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Cuadrado C, Cen-Pacheco F, Daranas AH. Computationally Assisted Analysis of NMR Chemical Shifts as a Tool in Conformational Analysis. Org Lett 2024. [PMID: 38888989 DOI: 10.1021/acs.orglett.4c01642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
A key to understanding the properties of functional molecules is to determine their conformation in solution. A conformational analysis procedure that relies on quantum mechanical calculations and the widely used DP4+ probability was evaluated to decipher the structural information encoded in NMR chemical shifts. The results underscore the potential utility of using NMR chemical shifts in advancing conformational analysis studies of complex molecules in solution.
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Affiliation(s)
- Cristina Cuadrado
- Instituto de Productos Naturales y Agrobiología del CSIC (IPNA-CSIC), La Laguna, 38206 Tenerife, Spain
| | - Francisco Cen-Pacheco
- Faculty of Bioanalysis, Iturbide s/n, Veracruz University, 91700 Veracruz, Veracruz, México
| | - Antonio Hernández Daranas
- Instituto de Productos Naturales y Agrobiología del CSIC (IPNA-CSIC), La Laguna, 38206 Tenerife, Spain
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2
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Calderón JC, Plut E, Keller M, Cabrele C, Reiser O, Gervasio FL, Clark T. Extended Metadynamics Protocol for Binding/Unbinding Free Energies of Peptide Ligands to Class A G-Protein-Coupled Receptors. J Chem Inf Model 2024; 64:205-218. [PMID: 38150388 DOI: 10.1021/acs.jcim.3c01574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
A metadynamics protocol is presented to characterize the binding and unbinding of peptide ligands to class A G-protein-coupled receptors (GPCRs). The protocol expands on the one previously presented for binding and unbinding small-molecule ligands to class A GPCRs and accounts for the more demanding nature of the peptide binding-unbinding process. It applies to almost all class A GPCRs. Exemplary simulations are described for subtypes Y1R, Y2R, and Y4R of the neuropeptide Y receptor family, vasopressin binding to the vasopressin V2 receptor (V2R), and oxytocin binding to the oxytocin receptor (OTR). Binding free energies and the positions of alternative binding sites are presented and, where possible, compared with the experiment.
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Affiliation(s)
- Jacqueline C Calderón
- Computer-Chemistry-Center, Department of Chemistry and Pharmacy, Friedrich-Alexander-University Erlangen-Nuernberg, Naegelsbachstr. 25, Erlangen 91052, Germany
| | - Eva Plut
- Institute of Organic Chemistry, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg 93040, Germany
| | - Max Keller
- Institute of Pharmacy, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg D-93040, Germany
| | - Chiara Cabrele
- Institute of Organic Chemistry, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg 93040, Germany
| | - Oliver Reiser
- Institute of Organic Chemistry, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg 93040, Germany
| | | | - Timothy Clark
- Computer-Chemistry-Center, Department of Chemistry and Pharmacy, Friedrich-Alexander-University Erlangen-Nuernberg, Naegelsbachstr. 25, Erlangen 91052, Germany
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3
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Hsueh SCC, Aina A, Plotkin SS. Ensemble Generation for Linear and Cyclic Peptides Using a Reservoir Replica Exchange Molecular Dynamics Implementation in GROMACS. J Phys Chem B 2022; 126:10384-10399. [PMID: 36410027 DOI: 10.1021/acs.jpcb.2c05470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
The profile of shapes presented by a cyclic peptide modulates its therapeutic efficacy and is represented by the ensemble of its sampled conformations. Although some algorithms excel at creating a diverse ensemble of cyclic peptide conformations, they seldom address the entropic contribution of flexible conformations and often have significant practical difficulty producing an ensemble with converged and reliable thermodynamic properties. In this study, an accelerated molecular dynamics (MD) method, namely, reservoir replica exchange MD (R-REMD or Res-REMD), was implemented in GROMACS ver. 4.6.7 and benchmarked on two small cyclic peptide model systems: a cyclized furin cleavage site of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (cyclo-(CGPRRARSG)) and oxytocin (disulfide-bonded CYIQNCPLG). Additionally, we also benchmarked Res-REMD on alanine dipeptide and Trpzip2 to demonstrate its validity and efficiency over REMD. For Trpzip2, Res-REMD coupled with an umbrella-sampling-derived reservoir generated similar folded fractions as regular REMD but on a much faster time scale. For cyclic peptides, Res-REMD appeared to be marginally faster than REMD in ensemble generation. Finally, Res-REMD was more effective in sampling rare events such as trans to cis peptide bond isomerization. We provide a GitHub page with the modified GROMACS source code for running Res-REMD at https://github.com/PlotkinLab/Reservoir-REMD.
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Affiliation(s)
- Shawn C C Hsueh
- Department of Physics and Astronomy, The University of British Columbia, Vancouver, BCV6T 1Z1, Canada
| | - Adekunle Aina
- Department of Physics and Astronomy, The University of British Columbia, Vancouver, BCV6T 1Z1, Canada
| | - Steven S Plotkin
- Department of Physics and Astronomy, The University of British Columbia, Vancouver, BCV6T 1Z1, Canada.,Genome Science and Technology Program, The University of British Columbia, Vancouver, BCV6T 1Z1, Canada
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4
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Miao J, Descoteaux ML, Lin YS. Structure prediction of cyclic peptides by molecular dynamics + machine learning. Chem Sci 2021; 12:14927-14936. [PMID: 34820109 PMCID: PMC8597836 DOI: 10.1039/d1sc05562c] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 10/14/2021] [Indexed: 12/27/2022] Open
Abstract
Recent computational methods have made strides in discovering well-structured cyclic peptides that preferentially populate a single conformation. However, many successful cyclic-peptide therapeutics adopt multiple conformations in solution. In fact, the chameleonic properties of some cyclic peptides are likely responsible for their high cell membrane permeability. Thus, we require the ability to predict complete structural ensembles for cyclic peptides, including the majority of cyclic peptides that have broad structural ensembles, to significantly improve our ability to rationally design cyclic-peptide therapeutics. Here, we introduce the idea of using molecular dynamics simulation results to train machine learning models to enable efficient structure prediction for cyclic peptides. Using molecular dynamics simulation results for several hundred cyclic pentapeptides as the training datasets, we developed machine-learning models that can provide molecular dynamics simulation-quality predictions of structural ensembles for all the hundreds of thousands of sequences in the entire sequence space. The prediction for each individual cyclic peptide can be made using less than 1 second of computation time. Even for the most challenging classes of poorly structured cyclic peptides with broad conformational ensembles, our predictions were similar to those one would normally obtain only after running multiple days of explicit-solvent molecular dynamics simulations. The resulting method, termed StrEAMM (Structural Ensembles Achieved by Molecular Dynamics and Machine Learning), is the first technique capable of efficiently predicting complete structural ensembles of cyclic peptides without relying on additional molecular dynamics simulations, constituting a seven-order-of-magnitude improvement in speed while retaining the same accuracy as explicit-solvent simulations.
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Affiliation(s)
- Jiayuan Miao
- Department of Chemistry, Tufts University Medford Massachusetts 02155 USA
| | - Marc L Descoteaux
- Department of Chemistry, Tufts University Medford Massachusetts 02155 USA
| | - Yu-Shan Lin
- Department of Chemistry, Tufts University Medford Massachusetts 02155 USA
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5
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Che K, Muttenthaler M, Kurzbach D. Conformational selection of vasopressin upon V 1a receptor binding. Comput Struct Biotechnol J 2021; 19:5826-5833. [PMID: 34765097 PMCID: PMC8567363 DOI: 10.1016/j.csbj.2021.10.024] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 10/14/2021] [Accepted: 10/14/2021] [Indexed: 11/30/2022] Open
Abstract
The neuropeptide vasopressin (VP) and its three G protein-coupled receptors (V1aR, V1bR and V2R) are of high interest in a wide array of drug discovery programs. V1aR is of particular importance due to its cardiovascular functions and diverse roles in the central nervous system. The structure–activity relationships underpinning ligand-receptor interactions remain however largely unclear, hindering rational drug design. This is not least due to the high structural flexibility of VP in its free as well as receptor-bound states. In this work, we developed a novel approach to reveal features of conformational selectivity upon VP-V1aR complex formation. We employed virtual screening strategies to probe VP’s conformational space for transiently adopted structures that favor binding to V1aR. To this end, we dissected the VP conformational space into three sub-ensembles, each containing distinct structural sets for VP’s three-residue C-terminal tail. We validated the computational results with experimental nuclear magnetic resonance (NMR) data and docked each sub-ensemble to V1aR. We observed that the conformation of VP’s three-residue tail significantly modulated the complex dissociation constants. Solvent-exposed and proline trans-configured VP tail conformations bound to the receptor with three-fold enhanced affinities compared to compacted or cis-configured conformations. The solvent-exposed and more flexible structures facilitated unique interaction patterns between VP and V1aR transmembrane helices 3, 4, and 6 which led to high binding energies. The presented “virtual conformational space screening” approach, integrated with NMR spectroscopy, thus enabled identification and characterization of a conformational selection-type complex formation mechanism that confers novel perspectives on targeting the VP-V1aR interactions at the level of the encounter complex – an aspect that opens novel research avenues for understanding the functionality of the evolutionary selected conformational properties of VP, as well as guidance for ligand design strategies to provide more potent and selective VP analogues.
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Affiliation(s)
- Kateryna Che
- University Vienna, Faculty of Chemistry, Institute of Biological Chemistry, Währinger Str. 38, A-1090 Vienna, Austria
| | - Markus Muttenthaler
- University Vienna, Faculty of Chemistry, Institute of Biological Chemistry, Währinger Str. 38, A-1090 Vienna, Austria
- The University of Queensland, Institute for Molecular Bioscience, 306 Carmody Rd, 4072 St Lucia, Brisbane, Queensland, Australia
| | - Dennis Kurzbach
- University Vienna, Faculty of Chemistry, Institute of Biological Chemistry, Währinger Str. 38, A-1090 Vienna, Austria
- Corresponding author.
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6
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Nazarski RB. Summary of DFT calculations coupled with current statistical and/or artificial neural network (ANN) methods to assist experimental NMR data in identifying diastereomeric structures. Tetrahedron Lett 2021. [DOI: 10.1016/j.tetlet.2020.152548] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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7
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Damjanovic J, Miao J, Huang H, Lin YS. Elucidating Solution Structures of Cyclic Peptides Using Molecular Dynamics Simulations. Chem Rev 2021; 121:2292-2324. [PMID: 33426882 DOI: 10.1021/acs.chemrev.0c01087] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Protein-protein interactions are vital to biological processes, but the shape and size of their interfaces make them hard to target using small molecules. Cyclic peptides have shown promise as protein-protein interaction modulators, as they can bind protein surfaces with high affinity and specificity. Dozens of cyclic peptides are already FDA approved, and many more are in various stages of development as immunosuppressants, antibiotics, antivirals, or anticancer drugs. However, most cyclic peptide drugs so far have been natural products or derivatives thereof, with de novo design having proven challenging. A key obstacle is structural characterization: cyclic peptides frequently adopt multiple conformations in solution, which are difficult to resolve using techniques like NMR spectroscopy. The lack of solution structural information prevents a thorough understanding of cyclic peptides' sequence-structure-function relationship. Here we review recent development and application of molecular dynamics simulations with enhanced sampling to studying the solution structures of cyclic peptides. We describe novel computational methods capable of sampling cyclic peptides' conformational space and provide examples of computational studies that relate peptides' sequence and structure to biological activity. We demonstrate that molecular dynamics simulations have grown from an explanatory technique to a full-fledged tool for systematic studies at the forefront of cyclic peptide therapeutic design.
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Affiliation(s)
- Jovan Damjanovic
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Jiayuan Miao
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - He Huang
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Yu-Shan Lin
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
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8
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Phillips ST, Dodds JN, Ellis BM, May JC, McLean JA. Chiral separation of diastereomers of the cyclic nonapeptides vasopressin and desmopressin by uniform field ion mobility mass spectrometry. Chem Commun (Camb) 2018; 54:9398-9401. [PMID: 30063231 DOI: 10.1039/c8cc03790f] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
In this study ion mobility-mass spectrometry (IM-MS) is used to distinguish chiral diastereomers of the nonapeptides desmopressin and vasopressin. The differences in gas phase cross sectional area (ca. 2%) were sufficient to directly resolve the enantiomers present in a binary mixture. Results from computational modeling indicate that chiral recognition by IM-MS for nonapeptides is possible due to their diastereomer-specific conformations adopted in the gas-phase, namely a compact ring-tail conformer specific to the l-diastereomer forms.
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Affiliation(s)
- Shawn T Phillips
- Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute of Chemical Biology, and Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN 3726, USA.
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9
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Bame J, Hoeck C, Carrington MJ, Butts CP, Jäger CM, Croft AK. Improved NOE fitting for flexible molecules based on molecular mechanics data – a case study with S-adenosylmethionine. Phys Chem Chem Phys 2018; 20:7523-7531. [DOI: 10.1039/c7cp07265a] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Using the important biomolecule S-adenosyl methionine as an exemplar, we provide a new, enhanced approach for fitting MD data to high-accuracy NOE data, providing improvements in structure determination.
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Affiliation(s)
- Jessica Bame
- University of Bristol
- School of Chemistry
- Clifton
- Bristol BS8 1TS
- UK
| | - Casper Hoeck
- University of Bristol
- School of Chemistry
- Clifton
- Bristol BS8 1TS
- UK
| | - Matthew J. Carrington
- University of Nottingham
- Department of Chemical and Environmental Engineering
- University Park
- Nottingham
- UK
| | - Craig P. Butts
- University of Bristol
- School of Chemistry
- Clifton
- Bristol BS8 1TS
- UK
| | - Christof M. Jäger
- University of Nottingham
- Department of Chemical and Environmental Engineering
- University Park
- Nottingham
- UK
| | - Anna K. Croft
- University of Nottingham
- Department of Chemical and Environmental Engineering
- University Park
- Nottingham
- UK
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10
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Haensele E, Mele N, Miljak M, Read CM, Whitley DC, Banting L, Delépée C, Sopkova-de Oliveira Santos J, Lepailleur A, Bureau R, Essex JW, Clark T. Conformation and Dynamics of Human Urotensin II and Urotensin Related Peptide in Aqueous Solution. J Chem Inf Model 2017; 57:298-310. [DOI: 10.1021/acs.jcim.6b00706] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
| | - Nawel Mele
- School
of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Marija Miljak
- School
of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | | | | | | | - Carla Delépée
- Normandie
Université, CS 14032 Caen Cedex 5, France, Centre d’Etudes
et de Recherche sur le Médicament de Normandie (CERMN, EA 4258,
FR CNRS 3038 INC3M SF 4206 ICORE), UFR des Sciences Pharmaceutiques, Université de Caen Basse−Normandie (UNICAEN), F-14032 Caen, France
| | - Jana Sopkova-de Oliveira Santos
- Normandie
Université, CS 14032 Caen Cedex 5, France, Centre d’Etudes
et de Recherche sur le Médicament de Normandie (CERMN, EA 4258,
FR CNRS 3038 INC3M SF 4206 ICORE), UFR des Sciences Pharmaceutiques, Université de Caen Basse−Normandie (UNICAEN), F-14032 Caen, France
| | - Alban Lepailleur
- Normandie
Université, CS 14032 Caen Cedex 5, France, Centre d’Etudes
et de Recherche sur le Médicament de Normandie (CERMN, EA 4258,
FR CNRS 3038 INC3M SF 4206 ICORE), UFR des Sciences Pharmaceutiques, Université de Caen Basse−Normandie (UNICAEN), F-14032 Caen, France
| | - Ronan Bureau
- Normandie
Université, CS 14032 Caen Cedex 5, France, Centre d’Etudes
et de Recherche sur le Médicament de Normandie (CERMN, EA 4258,
FR CNRS 3038 INC3M SF 4206 ICORE), UFR des Sciences Pharmaceutiques, Université de Caen Basse−Normandie (UNICAEN), F-14032 Caen, France
| | - Jonathan W. Essex
- School
of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Timothy Clark
- Computer-Chemie-Centrum
and Interdisciplinary Center for Molecular Materials, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nägelsbachstraße 25, 91052 Erlangen, Germany
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