1
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Borges R, Colby SM, Das S, Edison AS, Fiehn O, Kind T, Lee J, Merrill AT, Merz KM, Metz TO, Nunez JR, Tantillo DJ, Wang LP, Wang S, Renslow RS. Quantum Chemistry Calculations for Metabolomics. Chem Rev 2021; 121:5633-5670. [PMID: 33979149 PMCID: PMC8161423 DOI: 10.1021/acs.chemrev.0c00901] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Indexed: 02/07/2023]
Abstract
A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials ("standards"), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for "standards-free" identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.
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Affiliation(s)
- Ricardo
M. Borges
- Walter
Mors Institute of Research on Natural Products, Federal University of Rio de Janeiro, Rio de Janeiro 21941-901, Brazil
| | - Sean M. Colby
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Susanta Das
- Department
of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Arthur S. Edison
- Departments
of Genetics and Biochemistry and Molecular Biology, Complex Carbohydrate
Research Center and Institute of Bioinformatics, University of Georgia, Athens, Georgia 30602, United States
| | - Oliver Fiehn
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
| | - Tobias Kind
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
| | - Jesi Lee
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Amy T. Merrill
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Kenneth M. Merz
- Department
of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Thomas O. Metz
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Jamie R. Nunez
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Dean J. Tantillo
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Lee-Ping Wang
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Shunyang Wang
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Ryan S. Renslow
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
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2
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Hanzawa H, Shimada T, Takahashi M, Takahashi H. Revisiting biomolecular NMR spectroscopy for promoting small-molecule drug discovery. JOURNAL OF BIOMOLECULAR NMR 2020; 74:501-508. [PMID: 32306215 DOI: 10.1007/s10858-020-00314-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 04/09/2020] [Indexed: 06/11/2023]
Abstract
Recently, there has been increasing interest in new modalities such as therapeutic antibodies and gene therapy at a number of pharmaceutical companies. Moreover, in small-molecule drug discovery at such companies, efforts have focused on hard-to-drug targets such as inhibiting protein-protein interactions. Biomolecular NMR spectroscopy has been used in drug discovery in a variety of ways, such as for the reliable detection of binding and providing three-dimensional structural information for structure-based drug design. The advantages of using NMR spectroscopy have been known for decades (Jahnke in J Biomol NMR 39:87-90, (2007); Gossert and Jahnke in Prog Nucl Magn Reson Spectrosc 97:82-125, (2016)). For tackling hard-to-drug targets and increasing the success in discovering drug molecules, in-depth analysis of drug-target protein interactions performed by biophysical methods will be more and more essential. Here, we review the advantages of NMR spectroscopy as a key technology of biophysical methods and also discuss issues such as using cutting-edge NMR spectrometers and increasing the demand of utilizing conformational dynamics information for promoting small-molecule drug discovery.
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Affiliation(s)
- Hiroyuki Hanzawa
- Structure-Based Drug Design Group, Organic Synthesis Department, Daiichi Sankyo RD Novare Co., Ltd, 1-16-13 Kita-Kasai, Edogawa-ku, Tokyo, 134-8630, Japan.
| | - Takashi Shimada
- Structure-Based Drug Design Group, Organic Synthesis Department, Daiichi Sankyo RD Novare Co., Ltd, 1-16-13 Kita-Kasai, Edogawa-ku, Tokyo, 134-8630, Japan
| | - Mizuki Takahashi
- Structure-Based Drug Design Group, Organic Synthesis Department, Daiichi Sankyo RD Novare Co., Ltd, 1-16-13 Kita-Kasai, Edogawa-ku, Tokyo, 134-8630, Japan
| | - Hideo Takahashi
- Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, Japan
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3
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Orts J, Riek R. Protein-ligand structure determination with the NMR molecular replacement tool, NMR 2. JOURNAL OF BIOMOLECULAR NMR 2020; 74:633-642. [PMID: 32621003 DOI: 10.1007/s10858-020-00324-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 06/02/2020] [Indexed: 06/11/2023]
Abstract
We recently reported on a new method called NMR Molecular Replacement that efficiently derives the structure of a protein-ligand complex at the interaction site. The method was successfully applied to high and low affinity complexes covering ligands from peptides to small molecules. The algorithm used in the NMR Molecular Replacement program has until now not been described in detail. Here, we present a complete description of the NMR Molecular Replacement implementation as well as several new features that further reduce the time required for structure elucidation.
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Affiliation(s)
- Julien Orts
- Laboratory of Physical Chemistry, ETH, Swiss Federal Institute of Technology, Wolgang-Pauli-Strasse 10, 8093, Zürich, Switzerland.
| | - Roland Riek
- Laboratory of Physical Chemistry, ETH, Swiss Federal Institute of Technology, Wolgang-Pauli-Strasse 10, 8093, Zürich, Switzerland
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4
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Zheng Z, Borbulevych OY, Liu H, Deng J, Martin RI, Westerhoff LM. MovableType Software for Fast Free Energy-Based Virtual Screening: Protocol Development, Deployment, Validation, and Assessment. J Chem Inf Model 2020; 60:5437-5456. [PMID: 32791826 PMCID: PMC7781189 DOI: 10.1021/acs.jcim.0c00618] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
![]()
For decades, the
complicated energy surfaces found in macromolecular
protein:ligand structures, which require large amounts of computational
time and resources for energy state sampling, have been an inherent
obstacle to fast, routine free energy estimation in industrial drug
discovery efforts. Beginning in 2013, the Merz research group addressed
this cost with the introduction of a novel sampling methodology termed
“Movable Type” (MT). Using numerical integration methods,
the MT method reduces the computational expense for energy state sampling
by independently calculating each atomic partition function from an
initial molecular conformation in order to estimate the molecular
free energy using ensembles of the atomic partition functions. In
this work, we report a software package, the DivCon Discovery Suite
with the MovableType module from QuantumBio Inc., that performs this
MT free energy estimation protocol in a fast, fully encapsulated manner.
We discuss the computational procedures and improvements to the original
work, and we detail the corresponding settings for this software package.
Finally, we introduce two validation benchmarks to evaluate the overall
robustness of the method against a broad range of protein:ligand structural
cases. With these publicly available benchmarks, we show that the
method can use a variety of input types and parameters and exhibits
comparable predictability whether the method is presented with “expensive”
X-ray structures or “inexpensively docked” theoretical
models. We also explore some next steps for the method. The MovableType
software is available at http://www.quantumbioinc.com/
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Affiliation(s)
- Zheng Zheng
- QuantumBio Inc., 2790 West College Avenue, Suite 900, State College, Pennsylvania 16801, United States.,School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, P. R. China
| | - Oleg Y Borbulevych
- QuantumBio Inc., 2790 West College Avenue, Suite 900, State College, Pennsylvania 16801, United States
| | - Hao Liu
- School of Mechanical and Electronic Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, P. R. China
| | - Jianpeng Deng
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, P. R. China
| | - Roger I Martin
- QuantumBio Inc., 2790 West College Avenue, Suite 900, State College, Pennsylvania 16801, United States
| | - Lance M Westerhoff
- QuantumBio Inc., 2790 West College Avenue, Suite 900, State College, Pennsylvania 16801, United States
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5
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Wang Y, Hilty C. Determination of Ligand Binding Epitope Structures Using Polarization Transfer from Hyperpolarized Ligands. J Med Chem 2019; 62:2419-2427. [PMID: 30715877 DOI: 10.1021/acs.jmedchem.8b01711] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Drug discovery processes require the determination of the protein binding site structure, which can be achieved via nuclear magnetic resonance (NMR) spectroscopy. While traditional NMR spectroscopy suffers from low sensitivity, NMR signals can be significantly enhanced through hyperpolarization of nuclear spins. Here, folic acid is hyperpolarized by dissolution dynamic nuclear polarization (D-DNP). Polarization transfer to dihydrofolate reductase is compared to signal evolution predicted for docking-derived structures. The results demonstrate that a scoring function derived from the experimental data improves the ranking of structures. With data from six methyl groups, Spearman's correlation coefficient of the experimental scoring function to the root-mean-square deviation from a reference structure is 0.88 for five individually addressed ligand protons and 0.59 for the entire ligand, while the same correlation coefficient of the energy calculated from docking alone is 0.49. D-DNP NMR-derived ranking, therefore, is capable of determining the ligand structure with a small number of individually addressed source spins.
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Affiliation(s)
- Yunyi Wang
- Department of Chemistry , Texas A&M University , 3255 TAMU , College Station , Texas 77843 , United States
| | - Christian Hilty
- Department of Chemistry , Texas A&M University , 3255 TAMU , College Station , Texas 77843 , United States
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6
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Proudfoot A, Frank AO, Frommlet A, Lingel A. Selective Methyl Labeling of Proteins: Enabling Structural and Mechanistic Studies As Well As Drug Discovery Applications by Solution-State NMR. Methods Enzymol 2018; 614:1-36. [PMID: 30611421 DOI: 10.1016/bs.mie.2018.08.035] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Escherichia coli expression protocols for selective labeling of methyl groups in proteins have been essential in expanding the size range of targets that can be studied by biomolecular NMR. Based on the initial work achieving selective labeling of isoleucine, leucine, and valine residues, additional methods were developed over the past years which enabled the individual and/or simultaneous combinatorial labeling of all methyl containing amino acids. Together with the introduction of new methyl-optimized NMR experiments, this now allows the detailed characterization of protein-ligand interactions as well as mechanistic and dynamic processes of protein-protein complexes up to 1MDa in size. In this chapter, we provide a general introduction to selective labeling of proteins using E. coli-based expression systems, describe the considerations taken into account prior to the selective labeling of a protein, and include the protocols used to produce such proteins. An overview of applications using selectively labeled proteins with an emphasis on examples relevant to the drug discovery process is then presented.
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Affiliation(s)
- Andrew Proudfoot
- Structural and Biophysical Chemistry, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, Emeryville, CA, United States
| | - Andreas O Frank
- Structural and Biophysical Chemistry, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, Emeryville, CA, United States
| | - Alexandra Frommlet
- Structural and Biophysical Chemistry, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, Emeryville, CA, United States
| | - Andreas Lingel
- Structural and Biophysical Chemistry, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, Emeryville, CA, United States; Global Discovery Chemistry, Novartis Institutes for BioMedical Research, Novartis Campus, Basel, Switzerland.
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7
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Borbulevych O, Martin RI, Westerhoff LM. High-throughput quantum-mechanics/molecular-mechanics (ONIOM) macromolecular crystallographic refinement with PHENIX/DivCon: the impact of mixed Hamiltonian methods on ligand and protein structure. Acta Crystallogr D Struct Biol 2018; 74:1063-1077. [PMID: 30387765 PMCID: PMC6213575 DOI: 10.1107/s2059798318012913] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 09/12/2018] [Indexed: 12/28/2022] Open
Abstract
Conventional macromolecular crystallographic refinement relies on often dubious stereochemical restraints, the preparation of which often requires human validation for unusual species, and on rudimentary energy functionals that are devoid of nonbonding effects owing to electrostatics, polarization, charge transfer or even hydrogen bonding. While this approach has served the crystallographic community for decades, as structure-based drug design/discovery (SBDD) has grown in prominence it has become clear that these conventional methods are less rigorous than they need to be in order to produce properly predictive protein-ligand models, and that the human intervention that is required to successfully treat ligands and other unusual chemistries found in SBDD often precludes high-throughput, automated refinement. Recently, plugins to the Python-based Hierarchical ENvironment for Integrated Xtallography (PHENIX) crystallographic platform have been developed to augment conventional methods with the in situ use of quantum mechanics (QM) applied to ligand(s) along with the surrounding active site(s) at each step of refinement [Borbulevych et al. (2014), Acta Cryst D70, 1233-1247]. This method (Region-QM) significantly increases the accuracy of the X-ray refinement process, and this approach is now used, coupled with experimental density, to accurately determine protonation states, binding modes, ring-flip states, water positions and so on. In the present work, this approach is expanded to include a more rigorous treatment of the entire structure, including the ligand(s), the associated active site(s) and the entire protein, using a fully automated, mixed quantum-mechanics/molecular-mechanics (QM/MM) Hamiltonian recently implemented in the DivCon package. This approach was validated through the automatic treatment of a population of 80 protein-ligand structures chosen from the Astex Diverse Set. Across the entire population, this method results in an average 3.5-fold reduction in ligand strain and a 4.5-fold improvement in MolProbity clashscore, as well as improvements in Ramachandran and rotamer outlier analyses. Overall, these results demonstrate that the use of a structure-wide QM/MM Hamiltonian exhibits improvements in the local structural chemistry of the ligand similar to Region-QM refinement but with significant improvements in the overall structure beyond the active site.
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Affiliation(s)
- Oleg Borbulevych
- QuantumBio Inc., 2790 West College Avenue, State College, PA 16801, USA
| | - Roger I. Martin
- QuantumBio Inc., 2790 West College Avenue, State College, PA 16801, USA
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8
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Genoni A, Bučinský L, Claiser N, Contreras-García J, Dittrich B, Dominiak PM, Espinosa E, Gatti C, Giannozzi P, Gillet JM, Jayatilaka D, Macchi P, Madsen AØ, Massa L, Matta CF, Merz KM, Nakashima PNH, Ott H, Ryde U, Schwarz K, Sierka M, Grabowsky S. Quantum Crystallography: Current Developments and Future Perspectives. Chemistry 2018; 24:10881-10905. [PMID: 29488652 DOI: 10.1002/chem.201705952] [Citation(s) in RCA: 85] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 02/27/2018] [Indexed: 11/09/2022]
Abstract
Crystallography and quantum mechanics have always been tightly connected because reliable quantum mechanical models are needed to determine crystal structures. Due to this natural synergy, nowadays accurate distributions of electrons in space can be obtained from diffraction and scattering experiments. In the original definition of quantum crystallography (QCr) given by Massa, Karle and Huang, direct extraction of wavefunctions or density matrices from measured intensities of reflections or, conversely, ad hoc quantum mechanical calculations to enhance the accuracy of the crystallographic refinement are implicated. Nevertheless, many other active and emerging research areas involving quantum mechanics and scattering experiments are not covered by the original definition although they enable to observe and explain quantum phenomena as accurately and successfully as the original strategies. Therefore, we give an overview over current research that is related to a broader notion of QCr, and discuss options how QCr can evolve to become a complete and independent domain of natural sciences. The goal of this paper is to initiate discussions around QCr, but not to find a final definition of the field.
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Affiliation(s)
- Alessandro Genoni
- Université de Lorraine, CNRS, Laboratoire LPCT, 1 Boulevard Arago, F-57078, Metz, France
| | - Lukas Bučinský
- Institute of Physical Chemistry and Chemical Physics, Slovak University of Technology, FCHPT SUT, Radlinského 9, SK-812 37, Bratislava, Slovakia
| | - Nicolas Claiser
- Université de Lorraine, CNRS, Laboratoire CRM2, Boulevard des Aiguillettes, BP 70239, F-54506, Vandoeuvre-lès-Nancy, France
| | - Julia Contreras-García
- Sorbonne Universités, UPMC Université Paris 06, CNRS, Laboratoire de Chimie Théorique (LCT), 4 Place Jussieu, F-75252, Paris Cedex 05, France
| | - Birger Dittrich
- Anorganische und Strukturchemie II, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Paulina M Dominiak
- Biological and Chemical Research Centre, Department of Chemistry, University of Warsaw, ul. Żwirki i Wigury 101, 02-089, Warszawa, Poland
| | - Enrique Espinosa
- Université de Lorraine, CNRS, Laboratoire CRM2, Boulevard des Aiguillettes, BP 70239, F-54506, Vandoeuvre-lès-Nancy, France
| | - Carlo Gatti
- CNR-ISTM Istituto di Scienze e Tecnologie Molecolari, via Golgi 19, Milano, I-20133, Italy.,Istituto Lombardo Accademia di Scienze e Lettere, via Brera 28, 20121, Milano, Italy
| | - Paolo Giannozzi
- Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze 208, I-33100, Udine, Italy
| | - Jean-Michel Gillet
- Structure, Properties and Modeling of Solids Laboratory, CentraleSupelec, Paris-Saclay University, 3 rue Joliot-Curie, 91191, Gif-sur-Yvette, France
| | - Dylan Jayatilaka
- School of Molecular Sciences, University of Western Australia, 35 Stirling Highway, Perth, WA, 6009, Australia
| | - Piero Macchi
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, CH-3012, Bern, Switzerland
| | - Anders Ø Madsen
- Department of Pharmacy, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen, Denmark
| | - Lou Massa
- Hunter College & the Ph.D. Program of the Graduate Center, City University of New York, New York, USA
| | - Chérif F Matta
- Department of Chemistry and Physics, Mount Saint Vincent University, Halifax, Nova Scotia, B3M 2J6, Canada.,Department of Chemistry, Dalhousie University, Halifax, Nova Scotia, B3H 4J3, Canada.,Department of Chemistry, Saint Mary's University, Halifax, Nova Scotia, B3H 3C3, Canada.,Département de Chimie, Université Laval, Québec, QC G1V 0A6, Canada
| | - Kenneth M Merz
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan, 48824, USA.,Institute for Cyber Enabled Research, Michigan State University, 567 Wilson Road, Room 1440, East Lansing, Michigan, 48824, USA
| | - Philip N H Nakashima
- Department of Materials Science and Engineering, Monash University, Victoria, 3800, Australia
| | - Holger Ott
- Bruker AXS GmbH, Östliche Rheinbrückenstraße 49, 76187, Karlsruhe, Germany
| | - Ulf Ryde
- Department of Theoretical Chemistry, Lund University, Chemical Centre, P.O. Box 124, SE-22100, Lund, Sweden
| | - Karlheinz Schwarz
- Technische Universität Wien, Institut für Materialwissenschaften, Getreidemarkt 9, A-1060, Vienna, Austria
| | - Marek Sierka
- Otto Schott Institute of Materials Research, Friedrich Schiller University Jena, Löbdergraben 32, 07743, Jena, Germany
| | - Simon Grabowsky
- Fachbereich 2-Biologie/Chemie, Institut für Anorganische Chemie und Kristallographie, Universität Bremen, Leobener Str. 3, 28359, Bremen, Germany
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9
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Jin X, Zhu T, Zhang JZH, He X. Automated Fragmentation QM/MM Calculation of NMR Chemical Shifts for Protein-Ligand Complexes. Front Chem 2018; 6:150. [PMID: 29868556 PMCID: PMC5952040 DOI: 10.3389/fchem.2018.00150] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 04/16/2018] [Indexed: 01/13/2023] Open
Abstract
In this study, the automated fragmentation quantum mechanics/molecular mechanics (AF-QM/MM) method was applied for NMR chemical shift calculations of protein-ligand complexes. In the AF-QM/MM approach, the protein binding pocket is automatically divided into capped fragments (within ~200 atoms) for density functional theory (DFT) calculations of NMR chemical shifts. Meanwhile, the solvent effect was also included using the Poission-Boltzmann (PB) model, which properly accounts for the electrostatic polarization effect from the solvent for protein-ligand complexes. The NMR chemical shifts of neocarzinostatin (NCS)-chromophore binding complex calculated by AF-QM/MM accurately reproduce the large-sized system results. The 1H chemical shift perturbations (CSP) between apo-NCS and holo-NCS predicted by AF-QM/MM are also in excellent agreement with experimental results. Furthermore, the DFT calculated chemical shifts of the chromophore and residues in the NCS binding pocket can be utilized as molecular probes to identify the correct ligand binding conformation. By combining the CSP of the atoms in the binding pocket with the Glide scoring function, the new scoring function can accurately distinguish the native ligand pose from decoy structures. Therefore, the AF-QM/MM approach provides an accurate and efficient platform for protein-ligand binding structure prediction based on NMR derived information.
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Affiliation(s)
- Xinsheng Jin
- State Key Laboratory of Precision Spectroscopy, School of Chemistry and Molecular Engineering, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, East China Normal University, Shanghai, China
| | - Tong Zhu
- State Key Laboratory of Precision Spectroscopy, School of Chemistry and Molecular Engineering, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, East China Normal University, Shanghai, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, China
| | - John Z. H. Zhang
- State Key Laboratory of Precision Spectroscopy, School of Chemistry and Molecular Engineering, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, East China Normal University, Shanghai, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, China
- Department of Chemistry, New York University, New York, NY, United States
| | - Xiao He
- State Key Laboratory of Precision Spectroscopy, School of Chemistry and Molecular Engineering, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, East China Normal University, Shanghai, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, China
- National Engineering Research Centre for Nanotechnology, Shanghai, China
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10
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The NMR2 Method to Determine Rapidly the Structure of the Binding Pocket of a Protein–Ligand Complex with High Accuracy. MAGNETOCHEMISTRY 2018. [DOI: 10.3390/magnetochemistry4010012] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Structural characterization of complexes is crucial for a better understanding of biological processes and structure-based drug design. However, many protein–ligand structures are not solvable by X-ray crystallography, for example those with low affinity binders or dynamic binding sites. Such complexes are usually targeted by solution-state NMR spectroscopy. Unfortunately, structure calculation by NMR is very time consuming since all atoms in the complex need to be assigned to their respective chemical shifts. To circumvent this problem, we recently developed the Nuclear Magnetic Resonance Molecular Replacement (NMR2) method. NMR2 very quickly provides the complex structure of a binding pocket as measured by solution-state NMR. NMR2 circumvents the assignment of the protein by using previously determined structures and therefore speeds up the whole process from a couple of months to a couple of days. Here, we recall the main aspects of the method, show how to apply it, discuss its advantages over other methods and outline its limitations and future directions.
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11
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Zheng M, Zhao J, Cui C, Fu Z, Li X, Liu X, Ding X, Tan X, Li F, Luo X, Chen K, Jiang H. Computational chemical biology and drug design: Facilitating protein structure, function, and modulation studies. Med Res Rev 2018; 38:914-950. [DOI: 10.1002/med.21483] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Revised: 12/13/2017] [Accepted: 12/15/2017] [Indexed: 12/12/2022]
Affiliation(s)
- Mingyue Zheng
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Jihui Zhao
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Chen Cui
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Zunyun Fu
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Xutong Li
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Xiaohong Liu
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
- School of Life Science and Technology; ShanghaiTech University; Shanghai China
| | - Xiaoyu Ding
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Xiaoqin Tan
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Fei Li
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
- Department of Chemistry, College of Sciences; Shanghai University; Shanghai China
| | - Xiaomin Luo
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Kaixian Chen
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
- School of Life Science and Technology; ShanghaiTech University; Shanghai China
| | - Hualiang Jiang
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
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12
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Proudfoot A, Bussiere DE, Lingel A. High-Confidence Protein–Ligand Complex Modeling by NMR-Guided Docking Enables Early Hit Optimization. J Am Chem Soc 2017; 139:17824-17833. [DOI: 10.1021/jacs.7b07171] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Andrew Proudfoot
- Global
Discovery Chemistry, Novartis Institutes for BioMedical Research, 5300 Chiron Way, Emeryville, California 94608, United States
| | - Dirksen E. Bussiere
- Global
Discovery Chemistry, Novartis Institutes for BioMedical Research, 5300 Chiron Way, Emeryville, California 94608, United States
| | - Andreas Lingel
- Global
Discovery Chemistry, Novartis Institutes for BioMedical Research, 5300 Chiron Way, Emeryville, California 94608, United States
- Global
Discovery Chemistry, Novartis Institutes for BioMedical Research, Novartis Campus, 4056 Basel, Switzerland
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13
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Yu Z, Li P, Merz KM. Using Ligand-Induced Protein Chemical Shift Perturbations To Determine Protein–Ligand Structures. Biochemistry 2017; 56:2349-2362. [DOI: 10.1021/acs.biochem.7b00170] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Zhuoqin Yu
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824-1322, United States
| | - Pengfei Li
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824-1322, United States
| | - Kenneth M. Merz
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824-1322, United States
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14
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Orts J, Wälti MA, Marsh M, Vera L, Gossert AD, Güntert P, Riek R. NMR-Based Determination of the 3D Structure of the Ligand-Protein Interaction Site without Protein Resonance Assignment. J Am Chem Soc 2016; 138:4393-400. [PMID: 26943491 DOI: 10.1021/jacs.5b12391] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Molecular replacement in X-ray crystallography is the prime method for establishing structure-activity relationships of pharmaceutically relevant molecules. Such an approach is not available for NMR. Here, we establish a comparable method, called NMR molecular replacement (NMR(2)). The method requires experimentally measured ligand intramolecular NOEs and ligand-protein intermolecular NOEs as well as a previously known receptor structure or model. Our findings demonstrate that NMR(2) may open a new avenue for the fast and robust determination of the interaction site of ligand-protein complexes at atomic resolution.
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Affiliation(s)
- Julien Orts
- ETH Zürich , Laboratory of Physical Chemistry, HCI F217, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Marielle Aulikki Wälti
- ETH Zürich , Laboratory of Physical Chemistry, HCI F217, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - May Marsh
- Swiss Light Source, Paul Scherrer Institute , CH-5232 Villigen, Switzerland
| | - Laura Vera
- Swiss Light Source, Paul Scherrer Institute , CH-5232 Villigen, Switzerland
| | - Alvar D Gossert
- Novartis Institutes for BioMedical Research, Novartis AG , CH-4002 Basel, Switzerland
| | - Peter Güntert
- ETH Zürich , Laboratory of Physical Chemistry, HCI F217, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland.,Institute of Biophysical Chemistry, Center for Biomolecular Magnetic Resonance, and Frankfurt Institute of Advanced Studies, Goethe University Frankfurt am Main , Frankfurt am Main 60323, Germany
| | - Roland Riek
- ETH Zürich , Laboratory of Physical Chemistry, HCI F217, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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15
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Onila I, ten Brink T, Fredriksson K, Codutti L, Mazur A, Griesinger C, Carlomagno T, Exner TE. On-the-Fly Integration of Data from a Spin-Diffusion-Based NMR Experiment into Protein-Ligand Docking. J Chem Inf Model 2015; 55:1962-72. [PMID: 26226383 DOI: 10.1021/acs.jcim.5b00235] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
INPHARMA (interligand nuclear Overhauser enhancement for pharmacophore mapping) determines the relative orientation of two competitive ligands in the protein binding pocket. It is based on the observation of interligand transferred NOEs mediated by spin diffusion through protons of the protein and is, therefore, sensitive to the specific interactions of each of the two ligands with the protein. We show how this information can be directly included into a protein-ligand docking program to guide the prediction of the complex structures. Agreement between the experimental and back-calculated spectra based on the full relaxation matrix approach is translated into a score contribution that is combined with the scoring function ChemPLP of our docking tool PLANTS. This combined score is then used to predict the poses of five weakly bound cAMP-dependent protein kinase (PKA) ligands. After optimizing the setup, which finally also included trNOE data and optimized protonation states, very good success rates were obtained for all combinations of three ligands. For one additional ligand, no conclusive results could be obtained due to the ambiguous electron density of the ligand in the X-ray structure, which does not disprove alternative ligand poses. The failures of the remaining ligand are caused by suboptimal locations of specific protein side chains. Therefore, side-chain flexibility should be included in an improved INPHARMA-PLANTS version. This will reduce the strong dependence on the used protein input structure leading to improved scores overall, not only for this last ligand.
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Affiliation(s)
- Ionut Onila
- Institute of Pharmacy, Eberhard Karls Universität Tübingen , Auf der Morgenstelle 8, 72076 Tübingen, Germany.,Department of Chemistry and Zukunftskolleg, Universität Konstanz , 78457 Konstanz, Germany
| | - Tim ten Brink
- Department of Chemistry and Zukunftskolleg, Universität Konstanz , 78457 Konstanz, Germany
| | - Kai Fredriksson
- Institute of Pharmacy, Eberhard Karls Universität Tübingen , Auf der Morgenstelle 8, 72076 Tübingen, Germany.,Department of Chemistry and Zukunftskolleg, Universität Konstanz , 78457 Konstanz, Germany
| | - Luca Codutti
- Structural and Computational Biology Unit, EMBL , Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Adam Mazur
- Max Planck Institute for Biophysical Chemistry , Am Fassberg 11, 37077 Göttingen, Germany
| | - Christian Griesinger
- Max Planck Institute for Biophysical Chemistry , Am Fassberg 11, 37077 Göttingen, Germany
| | - Teresa Carlomagno
- Structural and Computational Biology Unit, EMBL , Meyerhofstrasse 1, 69117 Heidelberg, Germany.,Helmholtz Centre for Infection Research , Inhoffenstraße 7, 38124 Braunschweig, Germany
| | - Thomas E Exner
- Institute of Pharmacy, Eberhard Karls Universität Tübingen , Auf der Morgenstelle 8, 72076 Tübingen, Germany.,Department of Chemistry and Zukunftskolleg, Universität Konstanz , 78457 Konstanz, Germany
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16
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Sturlese M, Bellanda M, Moro S. NMR-Assisted Molecular Docking Methodologies. Mol Inform 2015; 34:513-25. [DOI: 10.1002/minf.201500012] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2015] [Accepted: 04/24/2015] [Indexed: 11/11/2022]
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17
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Abstract
Conspectus Quantum mechanics (QM) has revolutionized our understanding of the structure and reactivity of small molecular systems. Given the tremendous impact of QM in this research area, it is attractive to believe that this could also be brought into the biological realm where systems of a few thousand atoms and beyond are routine. Applying QM methods to biological problems brings an improved representation to these systems by the direct inclusion of inherently QM effects such as polarization and charge transfer. Because of the improved representation, novel insights can be gleaned from the application of QM tools to biomacromolecules in aqueous solution. To achieve this goal, the computational bottlenecks of QM methods had to be addressed. In semiempirical theory, matrix diagonalization is rate limiting, while in density functional theory or Hartree-Fock theory electron repulsion integral computation is rate-limiting. In this Account, we primarily focus on semiempirical models where the divide and conquer (D&C) approach linearizes the matrix diagonalization step with respect to the system size. Through the D&C approach, a number of applications to biological problems became tractable. Herein, we provide examples of QM studies on biological systems that focus on protein solvation as viewed by QM, QM enabled structure-based drug design, and NMR and X-ray biological structure refinement using QM derived restraints. Through the examples chosen, we show the power of QM to provide novel insights into biological systems, while also impacting practical applications such as structure refinement. While these methods can be more expensive than classical approaches, they make up for this deficiency by the more realistic modeling of the electronic nature of biological systems and in their ability to be broadly applied. Of the tools and applications discussed in this Account, X-ray structure refinement using QM models is now generally available to the community in the refinement package Phenix. While the power of this approach is manifest, challenges still remain. In particular, QM models are generally applied to static structures, so ways in which to include sampling is an ongoing challenge. Car-Parrinello or Born-Oppenheimer molecular dynamics approaches address the short time scale sampling issue, but how to effectively use QM to study phenomenon covering longer time scales will be the focus of future research. Finally, how to accurately and efficiently include electron correlation effects to facilitate the modeling of, for example, dispersive interactions, is also a major hurdle that a broad range of groups are addressing The use of QM models in biology is in its infancy, leading to the expectation that the most significant use of these tools to address biological problems will be seen in the coming years. It is hoped that while this Account summarizes where we have been, it will also help set the stage for future research directions at the interface of quantum mechanics and biology.
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Affiliation(s)
- Kenneth M Merz
- Department of Chemistry and the Department of Biochemistry and Molecular Biology, Michigan State University , 578 S. Shaw Lane, East Lansing Michigan 48824-1322, United States
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18
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Determinants of protein–ligand complex formation in the thyroid hormone receptor α: A molecular dynamics simulation study. COMPUT THEOR CHEM 2014. [DOI: 10.1016/j.comptc.2014.03.034] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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19
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Borbulevych OY, Plumley JA, Martin RI, Merz KM, Westerhoff LM. Accurate macromolecular crystallographic refinement: incorporation of the linear scaling, semiempirical quantum-mechanics program DivCon into the PHENIX refinement package. ACTA CRYSTALLOGRAPHICA. SECTION D, BIOLOGICAL CRYSTALLOGRAPHY 2014; 70:1233-47. [PMID: 24816093 PMCID: PMC4014119 DOI: 10.1107/s1399004714002260] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2013] [Accepted: 01/30/2014] [Indexed: 01/22/2023]
Abstract
Macromolecular crystallographic refinement relies on sometimes dubious stereochemical restraints and rudimentary energy functionals to ensure the correct geometry of the model of the macromolecule and any covalently bound ligand(s). The ligand stereochemical restraint file (CIF) requires a priori understanding of the ligand geometry within the active site, and creation of the CIF is often an error-prone process owing to the great variety of potential ligand chemistry and structure. Stereochemical restraints have been replaced with more robust functionals through the integration of the linear-scaling, semiempirical quantum-mechanics (SE-QM) program DivCon with the PHENIX X-ray refinement engine. The PHENIX/DivCon package has been thoroughly validated on a population of 50 protein-ligand Protein Data Bank (PDB) structures with a range of resolutions and chemistry. The PDB structures used for the validation were originally refined utilizing various refinement packages and were published within the past five years. PHENIX/DivCon does not utilize CIF(s), link restraints and other parameters for refinement and hence it does not make as many a priori assumptions about the model. Across the entire population, the method results in reasonable ligand geometries and low ligand strains, even when the original refinement exhibited difficulties, indicating that PHENIX/DivCon is applicable to both single-structure and high-throughput crystallography.
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Affiliation(s)
| | - Joshua A. Plumley
- QuantumBio Inc., 2790 West College Avenue, State College, PA 16801, USA
| | - Roger I. Martin
- QuantumBio Inc., 2790 West College Avenue, State College, PA 16801, USA
| | - Kenneth M. Merz
- Quantum Theory Project, University of Florida, Gainesville, Florida USA
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20
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Stark JL, Powers R. Application of NMR and molecular docking in structure-based drug discovery. Top Curr Chem (Cham) 2011; 326:1-34. [PMID: 21915777 DOI: 10.1007/128_2011_213] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Drug discovery is a complex and costly endeavor, where few drugs that reach the clinical testing phase make it to market. High-throughput screening (HTS) is the primary method used by the pharmaceutical industry to identify initial lead compounds. Unfortunately, HTS has a high failure rate and is not particularly efficient at identifying viable drug leads. These shortcomings have encouraged the development of alternative methods to drive the drug discovery process. Specifically, nuclear magnetic resonance (NMR) spectroscopy and molecular docking are routinely being employed as important components of drug discovery research. Molecular docking provides an extremely rapid way to evaluate likely binders from a large chemical library with minimal cost. NMR ligand-affinity screens can directly detect a protein-ligand interaction, can measure a corresponding dissociation constant, and can reliably identify the ligand binding site and generate a co-structure. Furthermore, NMR ligand affinity screens and molecular docking are perfectly complementary techniques, where the combination of the two has the potential to improve the efficiency and success rate of drug discovery. This review will highlight the use of NMR ligand affinity screens and molecular docking in drug discovery and describe recent examples where the two techniques were combined to identify new and effective therapeutic drugs.
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Affiliation(s)
- Jaime L Stark
- Department of Chemistry, University of Nebraska, Lincoln, NE 68588-0304, USA
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21
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Korb O, Möller HM, Exner TE. NMR-guided molecular docking of a protein-peptide complex based on ant colony optimization. ChemMedChem 2010; 5:1001-6. [PMID: 20486157 DOI: 10.1002/cmdc.201000090] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Standard docking approaches used for the prediction of protein-ligand complexes in the drug development process have problems identifying the correct binding mode of large flexible ligands. Herein we show how additional experimental data from NMR experiments can be used to predict the binding mode of a mucin 1 (MUC-1) pentapeptide recognized by the breast-cancer-selective monoclonal antibody SM3. Distance constraints derived from trNOE and saturation transfer difference NMR experiments are combined with the docking approach PLANTS. The resulting complex structures show excellent agreement with the NMR data and with a published X-ray crystal structure. The method was then further tested on two complexes in order to demonstrate its more general applicability: T-antigen disaccharide bound to Maclura pomifera agglutinin, and the inhibitor SBi279 bound to S100B protein. Our new approach has the advantages of being fully automatic, rapid, and unbiased; moreover, it is based on relatively easily obtainable experimental data and can greatly increase the reliability of the generated structures.
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Affiliation(s)
- Oliver Korb
- Department of Chemistry and Zukunftskolleg, University of Konstanz, 78457 Konstanz (Germany), Fax: (+49) 7531-88-3587
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22
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Pencheva T, Soumana OS, Pajeva I, Miteva MA. Post-docking virtual screening of diverse binding pockets: Comparative study using DOCK, AMMOS, X-Score and FRED scoring functions. Eur J Med Chem 2010; 45:2622-8. [PMID: 20227800 DOI: 10.1016/j.ejmech.2009.12.025] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2009] [Revised: 12/08/2009] [Accepted: 12/10/2009] [Indexed: 10/20/2022]
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23
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Pierri CL, Parisi G, Porcelli V. Computational approaches for protein function prediction: a combined strategy from multiple sequence alignment to molecular docking-based virtual screening. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2010; 1804:1695-712. [PMID: 20433957 DOI: 10.1016/j.bbapap.2010.04.008] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2010] [Revised: 03/04/2010] [Accepted: 04/14/2010] [Indexed: 12/12/2022]
Abstract
The functional characterization of proteins represents a daily challenge for biochemical, medical and computational sciences. Although finally proved on the bench, the function of a protein can be successfully predicted by computational approaches that drive the further experimental assays. Current methods for comparative modeling allow the construction of accurate 3D models for proteins of unknown structure, provided that a crystal structure of a homologous protein is available. Binding regions can be proposed by using binding site predictors, data inferred from homologous crystal structures, and data provided from a careful interpretation of the multiple sequence alignment of the investigated protein and its homologs. Once the location of a binding site has been proposed, chemical ligands that have a high likelihood of binding can be identified by using ligand docking and structure-based virtual screening of chemical libraries. Most docking algorithms allow building a list sorted by energy of the lowest energy docking configuration for each ligand of the library. In this review the state-of-the-art of computational approaches in 3D protein comparative modeling and in the study of protein-ligand interactions is provided. Furthermore a possible combined/concerted multistep strategy for protein function prediction, based on multiple sequence alignment, comparative modeling, binding region prediction, and structure-based virtual screening of chemical libraries, is described by using suitable examples. As practical examples, Abl-kinase molecular modeling studies, HPV-E6 protein multiple sequence alignment analysis, and some other model docking-based characterization reports are briefly described to highlight the importance of computational approaches in protein function prediction.
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Affiliation(s)
- Ciro Leonardo Pierri
- Department of Pharmaco-Biology, Laboratory of Biochemistry and Molecular Biology, University of Bari, Va E. Orabona, 4 - 70125 Bari, Italy.
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24
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Faver J, Merz KM. The Utility of the HSAB Principle via the Fukui Function in Biological Systems. J Chem Theory Comput 2010; 6:548-559. [PMID: 20369029 DOI: 10.1021/ct9005085] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The hard/soft acid-base principle has long been known to be an excellent predictor of chemical reactivity. The Fukui function, a reactivity descriptor from conceptual density functional theory, has been shown to be related to the local softness of a system. The usefulness of the Fukui function is explored and demonstrated herein for three common biological problems: ligand docking, active site detection, and protein folding. In each type of study, a scoring function is developed based on the local HSAB principle using atomic Fukui indices. Even with necessary approximations for its use in large systems, the Fukui function remains a useful descriptor for predicting chemical reactivity and understanding chemical systems.
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Affiliation(s)
- John Faver
- University of Florida Department of Chemistry Quantum Theory Project 2328 New Physics Building PO Box 118435 University of Florida Gainesville, FL 32611-8435
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25
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González-Ruiz D, Gohlke H. Steering Protein−Ligand Docking with Quantitative NMR Chemical Shift Perturbations. J Chem Inf Model 2009; 49:2260-71. [DOI: 10.1021/ci900188r] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Domingo González-Ruiz
- Fachbereich Biowissenschaften, Molekulare Bioinformatik, Goethe-Universität, Max-von-Laue-Str. 9, 60438 Frankfurt am Main, Germany, and Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität, Universitätstrasse 1, 40225 Düsseldorf, Germany
| | - Holger Gohlke
- Fachbereich Biowissenschaften, Molekulare Bioinformatik, Goethe-Universität, Max-von-Laue-Str. 9, 60438 Frankfurt am Main, Germany, and Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität, Universitätstrasse 1, 40225 Düsseldorf, Germany
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26
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Abstract
In the past decade, the potential of harnessing the ability of nuclear magnetic resonance (NMR) spectroscopy to monitor intermolecular interactions as a tool for drug discovery has been increasingly appreciated in academia and industry. In this Perspective, we highlight some of the major applications of NMR in drug discovery, focusing on hit and lead generation, and provide a critical analysis of its current and potential utility.
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27
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Riedinger C, Endicott JA, Kemp SJ, Smyth LA, Watson A, Valeur E, Golding BT, Griffin RJ, Hardcastle IR, Noble ME, McDonnell JM. Analysis of Chemical Shift Changes Reveals the Binding Modes of Isoindolinone Inhibitors of the MDM2-p53 Interaction. J Am Chem Soc 2008; 130:16038-44. [DOI: 10.1021/ja8062088] [Citation(s) in RCA: 85] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Christiane Riedinger
- Laboratory of Molecular Biophysics, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, U.K., and Northern Institute for Cancer Research, Bedson Building, University of Newcastle, NE1 4RW, U.K
| | - Jane A. Endicott
- Laboratory of Molecular Biophysics, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, U.K., and Northern Institute for Cancer Research, Bedson Building, University of Newcastle, NE1 4RW, U.K
| | - Stuart J. Kemp
- Laboratory of Molecular Biophysics, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, U.K., and Northern Institute for Cancer Research, Bedson Building, University of Newcastle, NE1 4RW, U.K
| | - Lynette A. Smyth
- Laboratory of Molecular Biophysics, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, U.K., and Northern Institute for Cancer Research, Bedson Building, University of Newcastle, NE1 4RW, U.K
| | - Anna Watson
- Laboratory of Molecular Biophysics, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, U.K., and Northern Institute for Cancer Research, Bedson Building, University of Newcastle, NE1 4RW, U.K
| | - Eric Valeur
- Laboratory of Molecular Biophysics, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, U.K., and Northern Institute for Cancer Research, Bedson Building, University of Newcastle, NE1 4RW, U.K
| | - Bernard T. Golding
- Laboratory of Molecular Biophysics, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, U.K., and Northern Institute for Cancer Research, Bedson Building, University of Newcastle, NE1 4RW, U.K
| | - Roger J. Griffin
- Laboratory of Molecular Biophysics, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, U.K., and Northern Institute for Cancer Research, Bedson Building, University of Newcastle, NE1 4RW, U.K
| | - Ian R. Hardcastle
- Laboratory of Molecular Biophysics, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, U.K., and Northern Institute for Cancer Research, Bedson Building, University of Newcastle, NE1 4RW, U.K
| | - Martin E. Noble
- Laboratory of Molecular Biophysics, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, U.K., and Northern Institute for Cancer Research, Bedson Building, University of Newcastle, NE1 4RW, U.K
| | - James M. McDonnell
- Laboratory of Molecular Biophysics, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, U.K., and Northern Institute for Cancer Research, Bedson Building, University of Newcastle, NE1 4RW, U.K
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