1
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Rahman M, Dannatt HRW, Blundell CD, Hughes LP, Blade H, Carson J, Tatman BP, Johnston ST, Brown SP. Polymorph Identification for Flexible Molecules: Linear Regression Analysis of Experimental and Calculated Solution- and Solid-State NMR Data. J Phys Chem A 2024; 128:1793-1816. [PMID: 38427685 PMCID: PMC10945485 DOI: 10.1021/acs.jpca.3c07732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 02/06/2024] [Accepted: 02/07/2024] [Indexed: 03/03/2024]
Abstract
The Δδ regression approach of Blade et al. [ J. Phys. Chem. A 2020, 124(43), 8959-8977] for accurately discriminating between solid forms using a combination of experimental solution- and solid-state NMR data with density functional theory (DFT) calculation is here extended to molecules with multiple conformational degrees of freedom, using furosemide polymorphs as an exemplar. As before, the differences in measured 1H and 13C chemical shifts between solution-state NMR and solid-state magic-angle spinning (MAS) NMR (Δδexperimental) are compared to those determined by gauge-including projector augmented wave (GIPAW) calculations (Δδcalculated) by regression analysis and a t-test, allowing the correct furosemide polymorph to be precisely identified. Monte Carlo random sampling is used to calculate solution-state NMR chemical shifts, reducing computation times by avoiding the need to systematically sample the multidimensional conformational landscape that furosemide occupies in solution. The solvent conditions should be chosen to match the molecule's charge state between the solution and solid states. The Δδ regression approach indicates whether or not correlations between Δδexperimental and Δδcalculated are statistically significant; the approach is differently sensitive to the popular root mean squared error (RMSE) method, being shown to exhibit a much greater dynamic range. An alternative method for estimating solution-state NMR chemical shifts by approximating the measured solution-state dynamic 3D behavior with an ensemble of 54 furosemide crystal structures (polymorphs and cocrystals) from the Cambridge Structural Database (CSD) was also successful in this case, suggesting new avenues for this method that may overcome its current dependency on the prior determination of solution dynamic 3D structures.
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Affiliation(s)
- Mohammed Rahman
- Department
of Physics, University of Warwick, Coventry CV4 7AL, U.K.
- Department
of Chemistry, University of Warwick, Coventry CV4 7AL, U.K.
| | | | | | - Leslie P. Hughes
- Oral
Product Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, Macclesfield SK10 2NA, U.K.
| | - Helen Blade
- Oral
Product Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, Macclesfield SK10 2NA, U.K.
| | - Jake Carson
- Mathematics
Institute at Warwick, University of Warwick, Coventry CV4 7AL, U.K.
| | - Ben P. Tatman
- Department
of Physics, University of Warwick, Coventry CV4 7AL, U.K.
- Department
of Chemistry, University of Warwick, Coventry CV4 7AL, U.K.
| | | | - Steven P. Brown
- Department
of Physics, University of Warwick, Coventry CV4 7AL, U.K.
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2
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Stirk AJ, Holmes ST, Souza FES, Hung I, Gan Z, Britten JF, Rey AW, Schurko RW. An unusual ionic cocrystal of ponatinib hydrochloride: characterization by single-crystal X-ray diffraction and ultra-high field NMR spectroscopy. CrystEngComm 2024; 26:1219-1233. [PMID: 38419975 PMCID: PMC10897533 DOI: 10.1039/d3ce01062g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 01/26/2024] [Indexed: 03/02/2024]
Abstract
This study describes the discovery of a unique ionic cocrystal of the active pharmaceutical ingredient (API) ponatinib hydrochloride (pon·HCl), and characterization using single-crystal X-ray diffraction (SCXRD) and solid-state NMR (SSNMR) spectroscopy. Pon·HCl is a multicomponent crystal that features an unusual stoichiometry, with an asymmetric unit containing both monocations and dications of the ponatinib molecule, three water molecules, and three chloride ions. Structural features include (i) a charged imidazopyridazine moiety that forms a hydrogen bond between the ponatinib monocations and dications and (ii) a chloride ion that does not feature hydrogen bonds involving any organic moiety, instead being situated in a "square" arrangement with three water molecules. Multinuclear SSNMR, featuring high and ultra-high fields up to 35.2 T, provides the groundwork for structural interpretation of complex multicomponent crystals in the absence of diffraction data. A 13C CP/MAS spectrum confirms the presence of two crystallographically distinct ponatinib molecules, whereas 1D 1H and 2D 1H-1H DQ-SQ spectra identify and assign the unusually deshielded imidazopyridazine proton. 1D 35Cl spectra obtained at multiple fields confirm the presence of three distinct chloride ions, with density functional theory calculations providing key relationships between the SSNMR spectra and H⋯Cl- hydrogen bonding arrangements. A 2D 35Cl → 1H D-RINEPT spectrum confirms the spatial proximities between the chloride ions, water molecules, and amine moieties. This all suggests future application of multinuclear SSNMR at high and ultra-high fields to the study of complex API solid forms for which SCXRD data are unavailable, with potential application to heterogeneous mixtures or amorphous solid dispersions.
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Affiliation(s)
| | - Sean T Holmes
- Department of Chemistry & Biochemistry, Florida State University Tallahassee FL 32306 USA
- National High Magnetic Field Laboratory Tallahassee FL 32310 USA
| | | | - Ivan Hung
- National High Magnetic Field Laboratory Tallahassee FL 32310 USA
| | - Zhehong Gan
- National High Magnetic Field Laboratory Tallahassee FL 32310 USA
| | - James F Britten
- MAX Diffraction Facility, McMaster University Hamilton ON L8S 4M1 Canada
| | - Allan W Rey
- Apotex Pharmachem Inc. Brantford ON N3T 6B8 Canada
| | - Robert W Schurko
- Department of Chemistry & Biochemistry, Florida State University Tallahassee FL 32306 USA
- National High Magnetic Field Laboratory Tallahassee FL 32310 USA
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3
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Xue X, Sun H, Yang M, Liu X, Hu HY, Deng Y, Wang X. Advances in the Application of Artificial Intelligence-Based Spectral Data Interpretation: A Perspective. Anal Chem 2023; 95:13733-13745. [PMID: 37688541 DOI: 10.1021/acs.analchem.3c02540] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/11/2023]
Abstract
The interpretation of spectral data, including mass, nuclear magnetic resonance, infrared, and ultraviolet-visible spectra, is critical for obtaining molecular structural information. The development of advanced sensing technology has multiplied the amount of available spectral data. Chemical experts must use basic principles corresponding to the spectral information generated by molecular fragments and functional groups. This is a time-consuming process that requires a solid professional knowledge base. In recent years, the rapid development of computer science and its applications in cheminformatics and the emergence of computer-aided expert systems have greatly reduced the difficulty in analyzing large quantities of data. For expert systems, however, the problem-solving strategy must be known in advance or extracted by human experts and translated into algorithms. Gratifyingly, the development of artificial intelligence (AI) methods has shown great promise for solving such problems. Traditional algorithms, including the latest neural network algorithms, have shown great potential for both extracting useful information and processing massive quantities of data. This Perspective highlights recent innovations covering all of the emerging AI-based spectral interpretation techniques. In addition, the main limitations and current obstacles are presented, and the corresponding directions for further research are proposed. Moreover, this Perspective gives the authors' personal outlook on the development and future applications of spectral interpretation.
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Affiliation(s)
- Xi Xue
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
- Beijing Key Laboratory of Active Substances Discovery and Drugability Evaluation, Department of Medicinal Chemistry, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, P. R. China
| | - Hanyu Sun
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
- Beijing Key Laboratory of Active Substances Discovery and Drugability Evaluation, Department of Medicinal Chemistry, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, P. R. China
| | - Minjian Yang
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
- Beijing Key Laboratory of Active Substances Discovery and Drugability Evaluation, Department of Medicinal Chemistry, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, P. R. China
| | - Xue Liu
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
| | - Hai-Yu Hu
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd. Beijing 100080, China
- Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xiaojian Wang
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
- CarbonSilicon AI Technology Co., Ltd. Beijing 100080, China
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4
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Bassani D, Brigo A, Andrews-Morger A. Federated Learning in Computational Toxicology: An Industrial Perspective on the Effiris Hackathon. Chem Res Toxicol 2023; 36:1503-1517. [PMID: 37584277 PMCID: PMC10523574 DOI: 10.1021/acs.chemrestox.3c00137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Indexed: 08/17/2023]
Abstract
In silico approaches have acquired a towering role in pharmaceutical research and development, allowing laboratories all around the world to design, create, and optimize novel molecular entities with unprecedented efficiency. From a toxicological perspective, computational methods have guided the choices of medicinal chemists toward compounds displaying improved safety profiles. Even if the recent advances in the field are significant, many challenges remain active in the on-target and off-target prediction fields. Machine learning methods have shown their ability to identify molecules with safety concerns. However, they strongly depend on the abundance and diversity of data used for their training. Sharing such information among pharmaceutical companies remains extremely limited due to confidentiality reasons, but in this scenario, a recent concept named "federated learning" can help overcome such concerns. Within this framework, it is possible for companies to contribute to the training of common machine learning algorithms, using, but not sharing, their proprietary data. Very recently, Lhasa Limited organized a hackathon involving several industrial partners in order to assess the performance of their federated learning platform, called "Effiris". In this paper, we share our experience as Roche in participating in such an event, evaluating the performance of the federated algorithms and comparing them with those coming from our in-house-only machine learning models. Our aim is to highlight the advantages of federated learning and its intrinsic limitations and also suggest some points for potential improvements in the method.
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Affiliation(s)
- Davide Bassani
- Pharmaceutical Research &
Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
| | - Alessandro Brigo
- Pharmaceutical Research &
Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
| | - Andrea Andrews-Morger
- Pharmaceutical Research &
Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
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5
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Rehman Z, Franks WT, Nguyen B, Schmidt HF, Scrivens G, Brown SP. Discovering the Solid-State Secrets of Lorlatinib by NMR Crystallography: To Hydrogen Bond or not to Hydrogen Bond. J Pharm Sci 2023; 112:1915-1928. [PMID: 36868358 DOI: 10.1016/j.xphs.2023.02.022] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 02/22/2023] [Accepted: 02/22/2023] [Indexed: 03/05/2023]
Abstract
Lorlatinib is an active pharmaceutical ingredient (API) used in the treatment of lung cancer. Here, an NMR crystallography analysis is presented whereby the single-crystal X-ray diffraction structure (CSD: 2205098) determination is complemented by multinuclear (1H, 13C, 14/15N, 19F) magic-angle spinning (MAS) solid-state NMR and gauge-including projector augmented wave (GIPAW) calculation of NMR chemical shifts. Lorlatinib crystallises in the P21 space group, with two distinct molecules in the asymmetric unit cell, Z' = 2. Three of the four NH2 hydrogen atoms form intermolecular hydrogen bonds, N30-H…N15 between the two distinct molecules and N30-H…O2 between two equivalent molecules. This is reflected in one of the NH21H chemical shifts being significantly lower, 4.0 ppm compared to 7.0 ppm. Two-dimensional 1H-13C, 14N-1H and 1H (double-quantum, DQ)-1H (single-quantum, SQ) MAS NMR spectra are presented. The 1H resonances are assigned and specific HH proximities corresponding to the observed DQ peaks are identified. The resolution enhancement at a 1H Larmor frequency of 1 GHz as compared to 500 or 600 MHz is demonstrated.
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Affiliation(s)
- Zainab Rehman
- Department of Physics, University of Warwick, Coventry, CV4 7AL, UK
| | - W Trent Franks
- Department of Physics, University of Warwick, Coventry, CV4 7AL, UK
| | | | | | | | - Steven P Brown
- Department of Physics, University of Warwick, Coventry, CV4 7AL, UK.
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6
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Gugler S, Reiher M. Quantum Chemical Roots of Machine-Learning Molecular Similarity Descriptors. J Chem Theory Comput 2022; 18:6670-6689. [PMID: 36218328 DOI: 10.1021/acs.jctc.2c00718] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
In this work, we explore the quantum chemical foundations of descriptors for molecular similarity. Such descriptors are key for traversing chemical compound space with machine learning. Our focus is on the Coulomb matrix and on the smooth overlap of atomic positions (SOAP). We adopt a basic framework that allows us to connect both descriptors to electronic structure theory. This framework enables us to then define two new descriptors that are more closely related to electronic structure theory, which we call Coulomb lists and smooth overlap of electron densities (SOED). By investigating their usefulness as molecular similarity descriptors, we gain new insights into how and why Coulomb matrix and SOAP work. Moreover, Coulomb lists avoid the somewhat mysterious diagonalization step of the Coulomb matrix and might provide a direct means to extract subsystem information that can be compared across Born-Oppenheimer surfaces of varying dimension. For the electron density, we derive the necessary formalism to create the SOED measure in close analogy to SOAP. Because this formalism is more involved than that of SOAP, we review the essential theory as well as introduce a set of approximations that eventually allow us to work with SOED in terms of the same implementation available for the evaluation of SOAP. We focus our analysis on elementary reaction steps, where transition state structures are more similar to either reactant or product structures than the latter two are with respect to one another. The prediction of electronic energies of transition state structures can, however, be more difficult than that of stable intermediates due to multi-configurational effects. The question arises to what extent molecular similarity descriptors rooted in electronic structure theory can resolve these intricate effects.
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Affiliation(s)
- Stefan Gugler
- Laboratorium für Physikalische Chemie, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Markus Reiher
- Laboratorium für Physikalische Chemie, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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7
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Cordova M, Engel EA, Stefaniuk A, Paruzzo F, Hofstetter A, Ceriotti M, Emsley L. A Machine Learning Model of Chemical Shifts for Chemically and Structurally Diverse Molecular Solids. THE JOURNAL OF PHYSICAL CHEMISTRY. C, NANOMATERIALS AND INTERFACES 2022; 126:16710-16720. [PMID: 36237276 PMCID: PMC9549463 DOI: 10.1021/acs.jpcc.2c03854] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 08/24/2022] [Indexed: 06/16/2023]
Abstract
Nuclear magnetic resonance (NMR) chemical shifts are a direct probe of local atomic environments and can be used to determine the structure of solid materials. However, the substantial computational cost required to predict accurate chemical shifts is a key bottleneck for NMR crystallography. We recently introduced ShiftML, a machine-learning model of chemical shifts in molecular solids, trained on minimum-energy geometries of materials composed of C, H, N, O, and S that provides rapid chemical shift predictions with density functional theory (DFT) accuracy. Here, we extend the capabilities of ShiftML to predict chemical shifts for both finite temperature structures and more chemically diverse compounds, while retaining the same speed and accuracy. For a benchmark set of 13 molecular solids, we find a root-mean-squared error of 0.47 ppm with respect to experiment for 1H shift predictions (compared to 0.35 ppm for explicit DFT calculations), while reducing the computational cost by over four orders of magnitude.
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Affiliation(s)
- Manuel Cordova
- Laboratory
of Magnetic Resonance, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
- National
Centre for Computational Design and Discovery of Novel Materials MARVEL, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Edgar A. Engel
- Theory
of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, U.K.
| | - Artur Stefaniuk
- Laboratory
of Magnetic Resonance, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Federico Paruzzo
- Laboratory
of Magnetic Resonance, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Albert Hofstetter
- Laboratory
of Magnetic Resonance, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Michele Ceriotti
- National
Centre for Computational Design and Discovery of Novel Materials MARVEL, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
- Laboratory
of Computational Science and Modelling, Institute of Materials, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Lyndon Emsley
- Laboratory
of Magnetic Resonance, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
- National
Centre for Computational Design and Discovery of Novel Materials MARVEL, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
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8
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Sajjan M, Li J, Selvarajan R, Sureshbabu SH, Kale SS, Gupta R, Singh V, Kais S. Quantum machine learning for chemistry and physics. Chem Soc Rev 2022; 51:6475-6573. [PMID: 35849066 DOI: 10.1039/d2cs00203e] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.
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Affiliation(s)
- Manas Sajjan
- Department of Chemistry, Purdue University, West Lafayette, IN-47907, USA. .,Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, USA
| | - Junxu Li
- Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, USA.,Department of Physics and Astronomy, Purdue University, West Lafayette, IN-47907, USA
| | - Raja Selvarajan
- Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, USA.,Department of Physics and Astronomy, Purdue University, West Lafayette, IN-47907, USA
| | - Shree Hari Sureshbabu
- Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, USA.,Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN-47907, USA
| | - Sumit Suresh Kale
- Department of Chemistry, Purdue University, West Lafayette, IN-47907, USA. .,Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, USA
| | - Rishabh Gupta
- Department of Chemistry, Purdue University, West Lafayette, IN-47907, USA. .,Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, USA
| | - Vinit Singh
- Department of Chemistry, Purdue University, West Lafayette, IN-47907, USA. .,Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, USA
| | - Sabre Kais
- Department of Chemistry, Purdue University, West Lafayette, IN-47907, USA. .,Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, USA.,Department of Physics and Astronomy, Purdue University, West Lafayette, IN-47907, USA.,Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN-47907, USA
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9
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Zheng DN, Szell PMJ, Khiri S, Ovens JS, Bryce DL. Solid-state multinuclear magnetic resonance and X-ray crystallographic investigation of the phosphorus...iodine halogen bond in a bis(dicyclohexylphenylphosphine)(1,6-diiodoperfluorohexane) cocrystal. ACTA CRYSTALLOGRAPHICA SECTION B, STRUCTURAL SCIENCE, CRYSTAL ENGINEERING AND MATERIALS 2022; 78:557-563. [PMID: 35702972 DOI: 10.1107/s2052520622004322] [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: 12/17/2021] [Accepted: 04/22/2022] [Indexed: 06/15/2023]
Abstract
Halogen bonding to phosphorus atoms remains uncommon, with relatively few examples reported in the literature. Here, the preparation and investigation of the cocrystal bis(dicyclohexylphenylphosphine)(1,6-diiodoperfluorohexane) by X-ray crystallography and solid-state multinuclear magnetic resonance spectroscopy is described. The crystal structure features two crystallographically unique C-I...P halogen bonds [dI...P = 3.090 (5) Å, 3.264 (5) Å] and crystallographic disorder of one of the 1,6-diiodoperfluorohexane molecules. The first of these is the shortest and most linear I...P halogen bond reported to date. 13C, 19F, and 31P magic angle spinning solid-state NMR spectra are reported. A 31P chemical shift change of -7.0 p.p.m. in the cocrystal relative to pure dicyclohexylphenylphosphine, consistent with halogen bond formation, is noted. This work establishes iodoperfluoroalkanes as viable halogen bond donors when paired with phosphorus acceptors, and also shows that dicyclohexylphenylphosphine can act as a practical halogen bond acceptor.
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Affiliation(s)
- Dan Ni Zheng
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie Private, Ottawa, Ontario K1N 6N5, Canada
| | - Patrick M J Szell
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie Private, Ottawa, Ontario K1N 6N5, Canada
| | - Safaa Khiri
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie Private, Ottawa, Ontario K1N 6N5, Canada
| | - Jeffrey S Ovens
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie Private, Ottawa, Ontario K1N 6N5, Canada
| | - David L Bryce
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie Private, Ottawa, Ontario K1N 6N5, Canada
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10
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Balodis M, Cordova M, Hofstetter A, Day GM, Emsley L. De Novo Crystal Structure Determination from Machine Learned Chemical Shifts. J Am Chem Soc 2022; 144:7215-7223. [PMID: 35416661 PMCID: PMC9052749 DOI: 10.1021/jacs.1c13733] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Determination of the three-dimensional atomic-level structure of powdered solids is one of the key goals in current chemistry. Solid-state NMR chemical shifts can be used to solve this problem, but they are limited by the high computational cost associated with crystal structure prediction methods and density functional theory chemical shift calculations. Here, we successfully determine the crystal structures of ampicillin, piroxicam, cocaine, and two polymorphs of the drug molecule AZD8329 using on-the-fly generated machine-learned isotropic chemical shifts to directly guide a Monte Carlo-based structure determination process starting from a random gas-phase conformation.
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Affiliation(s)
- Martins Balodis
- Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Manuel Cordova
- Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials MARVEL, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Albert Hofstetter
- Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Graeme M Day
- School of Chemistry, University of Southampton, Highfield SO17 1BJ, Southampton, United Kingdom
| | - Lyndon Emsley
- Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials MARVEL, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
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11
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Immel S, Köck M, Reggelin M. Bayesian Inference Applied to NMR-Based Configurational Assignments by Floating Chirality Distance Geometry Calculations. J Am Chem Soc 2022; 144:6830-6838. [PMID: 35412312 DOI: 10.1021/jacs.2c00813] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Using NMR data, the assignment of the correct 3D configuration and conformation to unknown natural products is of pivotal importance in pharmaceutical and medicinal chemistry. In this report, we quantify the quality and probability of structural elucidations using Bayesian inference in combination with floating chirality distance geometry simulations. Here, we will discuss the configurational analysis of three complex natural products including isopinocampheol (1), plakilactone H (2), and iodocallophycoic acid A (3) using NMR restraints of various types and in different combinations (residual dipolar couplings (RDCs) and NOE-derived distances). Our results quantitatively demonstrate how reliably molecular geometries can be inferred from experimental NMR data, unequivocally unveiling remaining assignment ambiguities. The methodology presented here can dramatically reduce the risk of incorrect structural assignments based on the overinterpretation of incomplete data in chemistry.
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Affiliation(s)
- Stefan Immel
- Clemens Schöpf Institut für Organische Chemie und Biochemie, Technische Universität Darmstadt, Alarich-Weiss-Straße 4, 64287 Darmstadt, Germany
| | - Matthias Köck
- Alfred-Wegener-Institut für Polar- and Meeresforschung in der Helmholtz-Gemeinschaft, Am Handelshafen 12, 27570 Bremerhaven, Germany
| | - Michael Reggelin
- Clemens Schöpf Institut für Organische Chemie und Biochemie, Technische Universität Darmstadt, Alarich-Weiss-Straße 4, 64287 Darmstadt, Germany
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12
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Klein A, Rovó P, Sakhrani VV, Wang Y, Holmes JB, Liu V, Skowronek P, Kukuk L, Vasa SK, Güntert P, Mueller LJ, Linser R. Atomic-resolution chemical characterization of (2x)72-kDa tryptophan synthase via four- and five-dimensional 1H-detected solid-state NMR. Proc Natl Acad Sci U S A 2022; 119:e2114690119. [PMID: 35058365 PMCID: PMC8795498 DOI: 10.1073/pnas.2114690119] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 12/13/2021] [Indexed: 02/07/2023] Open
Abstract
NMR chemical shifts provide detailed information on the chemical properties of molecules, thereby complementing structural data from techniques like X-ray crystallography and electron microscopy. Detailed analysis of protein NMR data, however, often hinges on comprehensive, site-specific assignment of backbone resonances, which becomes a bottleneck for molecular weights beyond 40 to 45 kDa. Here, we show that assignments for the (2x)72-kDa protein tryptophan synthase (665 amino acids per asymmetric unit) can be achieved via higher-dimensional, proton-detected, solid-state NMR using a single, 1-mg, uniformly labeled, microcrystalline sample. This framework grants access to atom-specific characterization of chemical properties and relaxation for the backbone and side chains, including those residues important for the catalytic turnover. Combined with first-principles calculations, the chemical shifts in the β-subunit active site suggest a connection between active-site chemistry, the electrostatic environment, and catalytically important dynamics of the portal to the β-subunit from solution.
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Affiliation(s)
- Alexander Klein
- Department of Chemistry and Pharmacy, Ludwig Maximilians University, 81377 Munich, Germany
- Department of Chemistry and Chemical Biology, TU Dortmund University, 44227 Dortmund, Germany
| | - Petra Rovó
- Department of Chemistry and Pharmacy, Ludwig Maximilians University, 81377 Munich, Germany
| | - Varun V Sakhrani
- Department of Chemistry, University of California, Riverside, CA 92521
| | - Yangyang Wang
- Department of Chemistry, University of California, Riverside, CA 92521
| | - Jacob B Holmes
- Department of Chemistry, University of California, Riverside, CA 92521
| | - Viktoriia Liu
- Department of Chemistry, University of California, Riverside, CA 92521
| | - Patricia Skowronek
- Department of Chemistry and Pharmacy, Ludwig Maximilians University, 81377 Munich, Germany
| | - Laura Kukuk
- Department of Chemistry and Chemical Biology, TU Dortmund University, 44227 Dortmund, Germany
| | - Suresh K Vasa
- Department of Chemistry and Pharmacy, Ludwig Maximilians University, 81377 Munich, Germany
- Department of Chemistry and Chemical Biology, TU Dortmund University, 44227 Dortmund, Germany
| | - Peter Güntert
- Institute of Biophysical Chemistry, Goethe University, 60438 Frankfurt am Main, Germany
- Laboratory of Physical Chemistry, Eidgenössische Technische Hochschule (ETH) Zürich, 8093 Zürich, Switzerland
- Department of Chemistry, Tokyo Metropolitan University, Tokyo 192-0397, Japan
| | - Leonard J Mueller
- Department of Chemistry, University of California, Riverside, CA 92521
| | - Rasmus Linser
- Department of Chemistry and Pharmacy, Ludwig Maximilians University, 81377 Munich, Germany;
- Department of Chemistry and Chemical Biology, TU Dortmund University, 44227 Dortmund, Germany
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13
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Holmes JB, Liu V, Caulkins BG, Hilario E, Ghosh RK, Drago VN, Young RP, Romero JA, Gill AD, Bogie PM, Paulino J, Wang X, Riviere G, Bosken YK, Struppe J, Hassan A, Guidoulianov J, Perrone B, Mentink-Vigier F, Chang CEA, Long JR, Hooley RJ, Mueser TC, Dunn MF, Mueller LJ. Imaging active site chemistry and protonation states: NMR crystallography of the tryptophan synthase α-aminoacrylate intermediate. Proc Natl Acad Sci U S A 2022; 119:e2109235119. [PMID: 34996869 PMCID: PMC8764694 DOI: 10.1073/pnas.2109235119] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/15/2021] [Indexed: 02/07/2023] Open
Abstract
NMR-assisted crystallography-the integrated application of solid-state NMR, X-ray crystallography, and first-principles computational chemistry-holds significant promise for mechanistic enzymology: by providing atomic-resolution characterization of stable intermediates in enzyme active sites, including hydrogen atom locations and tautomeric equilibria, NMR crystallography offers insight into both structure and chemical dynamics. Here, this integrated approach is used to characterize the tryptophan synthase α-aminoacrylate intermediate, a defining species for pyridoxal-5'-phosphate-dependent enzymes that catalyze β-elimination and replacement reactions. For this intermediate, NMR-assisted crystallography is able to identify the protonation states of the ionizable sites on the cofactor, substrate, and catalytic side chains as well as the location and orientation of crystallographic waters within the active site. Most notable is the water molecule immediately adjacent to the substrate β-carbon, which serves as a hydrogen bond donor to the ε-amino group of the acid-base catalytic residue βLys87. From this analysis, a detailed three-dimensional picture of structure and reactivity emerges, highlighting the fate of the L-serine hydroxyl leaving group and the reaction pathway back to the preceding transition state. Reaction of the α-aminoacrylate intermediate with benzimidazole, an isostere of the natural substrate indole, shows benzimidazole bound in the active site and poised for, but unable to initiate, the subsequent bond formation step. When modeled into the benzimidazole position, indole is positioned with C3 in contact with the α-aminoacrylate Cβ and aligned for nucleophilic attack. Here, the chemically detailed, three-dimensional structure from NMR-assisted crystallography is key to understanding why benzimidazole does not react, while indole does.
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Affiliation(s)
- Jacob B Holmes
- Department of Chemistry, University of California, Riverside, CA 92521
| | - Viktoriia Liu
- Department of Chemistry, University of California, Riverside, CA 92521
| | - Bethany G Caulkins
- Department of Chemistry, University of California, Riverside, CA 92521
- W.M. Keck Science Department, Claremont McKenna, Pitzer, and Scripps Colleges, Claremont, CA 91711
| | - Eduardo Hilario
- Department of Chemistry, University of California, Riverside, CA 92521
| | - Rittik K Ghosh
- Department of Biochemistry, University of California, Riverside, CA 92521
| | - Victoria N Drago
- Department of Chemistry and Biochemistry, University of Toledo, Toledo, OH 43606
| | - Robert P Young
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354
| | - Jennifer A Romero
- Department of Chemistry, University of California, Riverside, CA 92521
| | - Adam D Gill
- Department of Biochemistry, University of California, Riverside, CA 92521
| | - Paul M Bogie
- Department of Chemistry, University of California, Riverside, CA 92521
| | - Joana Paulino
- National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL 32310
| | - Xiaoling Wang
- National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL 32310
| | - Gwladys Riviere
- Department of Biochemistry and Molecular Biology, McKnight Brain Institute, National High Magnetic Field Laboratory, University of Florida, Gainesville, FL 32610
| | - Yuliana K Bosken
- Department of Biochemistry, University of California, Riverside, CA 92521
| | | | - Alia Hassan
- Bruker Switzerland AG 8117 Fällanden, Switzerland
| | | | | | | | - Chia-En A Chang
- Department of Chemistry, University of California, Riverside, CA 92521
| | - Joanna R Long
- Department of Biochemistry and Molecular Biology, McKnight Brain Institute, National High Magnetic Field Laboratory, University of Florida, Gainesville, FL 32610
| | - Richard J Hooley
- Department of Chemistry, University of California, Riverside, CA 92521
- Department of Biochemistry, University of California, Riverside, CA 92521
| | - Timothy C Mueser
- Department of Chemistry and Biochemistry, University of Toledo, Toledo, OH 43606
| | - Michael F Dunn
- Department of Biochemistry, University of California, Riverside, CA 92521;
| | - Leonard J Mueller
- Department of Chemistry, University of California, Riverside, CA 92521;
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14
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Immel S, Köck M, Reggelin M. NMR-Based Configurational Assignments of Natural Products: Gibbs Sampling and Bayesian Inference Using Floating Chirality Distance Geometry Calculations. Mar Drugs 2021; 20:14. [PMID: 35049868 PMCID: PMC8781118 DOI: 10.3390/md20010014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/12/2021] [Accepted: 12/20/2021] [Indexed: 02/07/2023] Open
Abstract
Floating chirality restrained distance geometry (fc-rDG) calculations are used to directly evolve structures from NMR data such as NOE-derived intramolecular distances or anisotropic residual dipolar couplings (RDCs). In contrast to evaluating pre-calculated structures against NMR restraints, multiple configurations (diastereomers) and conformations are generated automatically within the experimental limits. In this report, we show that the "unphysical" rDG pseudo energies defined from NMR violations bear statistical significance, which allows assigning probabilities to configurational assignments made that are fully compatible with the method of Bayesian inference. These "diastereomeric differentiabilities" then even become almost independent of the actual values of the force constants used to model the restraints originating from NOE or RDC data.
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Affiliation(s)
- Stefan Immel
- Clemens-Schöpf-Institut für Organische Chemie und Biochemie, Technische Universität Darmstadt, Alarich-Weiss-Straße 4, 64287 Darmstadt, Germany
| | - Matthias Köck
- Alfred-Wegener-Institut für Polar-und Meeresforschung in der Helmholtz-Gemeinschaft, Am Handelshafen 12, 27570 Bremerhaven, Germany;
| | - Michael Reggelin
- Clemens-Schöpf-Institut für Organische Chemie und Biochemie, Technische Universität Darmstadt, Alarich-Weiss-Straße 4, 64287 Darmstadt, Germany
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15
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Szell PMJ, Nilsson Lill SO, Blade H, Brown SP, Hughes LP. A toolbox for improving the workflow of NMR crystallography. SOLID STATE NUCLEAR MAGNETIC RESONANCE 2021; 116:101761. [PMID: 34736104 DOI: 10.1016/j.ssnmr.2021.101761] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/04/2021] [Accepted: 10/04/2021] [Indexed: 06/13/2023]
Abstract
NMR crystallography is a powerful tool with applications in structural characterization and crystal structure verification, to name two. However, applying this tool presents several challenges, especially for industrial users, in terms of consistency, workflow, time consumption, and the requirement for a high level of understanding of experimental solid-state NMR and GIPAW-DFT calculations. Here, we have developed a series of fully parameterized scripts for use in Materials Studio and TopSpin, based on the .magres file format, with a focus on organic molecules (e.g. pharmaceuticals), improving efficiency, robustness, and workflow. We separate these tools into three major categories: performing the DFT calculations, extracting & visualizing the results, and crystallographic modelling. These scripts will rapidly submit fully parameterized CASTEP jobs, extract data from the calculations, assist in visualizing the results, and expedite the process of structural modelling. Accompanied with these tools is a description on their functionality, documentation on how to get started and use the scripts, and links to video tutorials for guiding new users. Through the use of these tools, we hope to facilitate NMR crystallography and to harmonize the process across users.
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Affiliation(s)
| | - Sten O Nilsson Lill
- Early Product Development and Manufacturing, Pharmaceutical Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Helen Blade
- Oral Product Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, Macclesfield, UK
| | - Steven P Brown
- Department of Physics, University of Warwick, Coventry, CV4 7AL, UK.
| | - Leslie P Hughes
- Oral Product Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, Macclesfield, UK.
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16
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Cordova M, Balodis M, Simões de Almeida B, Ceriotti M, Emsley L. Bayesian probabilistic assignment of chemical shifts in organic solids. SCIENCE ADVANCES 2021; 7:eabk2341. [PMID: 34826232 PMCID: PMC8626066 DOI: 10.1126/sciadv.abk2341] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/08/2021] [Indexed: 06/13/2023]
Abstract
A prerequisite for NMR studies of organic materials is assigning each experimental chemical shift to a set of geometrically equivalent nuclei. Obtaining the assignment experimentally can be challenging and typically requires time-consuming multidimensional correlation experiments. An alternative solution for determining the assignment involves statistical analysis of experimental chemical shift databases, but no such database exists for molecular solids. Here, by combining the Cambridge Structural Database with a machine learning model of chemical shifts, we construct a statistical basis for probabilistic chemical shift assignment of organic crystals by calculating shifts for more than 200,000 compounds, enabling the probabilistic assignment of organic crystals directly from their two-dimensional chemical structure. The approach is demonstrated with the 13C and 1H assignment of 11 molecular solids with experimental shifts and benchmarked on 100 crystals using predicted shifts. The correct assignment was found among the two most probable assignments in more than 80% of cases.
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Affiliation(s)
- Manuel Cordova
- Laboratory of Magnetic Resonance, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
- National Centre for Computational Design and Discovery of Novel Materials MARVEL, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Martins Balodis
- Laboratory of Magnetic Resonance, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Bruno Simões de Almeida
- Laboratory of Magnetic Resonance, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Michele Ceriotti
- National Centre for Computational Design and Discovery of Novel Materials MARVEL, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
- Laboratory of Computational Science and Modelling, Institute of Materials, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Lyndon Emsley
- Laboratory of Magnetic Resonance, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
- National Centre for Computational Design and Discovery of Novel Materials MARVEL, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
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17
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Deringer VL, Bartók AP, Bernstein N, Wilkins DM, Ceriotti M, Csányi G. Gaussian Process Regression for Materials and Molecules. Chem Rev 2021; 121:10073-10141. [PMID: 34398616 PMCID: PMC8391963 DOI: 10.1021/acs.chemrev.1c00022] [Citation(s) in RCA: 232] [Impact Index Per Article: 77.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Indexed: 12/18/2022]
Abstract
We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come.
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Affiliation(s)
- Volker L. Deringer
- Department
of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford OX1 3QR, United Kingdom
| | - Albert P. Bartók
- Department
of Physics and Warwick Centre for Predictive Modelling, School of
Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Noam Bernstein
- Center
for Computational Materials Science, U.S.
Naval Research Laboratory, Washington D.C. 20375, United States
| | - David M. Wilkins
- Atomistic
Simulation Centre, School of Mathematics and Physics, Queen’s University Belfast, Belfast BT7 1NN, Northern Ireland, United Kingdom
| | - Michele Ceriotti
- Laboratory
of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
- National
Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale
de Lausanne, Lausanne, Switzerland
| | - Gábor Csányi
- Engineering
Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
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18
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Abstract
Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first-principles based virtual sampling of this space, for example, in search of novel molecules or materials which exhibit desirable properties, is therefore prohibitive for all but the smallest subsets and simplest properties. We review studies aimed at tackling this challenge using modern machine learning techniques based on (i) synthetic data, typically generated using quantum mechanics based methods, and (ii) model architectures inspired by quantum mechanics. Such Quantum mechanics based Machine Learning (QML) approaches combine the numerical efficiency of statistical surrogate models with an ab initio view on matter. They rigorously reflect the underlying physics in order to reach universality and transferability across CCS. While state-of-the-art approximations to quantum problems impose severe computational bottlenecks, recent QML based developments indicate the possibility of substantial acceleration without sacrificing the predictive power of quantum mechanics.
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Affiliation(s)
- Bing Huang
- Faculty
of Physics, University of Vienna, 1090 Vienna, Austria
| | - O. Anatole von Lilienfeld
- Faculty
of Physics, University of Vienna, 1090 Vienna, Austria
- Institute
of Physical Chemistry and National Center for Computational Design
and Discovery of Novel Materials (MARVEL), Department of Chemistry, University of Basel, 4056 Basel, Switzerland
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19
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Engel EA, Kapil V, Ceriotti M. Importance of Nuclear Quantum Effects for NMR Crystallography. J Phys Chem Lett 2021; 12:7701-7707. [PMID: 34355903 DOI: 10.1021/acs.jpclett.1c01987] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The resolving power of solid-state nuclear magnetic resonance (NMR) crystallography depends heavily on the accuracy of computational predictions of NMR chemical shieldings of candidate structures, which are usually taken to be local minima in the potential energy. To test the limits of this approximation, we systematically study the importance of finite-temperature and quantum nuclear fluctuations for 1H, 13C, and 15N shieldings in polymorphs of three paradigmatic molecular crystals: benzene, glycine, and succinic acid. The effect of quantum fluctuations is comparable to the typical errors of shielding predictions for static nuclei with respect to experiments, and their inclusion improves the agreement with measurements, translating to more reliable assignment of the NMR spectra to the correct candidate structure. The use of integrated machine-learning models, trained on first-principles energies and shieldings, renders rigorous sampling of nuclear fluctuations affordable, setting a new standard for the calculations underlying NMR structure determinations.
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Affiliation(s)
- Edgar A Engel
- TCM Group, Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom
| | - Venkat Kapil
- Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Michele Ceriotti
- Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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20
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Musil F, Grisafi A, Bartók AP, Ortner C, Csányi G, Ceriotti M. Physics-Inspired Structural Representations for Molecules and Materials. Chem Rev 2021; 121:9759-9815. [PMID: 34310133 DOI: 10.1021/acs.chemrev.1c00021] [Citation(s) in RCA: 144] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable representation. The development of atomic-scale representations has played, and continues to play, a central role in the success of machine-learning methods for chemistry and materials science. This review summarizes the current understanding of the nature and characteristics of the most commonly used structural and chemical descriptions of atomistic structures, highlighting the deep underlying connections between different frameworks and the ideas that lead to computationally efficient and universally applicable models. It emphasizes the link between properties, structures, their physical chemistry, and their mathematical description, provides examples of recent applications to a diverse set of chemical and materials science problems, and outlines the open questions and the most promising research directions in the field.
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Affiliation(s)
- Felix Musil
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Andrea Grisafi
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Albert P Bartók
- Department of Physics and Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Christoph Ortner
- University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada
| | - Gábor Csányi
- Engineering Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom
| | - Michele Ceriotti
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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21
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Cersonsky RK, Helfrecht BA, Engel EA, Kliavinek S, Ceriotti M. Improving sample and feature selection with principal covariates regression. MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2021. [DOI: 10.1088/2632-2153/abfe7c] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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22
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Cordova M, Balodis M, Hofstetter A, Paruzzo F, Nilsson Lill SO, Eriksson ESE, Berruyer P, Simões de Almeida B, Quayle MJ, Norberg ST, Svensk Ankarberg A, Schantz S, Emsley L. Structure determination of an amorphous drug through large-scale NMR predictions. Nat Commun 2021; 12:2964. [PMID: 34016980 PMCID: PMC8137699 DOI: 10.1038/s41467-021-23208-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 03/25/2021] [Indexed: 11/17/2022] Open
Abstract
Knowledge of the structure of amorphous solids can direct, for example, the optimization of pharmaceutical formulations, but atomic-level structure determination in amorphous molecular solids has so far not been possible. Solid-state nuclear magnetic resonance (NMR) is among the most popular methods to characterize amorphous materials, and molecular dynamics (MD) simulations can help describe the structure of disordered materials. However, directly relating MD to NMR experiments in molecular solids has been out of reach until now because of the large size of these simulations. Here, using a machine learning model of chemical shifts, we determine the atomic-level structure of the hydrated amorphous drug AZD5718 by combining dynamic nuclear polarization-enhanced solid-state NMR experiments with predicted chemical shifts for MD simulations of large systems. From these amorphous structures we then identify H-bonding motifs and relate them to local intermolecular complex formation energies.
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Affiliation(s)
- Manuel Cordova
- Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- National Centre for Computational Design and Discovery of Novel Materials MARVEL, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Martins Balodis
- Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Albert Hofstetter
- Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Federico Paruzzo
- Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Sten O Nilsson Lill
- Early Product Development and Manufacturing, Pharmaceutical Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Emma S E Eriksson
- Early Product Development and Manufacturing, Pharmaceutical Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Pierrick Berruyer
- Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bruno Simões de Almeida
- Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Michael J Quayle
- New Modalities and Parenteral Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, Gothenburg, Sweden
| | - Stefan T Norberg
- Oral Product Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, Gothenburg, Sweden
| | - Anna Svensk Ankarberg
- Oral Product Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, Gothenburg, Sweden
| | - Staffan Schantz
- Oral Product Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, Gothenburg, Sweden.
| | - Lyndon Emsley
- Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
- National Centre for Computational Design and Discovery of Novel Materials MARVEL, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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23
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Gupta A, Chakraborty S, Ramakrishnan R. Revving up 13C NMR shielding predictions across chemical space: benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2021. [DOI: 10.1088/2632-2153/abe347] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Abstract
The requirement for accelerated and quantitatively accurate screening of nuclear magnetic resonance spectra across the small molecules chemical compound space is two-fold: (1) a robust ‘local’ machine learning (ML) strategy capturing the effect of the neighborhood on an atom’s ‘near-sighted’ property—chemical shielding; (2) an accurate reference dataset generated with a state-of-the-art first-principles method for training. Herein we report the QM9-NMR dataset comprising isotropic shielding of over 0.8 million C atoms in 134k molecules of the QM9 dataset in gas and five common solvent phases. Using these data for training, we present benchmark results for the prediction transferability of kernel-ridge regression models with popular local descriptors. Our best model, trained on 100k samples, accurately predicts isotropic shielding of 50k ‘hold-out’ atoms with a mean error of less than 1.9 ppm. For the rapid prediction of new query molecules, the models were trained on geometries from an inexpensive theory. Furthermore, by using a Δ-ML strategy, we quench the error below 1.4 ppm. Finally, we test the transferability on non-trivial benchmark sets that include benchmark molecules comprising 10–17 heavy atoms and drugs.
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24
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Hope MA, Nakamura T, Ahlawat P, Mishra A, Cordova M, Jahanbakhshi F, Mladenović M, Runjhun R, Merten L, Hinderhofer A, Carlsen BI, Kubicki DJ, Gershoni-Poranne R, Schneeberger T, Carbone LC, Liu Y, Zakeeruddin SM, Lewinski J, Hagfeldt A, Schreiber F, Rothlisberger U, Grätzel M, Milić JV, Emsley L. Nanoscale Phase Segregation in Supramolecular π-Templating for Hybrid Perovskite Photovoltaics from NMR Crystallography. J Am Chem Soc 2021; 143:1529-1538. [DOI: 10.1021/jacs.0c11563] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Michael A. Hope
- Laboratory of Magnetic Resonance, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Toru Nakamura
- Laboratory of Photonics and Interfaces, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
- Technology Innovation Division, Panasonic Corporation, Osaka 570-8501, Japan
| | - Paramvir Ahlawat
- Laboratory of Computational Chemistry and Biochemistry, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Aditya Mishra
- Laboratory of Magnetic Resonance, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Manuel Cordova
- Laboratory of Magnetic Resonance, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Farzaneh Jahanbakhshi
- Laboratory of Computational Chemistry and Biochemistry, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Marko Mladenović
- Laboratory of Computational Chemistry and Biochemistry, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Rashmi Runjhun
- Laboratory of Photonics and Interfaces, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
- Institute of Physical Chemistry, Polish Academy of Sciences, Warsaw 01-224, Poland
| | - Lena Merten
- Institut für Angewandte Physik, Universität Tübingen, 72076 Tübingen, Germany
| | | | - Brian I. Carlsen
- Laboratory of Photomolecular Science, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Dominik J. Kubicki
- Laboratory of Magnetic Resonance, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | | | - Thomas Schneeberger
- Laboratory of Photonics and Interfaces, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Loï C. Carbone
- Laboratory of Photonics and Interfaces, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Yuhang Liu
- Laboratory of Photonics and Interfaces, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Shaik M. Zakeeruddin
- Laboratory of Photonics and Interfaces, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Janusz Lewinski
- Institute of Physical Chemistry, Polish Academy of Sciences, Warsaw 01-224, Poland
| | - Anders Hagfeldt
- Laboratory of Photomolecular Science, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Frank Schreiber
- Institut für Angewandte Physik, Universität Tübingen, 72076 Tübingen, Germany
| | - Ursula Rothlisberger
- Laboratory of Computational Chemistry and Biochemistry, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Michael Grätzel
- Laboratory of Photonics and Interfaces, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Jovana V. Milić
- Laboratory of Photonics and Interfaces, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | - Lyndon Emsley
- Laboratory of Magnetic Resonance, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
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25
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Reif B, Ashbrook SE, Emsley L, Hong M. Solid-state NMR spectroscopy. NATURE REVIEWS. METHODS PRIMERS 2021; 1:2. [PMID: 34368784 PMCID: PMC8341432 DOI: 10.1038/s43586-020-00002-1] [Citation(s) in RCA: 172] [Impact Index Per Article: 57.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/29/2020] [Indexed: 12/18/2022]
Abstract
Solid-state nuclear magnetic resonance (NMR) spectroscopy is an atomic-level method used to determine the chemical structure, three-dimensional structure, and dynamics of solids and semi-solids. This Primer summarizes the basic principles of NMR as applied to the wide range of solid systems. The fundamental nuclear spin interactions and the effects of magnetic fields and radiofrequency pulses on nuclear spins are the same as in liquid-state NMR. However, because of the anisotropy of the interactions in the solid state, the majority of high-resolution solid-state NMR spectra is measured under magic-angle spinning (MAS), which has profound effects on the types of radiofrequency pulse sequences required to extract structural and dynamical information. We describe the most common MAS NMR experiments and data analysis approaches for investigating biological macromolecules, organic materials, and inorganic solids. Continuing development of sensitivity-enhancement approaches, including 1H-detected fast MAS experiments, dynamic nuclear polarization, and experiments tailored to ultrahigh magnetic fields, is described. We highlight recent applications of solid-state NMR to biological and materials chemistry. The Primer ends with a discussion of current limitations of NMR to study solids, and points to future avenues of development to further enhance the capabilities of this sophisticated spectroscopy for new applications.
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Affiliation(s)
- Bernd Reif
- Technische Universität München, Department Chemie, Lichtenbergstr. 4, D-85747 Garching, Germany
| | - Sharon E. Ashbrook
- School of Chemistry, University of St Andrews, North Haugh, St Andrews, KY16 9ST, UK
| | - Lyndon Emsley
- École Polytechnique Fédérale de Lausanne (EPFL), Institut des sciences et ingénierie chimiques, CH-1015 Lausanne, Switzerland
| | - Mei Hong
- Department of Chemistry and Francis Bitter Magnet Laboratory, Massachusetts Institute of Technology, 170 Albany Street, Cambridge, MA 02139
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26
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Dračínský M, Vícha J, Bártová K, Hodgkinson P. Towards Accurate Predictions of Proton NMR Spectroscopic Parameters in Molecular Solids. Chemphyschem 2020; 21:2075-2083. [PMID: 32691463 DOI: 10.1002/cphc.202000629] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 07/20/2020] [Indexed: 12/18/2022]
Abstract
The factors contributing to the accuracy of quantum-chemical calculations for the prediction of proton NMR chemical shifts in molecular solids are systematically investigated. Proton chemical shifts of six solid amino acids with hydrogen atoms in various bonding environments (CH, CH2 , CH3 , OH, SH and NH3 ) were determined experimentally using ultra-fast magic-angle spinning and proton-detected 2D NMR experiments. The standard DFT method commonly used for the calculations of NMR parameters of solids is shown to provide chemical shifts that deviate from experiment by up to 1.5 ppm. The effects of the computational level (hybrid DFT functional, coupled-cluster calculation, inclusion of relativistic spin-orbit coupling) are thoroughly discussed. The effect of molecular dynamics and nuclear quantum effects are investigated using path-integral molecular dynamics (PIMD) simulations. It is demonstrated that the accuracy of the calculated proton chemical shifts is significantly better when these effects are included in the calculations.
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Affiliation(s)
- Martin Dračínský
- Institute of Organic Chemistry and Biochemistry, AS CR, Flemingovo nám. 2, Prague, CZ-16610, Czech Republic
| | - Jan Vícha
- Institute of Organic Chemistry and Biochemistry, AS CR, Flemingovo nám. 2, Prague, CZ-16610, Czech Republic.,Centre of Polymer Systems, Tomas Bata University in Zlín, Tomáše Bati 5678, Zlín, CZ-760 01, Czech Republic
| | - Kateřina Bártová
- Institute of Organic Chemistry and Biochemistry, AS CR, Flemingovo nám. 2, Prague, CZ-16610, Czech Republic
| | - Paul Hodgkinson
- Department of Chemistry, Durham University, South Road, DH1 3LE, Durham, UK
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27
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Corlett EK, Blade H, Hughes LP, Sidebottom PJ, Walker D, Walton RI, Brown SP. Investigating discrepancies between experimental solid-state NMR and GIPAW calculation: NC-N 13C and OH⋯O 1H chemical shifts in pyridinium fumarates and their cocrystals. SOLID STATE NUCLEAR MAGNETIC RESONANCE 2020; 108:101662. [PMID: 32590237 DOI: 10.1016/j.ssnmr.2020.101662] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 03/25/2020] [Accepted: 03/25/2020] [Indexed: 06/11/2023]
Abstract
An NMR crystallography analysis is presented for four solid-state structures of pyridine fumarates and their cocrystals, using crystal structures deposited in the Cambridge Crystallographic Data Centre, CCDC. Experimental one-dimensional one-pulse 1H and 13C cross-polarisation (CP) magic-angle spinning (MAS) nuclear magnetic resonance (NMR) and two-dimensional 14N-1H heteronuclear multiple-quantum coherence MAS NMR spectra are compared with gauge-including projector augmented wave (GIPAW) calculations of the 1H and 13C chemical shifts and the 14N shifts that additionally depend on the quadrupolar interaction. Considering the high ppm (>10 ppm) 1H resonances, while there is good agreement (within 0.4 ppm) between experiment and GIPAW calculation for the hydrogen-bonded NH moieties, the hydrogen-bonded fumaric acid OH resonances are 1.2-1.9 ppm higher in GIPAW calculation as compared to experiment. For the cocrystals of a salt and a salt formed by 2-amino-5-methylpyridinium and 2-amino-6-methylpyridinium ions, a large discrepancy of 4.2 and 5.9 ppm between experiment and GIPAW calculation is observed for the quaternary ring carbon 13C resonance that is directly bonded to two nitrogens (in the ring and in the amino group). By comparison, there is excellent agreement (within 0.2 ppm) for the quaternary ring carbon 13C resonance directly bonded to the ring nitrogen for the salt and cocrystal of a salt formed by 2,6-lutidinium and 2,5-lutidinium, respectively.
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Affiliation(s)
- Emily K Corlett
- Department of Physics, University of Warwick, Coventry, CV4 7AL, UK
| | - Helen Blade
- Pharmaceutical Development, AstraZeneca, Macclesfield, SK10 2NA, UK
| | - Leslie P Hughes
- Pharmaceutical Development, AstraZeneca, Macclesfield, SK10 2NA, UK
| | - Philip J Sidebottom
- Syngenta, Jealott's Hill International Research Centre, Bracknell, Berkshire, RG42 6EY, UK
| | - David Walker
- Department of Physics, University of Warwick, Coventry, CV4 7AL, UK
| | - Richard I Walton
- Department of Chemistry, University of Warwick, Coventry, CV4 7AL, UK
| | - Steven P Brown
- Department of Physics, University of Warwick, Coventry, CV4 7AL, UK.
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28
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Vícha J, Švec P, Růžičková Z, Samsonov MA, Bártová K, Růžička A, Straka M, Dračínský M. Experimental and Theoretical Evidence of Spin‐Orbit Heavy Atom on the Light Atom
1
H NMR Chemical Shifts Induced through H⋅⋅⋅I
−
Hydrogen Bond. Chemistry 2020; 26:8698-8702. [DOI: 10.1002/chem.202001532] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Indexed: 01/11/2023]
Affiliation(s)
- Jan Vícha
- Institute of Organic Chemistry and Biochemistry, AS CR Flemingovo nám. 2 Prague 16610 Czech Republic
- Centre of Polymer SystemsTomas Bata University in Zlín Tomáše Bati 5678 Zlín 760 01 Czech Republic
| | - Petr Švec
- Department of General and Inorganic ChemistryUniversity of Pardubice Studentská 573 Pardubice 53210 Czech Republic
| | - Zdeňka Růžičková
- Department of General and Inorganic ChemistryUniversity of Pardubice Studentská 573 Pardubice 53210 Czech Republic
| | - Maksim A. Samsonov
- Department of General and Inorganic ChemistryUniversity of Pardubice Studentská 573 Pardubice 53210 Czech Republic
| | - Kateřina Bártová
- Institute of Organic Chemistry and Biochemistry, AS CR Flemingovo nám. 2 Prague 16610 Czech Republic
| | - Aleš Růžička
- Department of General and Inorganic ChemistryUniversity of Pardubice Studentská 573 Pardubice 53210 Czech Republic
| | - Michal Straka
- Institute of Organic Chemistry and Biochemistry, AS CR Flemingovo nám. 2 Prague 16610 Czech Republic
| | - Martin Dračínský
- Institute of Organic Chemistry and Biochemistry, AS CR Flemingovo nám. 2 Prague 16610 Czech Republic
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29
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Xie J, Zhang K, Frank AT. PyShifts: A PyMOL Plugin for Chemical Shift-Based Analysis of Biomolecular Ensembles. J Chem Inf Model 2020; 60:1073-1078. [PMID: 32011127 DOI: 10.1021/acs.jcim.9b01039] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Here, we present PyShifts-a PyMOL plugin for chemical shift-based analysis of biomolecular ensembles. With PyShifts, users can compare and visualize differences between experimentally measured and computationally predicted chemical shifts. When analyzing multiple conformations of a biomolecule with PyShifts, users can also sort a set of conformations based on chemical shift differences and identify the conformers that exhibit the best agreement between measured and predicted chemical shifts. Although we have integrated PyShifts with the chemical shift predictors LARMORD and LARMORCα, PyShifts can read in chemical shifts from any source, and so, users can employ PyShifts to analyze biomolecular structures using chemical shifts computed by any chemical shift predictor. We envision, therefore, that PyShifts (https://github.com/atfrank/PyShifts) will find utility as a general-purpose tool for exploring chemical shift-structure relationships in biomolecular ensembles.
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Affiliation(s)
- Jingru Xie
- Physics Department, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
| | - Kexin Zhang
- Department of Chemistry, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
| | - Aaron T Frank
- Departments of Biophysics and Chemistry, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
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Abstract
As the quantum chemistry (QC) community embraces machine learning (ML), the number of new methods and applications based on the combination of QC and ML is surging. In this Perspective, a view of the current state of affairs in this new and exciting research field is offered, challenges of using machine learning in quantum chemistry applications are described, and potential future developments are outlined. Specifically, examples of how machine learning is used to improve the accuracy and accelerate quantum chemical research are shown. Generalization and classification of existing techniques are provided to ease the navigation in the sea of literature and to guide researchers entering the field. The emphasis of this Perspective is on supervised machine learning.
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Affiliation(s)
- Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
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