1
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Krivoshchapov NV, Medvedev MG. Accurate and Efficient Conformer Sampling of Cyclic Drug-Like Molecules with Inverse Kinematics. J Chem Inf Model 2024; 64:4542-4552. [PMID: 38776465 DOI: 10.1021/acs.jcim.3c02040] [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: 05/25/2024]
Abstract
Identification of all of the influential conformers of biomolecules is a crucial step in many tasks of computational biochemistry. Specifically, molecular docking, a key component of in silico drug development, requires a comprehensive set of conformations for potential candidates in order to generate the optimal ligand-receptor poses and, ultimately, find the best drug candidates. However, the presence of flexible cycles in a molecule complicates the initial search for conformers since exhaustive sampling algorithms via torsional random and systematic searches become very inefficient. The devised inverse-kinematics-based Monte Carlo with refinement (MCR) algorithm identifies independently rotatable dihedral angles in (poly)cyclic molecules and uses them to perform global conformational sampling, outperforming popular alternatives (MacroModel, CREST, and RDKit) in terms of speed and diversity of the resulting conformer ensembles. Moreover, MCR quickly and accurately recovers naturally occurring macrocycle conformations for most of the considered molecules.
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Affiliation(s)
- Nikolai V Krivoshchapov
- N. D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow 119991, Russian Federation
| | - Michael G Medvedev
- N. D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow 119991, Russian Federation
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2
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Kuznetsov M, Ryabov F, Schutski R, Shayakhmetov R, Lin YC, Aliper A, Polykovskiy D. COSMIC: Molecular Conformation Space Modeling in Internal Coordinates with an Adversarial Framework. J Chem Inf Model 2024; 64:3610-3620. [PMID: 38668753 PMCID: PMC11094738 DOI: 10.1021/acs.jcim.3c00989] [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: 10/27/2023] [Revised: 03/29/2024] [Accepted: 04/02/2024] [Indexed: 05/14/2024]
Abstract
The fast and accurate conformation space modeling is an essential part of computational approaches for solving ligand and structure-based drug discovery problems. Recent state-of-the-art diffusion models for molecular conformation generation show promising distribution coverage and physical plausibility metrics but suffer from a slow sampling procedure. We propose a novel adversarial generative framework, COSMIC, that shows comparable generative performance but provides a time-efficient sampling and training procedure. Given a molecular graph and random noise, the generator produces a conformation in two stages. First, it constructs a conformation in a rotation and translation invariant representation─internal coordinates. In the second step, the model predicts the distances between neighboring atoms and performs a few fast optimization steps to refine the initial conformation. The proposed model considers conformation energy, achieving comparable space coverage, and diversity metrics results.
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Affiliation(s)
- Maksim Kuznetsov
- Insilico
Medicine Canada Inc., 1250 René-Lévesque Ouest, Suite 3710, Montréal, Québec H3B 4W8, Canada
| | - Fedor Ryabov
- Insilico
Medicine Hong Kong Ltd., Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak
Shek Kok, New Territories, Hong Kong 999077, China
| | - Roman Schutski
- Insilico
Medicine Hong Kong Ltd., Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak
Shek Kok, New Territories, Hong Kong 999077, China
| | - Rim Shayakhmetov
- Insilico
Medicine Canada Inc., 1250 René-Lévesque Ouest, Suite 3710, Montréal, Québec H3B 4W8, Canada
| | - Yen-Chu Lin
- Insilico
Medicine Taiwan Ltd., Taipei City 110208, Taiwan
| | - Alex Aliper
- Insilico
Medicine Hong Kong Ltd., Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak
Shek Kok, New Territories, Hong Kong 999077, China
| | - Daniil Polykovskiy
- Insilico
Medicine Canada Inc., 1250 René-Lévesque Ouest, Suite 3710, Montréal, Québec H3B 4W8, Canada
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3
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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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4
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Das S, Merz KM. Molecular Gas-Phase Conformational Ensembles. J Chem Inf Model 2024; 64:749-760. [PMID: 38206321 DOI: 10.1021/acs.jcim.3c01309] [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: 01/12/2024]
Abstract
Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.
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Affiliation(s)
- Susanta Das
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
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5
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Baillif B, Cole J, Giangreco I, McCabe P, Bender A. Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations. J Cheminform 2023; 15:124. [PMID: 38129933 PMCID: PMC10740246 DOI: 10.1186/s13321-023-00794-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 12/10/2023] [Indexed: 12/23/2023] Open
Abstract
Identifying bioactive conformations of small molecules is an essential process for virtual screening applications relying on three-dimensional structure such as molecular docking. For most small molecules, conformer generators retrieve at least one bioactive-like conformation, with an atomic root-mean-square deviation (ARMSD) lower than 1 Å, among the set of low-energy conformers generated. However, there is currently no general method to prioritise these likely target-bound conformations in the ensemble. In this work, we trained atomistic neural networks (AtNNs) on 3D information of generated conformers of a curated subset of PDBbind ligands to predict the ARMSD to their closest bioactive conformation, and evaluated the early enrichment of bioactive-like conformations when ranking conformers by AtNN prediction. AtNN ranking was compared with bioactivity-unaware baselines such as ascending Sage force field energy ranking, and a slower bioactivity-based baseline ranking by ascending Torsion Fingerprint Deviation to the Maximum Common Substructure to the most similar molecule in the training set (TFD2SimRefMCS). On test sets from random ligand splits of PDBbind, ranking conformers using ComENet, the AtNN encoding the most 3D information, leads to early enrichment of bioactive-like conformations with a median BEDROC of 0.29 ± 0.02, outperforming the best bioactivity-unaware Sage energy ranking baseline (median BEDROC of 0.18 ± 0.02), and performing on a par with the bioactivity-based TFD2SimRefMCS baseline (median BEDROC of 0.31 ± 0.02). The improved performance of the AtNN and TFD2SimRefMCS baseline is mostly observed on test set ligands that bind proteins similar to proteins observed in the training set. On a more challenging subset of flexible molecules, the bioactivity-unaware baselines showed median BEDROCs up to 0.02, while AtNNs and TFD2SimRefMCS showed median BEDROCs between 0.09 and 0.13. When performing rigid ligand re-docking of PDBbind ligands with GOLD using the 1% top-ranked conformers, ComENet ranked conformers showed a higher successful docking rate than bioactivity-unaware baselines, with a rate of 0.48 ± 0.02 compared to CSD probability baseline with a rate of 0.39 ± 0.02. Similarly, on a pharmacophore searching experiment, selecting the 20% top-ranked conformers ranked by ComENet showed higher hit rate compared to baselines. Hence, the approach presented here uses AtNNs successfully to focus conformer ensembles towards bioactive-like conformations, representing an opportunity to reduce computational expense in virtual screening applications on known targets that require input conformations.
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Affiliation(s)
- Benoit Baillif
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, Cambridge, CB2 1EW, UK
| | - Jason Cole
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK
| | - Ilenia Giangreco
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK
- Exscientia plc, The Schrödinger Building, Oxford Science Park, Oxford, OX4 4GE, UK
| | - Patrick McCabe
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK
| | - Andreas Bender
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, Cambridge, CB2 1EW, UK.
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6
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Lee GR, Pellock SJ, Norn C, Tischer D, Dauparas J, Anischenko I, Mercer JAM, Kang A, Bera A, Nguyen H, Goreshnik I, Vafeados D, Roullier N, Han HL, Coventry B, Haddox HK, Liu DR, Yeh AHW, Baker D. Small-molecule binding and sensing with a designed protein family. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.01.565201. [PMID: 37961294 PMCID: PMC10635051 DOI: 10.1101/2023.11.01.565201] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Despite transformative advances in protein design with deep learning, the design of small-molecule-binding proteins and sensors for arbitrary ligands remains a grand challenge. Here we combine deep learning and physics-based methods to generate a family of proteins with diverse and designable pocket geometries, which we employ to computationally design binders for six chemically and structurally distinct small-molecule targets. Biophysical characterization of the designed binders revealed nanomolar to low micromolar binding affinities and atomic-level design accuracy. The bound ligands are exposed at one edge of the binding pocket, enabling the de novo design of chemically induced dimerization (CID) systems; we take advantage of this to create a biosensor with nanomolar sensitivity for cortisol. Our approach provides a general method to design proteins that bind and sense small molecules for a wide range of analytical, environmental, and biomedical applications.
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7
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Talmazan RA, Podewitz M. PyConSolv: A Python Package for Conformer Generation of (Metal-Containing) Systems in Explicit Solvent. J Chem Inf Model 2023; 63:5400-5407. [PMID: 37606893 PMCID: PMC10498442 DOI: 10.1021/acs.jcim.3c00798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Indexed: 08/23/2023]
Abstract
We introduce PyConSolv, a freely available Python package that automates the generation of conformers of metal- and nonmetal-containing complexes in explicit solvent, through classical molecular dynamics simulations. Using a streamlined workflow and interfacing with widely used computational chemistry software, PyConSolv is an all-in-one tool for the generation of conformers in any solvent. Input requirements are minimal; only the geometry of the structure and the desired solvent in xyz (XMOL) format are needed. The package can also account for charged systems, by including arbitrary counterions in the simulation. A bonded model parametrization is performed automatically, utilizing AmberTools, ORCA, and Multiwfn software packages. PyConSolv provides a selection of preparametrized solvents and counterions for use in classical molecular dynamics simulations. We show the applicability of our package on a number of (transition-metal-containing) systems. The software is provided open source and free of charge.
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Affiliation(s)
- R. A. Talmazan
- Institute
of Materials Chemistry, TU Wien, Getreidemarkt 9, A-1060 Wien, Austria
| | - M. Podewitz
- Institute
of Materials Chemistry, TU Wien, Getreidemarkt 9, A-1060 Wien, Austria
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8
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Zhang T, Jiang S, Li T, Liu Y, Zhang Y. Identified Isosteric Replacements of Ligands' Glycosyl Domain by Data Mining. ACS OMEGA 2023; 8:25165-25184. [PMID: 37483233 PMCID: PMC10357434 DOI: 10.1021/acsomega.3c02243] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 06/09/2023] [Indexed: 07/25/2023]
Abstract
Biologically equivalent replacements of key moieties in molecules rationalize scaffold hopping, patent busting, or R-group enumeration. Yet, this information may depend upon the expert-defined space, and might be subjective and biased toward the chemistries they get used to. Most importantly, these practices are often informatively incomplete since they are often compromised by a try-and-error cycle, and although they depict what kind of substructures are suitable for the replacement occurrence, they fail to explain the driving forces to support such interchanges. The protein data bank (PDB) encodes a receptor-ligand interaction pattern and could be an optional source to mine structural surrogates. However, manual decoding of PDB has become almost impossible and redundant to excavate the bioisosteric know-how. Therefore, a text parsing workflow has been developed to automatically extract the local structural replacement of a specific structure from PDB by finding spatial and steric interaction overlaps between the fragments in endogenous ligands and particular ligand fragments. Taking the glycosyl domain for instance, a total of 49 520 replacements that overlap on nucleotide ribose were identified and categorized based on their SMILE codes. A predominately ring system, such as aliphatic and aromatic rings, was observed; yet, amide and sulfonamide replacements also occur. We believe these findings may enlighten medicinal chemists on the structure design and optimization of ligands using the bioisosteric replacement strategy.
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Affiliation(s)
- Tinghao Zhang
- Xi’an
Institute of Flexible Electronics (IFE) and Xi’an Institute
of Biomedical Materials & Engineering (IBME), Northwestern Polytechnical
University, 127 West Youyi Road, Xi’an 710072, China
| | - Shenghao Jiang
- School of
Computer Science, Northwestern Polytechnical
University, 127 West
Youyi Road, Xi’an 710072, China
| | - Ting Li
- Xi’an
Institute of Flexible Electronics (IFE) and Xi’an Institute
of Biomedical Materials & Engineering (IBME), Northwestern Polytechnical
University, 127 West Youyi Road, Xi’an 710072, China
| | - Yan Liu
- Xi’an
Institute of Flexible Electronics (IFE) and Xi’an Institute
of Biomedical Materials & Engineering (IBME), Northwestern Polytechnical
University, 127 West Youyi Road, Xi’an 710072, China
| | - Yuezhou Zhang
- Xi’an
Institute of Flexible Electronics (IFE) and Xi’an Institute
of Biomedical Materials & Engineering (IBME), Northwestern Polytechnical
University, 127 West Youyi Road, Xi’an 710072, China
- Ningbo
Institute of Northwestern Polytechnical University, Frontiers Science
Center for Flexible Electronics (FSCFE), Key laboratory of Flexible
Electronics of Zhejiang Province, Ningbo Institute of Northwestern
Polytechnical University, 218 Qingyi Road, Ningbo 315103, China
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9
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Baillif B, Cole J, McCabe P, Bender A. Deep generative models for 3D molecular structure. Curr Opin Struct Biol 2023; 80:102566. [DOI: 10.1016/j.sbi.2023.102566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 02/05/2023] [Accepted: 02/15/2023] [Indexed: 03/30/2023]
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10
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Kojasoy V, Tantillo DJ. Impacts of noncovalent interactions involving sulfur atoms on protein stability, structure, folding, and bioactivity. Org Biomol Chem 2022; 21:11-23. [PMID: 36345987 DOI: 10.1039/d2ob01602h] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This review discusses the various types of noncovalent interactions in which sulfur atoms participate and their effects on protein stability, structure, folding and bioactivity. Current approaches and recommendations for modelling these noncovalent interactions (in terms of both geometries and interaction energies) are highlighted.
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Affiliation(s)
- Volga Kojasoy
- Department of Chemistry, University of California, Davis, 1 Shields Avenue, Davis, CA, 95616, USA.
| | - Dean J Tantillo
- Department of Chemistry, University of California, Davis, 1 Shields Avenue, Davis, CA, 95616, USA.
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11
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Jiang R, Gogineni T, Kammeraad J, He Y, Tewari A, Zimmerman PM. Conformer-RL: A deep reinforcement learning library for conformer generation. J Comput Chem 2022; 43:1880-1886. [PMID: 36000759 PMCID: PMC9542157 DOI: 10.1002/jcc.26984] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 07/21/2022] [Accepted: 07/31/2022] [Indexed: 11/21/2022]
Abstract
Conformer‐RL is an open‐source Python package for applying deep reinforcement learning (RL) to the task of generating a diverse set of low‐energy conformations for a single molecule. The library features a simple interface to train a deep RL conformer generation model on any covalently bonded molecule or polymer, including most drug‐like molecules. Under the hood, it implements state‐of‐the‐art RL algorithms and graph neural network architectures tuned specifically for molecular structures. Conformer‐RL is also a platform for researching new algorithms and neural network architectures for conformer generation, as the library contains modular class interfaces for RL environments and agents, allowing users to easily swap components with their own implementations. Additionally, it comes with tools to visualize and save generated conformers for further analysis. Conformer‐RL is well‐tested and thoroughly documented with tutorials for each of the functionalities mentioned above, and is available on PyPi and Github: https://github.com/ZimmermanGroup/conformer-rl.
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Affiliation(s)
- Runxuan Jiang
- Department of EECS, University of Michigan, Ann Arbor, Michigan, USA
| | - Tarun Gogineni
- Department of EECS, University of Michigan, Ann Arbor, Michigan, USA
| | - Joshua Kammeraad
- Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA.,Department of Chemistry, University of Michigan, Ann Arbor, Michigan, USA
| | - Yifei He
- Department of EECS, University of Michigan, Ann Arbor, Michigan, USA
| | - Ambuj Tewari
- Department of EECS, University of Michigan, Ann Arbor, Michigan, USA.,Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Paul M Zimmerman
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan, USA
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12
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Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system. Mol Divers 2022; 27:959-985. [PMID: 35819579 DOI: 10.1007/s11030-022-10489-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/21/2022] [Indexed: 12/11/2022]
Abstract
CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.
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13
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Meixner M, Zachmann M, Metzler S, Scheerer J, Zacharias M, Antes I. Dynamic Docking of Macrocycles in Bound and Unbound Protein Structures with DynaDock. J Chem Inf Model 2022; 62:3426-3441. [PMID: 35796228 DOI: 10.1021/acs.jcim.2c00436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Macrocycles are interesting molecules with unique features due to their conformationally constrained yet flexible ring structure. This characteristic poses a difficult challenge for computational modeling studies since they rely on accurate structural descriptions. In particular, molecular docking calculations suffer from the lack of ring flexibility during pose generation, which is often compensated by using pregenerated ligand conformer ensembles. Moreover, receptor structures are mainly treated rigidly, which limits the use of many docking tools. In this study, we optimized our previous molecular dynamics-based sampling and docking pipeline specifically designed for the accurate prediction of macrocyclic compounds. We developed a dihedral classification procedure for in-depth conformational analysis of the macrocyclic rings and extracted structural ensembles that were subsequently docked in both bound and unbound protein structures employing a fully flexible approach. Our results suggest that including a ring conformer close to the bound state in the starting ensemble increases the chance of successful docking. The bioactive conformations of a diverse set of ligands could be predicted with high and decent accuracy in bound and unbound protein structures, respectively, due to the incorporation of full molecular flexibility in our approach. The remaining unsuccessful docking calculations were mainly caused by large flexible substituents that bind to surface-exposed binding sites, rather than the macrocyclic ring per se and could be further improved by explicit molecular dynamics simulations of the docked complex.
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Affiliation(s)
- Maximilian Meixner
- TUM School of Life Sciences, Technical University Munich, Am Staudengarten 2, Freising 85354, Germany
| | - Martin Zachmann
- TUM School of Life Sciences, Technical University Munich, Am Staudengarten 2, Freising 85354, Germany
| | - Sebastian Metzler
- TUM School of Life Sciences, Technical University Munich, Am Staudengarten 2, Freising 85354, Germany
| | - Jonathan Scheerer
- TUM School of Life Sciences, Technical University Munich, Am Staudengarten 2, Freising 85354, Germany
| | - Martin Zacharias
- Center of Functional Protein Assemblies, Technical University Munich, Ernst-Otto-Fischer-Straße 8, Garching bei München 85748, Germany
| | - Iris Antes
- TUM School of Life Sciences, Technical University Munich, Am Staudengarten 2, Freising 85354, Germany
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14
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Abramov YA, Sun G, Zeng Q. Emerging Landscape of Computational Modeling in Pharmaceutical Development. J Chem Inf Model 2022; 62:1160-1171. [PMID: 35226809 DOI: 10.1021/acs.jcim.1c01580] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Computational chemistry applications have become an integral part of the drug discovery workflow over the past 35 years. However, computational modeling in support of drug development has remained a relatively uncharted territory for a significant part of both academic and industrial communities. This review considers the computational modeling workflows for three key components of drug preclinical and clinical development, namely, process chemistry, analytical research and development, as well as drug product and formulation development. An overview of the computational support for each step of the respective workflows is presented. Additionally, in context of solid form design, special consideration is given to modern physics-based virtual screening methods. This covers rational approaches to polymorph, coformer, counterion, and solvent virtual screening in support of solid form selection and design.
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Affiliation(s)
- Yuriy A Abramov
- XtalPi, Inc., 245 Main St., Cambridge, Massachusetts 02142, United States.,Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Guangxu Sun
- XtalPi, Inc., Shenzhen Jingtai Technology Co., Ltd., Floor 3, Sf Industrial Plant, No. 2 Hongliu road, Fubao Community, Fubao Street, Futian District, Shenzhen 518100, China
| | - Qun Zeng
- XtalPi, Inc., Shenzhen Jingtai Technology Co., Ltd., Floor 3, Sf Industrial Plant, No. 2 Hongliu road, Fubao Community, Fubao Street, Futian District, Shenzhen 518100, China
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15
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Rai BK, Sresht V, Yang Q, Unwalla R, Tu M, Mathiowetz AM, Bakken GA. TorsionNet: A Deep Neural Network to Rapidly Predict Small-Molecule Torsional Energy Profiles with the Accuracy of Quantum Mechanics. J Chem Inf Model 2022; 62:785-800. [PMID: 35119861 DOI: 10.1021/acs.jcim.1c01346] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Fast and accurate assessment of small-molecule dihedral energetics is crucial for molecular design and optimization in medicinal chemistry. Yet, accurate prediction of torsion energy profiles remains challenging as the current molecular mechanics (MM) methods are limited by insufficient coverage of drug-like chemical space and accurate quantum mechanical (QM) methods are too expensive. To address this limitation, we introduce TorsionNet, a deep neural network (DNN) model specifically developed to predict small-molecule torsion energy profiles with QM-level accuracy. We applied active learning to identify nearly 50k fragments (with elements H, C, N, O, F, S, and Cl) that maximized the coverage of our corporate compound library and leveraged massively parallel cloud computing resources for density functional theory (DFT) torsion scans of these fragments, generating a training data set of 1.2 million DFT energies. After training TorsionNet on this data set, we obtain a model that can rapidly predict the torsion energy profile of typical drug-like fragments with DFT-level accuracy. Importantly, our method also provides an uncertainty estimate for the predicted profiles without any additional calculations. In this report, we show that TorsionNet can accurately identify the preferred dihedral geometries observed in crystal structures. Our TorsionNet-based analysis of a diverse set of protein-ligand complexes with measured binding affinity shows a strong association between high ligand strain and low potency. We also present practical applications of TorsionNet that demonstrate how consideration of DNN-based strain energy leads to substantial improvement in existing lead discovery and design workflows. TorsionNet500, a benchmark data set comprising 500 chemically diverse fragments with DFT torsion profiles (12k MM- and DFT-optimized geometries and energies), has been created and is made publicly available.
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Affiliation(s)
- Brajesh K Rai
- Simulation and Modeling Sciences, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Vishnu Sresht
- Simulation and Modeling Sciences, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Qingyi Yang
- Medicine Design, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Ray Unwalla
- Medicine Design, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Meihua Tu
- Medicine Design, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Alan M Mathiowetz
- Medicine Design, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Gregory A Bakken
- Digital, Pfizer, Eastern Point Road, Groton, Connecticut 06340, United States
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16
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Spillman MJ, Shankland N, Shankland K. An efficient treatment of ring conformations during molecular crystal structure determination from powder diffraction data. CrystEngComm 2022. [DOI: 10.1039/d2ce00520d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
An effective and efficient method for dealing with ring systems during global optimisation-based crystal structure determination from powder diffraction data.
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Affiliation(s)
| | - Norman Shankland
- CrystallografX Ltd, 2 Stewart Street, Glasgow, Strathclyde G62 6BW, Scotland, UK
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17
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Giangreco I, Mukhopadhyay A, Cole JC. Validation of a Field-Based Ligand Screener Using a Novel Benchmarking Data Set for Assessing 3D-Based Virtual Screening Methods. J Chem Inf Model 2021; 61:5841-5852. [PMID: 34792345 DOI: 10.1021/acs.jcim.1c00866] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Ligand-based methods play a crucial role in virtual screening when the 3D structure of the target is not available. This study discusses the results of a validation study of the CSD field-based ligand screener using a novel benchmarking data set containing 56 targets. The data set was created starting from the target UniProt IDs in a previously published data set (i.e., the AZ data set), by mining ChEMBL to find known active molecules for these targets and by using DUD-E to generate property-matched decoys of the identified actives. Several experiments were performed to assess the virtual screening performance of the new method. One of its strengths is that it can use an overlay of multiple flexible ligands as a query without the need to run several parallel calculations with one ligand at a time. Here, we discuss how changes to different parameter settings or adoption of different query models can influence the final performance compared to the performance when using the experimentally observed overlay of ligands. We have also generated the enrichment scores based on three external benchmark data sets to enable the comparison with existing methods previously validated using these data sets. Here, we present results for the standard DUD-E data set, the DUD-E+ data set, as well as the DUD_Lib_VS_1.0 data set which was designed for ligand-based virtual screening validation and hence is more suitable for this type of methods.
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Affiliation(s)
- Ilenia Giangreco
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, U.K
| | - Abhik Mukhopadhyay
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, U.K
| | - Jason C Cole
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, U.K
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18
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Gu S, Smith MS, Yang Y, Irwin JJ, Shoichet BK. Ligand Strain Energy in Large Library Docking. J Chem Inf Model 2021; 61:4331-4341. [PMID: 34467754 DOI: 10.1021/acs.jcim.1c00368] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
While small molecule internal strain is crucial to molecular docking, using it in evaluating ligand scores has remained elusive. Here, we investigate a technique that calculates strain using relative torsional populations in the Cambridge Structural Database, enabling fast precalculation of these energies. In retrospective studies of large docking screens of the dopamine D4 receptor and of AmpC β-lactamase, where close to 600 docking hits were tested experimentally, including such strain energies improved hit rates by preferentially reducing the ranks of strained high-scoring decoy molecules. In a 40-target subset of the DUD-E benchmark, we found two thresholds that usefully distinguished between ligands and decoys: one based on the total strain energy of the small molecules and another based on the maximum strain allowed for any given torsion within them. Using these criteria, about 75% of the benchmark targets had improved enrichment after strain filtering. Relying on precalculated population distributions, this approach is rapid, taking less than 0.04 s to evaluate a conformation on a standard core, making it pragmatic for precalculating strain in even ultralarge libraries. Since it is scoring function agnostic, it may be useful to multiple docking approaches; it is openly available at http://tldr.docking.org.
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Affiliation(s)
- Shuo Gu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 Fourth Street, San Francisco, California 94143-2550, United States
| | - Matthew S Smith
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 Fourth Street, San Francisco, California 94143-2550, United States.,Program of Biophysics, University of California, San Francisco, 1700 Fourth Street, San Francisco, California 94143-2550, United States
| | - Ying Yang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 Fourth Street, San Francisco, California 94143-2550, United States
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 Fourth Street, San Francisco, California 94143-2550, United States
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 Fourth Street, San Francisco, California 94143-2550, United States
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19
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Stanzione F, Giangreco I, Cole JC. Use of molecular docking computational tools in drug discovery. PROGRESS IN MEDICINAL CHEMISTRY 2021; 60:273-343. [PMID: 34147204 DOI: 10.1016/bs.pmch.2021.01.004] [Citation(s) in RCA: 125] [Impact Index Per Article: 41.7] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.
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Affiliation(s)
| | - Ilenia Giangreco
- Cambridge Crystallographic Data Centre, Cambridge, United Kingdom
| | - Jason C Cole
- Cambridge Crystallographic Data Centre, Cambridge, United Kingdom
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20
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Croll TI, Read RJ. Adaptive Cartesian and torsional restraints for interactive model rebuilding. Acta Crystallogr D Struct Biol 2021; 77:438-446. [PMID: 33825704 PMCID: PMC8025879 DOI: 10.1107/s2059798321001145] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 02/01/2021] [Indexed: 12/18/2022] Open
Abstract
When building atomic models into weak and/or low-resolution density, a common strategy is to restrain their conformation to that of a higher resolution model of the same or similar sequence. When doing so, it is important to avoid over-restraining to the reference model in the face of disagreement with the experimental data. The most common strategy for this is the use of `top-out' potentials. These act like simple harmonic restraints within a defined range, but gradually weaken when the deviation between the model and reference grows beyond that range. In each current implementation the rate at which the potential flattens at large deviations follows a fixed form, although the form chosen varies among implementations. A restraint potential with a tuneable rate of flattening would provide greater flexibility to encode the confidence in any given restraint. Here, two new such potentials are described: a Cartesian distance restraint derived from a recent generalization of common loss functions and a periodic torsion restraint based on a renormalization of the von Mises distribution. Further, their implementation as user-adjustable/switchable restraints in ISOLDE is described and their use in some real-world examples is demonstrated.
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Affiliation(s)
- Tristan Ian Croll
- Cambridge Institute for Medical Research, Keith Peters Building, Cambridge CB2 0XY, United Kingdom
| | - Randy J. Read
- Cambridge Institute for Medical Research, Keith Peters Building, Cambridge CB2 0XY, United Kingdom
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21
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Chan L, Hutchison GR, Morris GM. Understanding Ring Puckering in Small Molecules and Cyclic Peptides. J Chem Inf Model 2021; 61:743-755. [PMID: 33544592 PMCID: PMC8023587 DOI: 10.1021/acs.jcim.0c01144] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Indexed: 12/11/2022]
Abstract
The geometry of a molecule plays a significant role in determining its physical and chemical properties. Despite its importance, there are relatively few studies on ring puckering and conformations, often focused on small cycloalkanes, 5- and 6-membered carbohydrate rings, and specific macrocycle families. We lack a general understanding of the puckering preferences of medium-sized rings and macrocycles. To address this, we provide an extensive conformational analysis of a diverse set of rings. We used Cremer-Pople puckering coordinates to study the trends of the ring conformation across a set of 140 000 diverse small molecules, including small rings, macrocycles, and cyclic peptides. By standardizing using key atoms, we show that the ring conformations can be classified into relatively few conformational clusters, based on their canonical forms. The number of such canonical clusters increases slowly with ring size. Ring puckering motions, especially pseudo-rotations, are generally restricted and differ between clusters. More importantly, we propose models to map puckering preferences to torsion space, which allows us to understand the inter-related changes in torsion angles during pseudo-rotation and other puckering motions. Beyond ring puckers, our models also explain the change in substituent orientation upon puckering. We also present a novel knowledge-based sampling method using the puckering preferences and coupled substituent motion to generate ring conformations efficiently. In summary, this work provides an improved understanding of general ring puckering preferences, which will in turn accelerate the identification of low-energy ring conformations for applications from polymeric materials to drug binding.
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Affiliation(s)
- Lucian Chan
- Department
of Statistics, University of Oxford, 24-29 St Giles’, Oxford OX1 3LB, U.K.
| | - Geoffrey R. Hutchison
- Department
of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, United States
- Department
of Chemical and Petroleum Engineering, University
of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Garrett M. Morris
- Department
of Statistics, University of Oxford, 24-29 St Giles’, Oxford OX1 3LB, U.K.
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22
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Fang L, Makkonen E, Todorović M, Rinke P, Chen X. Efficient Amino Acid Conformer Search with Bayesian Optimization. J Chem Theory Comput 2021; 17:1955-1966. [PMID: 33577313 PMCID: PMC8023666 DOI: 10.1021/acs.jctc.0c00648] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
![]()
Finding low-energy molecular conformers
is challenging due to the
high dimensionality of the search space and the computational cost
of accurate quantum chemical methods for determining conformer structures
and energies. Here, we combine active-learning Bayesian optimization
(BO) algorithms with quantum chemistry methods to address this challenge.
Using cysteine as an example, we show that our procedure is both efficient
and accurate. After only 1000 single-point calculations and approximately
80 structure relaxations, which is less than 10% computational cost
of the current fastest method, we have found the low-energy conformers
in good agreement with experimental measurements and reference calculations.
To test the transferability of our method, we also repeated the conformer
search of serine, tryptophan, and aspartic acid. The results agree
well with previous conformer search studies.
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Affiliation(s)
- Lincan Fang
- Department of Applied Physics, Aalto University, AALTO 00076, Finland
| | - Esko Makkonen
- Department of Applied Physics, Aalto University, AALTO 00076, Finland
| | - Milica Todorović
- Department of Applied Physics, Aalto University, AALTO 00076, Finland
| | - Patrick Rinke
- Department of Applied Physics, Aalto University, AALTO 00076, Finland
| | - Xi Chen
- Department of Applied Physics, Aalto University, AALTO 00076, Finland
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23
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Malapile RJ, Nyamayaro K, Nassimbeni LR, Báthori NB. Multicomponent crystals of baclofen with acids and bases—conformational flexibility and synthon versatility. CrystEngComm 2021. [DOI: 10.1039/d0ce01522a] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Crystals of baclofen with acidic or basic coformers suggest that the robustness of the hydrogen bonding between the adjacent baclofen molecules aids the formation of the alternating hydrophilic and hydrophobic layers in the crystal structures.
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Affiliation(s)
| | - Kudzanai Nyamayaro
- Department of Chemistry
- Cape Peninsula University of Technology
- Cape Town
- South Africa
| | | | - Nikoletta B. Báthori
- Department of Chemistry
- Cape Peninsula University of Technology
- Cape Town
- South Africa
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24
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Mendenhall J, Brown BP, Kothiwale S, Meiler J. BCL::Conf: Improved Open-Source Knowledge-Based Conformation Sampling Using the Crystallography Open Database. J Chem Inf Model 2020; 61:189-201. [PMID: 33351632 DOI: 10.1021/acs.jcim.0c01140] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
We previously described BCL::Conf, a knowledge-based conformation sampling algorithm utilizing a small molecule fragment rotamer library derived from the Cambridge Structural Database (CSD, license required), as a component of the BioChemical Library (BCL) cheminformatics toolkit. This paper describes substantial improvements made to the BCL::Conf algorithm and a transition to a rotamer library derived from molecules in the Crystallography Open Database (COD, no license required). We demonstrate the performance of the new BCL::Conf on native conformer recovery in the Platinum dataset of high-quality protein-ligand complexes. This set of 2859 structures has previously been used to assess the performance of over a dozen conformer generation algorithms, including the Conformator, Balloon, RDKit DG, ETKDG, Confab, Frog2, MultiConf-DOCK, CSD conformer generator, ConfGenX-OPSL3 force field, Omega, excalc, iCon, and MOE. These benchmarks suggest that the CSD conformer generator is at the state of the art of reported conformer generators. Our results indicate that the improved BCL::Conf significantly outperforms the CSD conformer generation algorithm at binding conformer recovery across a range of ensemble sizes and with similarly fast rates of conformer generation. BCL::Conf is now distributed with the COD-derived rotamer library and is free for academic use. The BCL can be downloaded at http://meilerlab.org/bclcommons for Windows, Linux, or Apple operating systems. BCL::Conf can now also be accessed via webserver at http://meilerlab.org/bclconf.
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Affiliation(s)
- Jeffrey Mendenhall
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232 United States
| | - Benjamin P Brown
- Chemical and Physical Biology Program, Medical Scientist Training Program, Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232 United States
| | - Sandeepkumar Kothiwale
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232 United States
| | - Jens Meiler
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232 United States.,Departments of Pharmacology and Biomedical Informatics, Vanderbilt University, Nashville, Tennessee 37212 United States.,Institute for Drug Discovery, Leipzig University Medical School, Leipzig SAC 04103, Germany
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25
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Reyes Romero A, Ruiz-Moreno AJ, Groves MR, Velasco-Velázquez M, Dömling A. Benchmark of Generic Shapes for Macrocycles. J Chem Inf Model 2020; 60:6298-6313. [PMID: 33270455 PMCID: PMC7768607 DOI: 10.1021/acs.jcim.0c01038] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
![]()
Macrocycles
target proteins that are otherwise considered undruggable
because of a lack of hydrophobic cavities and the presence of extended
featureless surfaces. Increasing efforts by computational chemists
have developed effective software to overcome the restrictions of
torsional and conformational freedom that arise as a consequence of
macrocyclization. Moloc is an efficient algorithm, with an emphasis
on high interactivity, and has been constantly updated since 1986
by drug designers and crystallographers of the Roche biostructural
community. In this work, we have benchmarked the shape-guided algorithm
using a dataset of 208 macrocycles, carefully selected on the basis
of structural complexity. We have quantified the accuracy, diversity,
speed, exhaustiveness, and sampling efficiency in an automated fashion
and we compared them with four commercial (Prime, MacroModel, molecular
operating environment, and molecular dynamics) and four open-access
(experimental-torsion distance geometry with additional “basic
knowledge” alone and with Merck molecular force field minimization
or universal force field minimization, Cambridge Crystallographic
Data Centre conformer generator, and conformator) packages. With three-quarters
of the database processed below the threshold of high ring accuracy,
Moloc was identified as having the highest sampling efficiency and
exhaustiveness without producing thousands of conformations, random
ring splitting into two half-loops, and possibility to interactively
produce globular or flat conformations with diversity similar to Prime,
MacroModel, and molecular dynamics. The algorithm and the Python scripts
for full automatization of these parameters are freely available for
academic use.
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Affiliation(s)
- Atilio Reyes Romero
- Drug Design, Department of Pharmacy, University of Groningen, Antonius Deusinglaan 1, XB20, 9713 AV Groningen, The Netherlands
| | - Angel Jonathan Ruiz-Moreno
- Drug Design, Department of Pharmacy, University of Groningen, Antonius Deusinglaan 1, XB20, 9713 AV Groningen, The Netherlands.,Departamento de Farmacología y Unidad Periférica de Investigación en Biomedicina Trasnacional, Facultad de Medicina, Universidad Nacional Autónoma de México (UNAM), Av. Universidad 3000, Circuito Exterior S/N, Delegación Coyoacán, Ciudad Universitaria, 04510 Ciudad de México, Mexico.,Programa de Doctorado en Ciencias Biomédicas, UNAM, Av. Universidad 3000, Circuito Exterior S/N. Delegación Coyoacán, Ciudad Universitaria, 04510 Ciudad de México, Mexico
| | - Matthew R Groves
- Drug Design, Department of Pharmacy, University of Groningen, Antonius Deusinglaan 1, XB20, 9713 AV Groningen, The Netherlands
| | - Marco Velasco-Velázquez
- Departamento de Farmacología y Unidad Periférica de Investigación en Biomedicina Trasnacional, Facultad de Medicina, Universidad Nacional Autónoma de México (UNAM), Av. Universidad 3000, Circuito Exterior S/N, Delegación Coyoacán, Ciudad Universitaria, 04510 Ciudad de México, Mexico
| | - Alexander Dömling
- Drug Design, Department of Pharmacy, University of Groningen, Antonius Deusinglaan 1, XB20, 9713 AV Groningen, The Netherlands
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26
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de Oliveira MD, Araújo JDO, Galúcio JMP, Santana K, Lima AH. Targeting shikimate pathway: In silico analysis of phosphoenolpyruvate derivatives as inhibitors of EPSP synthase and DAHP synthase. J Mol Graph Model 2020; 101:107735. [PMID: 32947107 DOI: 10.1016/j.jmgm.2020.107735] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 08/13/2020] [Accepted: 08/31/2020] [Indexed: 02/03/2023]
Abstract
The shikimate pathway consists of seven enzymatic steps involved in the conversion of erythrose-4-phosphate and phosphoenolpyruvate to chorismate and also responsible to the production of aromatic amino acids, such as phenylalanine, tyrosine, and tryptophan which are essential to the bacterial metabolism. The 3-deoxy-D-arabino-heptulosonate-7-phosphate synthase (DAHPS) and 5-enolpyruvylshikimate 3-phosphate synthase (EPSPS) catalyze important steps in the shikimate pathway using as substrate the phosphoenolpyruvate (PEP). Due to the importance of PEP in shikimate pathway, its structure has been investigated to develop new bioinspired competitive inhibitors against DAHPS and EPSPS. In the present study, we perform a literature survey of 28 PEP derivatives, then we analyzed the selectivity and affinity of these compounds against the EPSPS and DAHPS structures using consensual molecular docking, pharmacophore prediction, molecular dynamics (MD) simulations, and binding free energy calculations. Here, we propose consistent binding modes of the selected ligands and indicate that their structures show interesting pharmacophoric properties related to multi-targets inhibitors for both enzymes. Our computational results are supported by previous experimental findings related to the interactions of PEP derivatives with DAHPS and EPSPS structures.
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Affiliation(s)
- Maycon D de Oliveira
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, 66075-110, Belém, Pará, Brazil
| | - Jéssica de O Araújo
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, 66075-110, Belém, Pará, Brazil
| | - João M P Galúcio
- Instituto de Biodiversidade. Universidade Federal do Oeste do Pará, 68035-110, Santarém, Pará, Brazil
| | - Kauê Santana
- Instituto de Biodiversidade. Universidade Federal do Oeste do Pará, 68035-110, Santarém, Pará, Brazil
| | - Anderson H Lima
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, 66075-110, Belém, Pará, Brazil.
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27
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Wang S, Witek J, Landrum GA, Riniker S. Improving Conformer Generation for Small Rings and Macrocycles Based on Distance Geometry and Experimental Torsional-Angle Preferences. J Chem Inf Model 2020; 60:2044-2058. [PMID: 32155061 DOI: 10.1021/acs.jcim.0c00025] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The conformer generator ETKDG is a stochastic search method that utilizes distance geometry together with knowledge derived from experimental crystal structures. It has been shown to generate good conformers for acyclic, flexible molecules. This work builds on ETKDG to improve conformer generation of molecules containing small or large aliphatic (i.e., non-aromatic) rings. For one, we devise additional torsional-angle potentials to describe small aliphatic rings and adapt the previously developed potentials for acyclic bonds to facilitate the sampling of macrocycles. However, due to the larger number of degrees of freedom of macrocycles, the conformational space to sample is much broader than for small molecules, creating a challenge for conformer generators. We therefore introduce different heuristics to restrict the search space of macrocycles and bias the sampling toward more experimentally relevant structures. Specifically, we show the usage of elliptical geometry and customizable Coulombic interactions as heuristics. The performance of the improved ETKDG is demonstrated on test sets of diverse macrocycles and cyclic peptides. The code developed here will be incorporated into the 2020.03 release of the open-source cheminformatics library RDKit.
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Affiliation(s)
- Shuzhe Wang
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Jagna Witek
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | | | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
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28
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Macrae CF, Sovago I, Cottrell SJ, Galek PTA, McCabe P, Pidcock E, Platings M, Shields GP, Stevens JS, Towler M, Wood PA. Mercury 4.0: from visualization to analysis, design and prediction. J Appl Crystallogr 2020; 53:226-235. [PMID: 32047413 PMCID: PMC6998782 DOI: 10.1107/s1600576719014092] [Citation(s) in RCA: 1962] [Impact Index Per Article: 490.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 10/15/2019] [Indexed: 12/31/2022] Open
Abstract
The program Mercury, developed at the Cambridge Crystallographic Data Centre, was originally designed primarily as a crystal structure visualization tool. Over the years the fields and scientific communities of chemical crystallography and crystal engineering have developed to require more advanced structural analysis software. Mercury has evolved alongside these scientific communities and is now a powerful analysis, design and prediction platform which goes a lot further than simple structure visualization.
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Affiliation(s)
- Clare F. Macrae
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK
| | - Ioana Sovago
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK
| | - Simon J. Cottrell
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK
| | - Peter T. A. Galek
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK
| | - Patrick McCabe
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK
| | - Elna Pidcock
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK
| | - Michael Platings
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK
| | - Greg P. Shields
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK
| | - Joanna S. Stevens
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK
| | - Matthew Towler
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK
| | - Peter A. Wood
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK
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29
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Crystal structure and conformational diversity of fluorinated alkyl tosylates. MENDELEEV COMMUNICATIONS 2020. [DOI: 10.1016/j.mencom.2020.01.034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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30
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Nunes Costa R, Choquesillo-Lazarte D, Cuffini SL, Pidcock E, Infantes L. Optimization and comparison of statistical tools for the prediction of multicomponent forms of a molecule: the antiretroviral nevirapine as a case study. CrystEngComm 2020. [DOI: 10.1039/d0ce00948b] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A methodology is proposed to assess the propensity to obtain multicomponent forms of an API based on the combination of modified statistical analytical tools to order the possible co-formers in a ranking index.
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Affiliation(s)
- Rogeria Nunes Costa
- Instituto de Ciência e Tecnologia
- Universidade Federal de São Paulo
- 12331-280 São José dos Campos
- Brazil
| | | | - Silvia Lucía Cuffini
- Instituto de Ciência e Tecnologia
- Universidade Federal de São Paulo
- 12331-280 São José dos Campos
- Brazil
| | - Elna Pidcock
- Cambridge Crystallographic Data Centre
- Cambridge
- UK
| | - Lourdes Infantes
- Instituto de Química Fisica Rocasolano
- Consejo Superior de Investigaciones Científicas
- Madrid
- Spain
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31
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Odounga Odounga JE, Báthori NB. Systematic comparison of racemic and enantiopure multicomponent crystals of phenylsuccinic acid—the role of chirality. CrystEngComm 2020. [DOI: 10.1039/d0ce00072h] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Comparison of binary cocrystals of chiral and racemic carboxylic acids showed that the introduction of chiral building blocks may lead to the formation of subclasses of multicomponent crystals with unique Z′′/Zr values combined with complex protonation stages of the molecules.
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Affiliation(s)
| | - Nikoletta B. Báthori
- Department of Chemistry
- Cape Peninsula University of Technology
- Cape Town
- South Africa
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32
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Chan L, Hutchison GR, Morris GM. BOKEI: Bayesian optimization using knowledge of correlated torsions and expected improvement for conformer generation. Phys Chem Chem Phys 2020; 22:5211-5219. [PMID: 32091055 DOI: 10.1039/c9cp06688h] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
A key challenge in conformer sampling is finding low-energy conformations using a small number of energy evaluations. By extracting patterns of correlated torsions, we improve the efficiency of Bayesian Optimization in finding optimal conformations.
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Affiliation(s)
- Lucian Chan
- Department of Statistics
- University of Oxford
- Oxford
- UK
| | - Geoffrey R. Hutchison
- Department of Chemistry and Chemical Engineering
- University of Pittsburgh
- Pittsburgh
- USA
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33
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Fukunishi Y, Mashimo T, Kurosawa T, Wakabayashi Y, Nakamura HK, Takeuchi K. Prediction of Passive Membrane Permeability by Semi-Empirical Method Considering Viscous and Inertial Resistances and Different Rates of Conformational Change and Diffusion. Mol Inform 2020; 39:e1900071. [PMID: 31609549 PMCID: PMC7050510 DOI: 10.1002/minf.201900071] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 09/22/2019] [Indexed: 12/24/2022]
Abstract
Membrane permeability is an important property of drugs in adsorption. Many prediction methods work well for small molecules, but the prediction of middle-molecule permeability is still difficult. In the present study, we modified a classical permeability model based on Fick's law to study passive membrane permeability. The model consisted of the distribution of solute from water to membrane and the diffusion of solute in each solvent. The diffusion coefficient is the inverse of the resistance, and we examined the inertial resistance in addition to the viscous resistance, the latter of which has been widely used in permeability prediction. Also, we examined three models changing the balance between the diffusion of solute in membrane and the conformational change of solute. The inertial resistance improved the prediction results in addition to the viscous resistance. The models worked well not only for small molecules but also for middle molecules, whose structures have more conformational freedom.
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Affiliation(s)
- Yoshifumi Fukunishi
- Molecular Profiling Research Center for Drug Discovery (molprof)National Institute of Advanced Industrial Science and Technology (AIST)2-3-26, Aomi, Koto-kuTokyo135-0064Japan
| | - Tadaaki Mashimo
- Technology Research Association for Next-Generation Natural Products Chemistry2-3-26, Aomi, Koto-kuTokyo135-0064Japan
- IMSBIO Co., Ltd.Owl Tower, 4–21-1, Higashi-Ikebukuro, Toshima-kuTokyo170-0013Japan
| | - Takashi Kurosawa
- Technology Research Association for Next-Generation Natural Products Chemistry2-3-26, Aomi, Koto-kuTokyo135-0064Japan
- Hitachi Solutions East Japan, 12–1 Ekimaehoncho, Kawasaki-ku, KawasakiKanagawa210-0007Japan
| | | | - Hironori K. Nakamura
- Biomodeling Research Co., Ltd.1-704-2 Uedanishi, Tenpaku-ku, NagoyaAichi468-0058Japan
| | - Koh Takeuchi
- Molecular Profiling Research Center for Drug Discovery (molprof)National Institute of Advanced Industrial Science and Technology (AIST)2-3-26, Aomi, Koto-kuTokyo135-0064Japan
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34
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Steinhauer D, Salat M, Frey R, Mosbach A, Luksch T, Balmer D, Hansen R, Widdison S, Logan G, Dietrich RA, Kema GHJ, Bieri S, Sierotzki H, Torriani SFF, Scalliet G. A dispensable paralog of succinate dehydrogenase subunit C mediates standing resistance towards a subclass of SDHI fungicides in Zymoseptoria tritici. PLoS Pathog 2019; 15:e1007780. [PMID: 31860693 PMCID: PMC6941823 DOI: 10.1371/journal.ppat.1007780] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Revised: 01/03/2020] [Accepted: 11/20/2019] [Indexed: 11/24/2022] Open
Abstract
Succinate dehydrogenase inhibitor (SDHI) fungicides are widely used for the control of a broad range of fungal diseases. This has been the most rapidly expanding fungicide group in terms of new molecules discovered and introduced for agricultural use over the past fifteen years. A particular pattern of differential sensitivity (resistance) to the stretched heterocycle amide SDHIs (SHA-SDHIs), a subclass of chemically-related SDHIs, was observed in naïve Zymoseptoria tritici populations not previously exposed to these chemicals. Subclass-specific resistance was confirmed at the enzyme level but did not correlate with the genotypes of the succinate dehydrogenase (SDH) encoding genes. Mapping and characterization of the molecular mechanisms responsible for standing SHA-SDHI resistance in natural field isolates identified a gene paralog of SDHC, termed ZtSDHC3, which encodes for an alternative C subunit of succinate dehydrogenase, named alt-SDHC. Using reverse genetics, we showed that alt-SDHC associates with the three other SDH subunits, leading to a fully functional enzyme and that a unique Qp-site residue within the alt-SDHC protein confers SHA-SDHI resistance. Enzymatic assays, computational modelling and docking simulations for the two SQR enzymes (altC-SQR, WT_SQR) enabled us to describe enzyme-inhibitor interactions at an atomistic level and to propose rational explanations for differential potency and resistance across SHA-SDHIs. European Z. tritici populations displayed a presence (20–30%) / absence polymorphism of ZtSDHC3, as well as differences in ZtSDHC3 expression levels and splicing efficiency. These polymorphisms have a strong impact on SHA-SDHI resistance phenotypes. Characterization of the ZtSDHC3 promoter in European Z. tritici populations suggests that transposon insertions are associated with the strongest resistance phenotypes. These results establish that a dispensable paralogous gene determines SHA-SDHIs fungicide resistance in natural populations of Z. tritici. This study paves the way to an increased awareness of the role of fungicidal target paralogs in resistance to fungicides and demonstrates the paramount importance of population genomics in fungicide discovery. Zymoseptoria tritici is the causal agent of Septoria tritici leaf blotch (STB) of wheat, the most devastating disease for cereal production in Europe. Multiple succinate dehydrogenase inhibitor (SDHI) fungicides have been developed and introduced for the control of STB. We report the discovery and detailed characterization of a paralog of the C subunit of the SDH enzyme conferring standing resistance towards the SHA-SDHIs, a particular chemical subclass of the SDHIs. The SDHC paralog is characterized by its presence/absence, expression and alternative splicing polymorphisms, which in turn influence resistance levels. The identified mechanisms exemplify the importance of population genomics for the discovery and rational design of the most adapted solutions.
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Affiliation(s)
| | - Marie Salat
- Syngenta Crop Protection AG, Stein, Switzerland
| | - Regula Frey
- Syngenta Crop Protection AG, Stein, Switzerland
| | | | | | - Dirk Balmer
- Syngenta Crop Protection AG, Stein, Switzerland
| | - Rasmus Hansen
- Syngenta Jealott’s Hill Int. Research Centre, Bracknell Berkshire, United Kingdom
| | - Stephanie Widdison
- Syngenta Jealott’s Hill Int. Research Centre, Bracknell Berkshire, United Kingdom
| | - Grace Logan
- Syngenta Jealott’s Hill Int. Research Centre, Bracknell Berkshire, United Kingdom
| | - Robert A. Dietrich
- Syngenta Biotechnology Inc., Research Triangle Park, North Carolina, United States of America
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35
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Rai BK, Sresht V, Yang Q, Unwalla R, Tu M, Mathiowetz AM, Bakken GA. Comprehensive Assessment of Torsional Strain in Crystal Structures of Small Molecules and Protein–Ligand Complexes using ab Initio Calculations. J Chem Inf Model 2019; 59:4195-4208. [DOI: 10.1021/acs.jcim.9b00373] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
| | | | | | | | | | | | - Gregory A. Bakken
- Simulation and Modeling Sciences, Pfizer Worldwide Research and Development, Eastern Point Road, Groton, Connecticut 06340, United States
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36
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Intermolecular Interactions in Functional Crystalline Materials: From Data to Knowledge. CRYSTALS 2019. [DOI: 10.3390/cryst9090478] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Intermolecular interactions of organic, inorganic, and organometallic compounds are the key to many composition–structure and structure–property networks. In this review, some of these relations and the tools developed by the Cambridge Crystallographic Data Center (CCDC) to analyze them and design solid forms with desired properties are described. The potential of studies supported by the Cambridge Structural Database (CSD)-Materials tools for investigation of dynamic processes in crystals, for analysis of biologically active, high energy, optical, (electro)conductive, and other functional crystalline materials, and for the prediction of novel solid forms (polymorphs, co-crystals, solvates) are discussed. Besides, some unusual applications, the potential for further development and limitations of the CCDC software are reported.
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37
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Cole JC, Wiggin S, Stanzione F. New insights and innovation from a million crystal structures in the Cambridge Structural Database. STRUCTURAL DYNAMICS (MELVILLE, N.Y.) 2019; 6:054301. [PMID: 31489338 PMCID: PMC6713555 DOI: 10.1063/1.5116878] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 08/13/2019] [Indexed: 05/23/2023]
Abstract
The Cambridge Structural Database (CSD) is the world's largest and most comprehensive collection of organic, organometallic, and metal-organic crystal structure information. Analyses using the data have wide impact across the chemical sciences in allowing understanding of structural preferences. In this short review, we illustrate the more common methods by which CSD data influence molecular design. We show how more data could lead to more refined insights into the future using a simple example of trifluoromethylphenyl fragments, highlighting how with sufficient data one can build a reasonable model of geometric change in a chemical fragment with torsional rotation, and show some recent examples where the CSD has been used in conjunction with other methods to provide design ideas and more computationally tractable workflows for derivation of useful insights into structural design.
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Affiliation(s)
- Jason C Cole
- The Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, United Kingdom
| | - Seth Wiggin
- The Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, United Kingdom
| | - Francesca Stanzione
- The Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, United Kingdom
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38
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Taylor R, Wood PA. A Million Crystal Structures: The Whole Is Greater than the Sum of Its Parts. Chem Rev 2019; 119:9427-9477. [PMID: 31244003 DOI: 10.1021/acs.chemrev.9b00155] [Citation(s) in RCA: 134] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The founding in 1965 of what is now called the Cambridge Structural Database (CSD) has reaped dividends in numerous and diverse areas of chemical research. Each of the million or so crystal structures in the database was solved for its own particular reason, but collected together, the structures can be reused to address a multitude of new problems. In this Review, which is focused mainly on the last 10 years, we chronicle the contribution of the CSD to research into molecular geometries, molecular interactions, and molecular assemblies and demonstrate its value in the design of biologically active molecules and the solid forms in which they are delivered. Its potential in other commercially relevant areas is described, including gas storage and delivery, thin films, and (opto)electronics. The CSD also aids the solution of new crystal structures. Because no scientific instrument is without shortcomings, the limitations of CSD research are assessed. We emphasize the importance of maintaining database quality: notwithstanding the arrival of big data and machine learning, it remains perilous to ignore the principle of garbage in, garbage out. Finally, we explain why the CSD must evolve with the world around it to ensure it remains fit for purpose in the years ahead.
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Affiliation(s)
- Robin Taylor
- Cambridge Crystallographic Data Centre , 12 Union Road , Cambridge CB2 1EZ , United Kingdom
| | - Peter A Wood
- Cambridge Crystallographic Data Centre , 12 Union Road , Cambridge CB2 1EZ , United Kingdom
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39
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Abstract
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here, we present a new stochastic search method called the Bayesian optimization algorithm (BOA) for finding the lowest energy conformation of a given molecule. We compare BOA with uniform random search, and systematic search as implemented in Confab, to determine which method finds the lowest energy. Energetic difference, root-mean-square deviation, and torsion fingerprint deviation are used to quantify the performance of the conformer search algorithms. In general, we find BOA requires far fewer evaluations than systematic or uniform random search to find low-energy minima. For molecules with four or more rotatable bonds, Confab typically evaluates \documentclass[12pt]{minimal}
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\begin{document}$$10^{4}$$\end{document}104 (median) conformers in its search, while BOA only requires \documentclass[12pt]{minimal}
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\begin{document}$$10^{2}$$\end{document}102 energy evaluations to find top candidates. Despite using evaluating fewer conformers, 20–40% of the time BOA finds lower-energy conformations than a systematic Confab search for molecules with four or more rotatable bonds.
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