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Suh D, Arattu Thodika AR, Kim S, Nam K, Im W. CHARMM-GUI QM/MM Interfacer for a Quantum Mechanical and Molecular Mechanical (QM/MM) Simulation Setup: 1. Semiempirical Methods. J Chem Theory Comput 2024; 20:5337-5351. [PMID: 38856971 PMCID: PMC11209942 DOI: 10.1021/acs.jctc.4c00439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 05/17/2024] [Accepted: 05/24/2024] [Indexed: 06/11/2024]
Abstract
Quantum mechanical (QM) treatments, when combined with molecular mechanical (MM) force fields, can effectively handle enzyme-catalyzed reactions without significantly increasing the computational cost. In this context, we present CHARMM-GUI QM/MM Interfacer, a web-based cyberinfrastructure designed to streamline the preparation of various QM/MM simulation inputs with ligand modification. The development of QM/MM Interfacer has been achieved through integration with existing CHARMM-GUI modules, such as PDB Reader and Manipulator, Solution Builder, and Membrane Builder. In addition, new functionalities have been developed to facilitate the one-stop preparation of QM/MM systems and enable interactive and intuitive ligand modifications and QM atom selections. QM/MM Interfacer offers support for a range of semiempirical QM methods, including AM1(+/d), PM3(+/PDDG), MNDO(+/d, +/PDDG), PM6, RM1, and SCC-DFTB, tailored for both AMBER and CHARMM. A nontrivial setup related to ligand modification, link-atom insertion, and charge distribution is automatized through intuitive user interfaces. To illustrate the robustness of QM/MM Interfacer, we conducted QM/MM simulations of three enzyme-substrate systems: dihydrofolate reductase, insulin receptor kinase, and oligosaccharyltransferase. In addition, we have created three tutorial videos about building these systems, which can be found at https://www.charmm-gui.org/demo/qmi. QM/MM Interfacer is expected to be a valuable and accessible web-based tool that simplifies and accelerates the setup process for hybrid QM/MM simulations.
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Affiliation(s)
- Donghyuk Suh
- Department
of Biological Sciences, Chemistry, Bioengineering, and Computer Science
and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Abdul Raafik Arattu Thodika
- Department
of Chemistry and Biochemistry, The University
of Texas at Arlington, Arlington, Texas 76019-9800, United States
| | - Seonghoon Kim
- Department
of Biological Sciences, Chemistry, Bioengineering, and Computer Science
and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Kwangho Nam
- Department
of Chemistry and Biochemistry, The University
of Texas at Arlington, Arlington, Texas 76019-9800, United States
| | - Wonpil Im
- Department
of Biological Sciences, Chemistry, Bioengineering, and Computer Science
and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
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2
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Georgiou K, Konstantinidi A, Hutterer J, Freudenberger K, Kolarov F, Lambrinidis G, Stylianakis I, Stampelou M, Gauglitz G, Kolocouris A. Accurate calculation of affinity changes to the close state of influenza A M2 transmembrane domain in response to subtle structural changes of adamantyl amines using free energy perturbation methods in different lipid bilayers. BIOCHIMICA ET BIOPHYSICA ACTA. BIOMEMBRANES 2024; 1866:184258. [PMID: 37995846 DOI: 10.1016/j.bbamem.2023.184258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 10/18/2023] [Accepted: 11/15/2023] [Indexed: 11/25/2023]
Abstract
Experimental binding free energies of 27 adamantyl amines against the influenza M2(22-46) WT tetramer, in its closed form at pH 8, were measured by ITC in DPC micelles. The measured Kd's range is ~44 while the antiviral potencies (IC50) range is ~750 with a good correlation between binding free energies computed with Kd and IC50 values (r = 0.76). We explored with MD simulations (ff19sb, CHARMM36m) the binding profile of complexes with strong, moderate and weak binders embedded in DMPC, DPPC, POPC or a viral mimetic membrane and using different experimental starting structures of M2. To predict accurately differences in binding free energy in response to subtle changes in the structure of the ligands, we performed 18 alchemical perturbative single topology FEP/MD NPT simulations (OPLS2005) using the BAR estimator (Desmond software) and 20 dual topology calculations TI/MD NVT simulations (ff19sb) using the MBAR estimator (Amber software) for adamantyl amines in complex with M2(22-46) WT in DMPC, DPPC, POPC. We observed that both methods with all lipids show a very good correlation between the experimental and calculated relative binding free energies (r = 0.77-0.87, mue = 0.36-0.92 kcal mol-1) with the highest performance achieved with TI/MBAR and lowest performance with FEP/BAR in DMPC bilayers. When antiviral potencies are used instead of the Kd values for computing the experimental binding free energies we obtained also good performance with both FEP/BAR (r = 0.83, mue = 0.75 kcal mol-1) and TI/MBAR (r = 0.69, mue = 0.77 kcal mol-1).
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Affiliation(s)
- Kyriakos Georgiou
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens (NKUA), Panepistimiopolis-Zografou, 15771 Athens, Greece
| | - Athina Konstantinidi
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens (NKUA), Panepistimiopolis-Zografou, 15771 Athens, Greece
| | - Johanna Hutterer
- Institut für Physikalische und Theoretische Chemie, Eberhard-Karls-Universität, D-72076 Tübingen, Germany
| | - Kathrin Freudenberger
- Institut für Physikalische und Theoretische Chemie, Eberhard-Karls-Universität, D-72076 Tübingen, Germany
| | - Felix Kolarov
- Institut für Physikalische und Theoretische Chemie, Eberhard-Karls-Universität, D-72076 Tübingen, Germany; Roche, Penzberg, Bavaria, Germany
| | - George Lambrinidis
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens (NKUA), Panepistimiopolis-Zografou, 15771 Athens, Greece
| | - Ioannis Stylianakis
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens (NKUA), Panepistimiopolis-Zografou, 15771 Athens, Greece
| | - Margarita Stampelou
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens (NKUA), Panepistimiopolis-Zografou, 15771 Athens, Greece
| | - Günter Gauglitz
- Institut für Physikalische und Theoretische Chemie, Eberhard-Karls-Universität, D-72076 Tübingen, Germany
| | - Antonios Kolocouris
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens (NKUA), Panepistimiopolis-Zografou, 15771 Athens, Greece.
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3
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Herz AM, Kellici T, Morao I, Michel J. Alchemical Free Energy Workflows for the Computation of Protein-Ligand Binding Affinities. Methods Mol Biol 2024; 2716:241-264. [PMID: 37702943 DOI: 10.1007/978-1-0716-3449-3_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
Alchemical free energy methods can be used for the efficient computation of relative binding free energies during preclinical drug discovery stages. In recent years, this has been facilitated further by the implementation of workflows that enable non-experts to quickly and consistently set up the required simulations. Given the correct input structures, workflows handle the difficult aspects of setting up perturbations, including consistently defining the perturbable molecule, its atom mapping and topology generation, perturbation network generation, running of the simulations via different sampling methods, and analysis of the results. Different academic and commercial workflows are discussed, including FEW, FESetup, FEPrepare, CHARMM-GUI, Transformato, PMX, QLigFEP, TIES, ProFESSA, PyAutoFEP, BioSimSpace, FEP+, Flare, and Orion. These workflows differ in various aspects, such as mapping algorithms or enhanced sampling methods. Some workflows can accommodate more than one molecular dynamics (MD) engine and use external libraries for tasks. Differences between workflows can present advantages for different use cases, however a lack of interoperability of the workflows' components hinders systematic comparisons.
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Affiliation(s)
- Anna M Herz
- EaStChem School of Chemistry, Joseph Black Building, University of Edinburgh, Edinburgh, UK
| | - Tahsin Kellici
- Evotec (UK) Ltd., In Silico Research and Development, Abingdon, Oxfordshire, UK
- Merck & Co., Inc., Modelling and Informatics, West Point, PA, USA
| | - Inaki Morao
- Evotec (UK) Ltd., In Silico Research and Development, Abingdon, Oxfordshire, UK
| | - Julien Michel
- EaStChem School of Chemistry, Joseph Black Building, University of Edinburgh, Edinburgh, UK.
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4
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Morales-Salazar I, Garduño-Albino CE, Montes-Enríquez FP, Nava-Tapia DA, Navarro-Tito N, Herrera-Zúñiga LD, González-Zamora E, Islas-Jácome A. Synthesis of Pyrrolo[3,4- b]pyridin-5-ones via Ugi-Zhu Reaction and In Vitro-In Silico Studies against Breast Carcinoma. Pharmaceuticals (Basel) 2023; 16:1562. [PMID: 38004428 PMCID: PMC10674953 DOI: 10.3390/ph16111562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 10/31/2023] [Accepted: 11/02/2023] [Indexed: 11/26/2023] Open
Abstract
An Ugi-Zhu three-component reaction (UZ-3CR) coupled in a one-pot manner to a cascade process (N-acylation/aza Diels-Alder cycloaddition/decarboxylation/dehydration) was performed to synthesize a series of pyrrolo[3,4-b]pyridin-5-ones in 20% to 92% overall yields using ytterbium triflate as a catalyst, toluene as a solvent, and microwaves as a heat source. The synthesized molecules were evaluated in vitro against breast cancer cell lines MDA-MB-231 and MCF-7, finding that compound 1f, at a concentration of 6.25 μM, exhibited a potential cytotoxic effect. Then, to understand the interactions between synthesized compounds and the main proteins related to the cancer cell lines, docking studies were performed on the serine/threonine kinase 1 (AKT1) and Orexetine type 2 receptor (Ox2R), finding moderate to strong binding energies, which matched accurately with the in vitro results. Additionally, molecular dynamics were performed between proteins related to the studied cell lines and the three best ligands.
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Affiliation(s)
- Ivette Morales-Salazar
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, San Rafael Atlixco 186, Col. Vicentina, Iztapalapa, Ciudad de México 09340, Mexico; (I.M.-S.); (C.E.G.-A.); (F.P.M.-E.); (E.G.-Z.)
| | - Carlos E. Garduño-Albino
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, San Rafael Atlixco 186, Col. Vicentina, Iztapalapa, Ciudad de México 09340, Mexico; (I.M.-S.); (C.E.G.-A.); (F.P.M.-E.); (E.G.-Z.)
| | - Flora P. Montes-Enríquez
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, San Rafael Atlixco 186, Col. Vicentina, Iztapalapa, Ciudad de México 09340, Mexico; (I.M.-S.); (C.E.G.-A.); (F.P.M.-E.); (E.G.-Z.)
| | - Dania A. Nava-Tapia
- Laboratorio de Biología Celular del Cáncer, Universidad Autónoma de Guerrero, Chilpancingo de los Bravo 39086, Mexico;
| | - Napoleón Navarro-Tito
- Laboratorio de Biología Celular del Cáncer, Universidad Autónoma de Guerrero, Chilpancingo de los Bravo 39086, Mexico;
| | - Leonardo David Herrera-Zúñiga
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, San Rafael Atlixco 186, Col. Vicentina, Iztapalapa, Ciudad de México 09340, Mexico; (I.M.-S.); (C.E.G.-A.); (F.P.M.-E.); (E.G.-Z.)
| | - Eduardo González-Zamora
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, San Rafael Atlixco 186, Col. Vicentina, Iztapalapa, Ciudad de México 09340, Mexico; (I.M.-S.); (C.E.G.-A.); (F.P.M.-E.); (E.G.-Z.)
| | - Alejandro Islas-Jácome
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, San Rafael Atlixco 186, Col. Vicentina, Iztapalapa, Ciudad de México 09340, Mexico; (I.M.-S.); (C.E.G.-A.); (F.P.M.-E.); (E.G.-Z.)
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5
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Buckner J, Liu X, Chakravorty A, Wu Y, Cervantes LF, Lai TT, Brooks CL. pyCHARMM: Embedding CHARMM Functionality in a Python Framework. J Chem Theory Comput 2023; 19:3752-3762. [PMID: 37267404 PMCID: PMC10504603 DOI: 10.1021/acs.jctc.3c00364] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
CHARMM is rich in methodology and functionality as one of the first programs addressing problems of molecular dynamics and modeling of biological macromolecules and their partners, e.g., small molecule ligands. When combined with the highly developed CHARMM parameters for proteins, nucleic acids, small molecules, lipids, sugars, and other biologically relevant building blocks, and the versatile CHARMM scripting language, CHARMM has been a trendsetting platform for modeling studies of biological macromolecules. To further enhance the utility of accessing and using CHARMM functionality in increasingly complex workflows associated with modeling biological systems, we introduce pyCHARMM, Python bindings, functions, and modules to complement and extend the extensive set of modeling tools and methods already available in CHARMM. These include access to CHARMM function-generated variables associated with the system (psf), coordinates, velocities and forces, atom selection variables, and force field related parameters. The ability to augment CHARMM forces and energies with energy terms or methods derived from machine learning or other sources, written in Python, CUDA, or OpenCL and expressed as Python callable routines is introduced together with analogous functions callable during dynamics calculations. Integration of Python-based graphical engines for visualization of simulation models and results is also accessible. Loosely coupled parallelism is available for workflows such as free energy calculations, using MBAR/TI approaches or high-throughput multisite λ-dynamics (MSλD) free energy methods, string path optimization calculations, replica exchange, and molecular docking with a new Python-based CDOCKER module. CHARMM accelerated platform kernels through the CHARMM/OpenMM API, CHARMM/DOMDEC, and CHARMM/BLaDE API are also readily integrated into this Python framework. We anticipate that pyCHARMM will be a robust platform for the development of comprehensive and complex workflows utilizing Python and its extensive functionality as well as an optimal platform for users to learn molecular modeling methods and practices within a Python-friendly environment such as Jupyter Notebooks.
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Affiliation(s)
- Joshua Buckner
- Department of Chemistry, University of Michigan, Ann Arbor, MI
| | - Xiaorong Liu
- Department of Chemistry, University of Michigan, Ann Arbor, MI
| | | | - Yujin Wu
- Department of Chemistry, University of Michigan, Ann Arbor, MI
| | - Luis F. Cervantes
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI
| | - Thanh T. Lai
- Biophysics Program, University of Michigan, Ann Arbor, MI
| | - Charles L. Brooks
- Department of Chemistry, University of Michigan, Ann Arbor, MI
- Biophysics Program, University of Michigan, Ann Arbor, MI
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6
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Chio H, Guest EE, Hobman JL, Dottorini T, Hirst JD, Stekel DJ. Predicting bioactivity of antibiotic metabolites by molecular docking and dynamics. J Mol Graph Model 2023; 123:108508. [PMID: 37235902 DOI: 10.1016/j.jmgm.2023.108508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 04/27/2023] [Accepted: 05/04/2023] [Indexed: 05/28/2023]
Abstract
Antibiotics enter the environment through waste streams, where they can exert selective pressure for antimicrobial resistance in bacteria. However, many antibiotics are excreted as partly metabolized forms, or can be subject to partial breakdown in wastewater treatment, soil, or through natural processes in the environment. If a metabolite is bioactive, even at sub-lethal levels, and also stable in the environment, then it could provide selection pressure for resistance. (5S)-penicilloic acid of piperacillin has previously been found complexed to the binding pocket of penicillin binding protein 3 (PBP3) of Pseudomonas aeruginosa. Here, we predicted the affinities of all potentially relevant antibiotic metabolites of ten different penicillins to that target protein, using molecular docking and molecular dynamics simulations. Docking predicts that, in addition to penicilloic acid, pseudopenicillin derivatives of these penicillins, as well as 6-aminopenicillanic acid (6APA), could also bind to this target. MD simulations further confirmed that (5R)-pseudopenicillin and 6APA bind the target protein, in addition to (5S)-penicilloic acid. Thus, it is possible that these metabolites are bioactive, and, if stable in the environment, could be contaminants selective for antibiotic resistance. This could have considerable significance for environmental surveillance for antibiotics as a means to reduce antimicrobial resistance, because targeted mass spectrometry could be required for relevant metabolites as well as the native antibiotics.
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Affiliation(s)
- Hokin Chio
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD, UK
| | - Ellen E Guest
- School of Chemistry, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Jon L Hobman
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD, UK
| | - Tania Dottorini
- School of Veterinary Medicine and Sciences, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD, UK
| | - Jonathan D Hirst
- School of Chemistry, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Dov J Stekel
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD, UK; Department of Mathematics and Applied Mathematics, University of Johannesburg, Aukland Park Kingsway Campus, Rossmore, Johannesburg, South Africa.
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7
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Guterres H, Park S, Zhang H, Perone T, Kim J, Im W. CHARMM‐GUI
high‐throughput simulator
for efficient evaluation of protein–ligand interactions with different force fields. Protein Sci 2022. [DOI: 10.1002/pro.4413] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Hugo Guterres
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering Lehigh University Bethlehem Pennsylvania USA
| | - Sang‐Jun Park
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering Lehigh University Bethlehem Pennsylvania USA
| | - Han Zhang
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering Lehigh University Bethlehem Pennsylvania USA
| | - Thomas Perone
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering Lehigh University Bethlehem Pennsylvania USA
| | - Jongtaek Kim
- Department of Physics and Chemistry Korea Air Force Academy Cheongju South Korea
| | - Wonpil Im
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering Lehigh University Bethlehem Pennsylvania USA
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8
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Barnsley KK, Ondrechen MJ. Enzyme active sites: Identification and prediction of function using computational chemistry. Curr Opin Struct Biol 2022; 74:102384. [DOI: 10.1016/j.sbi.2022.102384] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/20/2022] [Accepted: 03/28/2022] [Indexed: 11/03/2022]
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