡Department of Chemistry, Temple University , Philadelphia, Pennsylvania19122, United States
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37
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Muddana HS, Yin J, Sapra NV, Fenley AT, Gilson MK. Blind prediction of SAMPL4 cucurbit[7]uril binding affinities with the mining minima method. J Comput Aided Mol Des 2014; 28:463-74. [PMID: 24510191 PMCID: PMC4053532 DOI: 10.1007/s10822-014-9726-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2013] [Accepted: 01/31/2014] [Indexed: 12/14/2022]
Abstract
Accurate methods for predicting protein-ligand binding affinities are of central interest to computer-aided drug design for hit identification and lead optimization. Here, we used the mining minima (M2) method to predict cucurbit[7]uril binding affinities from the SAMPL4 blind prediction challenge. We tested two different energy models, an empirical classical force field, CHARMm with VCharge charges, and the Poisson-Boltzmann surface area solvation model; and a semiempirical quantum mechanical (QM) Hamiltonian, PM6-DH+, coupled with the COSMO solvation model and a surface area term for nonpolar solvation free energy. Binding affinities based on the classical force field correlated strongly with the experiments with a correlation coefficient (R(2)) of 0.74. On the other hand, binding affinities based on the QM energy model correlated poorly with experiments (R(2) = 0.24), due largely to two major outliers. As we used extensive conformational search methods, these results point to possible inaccuracies in the PM6-DH+ energy model or the COSMO solvation model. Furthermore, the different binding free energy components, solute energy, solvation free energy, and configurational entropy showed significant deviations between the classical M2 and quantum M2 calculations. Comparison of different classical M2 free energy components to experiments show that the change in the total energy, i.e. the solute energy plus the solvation free energy, is the key driving force for binding, with a reasonable correlation to experiment (R(2) = 0.56); however, accounting for configurational entropy further improves the correlation.
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Affiliation(s)
- Hari S Muddana
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9500 Gilman Drive, Room# 3224, La Jolla, CA, 92093-0736, USA
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38
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The SAMPL4 host-guest blind prediction challenge: an overview. J Comput Aided Mol Des 2014; 28:305-17. [PMID: 24599514 DOI: 10.1007/s10822-014-9735-1] [Citation(s) in RCA: 138] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2014] [Accepted: 02/17/2014] [Indexed: 01/21/2023]
Abstract
Prospective validation of methods for computing binding affinities can help assess their predictive power and thus set reasonable expectations for their performance in drug design applications. Supramolecular host-guest systems are excellent model systems for testing such affinity prediction methods, because their small size and limited conformational flexibility, relative to proteins, allows higher throughput and better numerical convergence. The SAMPL4 prediction challenge therefore included a series of host-guest systems, based on two hosts, cucurbit[7]uril and octa-acid. Binding affinities in aqueous solution were measured experimentally for a total of 23 guest molecules. Participants submitted 35 sets of computational predictions for these host-guest systems, based on methods ranging from simple docking, to extensive free energy simulations, to quantum mechanical calculations. Over half of the predictions provided better correlations with experiment than two simple null models, but most methods underperformed the null models in terms of root mean squared error and linear regression slope. Interestingly, the overall performance across all SAMPL4 submissions was similar to that for the prior SAMPL3 host-guest challenge, although the experimentalists took steps to simplify the current challenge. While some methods performed fairly consistently across both hosts, no single approach emerged as consistent top performer, and the nonsystematic nature of the various submissions made it impossible to draw definitive conclusions regarding the best choices of energy models or sampling algorithms. Salt effects emerged as an issue in the calculation of absolute binding affinities of cucurbit[7]uril-guest systems, but were not expected to affect the relative affinities significantly. Useful directions for future rounds of the challenge might involve encouraging participants to carry out some calculations that replicate each others' studies, and to systematically explore parameter options.
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Ellingson BA, Geballe MT, Wlodek S, Bayly CI, Skillman AG, Nicholls A. Efficient calculation of SAMPL4 hydration free energies using OMEGA, SZYBKI, QUACPAC, and Zap TK. J Comput Aided Mol Des 2014; 28:289-98. [PMID: 24633516 PMCID: PMC4003403 DOI: 10.1007/s10822-014-9720-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2013] [Accepted: 01/24/2014] [Indexed: 12/14/2022]
Abstract
Several submissions for the SAMPL4 hydration free energy set were calculated using OpenEye tools, including many that were among the top performing submissions. All of our best submissions used AM1BCC charges and Poisson-Boltzmann solvation. Three submissions used a single conformer for calculating the hydration free energy and all performed very well with mean unsigned errors ranging from 0.94 to 1.08 kcal/mol. These calculations were very fast, only requiring 0.5-2.0 s per molecule. We observed that our two single-conformer methodologies have different types of failure cases and that these differences could be exploited for determining when the methods are likely to have substantial errors.
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40
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Coleman RG, Sterling T, Weiss DR. SAMPL4 & DOCK3.7: lessons for automated docking procedures. J Comput Aided Mol Des 2014; 28:201-9. [PMID: 24515818 DOI: 10.1007/s10822-014-9722-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2013] [Accepted: 01/28/2014] [Indexed: 12/14/2022]
Abstract
The SAMPL4 challenges were used to test current automated methods for solvation energy, virtual screening, pose and affinity prediction of the molecular docking pipeline DOCK 3.7. Additionally, first-order models of binding affinity were proposed as milestones for any method predicting binding affinity. Several important discoveries about the molecular docking software were made during the challenge: (1) Solvation energies of ligands were five-fold worse than any other method used in SAMPL4, including methods that were similarly fast, (2) HIV Integrase is a challenging target, but automated docking on the correct allosteric site performed well in terms of virtual screening and pose prediction (compared to other methods) but affinity prediction, as expected, was very poor, (3) Molecular docking grid sizes can be very important, serious errors were discovered with default settings that have been adjusted for all future work. Overall, lessons from SAMPL4 suggest many changes to molecular docking tools, not just DOCK 3.7, that could improve the state of the art. Future difficulties and projects will be discussed.
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Affiliation(s)
- Ryan G Coleman
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 4th St, Box 2550, San Francisco, CA, 94158, USA,
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41
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Virtual screening of integrase inhibitors by large scale binding free energy calculations: the SAMPL4 challenge. J Comput Aided Mol Des 2014; 28:475-90. [PMID: 24504704 DOI: 10.1007/s10822-014-9711-9] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2013] [Accepted: 01/16/2014] [Indexed: 10/25/2022]
Abstract
As part of the SAMPL4 blind challenge, filtered AutoDock Vina ligand docking predictions and large scale binding energy distribution analysis method binding free energy calculations have been applied to the virtual screening of a focused library of candidate binders to the LEDGF site of the HIV integrase protein. The computational protocol leveraged docking and high level atomistic models to improve enrichment. The enrichment factor of our blind predictions ranked best among all of the computational submissions, and second best overall. This work represents to our knowledge the first example of the application of an all-atom physics-based binding free energy model to large scale virtual screening. A total of 285 parallel Hamiltonian replica exchange molecular dynamics absolute protein-ligand binding free energy simulations were conducted starting from docked poses. The setup of the simulations was fully automated, calculations were distributed on multiple computing resources and were completed in a 6-weeks period. The accuracy of the docked poses and the inclusion of intramolecular strain and entropic losses in the binding free energy estimates were the major factors behind the success of the method. Lack of sufficient time and computing resources to investigate additional protonation states of the ligands was a major cause of mispredictions. The experiment demonstrated the applicability of binding free energy modeling to improve hit rates in challenging virtual screening of focused ligand libraries during lead optimization.
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42
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Perryman AL, Santiago DN, Forli S, Martins DS, Olson AJ. Virtual screening with AutoDock Vina and the common pharmacophore engine of a low diversity library of fragments and hits against the three allosteric sites of HIV integrase: participation in the SAMPL4 protein-ligand binding challenge. J Comput Aided Mol Des 2014; 28:429-441. [PMID: 24493410 DOI: 10.1007/s10822-014-9709-3] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2013] [Accepted: 01/11/2014] [Indexed: 01/01/2023]
Abstract
To rigorously assess the tools and protocols that can be used to understand and predict macromolecular recognition, and to gain more structural insight into three newly discovered allosteric binding sites on a critical drug target involved in the treatment of HIV infections, the Olson and Levy labs collaborated on the SAMPL4 challenge. This computational blind challenge involved predicting protein-ligand binding against the three allosteric sites of HIV integrase (IN), a viral enzyme for which two drugs (that target the active site) have been approved by the FDA. Positive control cross-docking experiments were utilized to select 13 receptor models out of an initial ensemble of 41 different crystal structures of HIV IN. These 13 models of the targets were selected using our new "Rank Difference Ratio" metric. The first stage of SAMPL4 involved using virtual screens to identify 62 active, allosteric IN inhibitors out of a set of 321 compounds. The second stage involved predicting the binding site(s) and crystallographic binding mode(s) for 57 of these inhibitors. Our team submitted four entries for the first stage that utilized: (1) AutoDock Vina (AD Vina) plus visual inspection; (2) a new common pharmacophore engine; (3) BEDAM replica exchange free energy simulations, and a Consensus approach that combined the predictions of all three strategies. Even with the SAMPL4's very challenging compound library that displayed a significantly lower amount of structural diversity than most libraries that are conventionally employed in prospective virtual screens, these approaches produced hit rates of 24, 25, 34, and 27 %, respectively, on a set with 19 % declared binders. Our only entry for the second stage challenge was based on the results of AD Vina plus visual inspection, and it ranked third place overall according to several different metrics provided by the SAMPL4 organizers. The successful results displayed by these approaches highlight the utility of the computational structure-based drug discovery tools and strategies that are being developed to advance the goals of the newly created, multi-institution, NIH-funded center called the "HIV Interaction and Viral Evolution Center".
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Affiliation(s)
- Alexander L Perryman
- Department of Integrative Structural & Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Daniel N Santiago
- Department of Integrative Structural & Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Stefano Forli
- Department of Integrative Structural & Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Diogo Santos Martins
- Department of Integrative Structural & Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
- Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto, Portugal
| | - Arthur J Olson
- Department of Integrative Structural & Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
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43
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Virtual screening of the SAMPL4 blinded HIV integrase inhibitors dataset. J Comput Aided Mol Des 2014; 28:455-62. [DOI: 10.1007/s10822-014-9707-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2013] [Accepted: 01/10/2014] [Indexed: 12/14/2022]
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44
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Voet ARD, Kumar A, Berenger F, Zhang KYJ. Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4. J Comput Aided Mol Des 2014; 28:363-73. [PMID: 24446075 DOI: 10.1007/s10822-013-9702-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2013] [Accepted: 12/17/2013] [Indexed: 12/14/2022]
Abstract
The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.
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Affiliation(s)
- Arnout R D Voet
- Zhang Initiative Research Unit, Institute Laboratories, RIKEN, 2-1 Hirosawa, Wakō, Saitama, 351-0198, Japan
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