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Sadino A, Saptarini NM, Levita J, Ramadhan DSF, Fristiohady A, Jiranusornkul S. Identifying Potential Human Monoacylglycerol Lipase Inhibitors from the Phytoconstituents of Morinda Citrifolia L. Fruits by in silico Pharmacology and in vitro Study. J Exp Pharmacol 2024; 16:295-309. [PMID: 39345798 PMCID: PMC11436673 DOI: 10.2147/jep.s477956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Accepted: 09/19/2024] [Indexed: 10/01/2024] Open
Abstract
Background Human monoacylglycerol lipase (MGL) is accountable for the hydrolysis of 2-arachidonoylglycerol (2-AG), thus contributing pivotally to neuroprotection because 2-AG is the main source of arachidonic acid, the precursor of prostaglandins production. Inhibiting MGL reduces inflammatory damage in the ischemic brain and enhances cerebral blood flow. Plants have been reported for their neuroprotective effect, such as Morinda citrifolia on pentylenetetrazol (PTZ)-induced kindling seizures in mice, by reducing the seizures and restoring behavioral and biochemical changes, although the mechanism is not described. Purpose To evaluate the binding affinity and stability of phytoconstituents in M. citrifolia fruits toward human MGL (PDB ID 3PE6), compared to the known MGL inhibitors (JZL195 and ZYH). The in silico pharmacology study was validated by an in vitro study of the phytosterols and the ethanol extract of M. citrifolia fruits (EEMC) towards MGL. Methods Initially, nine phytoconstituents of M. citrifolia fruits were docked to the catalytic pocket of human MGL (PDB ID: 3PE6), and compounds with the best affinity were subjected to a molecular dynamic (MD) simulation. The in vitro study was performed using the MGL inhibitor screening assay kit. Results The best binding affinity and stability toward human MGL were shown by stigmasterol and beta-sitosterol, and the MM-PBSA total binding energy of stigmasterol and beta-sitosterol to MGL is stronger than that of JZL195 and ZYH. Moreover, beta-sitosterol and EEMC inhibit MGL with an IC50 value of, respectively, 8.10 μg/mL and 196.20 μg/mL, while JZL195 shows an IC50 of 0.028 μg/mL. Conclusion Beta-sitosterol of Morinda citrifolia fruits may have the potential to protect human neurons by occupying the catalytic site of human MGL, thus competitively inhibiting the substrate of the enzyme. However, the inhibitory activity towards human MGL is lower than JZL195.
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Affiliation(s)
- Asman Sadino
- Doctoral Program in Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, West Java, 45363, Indonesia
- Department of Pharmacology and Clinical Pharmacy, Faculty of Mathematics and Natural Sciences, Garut University, Garut, West Java, 44151, Indonesia
| | - Nyi Mekar Saptarini
- Department of Pharmaceutical Analysis and Medicinal Chemistry, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, West Java, 45363, Indonesia
| | - Jutti Levita
- Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, West Java, 45363, West Java, Indonesia
| | - Dwi Syah Fitra Ramadhan
- Department of Pharmacy, Poltekkes Kemenkes Makassar, Makassar, South Sulawesi, 90222, Indonesia
| | - Adryan Fristiohady
- Faculty of Pharmacy, Halu Oleo University, Kendari, Southeast Sulawesi, 93132, Indonesia
| | - Supat Jiranusornkul
- Department of Pharmaceutical Science, Faculty of Pharmacy, Chiang Mai University, Chiang Mai, Thailand
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2
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Rothchild D, Rosen AS, Taw E, Robinson C, Gonzalez JE, Krishnapriyan AS. Investigating the behavior of diffusion models for accelerating electronic structure calculations. Chem Sci 2024; 15:13506-13522. [PMID: 39183908 PMCID: PMC11339969 DOI: 10.1039/d3sc05877h] [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: 11/03/2023] [Accepted: 07/11/2024] [Indexed: 08/27/2024] Open
Abstract
We present an investigation of diffusion models for molecular generation, with the aim of better understanding how their predictions compare to the results of physics-based calculations. The investigation into these models is driven by their potential to significantly accelerate electronic structure calculations using machine learning, without requiring expensive first-principles datasets for training interatomic potentials. We find that the inference process of a popular diffusion model for de novo molecular generation is divided into an exploration phase, where the model chooses the atomic species, and a relaxation phase, where it adjusts the atomic coordinates to find a low-energy geometry. As training proceeds, we show that the model initially learns about the first-order structure of the potential energy surface, and then later learns about higher-order structure. We also find that the relaxation phase of the diffusion model can be re-purposed to sample the Boltzmann distribution over conformations and to carry out structure relaxations. For structure relaxations, the model finds geometries with ∼10× lower energy than those produced by a classical force field for small organic molecules. Initializing a density functional theory (DFT) relaxation at the diffusion-produced structures yields a >2× speedup to the DFT relaxation when compared to initializing at structures relaxed with a classical force field.
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Affiliation(s)
- Daniel Rothchild
- Department of Electrical Engineering and Computer Science, University of California Berkeley USA
| | - Andrew S Rosen
- Department of Materials Science and Engineering, University of California Berkeley USA
- Materials Science Division, Lawrence Berkeley National Laboratory USA
| | - Eric Taw
- Department of Chemical and Biomolecular Engineering, University of California Berkeley USA
- Materials Science Division, Lawrence Berkeley National Laboratory USA
| | - Connie Robinson
- Department of Chemistry, University of California Berkeley USA
| | - Joseph E Gonzalez
- Department of Electrical Engineering and Computer Science, University of California Berkeley USA
| | - Aditi S Krishnapriyan
- Department of Electrical Engineering and Computer Science, University of California Berkeley USA
- Department of Chemical and Biomolecular Engineering, University of California Berkeley USA
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3
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Bånkestad M, Dorst KM, Widmalm G, Rönnols J. Carbohydrate NMR chemical shift prediction by GeqShift employing E(3) equivariant graph neural networks. RSC Adv 2024; 14:26585-26595. [PMID: 39175672 PMCID: PMC11340439 DOI: 10.1039/d4ra03428g] [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: 05/09/2024] [Accepted: 08/02/2024] [Indexed: 08/24/2024] Open
Abstract
Carbohydrates, vital components of biological systems, are well-known for their structural diversity. Nuclear Magnetic Resonance (NMR) spectroscopy plays a crucial role in understanding their intricate molecular arrangements and is essential in assessing and verifying the molecular structure of organic molecules. An important part of this process is to predict the NMR chemical shift from the molecular structure. This work introduces a novel approach that leverages E(3) equivariant graph neural networks to predict carbohydrate NMR spectral data. Notably, our model achieves a substantial reduction in mean absolute error, up to threefold, compared to traditional models that rely solely on two-dimensional molecular structure. Even with limited data, the model excels, highlighting its robustness and generalization capabilities. The model is dubbed GeqShift (geometric equivariant shift) and uses equivariant graph self-attention layers to learn about NMR chemical shifts, in particular since stereochemical arrangements in carbohydrate molecules are characteristics of their structures.
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Affiliation(s)
- Maria Bånkestad
- Department of Information Technology, Uppsala University Sweden
- RISE Research Institutes of Sweden Stockholm Sweden
| | - Kevin M Dorst
- Department of Organic Chemistry, Stockholm University Sweden
| | - Göran Widmalm
- Department of Organic Chemistry, Stockholm University Sweden
| | - Jerk Rönnols
- RISE Research Institutes of Sweden Stockholm Sweden
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4
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van Gerwen P, Briling KR, Bunne C, Somnath VR, Laplaza R, Krause A, Corminboeuf C. 3DReact: Geometric Deep Learning for Chemical Reactions. J Chem Inf Model 2024; 64:5771-5785. [PMID: 39007724 PMCID: PMC11323278 DOI: 10.1021/acs.jcim.4c00104] [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: 01/21/2024] [Revised: 07/03/2024] [Accepted: 07/08/2024] [Indexed: 07/16/2024]
Abstract
Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we introduce 3DReact, a geometric deep learning model to predict reaction properties from three-dimensional structures of reactants and products. We demonstrate that the invariant version of the model is sufficient for existing reaction data sets. We illustrate its competitive performance on the prediction of activation barriers on the GDB7-22-TS, Cyclo-23-TS, and Proparg-21-TS data sets in different atom-mapping regimes. We show that, compared to existing models for reaction property prediction, 3DReact offers a flexible framework that exploits atom-mapping information, if available, as well as geometries of reactants and products (in an invariant or equivariant fashion). Accordingly, it performs systematically well across different data sets, atom-mapping regimes, as well as both interpolation and extrapolation tasks.
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Affiliation(s)
- Puck van Gerwen
- Laboratory
for Computational Molecular Design, Institute of Chemical Sciences
and Engineering, École Polytechnique
Fédérale de Lausanne, 1015 Lausanne, Switzerland
- National
Center for Competence in Research − Catalysis (NCCR-Catalysis), École Polytechnique Fédérale
de Lausanne, 1015 Lausanne, Switzerland
| | - Ksenia R. Briling
- Laboratory
for Computational Molecular Design, Institute of Chemical Sciences
and Engineering, École Polytechnique
Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Charlotte Bunne
- National
Center for Competence in Research − Catalysis (NCCR-Catalysis), École Polytechnique Fédérale
de Lausanne, 1015 Lausanne, Switzerland
- Learning
& Adaptive Systems Group, Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland
| | - Vignesh Ram Somnath
- National
Center for Competence in Research − Catalysis (NCCR-Catalysis), École Polytechnique Fédérale
de Lausanne, 1015 Lausanne, Switzerland
- Learning
& Adaptive Systems Group, Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland
| | - Ruben Laplaza
- Laboratory
for Computational Molecular Design, Institute of Chemical Sciences
and Engineering, École Polytechnique
Fédérale de Lausanne, 1015 Lausanne, Switzerland
- National
Center for Competence in Research − Catalysis (NCCR-Catalysis), École Polytechnique Fédérale
de Lausanne, 1015 Lausanne, Switzerland
| | - Andreas Krause
- National
Center for Competence in Research − Catalysis (NCCR-Catalysis), École Polytechnique Fédérale
de Lausanne, 1015 Lausanne, Switzerland
- Learning
& Adaptive Systems Group, Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland
| | - Clemence Corminboeuf
- Laboratory
for Computational Molecular Design, Institute of Chemical Sciences
and Engineering, École Polytechnique
Fédérale de Lausanne, 1015 Lausanne, Switzerland
- National
Center for Competence in Research − Catalysis (NCCR-Catalysis), École Polytechnique Fédérale
de Lausanne, 1015 Lausanne, Switzerland
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5
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Larsen AS, Rekis T, Madsen AØ. PhAI: A deep-learning approach to solve the crystallographic phase problem. Science 2024; 385:522-528. [PMID: 39088613 DOI: 10.1126/science.adn2777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 05/21/2024] [Accepted: 06/24/2024] [Indexed: 08/03/2024]
Abstract
X-ray crystallography provides a distinctive view on the three-dimensional structure of crystals. To reconstruct the electron density map, the complex structure factors [Formula: see text] of a sufficiently large number of diffracted reflections must be known. In a conventional experiment, only the amplitudes [Formula: see text] are obtained, and the phases ϕ are lost. This is the crystallographic phase problem. In this work, we show that a neural network, trained on millions of artificial structure data, can solve the phase problem at a resolution of only 2 angstroms, using only 10 to 20% of the data needed for direct methods. The network works in common space groups and for modest unit-cell dimensions and suggests that neural networks could be used to solve the phase problem in the general case for weakly scattering crystals.
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Affiliation(s)
- Anders S Larsen
- Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark
| | - Toms Rekis
- Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark
| | - Anders Ø Madsen
- Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark
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6
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Borup RM, Ree N, Jensen JH. pKalculator: A p K a predictor for C-H bonds. Beilstein J Org Chem 2024; 20:1614-1622. [PMID: 39076289 PMCID: PMC11285060 DOI: 10.3762/bjoc.20.144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 07/02/2024] [Indexed: 07/31/2024] Open
Abstract
Determining the pK a values of various C-H sites in organic molecules offers valuable insights for synthetic chemists in predicting reaction sites. As molecular complexity increases, this task becomes more challenging. This paper introduces pKalculator, a quantum chemistry (QM)-based workflow for automatic computations of C-H pK a values, which is used to generate a training dataset for a machine learning (ML) model. The QM workflow is benchmarked against 695 experimentally determined C-H pK a values in DMSO. The ML model is trained on a diverse dataset of 775 molecules with 3910 C-H sites. Our ML model predicts C-H pK a values with a mean absolute error (MAE) and a root mean squared error (RMSE) of 1.24 and 2.15 pK a units, respectively. Furthermore, we employ our model on 1043 pK a-dependent reactions (aldol, Claisen, and Michael) and successfully indicate the reaction sites with a Matthew's correlation coefficient (MCC) of 0.82.
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Affiliation(s)
- Rasmus M Borup
- Department of Chemistry, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Nicolai Ree
- Department of Chemistry, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Jan H Jensen
- Department of Chemistry, University of Copenhagen, Copenhagen, DK-2100, Denmark
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7
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Pang HW, Dong X, Johnson MS, Green WH. Subgraph Isomorphic Decision Tree to Predict Radical Thermochemistry with Bounded Uncertainty Estimation. J Phys Chem A 2024; 128:2891-2907. [PMID: 38536892 DOI: 10.1021/acs.jpca.4c00569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Detailed chemical kinetic models offer valuable mechanistic insights into industrial applications. Automatic generation of reliable kinetic models requires fast and accurate radical thermochemistry estimation. Kineticists often prefer hydrogen bond increment (HBI) corrections from a closed-shell molecule to the corresponding radical for their interpretability, physical meaning, and facilitation of error cancellation as a relative quantity. Tree estimators, used due to limited data, currently rely on expert knowledge and manual construction, posing challenges in maintenance and improvement. In this work, we extend the subgraph isomorphic decision tree (SIDT) algorithm originally developed for rate estimation to estimate HBI corrections. We introduce a physics-aware splitting criterion, explore a bounded weighted uncertainty estimation method, and evaluate aleatoric uncertainty-based and model variance reduction-based prepruning methods. Moreover, we compile a data set of thermochemical parameters for 2210 radicals involving C, O, N, and H based on quantum chemical calculations from recently published works. We leverage the collected data set to train the SIDT model. Compared to existing empirical tree estimators, the SIDT model (1) offers an automatic approach to generating and extending the tree estimator for thermochemistry, (2) has better accuracy and R2, (3) provides significantly more realistic uncertainty estimates, and (4) has a tree structure much more advantageous in descent speed. Overall, the SIDT estimator marks a great leap in kinetic modeling, offering more precise, reliable, and scalable predictions for radical thermochemistry.
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Affiliation(s)
- Hao-Wei Pang
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Xiaorui Dong
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Matthew S Johnson
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - William H Green
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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8
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Yu F, Wu X, Chen W, Yan F, Li W. Computer-assisted discovery and evaluation of potential ribosomal protein S6 kinase beta 2 inhibitors. Comput Biol Med 2024; 172:108204. [PMID: 38484695 DOI: 10.1016/j.compbiomed.2024.108204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/11/2024] [Accepted: 02/19/2024] [Indexed: 03/26/2024]
Abstract
S6K2 is an important protein in mTOR signaling pathway and cancer. To identify potential S6K2 inhibitors for mTOR pathway treatment, a virtual screening of 1,575,957 active molecules was performed using PLANET, AutoDock GPU, and AutoDock Vina, with their classification abilities compared. The MM/PB(GB)SA method was used to identify four compounds with the strongest binding energies. These compounds were further investigated using molecular dynamics (MD) simulations to understand the properties of the S6K2/ligand complex. Due to a lack of available 3D structures of S6K2, OmegaFold served as a reliable 3D predictive model with higher evaluation scores in SAVES v6.0 than AlphaFold, AlphaFold2, and RoseTTAFold2. The 150 ns MD simulation revealed that the S6K2 structure in aqueous solvation experienced compression during conformational relaxation and encountered potential energy traps of about 19.6 kJ mol-1. The virtual screening results indicated that Lys75 and Lys99 in S6K2 are key binding sites in the binding cavity. Additionally, MD simulations revealed that the ligands remained attached to the activation cavity of S6K2. Among the compounds, compound 1 induced restrictive dissociation of S6K2 in the presence of a flexible region, compound 8 achieved strong stability through hydrogen bonding with Lys99, compound 9 caused S6K2 tightening, and the binding of compound 16 was heavily influenced by hydrophobic interactions. This study suggests that these four potential inhibitors with different mechanisms of action could provide potential therapeutic options.
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Affiliation(s)
- Fangyi Yu
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, China
| | - Xiaochuan Wu
- Institute of Pharmaceutics, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - WeiSong Chen
- Department of Respiratory Medicine, Jinhua Municipal Central Hospital, Jinhua, Zhejiang, 321000, China
| | - Fugui Yan
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, China
| | - Wen Li
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, China.
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9
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Sun R, Zheng P, Chen P, Wu D, Zheng J, Liu X, Hu Y. Enhancing the Catalytic Efficiency of D-lactonohydrolase through the Synergy of Tunnel Engineering, Evolutionary Analysis, and Force-Field Calculations. Chemistry 2024; 30:e202304164. [PMID: 38217521 DOI: 10.1002/chem.202304164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/11/2024] [Accepted: 01/12/2024] [Indexed: 01/15/2024]
Abstract
Computational design advances enzyme evolution and their use in biocatalysis in a faster and more efficient manner. In this study, a synergistic approach integrating tunnel engineering, evolutionary analysis, and force-field calculations has been employed to enhance the catalytic activity of D-lactonohydrolase (D-Lac), which is a pivotal enzyme involved in the resolution of racemic pantolactone during the production of vitamin B5. The best mutant, N96S/A271E/F274Y/F308G (M3), was obtained and its catalytic efficiency (kcat/KM) was nearly 23-fold higher than that of the wild-type. The M3 whole-cell converted 20 % of DL-pantolactone into D-pantoic acid (D-PA, >99 % e.e.) with a conversion rate of 47 % and space-time yield of 107.1 g L-1 h-1, demonstrating its great potential for industrial-scale D-pantothenic acid production. Molecular dynamics (MD) simulations revealed that the reduction in the steric hindrance within the substrate tunnel and conformational reconstruction of the distal loop resulted in a more favourable"catalytic" conformation, making it easier for the substrate and enzyme to enter their pre-reaction state. This study illustrates the potential of the distal residue on the pivotal loop at the entrance of the D-Lac substrate tunnel as a novel modification hotspot capable of reshaping energy patterns and consequently influencing the enzymatic activity.
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Affiliation(s)
- Ruobin Sun
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122, P. R. China
| | - Pu Zheng
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122, P. R. China
| | - Pengcheng Chen
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122, P. R. China
| | - Dan Wu
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122, P. R. China
| | - Jiangmei Zheng
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122, P. R. China
| | - Xueyu Liu
- Hangzhou Xinfu Technology Co., Ltd., Hangzhou, 311301, P. R. China
| | - Yunxiang Hu
- Hangzhou Xinfu Technology Co., Ltd., Hangzhou, 311301, P. R. China
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Ganeshpurkar A, Akotkar L, Kumar D, Kumar D, Ganeshpurkar A. Machine learning-based virtual screening and molecular modelling studies for identification of butyrylcholinesterase inhibitors as anti-Alzheimer's agent. J Biomol Struct Dyn 2024:1-17. [PMID: 38466084 DOI: 10.1080/07391102.2024.2326664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 02/28/2024] [Indexed: 03/12/2024]
Abstract
Butyrylcholinesterase (BChE) is a hydrolase involved in the metabolism and detoxification of specific esters in the blood. It is also implicated in the progression of Alzheimer's disease, a type of dementia. As the disease progresses, the level of BChE tends to increase, opting for a major role as an acetylcholine-degrading enzyme and surpassing the role of acetylcholinesterase. Hence, the development of BChE inhibitors could be beneficial for the latter stages of the disease. In the present study, machine learning (ML) models were developed and employed to identify new BChE inhibitors. Further, the identified molecules were subjected to molecular property filters. The filtered ligands were studied through molecular modelling techniques, viz. molecular docking and molecular dynamics (MD). Support vector machine-based ML models resulted in the identification of 3291 compounds that would have predicted IC50 values less than 200 nM. The docking study showed that compounds ART13069594, ART17350769 and LEG19710163 have mean binding energies of -9.62, -9.26 and -8.93 kcal/mol, respectively. The MD study displayed that all the selected ligands showed stable complexes with BChE. The trajectories of all the ligands were stable similar to the standard BChE inhibitors.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ankit Ganeshpurkar
- Department of Pharmaceutical Chemistry, Poona College of Pharmacy, Bharati Vidyapeeth (Deemed to be University), Pune, India
| | - Likhit Akotkar
- Department of Pharmacology, Poona College of Pharmacy, Bharati Vidyapeeth (Deemed to be University), Pune, India
| | - Devendra Kumar
- School of Pharmacy & Technology Management, SVKM's NMIMS University, Shirpur, India
| | - Dileep Kumar
- Department of Pharmaceutical Chemistry, Poona College of Pharmacy, Bharati Vidyapeeth (Deemed to be University), Pune, India
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11
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Paydar S, Feizi F, Shamsipur M, Barati A, Mousavi F, Matt D. A novel ratiometric fluorescence probe based on calix[4]arene functionalized polymer dots for bisphenol A detection in real water samples. Talanta 2024; 269:125450. [PMID: 38042141 DOI: 10.1016/j.talanta.2023.125450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 11/17/2023] [Accepted: 11/19/2023] [Indexed: 12/04/2023]
Abstract
Bisphenol A (BPA) is one of key raw materials used in the production of epoxy resins and plastics, which has toxicological effects on humans by disrupting cell functions through a variety of cell signaling pathways. Therefore, it is of great significance to develop a simple, rapid, and accurate BPA detection method in real water samples. In this study, a ratiometric fluorescence method based on yellow-emitting surface-functionalized polymer dots (PFBT@L Pdots) and blue-emitting carbon dots (Cdots) was described for the detection of BPA. Pdots as the detecting part were synthesized by using highly fluorescent hydrophobic Poly[(9,9-dioctylfluorenyl-2,7-diyl)-alt-co-(1,4-benzo-(2,1',3)-thiadiazole)] (PFBT) polymer and (R)-5,11,17,23-Tetra-tert-butyl-25,27-bis[(diphenylphosphinoyl)methoxy]-26-(3-oxabutyloxy)-28-[(1-phenylethyl)- carbamoylmethoxy]calix [4]arene (L) functionalizing ligand, and Cdots as internal reference were prepared by hydrothermal treatment of citric acid and urea. In the presence of BPA, chemical binding of the phosphorus atoms of nearby PFBT@L Pdots with BPA hydroxyl functional groups led to the aggregation of the PFBT@L Pdots aggregation and quenching their yellow emission, but the blue emission of Cdots, on the other hand, remained stable. The proposed PFBT@L Pdots probe was successfully applied for the detection of BPA in real water samples, and the results were in good agreement with those obtained by HPLC-FLD. To the best of our knowledge, this is the first report that the calixarene has been utilized to modify Pdots.
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Affiliation(s)
- Sardar Paydar
- Department of Chemistry, Razi University, Kermanshah, Iran
| | - Foroozan Feizi
- Department of Chemistry, Razi University, Kermanshah, Iran.
| | | | - Ali Barati
- Department of Chemistry, Razi University, Kermanshah, Iran
| | | | - Dominique Matt
- Molecular Inorganic Chemistry Laboratory, Louis Pasteur University, France
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12
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van Tetering L, Spies S, Wildeman QDK, Houthuijs KJ, van Outersterp RE, Martens J, Wevers RA, Wishart DS, Berden G, Oomens J. A spectroscopic test suggests that fragment ion structure annotations in MS/MS libraries are frequently incorrect. Commun Chem 2024; 7:30. [PMID: 38355930 PMCID: PMC10867025 DOI: 10.1038/s42004-024-01112-7] [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: 11/18/2023] [Accepted: 01/22/2024] [Indexed: 02/16/2024] Open
Abstract
Modern untargeted mass spectrometry (MS) analyses quickly detect and resolve thousands of molecular compounds. Although features are readily annotated with a molecular formula in high-resolution small-molecule MS applications, the large majority of them remains unidentified in terms of their full molecular structure. Collision-induced dissociation tandem mass spectrometry (CID-MS2) provides a diagnostic molecular fingerprint to resolve the molecular structure through a library search. However, for de novo identifications, one must often rely on in silico generated MS2 spectra as reference. The ability of different in silico algorithms to correctly predict MS2 spectra and thus to retrieve correct molecular structures is a topic of lively debate, for instance in the CASMI contest. Underlying the predicted MS2 spectra are the in silico generated product ion structures, which are normally not used in de novo identification, but which can serve to critically assess the fragmentation algorithms. Here we evaluate in silico generated MSn product ion structures by comparison with structures established experimentally by infrared ion spectroscopy (IRIS). For a set of three dozen product ion structures from five precursor molecules, we find that virtually all fragment ion structure annotations in three major in silico MS2 libraries (HMDB, METLIN, mzCloud) are incorrect and caution the reader against their use for structure annotation of MS/MS ions.
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Affiliation(s)
- Lara van Tetering
- Radboud University, Institute for Molecules and Materials, FELIX Laboratory, Toernooiveld 7, 6525ED, Nijmegen, The Netherlands
| | - Sylvia Spies
- Radboud University, Institute for Molecules and Materials, FELIX Laboratory, Toernooiveld 7, 6525ED, Nijmegen, The Netherlands
| | - Quirine D K Wildeman
- Radboud University, Institute for Molecules and Materials, FELIX Laboratory, Toernooiveld 7, 6525ED, Nijmegen, The Netherlands
| | - Kas J Houthuijs
- Radboud University, Institute for Molecules and Materials, FELIX Laboratory, Toernooiveld 7, 6525ED, Nijmegen, The Netherlands
| | - Rianne E van Outersterp
- Radboud University, Institute for Molecules and Materials, FELIX Laboratory, Toernooiveld 7, 6525ED, Nijmegen, The Netherlands
| | - Jonathan Martens
- Radboud University, Institute for Molecules and Materials, FELIX Laboratory, Toernooiveld 7, 6525ED, Nijmegen, The Netherlands
| | - Ron A Wevers
- Department of Laboratory Medicine, Translational Metabolic Laboratory, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525GA, Nijmegen, The Netherlands
| | - David S Wishart
- Departments of Computing Science and Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Giel Berden
- Radboud University, Institute for Molecules and Materials, FELIX Laboratory, Toernooiveld 7, 6525ED, Nijmegen, The Netherlands
| | - Jos Oomens
- Radboud University, Institute for Molecules and Materials, FELIX Laboratory, Toernooiveld 7, 6525ED, Nijmegen, The Netherlands.
- van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098XH, Amsterdam, The Netherlands.
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13
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Le Berre M, Tubiana T, Reuterswärd Waldner P, Lazar N, Li de la Sierra I, Santos JM, Llinás M, Nessler S. Structural characterization of the ACDC domain from ApiAP2 proteins of the malaria parasite. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.09.579679. [PMID: 38370614 PMCID: PMC10871335 DOI: 10.1101/2024.02.09.579679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
The Apicomplexan AP2 (ApiAP2) proteins are the best characterized family of DNA-binding proteins in the malaria parasite. Apart from the AP2 DNA-binding domain, there is little sequence similarity between ApiAP2 proteins and no other functional domains have been extensively characterized. One protein domain, which is present in a subset of the ApiAP2 proteins, is the conserved AP2-coincident domain mostly at the C-terminus (ACDC domain). Here we solved for the first time the crystal structure of the ACDC domain from two distinct Plasmodium falciparum ApiAP2 proteins and one orthologue from P. vivax , revealing a non-canonical four-helix bundle. Despite little sequence conservation between the ACDC domains from the two proteins, the structures are remarkably similar and do not resemble that of any other known protein domains. Due to their unique protein architecture and lack of homologues in the human genome, we performed in silico docking calculations against a library of known antimalarial compounds and we identified a small molecule that can potentially bind to any Apicomplexan ACDC domain within a pocket highly conserved amongst ApiAP2 proteins. Inhibitors based on this compound would disrupt the function of the ACDC domain and thus of the ApiAP2 proteins containing it, providing a new therapeutic window for targeting the malaria parasite and other Apicomplexans.
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14
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Ma M, Lei X. A deep learning framework for predicting molecular property based on multi-type features fusion. Comput Biol Med 2024; 169:107911. [PMID: 38160501 DOI: 10.1016/j.compbiomed.2023.107911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 12/18/2023] [Accepted: 12/24/2023] [Indexed: 01/03/2024]
Abstract
Extracting expressive molecular features is essential for molecular property prediction. Sequence-based representation is a common representation of molecules, which ignores the structure information of molecules. While molecular graph representation has a weak ability in expressing the 3D structure. In this article, we try to make use of the advantages of different type representations simultaneously for molecular property prediction. Thus, we propose a fusion model named DLF-MFF, which integrates the multi-type molecular features. Specifically, we first extract four different types of features from molecular fingerprints, 2D molecular graph, 3D molecular graph and molecular image. Then, in order to learn molecular features individually, we use four essential deep learning frameworks, which correspond to four distinct molecular representations. The final molecular representation is created by integrating the four feature vectors and feeding them into prediction layer to predict molecular property. We compare DLF-MFF with 7 state-of-the-art methods on 6 benchmark datasets consisting of multiple molecular properties, the experimental results show that DLF-MFF achieves state-of-the-art performance on 6 benchmark datasets. Moreover, DLF-MFF is applied to identify potential anti-SARS-CoV-2 inhibitor from 2500 drugs. We predict probability of each drug being inferred as a 3CL protease inhibitor and also calculate the binding affinity scores between each drug and 3CL protease. The results show that DLF-MFF product better performance in the identification of anti-SARS-CoV-2 inhibitor. This work is expected to offer novel research perspectives for accurate prediction of molecular properties and provide valuable insights into drug repurposing for COVID-19.
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Affiliation(s)
- Mei Ma
- School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China; School of Mathematics and Statistics, Qinghai Normal University, Qinghai, 810000, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China.
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15
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Lolok N, Sumiwi SA, Ramadhan DSF, Levita J, Sahidin I. Molecular dynamics study of stigmasterol and beta-sitosterol of Morinda citrifolia L. towards α-amylase and α-glucosidase. J Biomol Struct Dyn 2024; 42:1952-1955. [PMID: 37539686 DOI: 10.1080/07391102.2023.2243519] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 04/08/2023] [Indexed: 08/05/2023]
Abstract
Previous in vivo studies of Morinda citrifolia (Rubiaceae) reported that the extract inhibited α-amylase and reduced blood glucose levels in streptozotocin-induced diabetes mice. Moreover, molecular docking studies confirmed that ursolic acid and sterol compounds contained in the fruit interacted with important residues in the binding site of α-amylase and α-glucosidase. Our work aimed to study the complex stability of stigmasterol (which has been isolated from the M. citrifolia fruit for the first time) and beta-sitosterol towards α-amylase and α-glucosidase by employing molecular dynamics simulation on GROMACS 2016.3 embedded with the AMBER99SB-ILDN force field. The simulation was carried out for 100 ns at 310 oK. Based on the RMSD and RMSF graphs, the complexes of stigmasterol/α-amylase and stigmasterol/α-glucosidase are more stable compared to acarbose, the known inhibitor of both enzymes. Moreover, beta-sitosterol indicates a better stability complex with α-glucosidase compared to that of acarbose. Interestingly, the affinity of stigmasterol and beta-sitosterol to both enzymes, in terms of the total binding energy, is stronger than that of acarbose. Taken together, stigmasterol and beta-sitosterol in M. citrifolia fruit may have the potency to be developed as α-amylase and α-glucosidase inhibitors.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Nikeherpianti Lolok
- Faculty of Pharmacy, STIKES Mandala Waluya, Kendari, Southeast Sulawesi, Indonesia
- Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, West Java, Indonesia
| | - Sri Adi Sumiwi
- Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, West Java, Indonesia
| | | | - Jutti Levita
- Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, West Java, Indonesia
| | - I Sahidin
- Faculty of Pharmacy, Halu Oleo University, Kendari, Southeast Sulawesi, Indonesia
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16
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Abranches DO, Maginn EJ, Colón YJ. Boosting Graph Neural Networks with Molecular Mechanics: A Case Study of Sigma Profile Prediction. J Chem Theory Comput 2023; 19:9318-9328. [PMID: 38063153 DOI: 10.1021/acs.jctc.3c01003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
Abstract
Sigma profiles are quantum-chemistry-derived molecular descriptors that encode the polarity of molecules. They have shown great performance when used as a feature in machine learning applications. To accelerate the development of these models and the construction of large sigma profile databases, this work proposes a graph convolutional network (GCN) architecture to predict sigma profiles from molecule structures. To do so, the usage of molecular mechanics (force field atom types) is explored as a computationally inexpensive node-level featurization technique to encode the local and global chemical environments of atoms in molecules. The GCN models developed in this work accurately predict the sigma profiles of assorted organic and inorganic compounds. The best GCN model here reported, obtained using Merck molecular force field (MMFF) atom types, displayed training and testing set coefficients of determination of 0.98 and 0.96, respectively, which are superior to previous methodologies reported in the literature. This performance boost is shown to be due to both the usage of a convolutional architecture and node-level features based on force field atom types. Finally, to demonstrate their practical applicability, we used GCN-predicted sigma profiles as the input to machine learning models previously developed in the literature that predict boiling temperatures and aqueous solubilities. Using the predicted sigma profiles as input, these models were able to compute both physicochemical properties using significantly less computational resources and displayed only a slight decrease in performance when compared with sigma profiles obtained from quantum chemistry methods.
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Affiliation(s)
- Dinis O Abranches
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Edward J Maginn
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Yamil J Colón
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
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17
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Kadan A, Ryczko K, Wildman A, Wang R, Roitberg A, Yamazaki T. Accelerated Organic Crystal Structure Prediction with Genetic Algorithms and Machine Learning. J Chem Theory Comput 2023; 19:9388-9402. [PMID: 38059458 DOI: 10.1021/acs.jctc.3c00853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
We present a high-throughput, end-to-end pipeline for organic crystal structure prediction (CSP)─the problem of identifying the stable crystal structures that will form from a given molecule based only on its molecular composition. Our tool uses neural network potentials to allow for efficient screening and structural relaxation of generated crystal candidates. Our pipeline consists of two distinct stages: random search, whereby crystal candidates are randomly generated and screened, and optimization, where a genetic algorithm (GA) optimizes this screened population. We assess the performance of each stage of our pipeline on 21 molecules taken from the Cambridge Crystallographic Data Centre's CSP blind tests. We show that random search alone yields matches for ≈50% of targets. We then validate the potential of our full pipeline, making use of the GA to optimize the root-mean-square deviation between crystal candidates and the experimentally derived structure. With this approach, we are able to find matches for ≈80% of candidates with 10-100 times smaller initial population sizes than when using random search. Lastly, we run our full pipeline with an ANI model that is trained on a small data set of molecules extracted from crystal structures in the Cambridge Structural Database, generating ≈60% of targets. By leveraging machine learning models trained to predict energies at the density functional theory level, our pipeline has the potential to approach the accuracy of ab initio methods and the efficiency of empirical force fields.
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Affiliation(s)
- Amit Kadan
- Good Chemistry Company, 1285 W Pender Street, Vancouver, British Columbia V6E 4B1, Canada
| | - Kevin Ryczko
- Good Chemistry Company, 1285 W Pender Street, Vancouver, British Columbia V6E 4B1, Canada
| | - Andrew Wildman
- Good Chemistry Company, 1285 W Pender Street, Vancouver, British Columbia V6E 4B1, Canada
| | - Rodrigo Wang
- Good Chemistry Company, 1285 W Pender Street, Vancouver, British Columbia V6E 4B1, Canada
| | - Adrian Roitberg
- Department of Chemistry, University of Florida, P.O. Box 117200, Gainesville, Florida 32611-7200, United States
| | - Takeshi Yamazaki
- Good Chemistry Company, 1285 W Pender Street, Vancouver, British Columbia V6E 4B1, Canada
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18
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Stylianakis I, Zervos N, Lii JH, Pantazis DA, Kolocouris A. Conformational energies of reference organic molecules: benchmarking of common efficient computational methods against coupled cluster theory. J Comput Aided Mol Des 2023; 37:607-656. [PMID: 37597063 PMCID: PMC10618395 DOI: 10.1007/s10822-023-00513-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 06/03/2023] [Indexed: 08/21/2023]
Abstract
We selected 145 reference organic molecules that include model fragments used in computer-aided drug design. We calculated 158 conformational energies and barriers using force fields, with wide applicability in commercial and free softwares and extensive application on the calculation of conformational energies of organic molecules, e.g. the UFF and DREIDING force fields, the Allinger's force fields MM3-96, MM3-00, MM4-8, the MM2-91 clones MMX and MM+, the MMFF94 force field, MM4, ab initio Hartree-Fock (HF) theory with different basis sets, the standard density functional theory B3LYP, the second-order post-HF MP2 theory and the Domain-based Local Pair Natural Orbital Coupled Cluster DLPNO-CCSD(T) theory, with the latter used for accurate reference values. The data set of the organic molecules includes hydrocarbons, haloalkanes, conjugated compounds, and oxygen-, nitrogen-, phosphorus- and sulphur-containing compounds. We reviewed in detail the conformational aspects of these model organic molecules providing the current understanding of the steric and electronic factors that determine the stability of low energy conformers and the literature including previous experimental observations and calculated findings. While progress on the computer hardware allows the calculations of thousands of conformations for later use in drug design projects, this study is an update from previous classical studies that used, as reference values, experimental ones using a variety of methods and different environments. The lowest mean error against the DLPNO-CCSD(T) reference was calculated for MP2 (0.35 kcal mol-1), followed by B3LYP (0.69 kcal mol-1) and the HF theories (0.81-1.0 kcal mol-1). As regards the force fields, the lowest errors were observed for the Allinger's force fields MM3-00 (1.28 kcal mol-1), ΜΜ3-96 (1.40 kcal mol-1) and the Halgren's MMFF94 force field (1.30 kcal mol-1) and then for the MM2-91 clones MMX (1.77 kcal mol-1) and MM+ (2.01 kcal mol-1) and MM4 (2.05 kcal mol-1). The DREIDING (3.63 kcal mol-1) and UFF (3.77 kcal mol-1) force fields have the lowest performance. These model organic molecules we used are often present as fragments in drug-like molecules. The values calculated using DLPNO-CCSD(T) make up a valuable data set for further comparisons and for improved force field parameterization.
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Affiliation(s)
- Ioannis Stylianakis
- Department of Medicinal Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimioupolis Zografou, 15771, Athens, Greece
| | - Nikolaos Zervos
- Department of Medicinal Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimioupolis Zografou, 15771, Athens, Greece
| | - Jenn-Huei Lii
- Department of Chemistry, National Changhua University of Education, Changhua City, Taiwan
| | - Dimitrios A Pantazis
- Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470, Mülheim an der Ruhr, Germany
| | - Antonios Kolocouris
- Department of Medicinal Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimioupolis Zografou, 15771, Athens, Greece.
- Laboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou, 15771, Athens, Greece.
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19
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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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20
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Houthuijs KJ, Horn M, Vughs D, Martens J, Brunner AM, Oomens J, Berden G. Identification of organic micro-pollutants in surface water using MS-based infrared ion spectroscopy. CHEMOSPHERE 2023; 341:140046. [PMID: 37660788 DOI: 10.1016/j.chemosphere.2023.140046] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/29/2023] [Accepted: 08/31/2023] [Indexed: 09/05/2023]
Abstract
Comprehensive monitoring of organic micro-pollutants (OMPs) in drinking water sources relies on non-target screening (NTS) using liquid-chromatography and high-resolution mass spectrometry (LC-HRMS). Identification of OMPs is typically based on accurate mass and tandem mass spectrometry (MS/MS) data by matching against entries in compound databases and MS/MS spectral libraries. MS/MS spectra are, however, not always diagnostic for the full molecular structure and, moreover, emerging OMPs or OMP transformation products may not be present in libraries. Here we demonstrate how infrared ion spectroscopy (IRIS), an emerging MS-based method for structural elucidation, can aid in the identification of OMPs. IRIS measures the IR spectrum of an m/z-isolated ion in a mass spectrometer, providing an orthogonal diagnostic for molecular identification. Here, we demonstrate the workflow for identification of OMPs in river water and show how quantum-chemically predicted IR spectra can be used to screen potential candidates and suggest structural assignments. A crucial step herein is to define a set of candidate structures, presumably including the actual OMP, for which we present several strategies based on domain knowledge, the IR spectrum and MS/MS spectrum.
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Affiliation(s)
- Kas J Houthuijs
- Radboud University, Institute for Molecules and Materials, FELIX Laboratory, Toernooiveld 7, 6525 ED, Nijmegen, the Netherlands
| | - Marijke Horn
- Radboud University, Institute for Molecules and Materials, FELIX Laboratory, Toernooiveld 7, 6525 ED, Nijmegen, the Netherlands
| | - Dennis Vughs
- KWR Water Research Institute, Chemical Water Quality and Health, P.O. Box 1072, 3430 BB, Nieuwegein, the Netherlands
| | - Jonathan Martens
- Radboud University, Institute for Molecules and Materials, FELIX Laboratory, Toernooiveld 7, 6525 ED, Nijmegen, the Netherlands
| | - Andrea M Brunner
- KWR Water Research Institute, Chemical Water Quality and Health, P.O. Box 1072, 3430 BB, Nieuwegein, the Netherlands; TNO, Environmental Modelling, Sensing and Analysis (EMSA), Princetonlaan 8, 3584 CB, Utrecht, the Netherlands
| | - Jos Oomens
- Radboud University, Institute for Molecules and Materials, FELIX Laboratory, Toernooiveld 7, 6525 ED, Nijmegen, the Netherlands; van't Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands
| | - Giel Berden
- Radboud University, Institute for Molecules and Materials, FELIX Laboratory, Toernooiveld 7, 6525 ED, Nijmegen, the Netherlands.
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21
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Cheng Y, Ji C, Xu J, Chen R, Guo Y, Bian Q, Shen Z, Zhang B. LCK-SafeScreen-Model: An Advanced Ensemble Machine Learning Approach for Estimating the Binding Affinity between Compounds and LCK Target. Molecules 2023; 28:7382. [PMID: 37959801 PMCID: PMC10650606 DOI: 10.3390/molecules28217382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/24/2023] [Accepted: 10/24/2023] [Indexed: 11/15/2023] Open
Abstract
The lymphocyte-specific protein tyrosine kinase (LCK) is a critical target in leukemia treatment. However, potential off-target interactions involving LCK can lead to unintended consequences. This underscores the importance of accurately predicting the inhibitory reactions of drug molecules with LCK during the research and development stage. To address this, we introduce an advanced ensemble machine learning technique designed to estimate the binding affinity between molecules and LCK. This comprehensive method includes the generation and selection of molecular fingerprints, the design of the machine learning model, hyperparameter tuning, and a model ensemble. Through rigorous optimization, the predictive capabilities of our model have been significantly enhanced, raising test R2 values from 0.644 to 0.730 and reducing test RMSE values from 0.841 to 0.732. Utilizing these advancements, our refined ensemble model was employed to screen an MCE -like drug library. Through screening, we selected the top ten scoring compounds, and tested them using the ADP-Glo bioactivity assay. Subsequently, we employed molecular docking techniques to further validate the binding mode analysis of these compounds with LCK. The exceptional predictive accuracy of our model in identifying LCK inhibitors not only emphasizes its effectiveness in projecting LCK-related safety panel predictions but also in discovering new LCK inhibitors. For added user convenience, we have also established a webserver, and a GitHub repository to share the project.
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Affiliation(s)
- Ying Cheng
- College of Pharmaceutical Sciences, Hangzhou First People’s Hospital, Zhejiang Chinese Medical University, Hangzhou 311402, China; (Y.C.); (C.J.); (J.X.)
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; (R.C.); (Y.G.); (Q.B.)
| | - Cong Ji
- College of Pharmaceutical Sciences, Hangzhou First People’s Hospital, Zhejiang Chinese Medical University, Hangzhou 311402, China; (Y.C.); (C.J.); (J.X.)
| | - Jun Xu
- College of Pharmaceutical Sciences, Hangzhou First People’s Hospital, Zhejiang Chinese Medical University, Hangzhou 311402, China; (Y.C.); (C.J.); (J.X.)
- Department of Pharmacy, Huzhou Central Hospital, Huzhou 313000, China
| | - Roufen Chen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; (R.C.); (Y.G.); (Q.B.)
| | - Yu Guo
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; (R.C.); (Y.G.); (Q.B.)
| | - Qingyu Bian
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; (R.C.); (Y.G.); (Q.B.)
| | - Zheyuan Shen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; (R.C.); (Y.G.); (Q.B.)
| | - Bo Zhang
- College of Pharmaceutical Sciences, Hangzhou First People’s Hospital, Zhejiang Chinese Medical University, Hangzhou 311402, China; (Y.C.); (C.J.); (J.X.)
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22
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Lehner MT, Katzberger P, Maeder N, Schiebroek CC, Teetz J, Landrum GA, Riniker S. DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment. J Chem Inf Model 2023; 63:6014-6028. [PMID: 37738206 PMCID: PMC10565818 DOI: 10.1021/acs.jcim.3c00800] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Indexed: 09/24/2023]
Abstract
We present a robust and computationally efficient approach for assigning partial charges of atoms in molecules. The method is based on a hierarchical tree constructed from attention values extracted from a graph neural network (GNN), which was trained to predict atomic partial charges from accurate quantum-mechanical (QM) calculations. The resulting dynamic attention-based substructure hierarchy (DASH) approach provides fast assignment of partial charges with the same accuracy as the GNN itself, is software-independent, and can easily be integrated in existing parametrization pipelines, as shown for the Open force field (OpenFF). The implementation of the DASH workflow, the final DASH tree, and the training set are available as open source/open data from public repositories.
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Affiliation(s)
| | | | - Niels Maeder
- Department of Chemistry and
Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Carl C.G. Schiebroek
- Department of Chemistry and
Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Jakob Teetz
- Department of Chemistry and
Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Gregory A. Landrum
- Department of Chemistry and
Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Sereina Riniker
- Department of Chemistry and
Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
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23
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Li Z, Ren L, Wang X, Chen M, Wang T, Dai R, Wang Z. Anaerobic hydrolysis of recalcitrant tetramethylammonium from semiconductor wastewater: Performance and mechanisms. JOURNAL OF HAZARDOUS MATERIALS 2023; 459:132239. [PMID: 37567140 DOI: 10.1016/j.jhazmat.2023.132239] [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: 05/23/2023] [Revised: 07/23/2023] [Accepted: 08/04/2023] [Indexed: 08/13/2023]
Abstract
The treatment of tetramethylammonium hydroxide (TMAH)-bearing wastewater, generated in the electronic and semiconductor industries, raises significant concerns due to the neurotoxic, recalcitrant, and bio-inhibiting effects of TMAH. In this study, we proposed the use of an anaerobic hydrolysis bioreactor (AHBR) for TMAH removal, achieving a high removal efficiency of approximately 85%, which greatly surpassed the performance of widely-used advanced oxidation processes (AOPs). Density functional theory calculations indicated that the unexpectedly poor efficiency (5.8-8.0%) of selected AOPs can be attributed to the electrostatic repulsion between oxidants and the tightly bound electrons of TMAH. Metagenomic analyses of the AHBR revealed that Proteobacteria and Euryarchaeota played a dominant role in the transformation of TMAH through processes such as methyl transfer, methanogenesis, and acetyl-coenzyme A synthesis, utilizing methyl-tetrahydromethanopterin as a substrate. Moreover, several potential functional genes (e.g., mprF, basS, bcrB, sugE) related to TMAH resistance have been identified. Molecular docking studies between five selected proteins and tetramethylammonium further provided evidence supporting the roles of these potential functional genes. This study demonstrates the superiority of AHBR as a pretreatment technology compared to several widely-researched AOPs, paving the way for the proper design of treatment processes to abate TMAH in semiconductor wastewater.
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Affiliation(s)
- Zhouyan Li
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, Tongji Advanced Membrane Technology Center, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Lehui Ren
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, Tongji Advanced Membrane Technology Center, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Xueye Wang
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, Tongji Advanced Membrane Technology Center, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Mei Chen
- MOE Key Laboratory of Pollution Processes and Environmental Criteria/Tianjin Key Laboratory of Environmental Remediation and Pollution Control/College of Environmental Science and Engineering, Nankai University, No. 38 Tongyan Road, Jinnan District, Tianjin 300350, China
| | - Tianlin Wang
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, Tongji Advanced Membrane Technology Center, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Ruobin Dai
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, Tongji Advanced Membrane Technology Center, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Zhiwei Wang
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, Tongji Advanced Membrane Technology Center, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.
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24
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Ge S, Ren H, Guo Q, Wang X, Liu Y, Lin B, Huang K. Wuweixiaoduyin regulates TAZ-mediated immunoregulatory properties of Treg/TH17 cells in chronic osteomyelitis. Biotechnol Genet Eng Rev 2023; 39:980-999. [PMID: 36641597 DOI: 10.1080/02648725.2023.2166706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 01/05/2023] [Indexed: 01/16/2023]
Abstract
Wuwei xiaoduyin (WWXDY) is a prescription for Chronic osteomyelitis (COM) in traditional Chinese medicine (TCM). However, its specific mechanism remains unclear. The objective of this study was to investigate the mechanism of WWXDY in COM treatment. To clarify the potential role of TAZ in the treatment of COM by WWXDY via regulatory CD4+ T cells differentiation. The expressions of TAZ, RORγt and Foxp3 were determined by Quantitative Real-time PCR and Western blot. Besides, levels of IL-21, IL-17 and IL-10 in peripheral blood were detected by using ELISA. Molecular dynamics simulations and docking were further utilized to explore the binding mechanism. COM resulted in abnormal cell differentiation and an imbalance of Treg/Th17. In comparison with the control group, the percentage of Treg cells, Foxp3 expression and secretion of IL-17 and -21 cytokines decreased (P < 0.001), while the proportion of Th17 cells, the levels of TAZ and RORγt and concentration of IL-10 in PBMCs increased in the COM group (P < 0.001). Furthermore, the above abnormal differentiation and function of Treg/Th17 cells in COM were suppressed after treatment with WWXDY in vivo and in vitro. In addition, TEAD1 inhibited the therapeutic effect of WWXDY in terms of Treg/Th17 cells with COM. it was found that the main active ingredients were cichoric acid and isocarlinoside. WWXDY regulates immunoregulatory properties of Treg/Th17 cells in COM mainly by mediating TAZ expression. By inhibiting the chronic inflammation in COM, WWXDY is potentially used to inhibit the progression of COM into bone tumors.
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Affiliation(s)
- Shuyu Ge
- Department of Pharmacy, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Haiyong Ren
- Department of Orthopedics, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Qiaofeng Guo
- Department of Orthopedics, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Xiang Wang
- Department of Orthopedics, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Yiyang Liu
- Department of Orthopedics, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Bingyuan Lin
- Department of Orthopedics, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Kai Huang
- Department of Orthopedics, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
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25
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Dollar O, Joshi N, Pfaendtner J, Beck DAC. Efficient 3D Molecular Design with an E(3) Invariant Transformer VAE. J Phys Chem A 2023; 127:7844-7852. [PMID: 37670244 DOI: 10.1021/acs.jpca.3c04188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Abstract
This work introduces a three-dimensional (3D) invariant graph-to-string transformer variational autoencoders (VAE) (Vagrant) for generating molecules with accurate density functional theory (DFT)-level properties. Vagrant learns to model the joint probability distribution of a 3D molecular structure and its properties by encoding molecular structures into a 3D-aware latent space. Directed navigation through this latent space implicitly optimizes the 3D structure of a molecule, and the latent embedding can be used to condition a generative transformer to predict the candidate structure as a one-dimensional (1D) sequence. Additionally, we introduce two novel sampling methods that exploit the latent characteristics of a VAE to improve performance. We show that our method outperforms comparable 3D autoregressive and diffusion methods for predicting quantum chemical property values of novel molecules in terms of both sample quality and computational efficiency.
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Affiliation(s)
- Orion Dollar
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Nisarg Joshi
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Jim Pfaendtner
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - David A C Beck
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
- escience Institute, University of Washington, Seattle, Washington 98195, United States
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26
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Das S, Dinpazhoh L, Tanemura KA, Merz KM. Rapid and Automated Ab Initio Metabolite Collisional Cross Section Prediction from SMILES Input. J Chem Inf Model 2023; 63:4995-5000. [PMID: 37548575 DOI: 10.1021/acs.jcim.3c00890] [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] [Indexed: 08/08/2023]
Abstract
We implemented an ab initio CCS prediction workflow which incrementally refines generated structures using molecular mechanics, a deep learning potential, conformational clustering, and quantum mechanics (QM). Automating intermediate steps for a high performance computing (HPC) environment allows users to input the SMILES structure of small organic molecules and obtain a Boltzmann averaged collisional cross section (CCS) value as output. The CCS of a molecular species is a metric measured by ion mobility spectrometry (IMS) which can improve annotation of untargeted metabolomics experiments. We report only a minor drop in accuracy when we expedite the CCS calculation by replacing the QM geometry refinement step with a single-point energy calculation. Even though the workflow involves stochastic steps (i.e., conformation generation and clustering), the final CCS value was highly reproducible for multiple iterations on L-carnosine. Finally, we illustrate that the gas phase ensembles modeled for the workflow are intermediate files which can be used for the prediction of other properties such as aqueous phase nuclear magnetic resonance chemical shift prediction. The software is available at the following link: https://github.com/DasSusanta/snakemake_ccs.
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Affiliation(s)
- Susanta Das
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Laleh Dinpazhoh
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kiyoto Aramis Tanemura
- 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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27
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Lupo Pasini M, Mehta K, Yoo P, Irle S. Two excited-state datasets for quantum chemical UV-vis spectra of organic molecules. Sci Data 2023; 10:546. [PMID: 37604820 PMCID: PMC10442335 DOI: 10.1038/s41597-023-02408-4] [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: 03/07/2023] [Accepted: 07/24/2023] [Indexed: 08/23/2023] Open
Abstract
We present two open-source datasets that provide time-dependent density-functional tight-binding (TD-DFTB) electronic excitation spectra of organic molecules. These datasets represent predictions of UV-vis absorption spectra performed on optimized geometries of the molecules in their electronic ground state. The GDB-9-Ex dataset contains a subset of 96,766 organic molecules from the original open-source GDB-9 dataset. The ORNL_AISD-Ex dataset consists of 10,502,904 organic molecules that contain between 5 and 71 non-hydrogen atoms. The data reveals the close correlation between the magnitude of the gaps between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO), and the excitation energy of the lowest singlet excited state energies quantitatively. The chemical variability of the large number of molecules was examined with a topological fingerprint estimation based on extended-connectivity fingerprints (ECFPs) followed by uniform manifold approximation and projection (UMAP) for dimension reduction. Both datasets were generated using the DFTB+ software on the "Andes" cluster of the Oak Ridge Leadership Computing Facility (OLCF).
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Affiliation(s)
- Massimiliano Lupo Pasini
- Oak Ridge National Laboratory, Computational Sciences and Engineering Division, Oak Ridge, 37831, USA.
| | - Kshitij Mehta
- Oak Ridge National Laboratory, Computer Science and Mathematics Division, Oak Ridge, 37831, USA
| | - Pilsun Yoo
- Oak Ridge National Laboratory, Computational Sciences and Engineering Division, Oak Ridge, 37831, USA
| | - Stephan Irle
- Oak Ridge National Laboratory, Computational Sciences and Engineering Division, Oak Ridge, 37831, USA.
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28
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Ruth M, Gerbig D, Schreiner PR. Machine Learning for Bridging the Gap between Density Functional Theory and Coupled Cluster Energies. J Chem Theory Comput 2023. [PMID: 37418619 DOI: 10.1021/acs.jctc.3c00274] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2023]
Abstract
Accurate electronic energies and properties are crucial for successful reaction design and mechanistic investigations. Computing energies and properties of molecular structures has proven extremely useful, and, with increasing computational power, the limits of high-level approaches (such as coupled cluster theory) are expanding to ever larger systems. However, because scaling is highly unfavorable, these methods are still not universally applicable to larger systems. To address the need for fast and accurate electronic energies of larger systems, we created a database of around 8000 small organic monomers (2000 dimers) optimized at the B3LYP-D3(BJ)/cc-pVTZ level of theory. This database also includes single-point energies computed at various levels of theory, including PBE1PBE, ωΒ97Χ, M06-2X, revTPSS, B3LYP, and BP86, for density functional theory as well as DLPNO-CCSD(T) and CCSD(T) for coupled cluster theory, all in conjunction with a cc-pVTZ basis. We used this database to train machine learning models based on graph neural networks using two different graph representations. Our models are able to make energy predictions from B3LYP-D3(BJ)/cc-pVTZ inputs to CCSD(T)/cc-pVTZ outputs with a mean absolute error of 0.78 and to DLPNO-CCSD(T)/cc-pVTZ with an mean absolute error of 0.50 and 0.18 kcal mol-1 for monomers and dimers, respectively. The model for dimers was further validated on the S22 database, and the monomer model was tested on challenging systems, including those with highly conjugated or functionally complex molecules.
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Affiliation(s)
- Marcel Ruth
- Institute of Organic Chemistry, Justus Liebig University, Heinrich-Buff-Ring 17, 35392 Giessen, Germany
| | - Dennis Gerbig
- Institute of Organic Chemistry, Justus Liebig University, Heinrich-Buff-Ring 17, 35392 Giessen, Germany
| | - Peter R Schreiner
- Institute of Organic Chemistry, Justus Liebig University, Heinrich-Buff-Ring 17, 35392 Giessen, Germany
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29
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Ferri P, Li C, Schwalbe-Koda D, Xie M, Moliner M, Gómez-Bombarelli R, Boronat M, Corma A. Approaching enzymatic catalysis with zeolites or how to select one reaction mechanism competing with others. Nat Commun 2023; 14:2878. [PMID: 37208318 DOI: 10.1038/s41467-023-38544-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 05/08/2023] [Indexed: 05/21/2023] Open
Abstract
Approaching the level of molecular recognition of enzymes with solid catalysts is a challenging goal, achieved in this work for the competing transalkylation and disproportionation of diethylbenzene catalyzed by acid zeolites. The key diaryl intermediates for the two competing reactions only differ in the number of ethyl substituents in the aromatic rings, and therefore finding a selective zeolite able to recognize this subtle difference requires an accurate balance of the stabilization of reaction intermediates and transition states inside the zeolite microporous voids. In this work we present a computational methodology that, by combining a fast high-throughput screeening of all zeolite structures able to stabilize the key intermediates with a more computationally demanding mechanistic study only on the most promising candidates, guides the selection of the zeolite structures to be synthesized. The methodology presented is validated experimentally and allows to go beyond the conventional criteria of zeolite shape-selectivity.
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Affiliation(s)
- Pau Ferri
- Instituto de Tecnología Química, Universitat Politècnica de València - Consejo Superior de Investigaciones Científicas, Avenida de los Naranjos s/n, 46022, Valencia, Spain
| | - Chengeng Li
- Instituto de Tecnología Química, Universitat Politècnica de València - Consejo Superior de Investigaciones Científicas, Avenida de los Naranjos s/n, 46022, Valencia, Spain
| | - Daniel Schwalbe-Koda
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Mingrou Xie
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Manuel Moliner
- Instituto de Tecnología Química, Universitat Politècnica de València - Consejo Superior de Investigaciones Científicas, Avenida de los Naranjos s/n, 46022, Valencia, Spain
| | - Rafael Gómez-Bombarelli
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Mercedes Boronat
- Instituto de Tecnología Química, Universitat Politècnica de València - Consejo Superior de Investigaciones Científicas, Avenida de los Naranjos s/n, 46022, Valencia, Spain.
| | - Avelino Corma
- Instituto de Tecnología Química, Universitat Politècnica de València - Consejo Superior de Investigaciones Científicas, Avenida de los Naranjos s/n, 46022, Valencia, Spain.
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30
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Andrianov AM, Shuldau MA, Furs KV, Yushkevich AM, Tuzikov AV. AI-Driven De Novo Design and Molecular Modeling for Discovery of Small-Molecule Compounds as Potential Drug Candidates Targeting SARS-CoV-2 Main Protease. Int J Mol Sci 2023; 24:ijms24098083. [PMID: 37175788 PMCID: PMC10178971 DOI: 10.3390/ijms24098083] [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: 03/14/2023] [Revised: 04/21/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
Over the past three years, significant progress has been made in the development of novel promising drug candidates against COVID-19. However, SARS-CoV-2 mutations resulting in the emergence of new viral strains that can be resistant to the drugs used currently in the clinic necessitate the development of novel potent and broad therapeutic agents targeting different vulnerable spots of the viral proteins. In this study, two deep learning generative models were developed and used in combination with molecular modeling tools for de novo design of small molecule compounds that can inhibit the catalytic activity of SARS-CoV-2 main protease (Mpro), an enzyme critically important for mediating viral replication and transcription. As a result, the seven best scoring compounds that exhibited low values of binding free energy comparable with those calculated for two potent inhibitors of Mpro, via the same computational protocol, were selected as the most probable inhibitors of the enzyme catalytic site. In light of the data obtained, the identified compounds are assumed to present promising scaffolds for the development of new potent and broad-spectrum drugs inhibiting SARS-CoV-2 Mpro, an attractive therapeutic target for anti-COVID-19 agents.
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Affiliation(s)
- Alexander M Andrianov
- Institute of Bioorganic Chemistry, National Academy of Sciences of Belarus, 220141 Minsk, Belarus
| | - Mikita A Shuldau
- United Institute of Informatics Problems, National Academy of Sciences of Belarus, 220012 Minsk, Belarus
| | - Konstantin V Furs
- United Institute of Informatics Problems, National Academy of Sciences of Belarus, 220012 Minsk, Belarus
| | - Artsemi M Yushkevich
- United Institute of Informatics Problems, National Academy of Sciences of Belarus, 220012 Minsk, Belarus
| | - Alexander V Tuzikov
- United Institute of Informatics Problems, National Academy of Sciences of Belarus, 220012 Minsk, Belarus
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31
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Jablonka K, Rosen AS, Krishnapriyan AS, Smit B. An Ecosystem for Digital Reticular Chemistry. ACS CENTRAL SCIENCE 2023; 9:563-581. [PMID: 37122448 PMCID: PMC10141625 DOI: 10.1021/acscentsci.2c01177] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
The vastness of the materials design space makes it impractical to explore using traditional brute-force methods, particularly in reticular chemistry. However, machine learning has shown promise in expediting and guiding materials design. Despite numerous successful applications of machine learning to reticular materials, progress in the field has stagnated, possibly because digital chemistry is more an art than a science and its limited accessibility to inexperienced researchers. To address this issue, we present mofdscribe, a software ecosystem tailored to novice and seasoned digital chemists that streamlines the ideation, modeling, and publication process. Though optimized for reticular chemistry, our tools are versatile and can be used in nonreticular materials research. We believe that mofdscribe will enable a more reliable, efficient, and comparable field of digital chemistry.
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Affiliation(s)
- Kevin
Maik Jablonka
- Laboratory of molecular simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Rue de l’Industrie 17, CH-1951 Sion, Switzerland
| | - Andrew S. Rosen
- Department of Materials
Science and Engineering, University of California, Berkeley, California 94720, United States
- Miller Institute for Basic Research in Science, University of California, Berkeley, California 94720, United States
- Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Aditi S. Krishnapriyan
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720, United States
- Department of Electrical Engineering and
Computer Science, University of California, Berkeley, California 94720, United States
- Computational
Research Division, Lawrence Berkeley National
Laboratory, Berkeley, California 94720, United States
| | - Berend Smit
- Laboratory of molecular simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Rue de l’Industrie 17, CH-1951 Sion, Switzerland
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32
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Bagnolini G, Luu TB, Hargrove AE. Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA. RNA (NEW YORK, N.Y.) 2023; 29:473-488. [PMID: 36693763 PMCID: PMC10019373 DOI: 10.1261/rna.079497.122] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
RNA structures regulate a wide range of processes in biology and disease, yet small molecule chemical probes or drugs that can modulate these functions are rare. Machine learning and other computational methods are well poised to fill gaps in knowledge and overcome the inherent challenges in RNA targeting, such as the dynamic nature of RNA and the difficulty of obtaining RNA high-resolution structures. Successful tools to date include principal component analysis, linear discriminate analysis, k-nearest neighbor, artificial neural networks, multiple linear regression, and many others. Employment of these tools has revealed critical factors for selective recognition in RNA:small molecule complexes, predictable differences in RNA- and protein-binding ligands, and quantitative structure activity relationships that allow the rational design of small molecules for a given RNA target. Herein we present our perspective on the value of using machine learning and other computation methods to advance RNA:small molecule targeting, including select examples and their validation as well as necessary and promising future directions that will be key to accelerate discoveries in this important field.
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Affiliation(s)
- Greta Bagnolini
- Department of Chemistry, Duke University, Durham, North Carolina 27708, USA
| | - TinTin B Luu
- Department of Chemistry, Duke University, Durham, North Carolina 27708, USA
| | - Amanda E Hargrove
- Department of Chemistry, Duke University, Durham, North Carolina 27708, USA
- Department of Biochemistry, Duke University School of Medicine, Durham, North Carolina 27710, USA
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33
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Ganeshpurkar A, Singh R, Tripathi P, Alam Q, Krishnamurthy S, Kumar A, Singh SK. Effect of sulfonamide derivatives of phenylglycine on scopolamine-induced amnesia in rats. IBRAIN 2023; 9:13-31. [PMID: 37786521 PMCID: PMC10529173 DOI: 10.1002/ibra.12092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 01/23/2023] [Accepted: 01/30/2023] [Indexed: 10/04/2023]
Abstract
Alzheimer's disease is a neurodegenerative disease responsible for dementia and other neuropsychiatric symptoms. In the present study, compounds 30 and 33, developed earlier in our laboratory as selective butyrylcholinesterase inhibitors, were tested against scopolamine-induced amnesia to evaluate their pharmacodynamic effect. The efficacy of the compounds was determined by behavioral experiments using the Y-maze and the Barnes maze and neurochemical testing. Both compounds reduced the effect of scopolamine treatment in the behavioral tasks at a dose of 20 mg/kg. The results of the neurochemical experiment indicated a reduction in cholinesterase activity in the prefrontal cortex and the hippocampus. The levels of antioxidant enzymes superoxide dismutase and catalase were restored compared to the scopolamine-treated groups. The docking study on rat butyrylcholinesterase (BChE) indicated tight binding, with free energies of -9.66 and -10.23 kcal/mol for compounds 30 and 33, respectively. The two aromatic amide derivatives of 2-phenyl-2-(phenylsulfonamido) acetic acid produced stable complexes with rat BChE in the molecular dynamics investigation.
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Affiliation(s)
- Ankit Ganeshpurkar
- Department of Pharmaceutical Engineering and Technology, Pharmaceutical Chemistry Research Laboratory IIndian Institute of Technology (Banaras Hindu University)VaranasiIndia
| | - Ravi Singh
- Department of Pharmaceutical Engineering and Technology, Pharmaceutical Chemistry Research Laboratory IIndian Institute of Technology (Banaras Hindu University)VaranasiIndia
| | - Pratigya Tripathi
- Department of Pharmaceutical Engineering and Technology, Neurotherapeutics LaboratoryIndian Institute of Technology (Banaras Hindu University)VaranasiUttar PradeshIndia
| | - Qadir Alam
- Department of Pharmaceutical Engineering and Technology, Neurotherapeutics LaboratoryIndian Institute of Technology (Banaras Hindu University)VaranasiUttar PradeshIndia
| | - Sairam Krishnamurthy
- Department of Pharmaceutical Engineering and Technology, Neurotherapeutics LaboratoryIndian Institute of Technology (Banaras Hindu University)VaranasiUttar PradeshIndia
| | - Ashok Kumar
- Department of Pharmaceutical Engineering and Technology, Pharmaceutical Chemistry Research Laboratory IIndian Institute of Technology (Banaras Hindu University)VaranasiIndia
| | - Sushil K. Singh
- Department of Pharmaceutical Engineering and Technology, Pharmaceutical Chemistry Research Laboratory IIndian Institute of Technology (Banaras Hindu University)VaranasiIndia
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34
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Pinel P, Guichaoua G, Najm M, Labouille S, Drizard N, Gaston-Mathé Y, Hoffmann B, Stoven V. Exploring isofunctional molecules: Design of a benchmark and evaluation of prediction performance. Mol Inform 2023; 42:e2200216. [PMID: 36633361 DOI: 10.1002/minf.202200216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 12/19/2022] [Accepted: 01/11/2023] [Indexed: 01/13/2023]
Abstract
Identification of novel chemotypes with biological activity similar to a known active molecule is an important challenge in drug discovery called 'scaffold hopping'. Small-, medium-, and large-step scaffold hopping efforts may lead to increasing degrees of chemical structure novelty with respect to the parent compound. In the present paper, we focus on the problem of large-step scaffold hopping. We assembled a high quality and well characterized dataset of scaffold hopping examples comprising pairs of active molecules and including a variety of protein targets. This dataset was used to build a benchmark corresponding to the setting of real-life applications: one active molecule is known, and the second active is searched among a set of decoys chosen in a way to avoid statistical bias. This allowed us to evaluate the performance of computational methods for solving large-step scaffold hopping problems. In particular, we assessed how difficult these problems are, particularly for classical 2D and 3D ligand-based methods. We also showed that a machine-learning chemogenomic algorithm outperforms classical methods and we provided some useful hints for future improvements.
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Affiliation(s)
- Philippe Pinel
- Center for Computational Biology, Mines Paris-PSL, PSL Research University, 75006, Paris, France.,Institut Curie, 75248, Paris, France.,INSERM U900, 75428, Paris, France.,Iktos SAS, 75017, Paris, France
| | - Gwenn Guichaoua
- Center for Computational Biology, Mines Paris-PSL, PSL Research University, 75006, Paris, France.,Institut Curie, 75248, Paris, France.,INSERM U900, 75428, Paris, France
| | - Matthieu Najm
- Center for Computational Biology, Mines Paris-PSL, PSL Research University, 75006, Paris, France.,Institut Curie, 75248, Paris, France.,INSERM U900, 75428, Paris, France
| | | | | | | | | | - Véronique Stoven
- Center for Computational Biology, Mines Paris-PSL, PSL Research University, 75006, Paris, France.,Institut Curie, 75248, Paris, France.,INSERM U900, 75428, Paris, France
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35
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Tan Z, Li Y, Wu X, Zhang Z, Shi W, Yang S, Zhang W. De novo creation of fluorescent molecules via adversarial generative modeling. RSC Adv 2023; 13:1031-1040. [PMID: 36686951 PMCID: PMC9811934 DOI: 10.1039/d2ra07008a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 12/19/2022] [Indexed: 01/06/2023] Open
Abstract
The development of AI for fluorescent materials design is technologically demanding due to the issue of accurately forecasting fluorescent properties. Besides the huge efforts made in predicting the photoluminescent properties of organic dyes in terms of machine learning techniques, this article aims to introduce an adversarial generation paradigm for the rational design of fluorescent molecules. Molecular SMILES is employed as the input of a GRU based autoencoder, where the encoding and decoding of the string information are processed. A generative adversarial network is applied on the latent space with a generator to generate samples to mimic the latent space, and a discriminator to distinguish samples from the latent space. It is found that the excited state property distributions of generated molecules fully match those of the original samples, with the molecular synthesizability being accessible as well. Further screening of the generated samples delivers a remarkable luminescence efficiency of molecules epitomized by the significant oscillator strength and charge transfer characteristics, demonstrating the great potential of the adversarial model in enriching the fluorescent library.
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Affiliation(s)
- Zheng Tan
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China Chengdu 610054 P. R. China
| | - Yan Li
- Chengdu Polytechnic 83 Tianyi Street Chengdu 610000 P. R. China
| | - Xin Wu
- Xiyuan Quantitative Technology 388 Yizhou Road Chengdu 610000 P. R. China
| | - Ziying Zhang
- Guangzhou Yinfo Information Technology 2 Ruyi Road, Panyu District Guangzhou 511431 P. R. China
| | - Weimei Shi
- Chengdu Polytechnic 83 Tianyi Street Chengdu 610000 P. R. China
| | - Shiqing Yang
- Chengdu Polytechnic 83 Tianyi Street Chengdu 610000 P. R. China
| | - Wanli Zhang
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China Chengdu 610054 P. R. China
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36
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Ganeshpurkar A, Singh R, Kumar D, Gutti G, Sardana D, Shivhare S, Singh RB, Kumar A, Singh SK. Development of homology model, docking protocol and Machine-Learning based scoring functions for identification of Equus caballus's butyrylcholinesterase inhibitors. J Biomol Struct Dyn 2022; 40:13693-13710. [PMID: 34696689 DOI: 10.1080/07391102.2021.1994012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Machine learning (ML), an emerging field in drug design, has the potential to predict in silico toxicity, shape-based analysis of inhibitors, scoring function (SF) etc. In the present study, a homology model, docking protocol, and a dedicated SF have been developed to identify the inhibitors of horse butyrylcholinesterase (BChE) enzyme. Horse BChE enzyme has homology with human BChE and is a substitute for the screening of in vitro inhibitors. The developed homology model was validated and the active site residues were identified from Cavityplus to generate grid box for docking. The validation of docking involved comparison of interactions of ligands co-crystallised with human BChE and the docked poses of the corresponding ligands with horse BChE. A high degree of similarity in the interaction profiles of generated poses validated the docking protocol. Scoring of ligands was further validated by docking with known BChE inhibitors. The binding energies obtained from SF was correlated with IC50 values of inhibitors through classification and regression-based methods, which indicated poor predictivity of native SF. Therefore, protein-ligand binding energy, interaction profile, and ligand descriptors were used to develop and validate the classification and regression-based models. The validated extra tree binary classifier, random forest and extra tree regression-based models were compiled as a protein-ligand SF and were made available to the users through web application and python library. ML models exhibited improved area under the curve for ROC and good correlation between the predicted and observed IC50 values, than the Autodock SF. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ankit Ganeshpurkar
- Pharmaceutical Chemistry Research Laboratory I, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Ravi Singh
- Pharmaceutical Chemistry Research Laboratory I, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Devendra Kumar
- Pharmaceutical Chemistry Research Laboratory I, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Gopichand Gutti
- Pharmaceutical Chemistry Research Laboratory I, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Divya Sardana
- Pharmaceutical Chemistry Research Laboratory I, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Shalini Shivhare
- Pharmaceutical Chemistry Research Laboratory I, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Ravi Bhushan Singh
- Institute of Pharmacy Harish Chandra, Post Graduate College, Varanasi, India
| | - Ashok Kumar
- Pharmaceutical Chemistry Research Laboratory I, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Sushil Kumar Singh
- Pharmaceutical Chemistry Research Laboratory I, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
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Xiong F, Yu M, Xu H, Zhong Z, Li Z, Guo Y, Zhang T, Zeng Z, Jin F, He X. Discovery of TIGIT inhibitors based on DEL and machine learning. Front Chem 2022; 10:982539. [PMID: 35958238 PMCID: PMC9360614 DOI: 10.3389/fchem.2022.982539] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 07/11/2022] [Indexed: 11/13/2022] Open
Abstract
Drug discovery has entered a new period of vigorous development with advanced technologies such as DNA-encoded library (DEL) and artificial intelligence (AI). The previous DEL-AI combination has been successfully applied in the drug discovery of classical kinase and receptor targets mainly based on the known scaffold. So far, there is no report of the DEL-AI combination on inhibitors targeting protein-protein interaction, including those undruggable targets with few or unknown active scaffolds. Here, we applied DEL technology on the T cell immunoglobulin and ITIM domain (TIGIT) target, resulting in the unique hit compound 1 (IC50 = 20.7 μM). Based on the screening data from DEL and hit derivatives a1-a34, a machine learning (ML) modeling process was established to address the challenge of poor sample distribution uniformity, which is also frequently encountered in DEL screening on new targets. In the end, the established ML model achieved a satisfactory hit rate of about 75% for derivatives in a high-scored area.
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Affiliation(s)
- Feng Xiong
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
- *Correspondence: Feng Xiong, ; Feng Jin, ; Xun He,
| | - Mingao Yu
- Shenzhen NewDEL Biotech Co., Ltd., Shenzhen, China
| | - Honggui Xu
- Shenzhen NewDEL Biotech Co., Ltd., Shenzhen, China
| | - Zhenmin Zhong
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Zhenwei Li
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Yuhan Guo
- Shenzhen NewDEL Biotech Co., Ltd., Shenzhen, China
| | | | - Zhixuan Zeng
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
| | - Feng Jin
- Shenzhen NewDEL Biotech Co., Ltd., Shenzhen, China
- *Correspondence: Feng Xiong, ; Feng Jin, ; Xun He,
| | - Xun He
- Shenzhen Innovation Center for Small Molecule Drug Discovery Co., Ltd., Shenzhen, China
- *Correspondence: Feng Xiong, ; Feng Jin, ; Xun He,
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38
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Ruth M, Gerbig D, Schreiner PR. Machine Learning of Coupled Cluster (T)-Energy Corrections via Delta (Δ)-Learning. J Chem Theory Comput 2022; 18:4846-4855. [PMID: 35816588 DOI: 10.1021/acs.jctc.2c00501] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Accurate thermochemistry is essential in many chemical disciplines, such as astro-, atmospheric, or combustion chemistry. These areas often involve fleetingly existent intermediates whose thermochemistry is difficult to assess. Whenever direct calorimetric experiments are infeasible, accurate computational estimates of relative molecular energies are required. However, high-level computations, often using coupled cluster theory, are generally resource-intensive. To expedite the process using machine learning techniques, we generated a database of energies for small organic molecules at the CCSD(T)/cc-pVDZ, CCSD(T)/aug-cc-pVDZ, and CCSD(T)/cc-pVTZ levels of theory. Leveraging the power of deep learning by employing graph neural networks, we are able to predict the effect of perturbatively included triples (T), that is, the difference between CCSD and CCSD(T) energies, with a mean absolute error of 0.25, 0.25, and 0.28 kcal mol-1 (R2 of 0.998, 0.997, and 0.998) with the cc-pVDZ, aug-cc-pVDZ, and cc-pVTZ basis sets, respectively. Our models were further validated by application to three validation sets taken from the S22 Database as well as to a selection of known theoretically challenging cases.
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Affiliation(s)
- Marcel Ruth
- Institute of Organic Chemistry, Justus Liebig University, Heinrich-Buff-Ring 17, 35392 Giessen, Germany
| | - Dennis Gerbig
- Institute of Organic Chemistry, Justus Liebig University, Heinrich-Buff-Ring 17, 35392 Giessen, Germany
| | - Peter R Schreiner
- Institute of Organic Chemistry, Justus Liebig University, Heinrich-Buff-Ring 17, 35392 Giessen, Germany
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39
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A Study of Type II ɛ-PL Degrading Enzyme (pldII) in Streptomyces albulus through the CRISPRi System. Int J Mol Sci 2022; 23:ijms23126691. [PMID: 35743134 PMCID: PMC9223678 DOI: 10.3390/ijms23126691] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/11/2022] [Accepted: 06/13/2022] [Indexed: 02/04/2023] Open
Abstract
ε-Poly-L-lysine (ε-PL) is a widely used antibacterial peptide polymerized of 25–35 L-lysine residues. The antibacterial effect of ε-PL is closely related to the polymerization degree. However, the mechanism of ε-PL degradation in S. albulus remains unclear. This study utilized the integrative plasmid pSET152-based CRISPRi system to transcriptionally repress the ε-PL degrading enzyme (pldII). The expression of pldII is regulated by changing the recognition site of dCas9. Through the ε-PL bacteriostatic experiments of repression strains, it was found that the repression of pldII improves the antibacterial effect of the ε-PL product. The consecutive MALDI-TOF-MS results confirmed that the molecular weight distribution of the ε-PL was changed after repression. The repression strain S1 showed a particular peak with a polymerization degree of 44, and other repression strains also generated ε-PL with a polymerization degree of over 40. Furthermore, the homology modeling and substrate docking of pldII, a typical endo-type metallopeptidase, were performed to resolve the degradation mechanism of ε-PL in S. albulus. The hydrolysis of ε-PL within pldII, initiated from the N-terminus by two amino acid-binding residues, Thr194 and Glu281, led to varying levels of polymerization of ε-PL.
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40
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Isert C, Atz K, Jiménez-Luna J, Schneider G. QMugs, quantum mechanical properties of drug-like molecules. Sci Data 2022; 9:273. [PMID: 35672335 PMCID: PMC9174255 DOI: 10.1038/s41597-022-01390-7] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 05/17/2022] [Indexed: 12/16/2022] Open
Abstract
Machine learning approaches in drug discovery, as well as in other areas of the chemical sciences, benefit from curated datasets of physical molecular properties. However, there currently is a lack of data collections featuring large bioactive molecules alongside first-principle quantum chemical information. The open-access QMugs (Quantum-Mechanical Properties of Drug-like Molecules) dataset fills this void. The QMugs collection comprises quantum mechanical properties of more than 665 k biologically and pharmacologically relevant molecules extracted from the ChEMBL database, totaling ~2 M conformers. QMugs contains optimized molecular geometries and thermodynamic data obtained via the semi-empirical method GFN2-xTB. Atomic and molecular properties are provided on both the GFN2-xTB and on the density-functional levels of theory (DFT, ωB97X-D/def2-SVP). QMugs features molecules of significantly larger size than previously-reported collections and comprises their respective quantum mechanical wave functions, including DFT density and orbital matrices. This dataset is intended to facilitate the development of models that learn from molecular data on different levels of theory while also providing insight into the corresponding relationships between molecular structure and biological activity.
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Affiliation(s)
- Clemens Isert
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, 8093, Zurich, Switzerland
| | - Kenneth Atz
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, 8093, Zurich, Switzerland
| | - José Jiménez-Luna
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, 8093, Zurich, Switzerland.
- Department of Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397, Biberach an der Riss, Germany.
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, 8093, Zurich, Switzerland.
- ETH Singapore SEC Ltd, 1 CREATE Way, #06-01 CREATE Tower, Singapore, 138602, Singapore.
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41
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Tan Z, Li Y, Zhang Z, Wu X, Penfold T, Shi W, Yang S. Efficient Adversarial Generation of Thermally Activated Delayed Fluorescence Molecules. ACS OMEGA 2022; 7:18179-18188. [PMID: 35664624 PMCID: PMC9161419 DOI: 10.1021/acsomega.2c02253] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 05/11/2022] [Indexed: 06/15/2023]
Abstract
Adversarial generative models are becoming an essential tool in molecular design and discovery due to their efficiency in exploring the desired chemical space with the assistance of deep learning. In this article, we introduce an integrated framework by combining the modules of algorithmic synthesis, deep prediction, adversarial generation, and fine screening for the purpose of effective design of the thermally activated delayed fluorescence (TADF) molecules that can be used in the organic light-emitting diode devices. The retrosynthetic rules are employed to algorithmically synthesize the D-A complex based on the empirically defined donor and acceptor moieties, which is followed by the high-throughput labeling and prediction with the deep neural network. The new D-A molecules are subsequently generated via the adversarial autoencoder, with the excited-state property distributions perfectly matching those of the original samples. Fine screening of the generated molecules, including the spin-orbital coupling calculation and the excited-state optimization, is eventually implemented to select the qualified TADF candidates within the novel chemical space. Further investigation shows that the created structures fully mimic the original D-A samples by maintaining a significant charge transfer characteristic, a minimal adiabatic singlet-triplet gap, and a moderate spin-orbital coupling that are desirable for the delayed fluorescence.
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Affiliation(s)
- Zheng Tan
- Chengdu
Polytechnic, 83 Tianyi
Street, Chengdu, Sichuan 610000, P. R. China
| | - Yan Li
- Xiyuan
Quantitative Technology, 388 Yizhou Road, Chengdu, Sichuan 610000, P.
R. China
| | - Ziying Zhang
- Guangzhou
Yinfo Information Technology, 2 Ruyi Road, Panyu District, Guangzhou 511431, P. R. China
| | - Xin Wu
- Xiyuan
Quantitative Technology, 388 Yizhou Road, Chengdu, Sichuan 610000, P.
R. China
| | - Thomas Penfold
- Chemistry-School
of Natural and Environmental Sciences, Newcastle
University, Newcastle Upon Tyne NE1 7RU, U.K.
| | - Weimei Shi
- Chengdu
Polytechnic, 83 Tianyi
Street, Chengdu, Sichuan 610000, P. R. China
| | - Shiqing Yang
- Chengdu
Polytechnic, 83 Tianyi
Street, Chengdu, Sichuan 610000, P. R. China
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42
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Yang C, Zhang Y. Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein-Ligand Scoring Functions. J Chem Inf Model 2022; 62:2696-2712. [PMID: 35579568 DOI: 10.1021/acs.jcim.2c00485] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Protein-ligand scoring functions are widely used in structure-based drug design for fast evaluation of protein-ligand interactions, and it is of strong interest to develop scoring functions with machine-learning approaches. In this work, by expanding the training set, developing physically meaningful features, employing our recently developed linear empirical scoring function Lin_F9 (Yang, C. J. Chem. Inf. Model. 2021, 61, 4630-4644) as the baseline, and applying extreme gradient boosting (XGBoost) with Δ-machine learning, we have further improved the robustness and applicability of machine-learning scoring functions. Besides the top performances for scoring-ranking-screening power tests of the CASF-2016 benchmark, the new scoring function ΔLin_F9XGB also achieves superior scoring and ranking performances in different structure types that mimic real docking applications. The scoring powers of ΔLin_F9XGB for locally optimized poses, flexible redocked poses, and ensemble docked poses of the CASF-2016 core set achieve Pearson's correlation coefficient (R) values of 0.853, 0.839, and 0.813, respectively. In addition, the large-scale docking-based virtual screening test on the LIT-PCBA data set demonstrates the reliability and robustness of ΔLin_F9XGB in virtual screening application. The ΔLin_F9XGB scoring function and its code are freely available on the web at (https://yzhang.hpc.nyu.edu/Delta_LinF9_XGB).
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Affiliation(s)
- Chao Yang
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Yingkai Zhang
- Department of Chemistry, New York University, New York, New York 10003, United States.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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43
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Vilseck JZ, Cervantes LF, Hayes RL, Brooks CL. Optimizing Multisite λ-Dynamics Throughput with Charge Renormalization. J Chem Inf Model 2022; 62:1479-1488. [PMID: 35286093 PMCID: PMC9700484 DOI: 10.1021/acs.jcim.2c00047] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
With the ability to sample combinations of alchemical perturbations at multiple sites off a small molecule core, multisite λ-dynamics (MSλD) has become an attractive alternative to conventional alchemical free energy methods for exploring large combinatorial chemical spaces. However, current software implementations dictate that combinatorial sampling with MSλD must be performed with a multiple topology model (MTM), which is nontrivial to create by hand, especially for a series of ligand analogues which may have diverse functional groups attached. This work introduces an automated workflow, referred to as msld_py_prep, to assist in the creation of a MTM for use with MSλD. One approach for partitioning partial atomic charges between ligands to create a MTM, called charge renormalization, is also presented and rigorously evaluated. We find that msld_py_prep greatly accelerates the preparation of MSλD ready-to-use files and that charge renormalization can provide a successful approach for MTM generation, as long as bookending calculations are applied to correct small differences introduced by charge renormalization. Charge renormalization also facilitates the use of many different force field parameters with MSλD, broadening the applicability of MSλD for computer-aided drug design.
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Affiliation(s)
- Jonah Z. Vilseck
- Department of Chemistry, University of Michigan, Ann Arbor, MI 48109
- Department of Biochemistry and Molecular Biology, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, United States
| | - Luis F. Cervantes
- Department of Medicinal Chemistry College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, United States
| | - Ryan L. Hayes
- Department of Chemistry, University of Michigan, Ann Arbor, MI 48109
| | - Charles L. Brooks
- Department of Chemistry, University of Michigan, Ann Arbor, MI 48109
- Biophysics Program, University of Michigan, Ann Arbor, MI 48109
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44
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Piras P. New mathematical measures for apprehending complexity of chiral molecules using information entropy. Chirality 2022; 34:646-666. [PMID: 35146805 DOI: 10.1002/chir.23423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 01/02/2022] [Accepted: 01/08/2022] [Indexed: 11/09/2022]
Abstract
In this paper, we present several new theoretical measures based on information entropy that can be used to analyze the information content of a chiral molecule. Starting from a differentiation between "chiral" and "achiral" portions in a chiral molecule, we define a new concept that allows us to quantify the complexity of chiral constitutional 2D-isomers of C10 to C20 alkanes. Various new chiral and achiral information measures founded on joint entropy, mutual information, and conditional entropy are presented providing an access to a set of regression equations. Then, introducing a case-based measure of entropy, we demonstrate that the distribution of the chiral complexity in these molecules is mostly skewed-right: 60% of the chiral isomers follow a 60/40 distribution rule, which indicates a concentration of chiral complexity in a small number of topological features. Furthermore, by replacing 2D topological distances by 3D distances, the application of these new information measures goes from conformational to racemization and deracemization studies. Interestingly, when the geometrical distances between atoms and the chiral center(s) are taken into account when determining the chiral information entropy, one can observe a significative Pearson correlation coefficient (R = 0.70) between the chiral entropy of 3D molecules and the continuous chirality measure. Finally, we show that our approach is applicable to almost any type of chiral organic chemical structures if in the entropy equation, atoms are represented by their electrotopological state (E-state) index instead of connectivity.
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Affiliation(s)
- Patrick Piras
- Aix Marseille Univ, CNRS, Centrale Marseille, iSm2, Marseille, France
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45
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Sen B, Roy S, Garai S, Roy S, Anoop A, Hajra S. Stereochemistry of the Benzylidene γ-Butyrolactone Dictates the Reductive Heck Cyclization Mode in the Asymmetric Synthesis of Aryltetralin Lignans: A Detailed Experimental and Theoretical Study. J Org Chem 2022; 87:3910-3921. [PMID: 35130698 DOI: 10.1021/acs.joc.1c02174] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The reductive Heck cyclization of nonracemic benzylidene γ-butyrolactone is studied toward the asymmetric synthesis of aryltetralin lignans, where the stereochemistry of the benzylidene lactone is found to control the mode of cyclization. The Z-isomer undergoes mostly 6-endo-cyclization and provides the desired (-)-isopodophyllotoxin along with a minor amount of 5-exo-cyclized product, but the E-isomer goes through exclusively 5-exo-cyclization, leading to the undesired dihydroindenolactone compound instead of (-)-podophyllotoxin. The experimental results are well-supported by the DFT studies.
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Affiliation(s)
- Biswajit Sen
- Center of Biomedical Research, Sanjay Gandhi Post-Graduate Institute of Medical Sciences Campus, Raebareli Road, Lucknow 226014, India
| | - Saikat Roy
- Department of Chemistry, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Sujay Garai
- Center of Biomedical Research, Sanjay Gandhi Post-Graduate Institute of Medical Sciences Campus, Raebareli Road, Lucknow 226014, India.,Department of Chemistry, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Sayan Roy
- Center of Biomedical Research, Sanjay Gandhi Post-Graduate Institute of Medical Sciences Campus, Raebareli Road, Lucknow 226014, India
| | - Anakuthil Anoop
- Department of Chemistry, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Saumen Hajra
- Center of Biomedical Research, Sanjay Gandhi Post-Graduate Institute of Medical Sciences Campus, Raebareli Road, Lucknow 226014, India
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A THEORETICAL AND EXPERIMENTAL STUDY OF LIQUID-LIQUID EQUILIBRIUM TO REFINE RAW GLYCEROL OBTAINED AS A BYPRODUCT ON THE BIODIESEL PRODUCTION. CHEMICAL ENGINEERING JOURNAL ADVANCES 2022. [DOI: 10.1016/j.ceja.2022.100257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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47
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Schwalbe-Koda D, Kwon S, Paris C, Bello-Jurado E, Jensen Z, Olivetti E, Willhammar T, Corma A, Román-Leshkov Y, Moliner M, Gómez-Bombarelli R. A priori control of zeolite phase competition and intergrowth with high-throughput simulations. Science 2021; 374:308-315. [PMID: 34529493 DOI: 10.1126/science.abh3350] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
[Figure: see text].
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Affiliation(s)
- Daniel Schwalbe-Koda
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Soonhyoung Kwon
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Cecilia Paris
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas, 46022 Valencia, Spain
| | - Estefania Bello-Jurado
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas, 46022 Valencia, Spain
| | - Zach Jensen
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Elsa Olivetti
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Tom Willhammar
- Department of Materials and Environmental Chemistry, Stockholm University, SE-106 91 Stockholm, Sweden
| | - Avelino Corma
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas, 46022 Valencia, Spain
| | - Yuriy Román-Leshkov
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Manuel Moliner
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas, 46022 Valencia, Spain
| | - Rafael Gómez-Bombarelli
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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48
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Dej-adisai S, Rais IR, Wattanapiromsakul C, Pitakbut T. Alpha-Glucosidase Inhibitory Assay-Screened Isolation and Molecular Docking Model from Bauhinia pulla Active Compounds. Molecules 2021; 26:molecules26195970. [PMID: 34641514 PMCID: PMC8512368 DOI: 10.3390/molecules26195970] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/26/2021] [Accepted: 09/28/2021] [Indexed: 11/16/2022] Open
Abstract
The aim of this research was to establish the constituents of Bauhinia pulla as anti-diabetic agents. A phytochemistry analysis was conducted by chromatographic and spectroscopic techniques. The alpha-glucosidase inhibitory assay screening resulted in the isolation of eight known compounds of quercetin, quercitrin, luteolin, 5-deoxyluteolin, 4-methyl ether isoliquiritigenin, 3,2',4'-trihydroxy-4-methoxychalcone, stigmasterol and β-sitosterol. Ethanol leaf extracts showed potential effects, which led to a strong inhibitory activity of isolated quercetin at 138.95 µg/mL and 5.41 µg/mL of IC50, respectively. The docking confirmed that flavonoids and chalcones had the same potential binding sites and responsibilities for their activity. This study was the first report of Bauhinia pulla chemical constituents and its alpha-glucosidase inhibition.
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Affiliation(s)
- Sukanya Dej-adisai
- Department of Pharmacognosy and Pharmaceutical Botany, Faculty of Pharmaceutical Sciences, Prince of Songkla University, Hat Yai 90112, Songkhla, Thailand; (I.R.R.); (C.W.)
- Correspondence: ; Tel.: +66-74-288888; Fax: +66-74-288891
| | - Ichwan Ridwan Rais
- Department of Pharmacognosy and Pharmaceutical Botany, Faculty of Pharmaceutical Sciences, Prince of Songkla University, Hat Yai 90112, Songkhla, Thailand; (I.R.R.); (C.W.)
- Department of Pharmaceutical Biology, Faculty of Pharmacy, Universitas Ahmad Dahlan, Yogyakarta 55164, Indonesia
| | - Chatchai Wattanapiromsakul
- Department of Pharmacognosy and Pharmaceutical Botany, Faculty of Pharmaceutical Sciences, Prince of Songkla University, Hat Yai 90112, Songkhla, Thailand; (I.R.R.); (C.W.)
| | - Thanet Pitakbut
- Department of Biochemical and Chemical Engineering, Technical University of Dortmund, 44227 Dortmund, Germany;
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49
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Shin HK, Lee S, Oh HN, Yoo D, Park S, Kim WK, Kang MG. Development of blood brain barrier permeation prediction models for organic and inorganic biocidal active substances. CHEMOSPHERE 2021; 277:130330. [PMID: 33780678 DOI: 10.1016/j.chemosphere.2021.130330] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 03/16/2021] [Accepted: 03/16/2021] [Indexed: 05/04/2023]
Abstract
Biocidal products are broadly used in homes and industries. However, the safety of biocidal active substances (BASs) is not yet fully understood. In particular, the neurotoxic action of BASs needs to be studied as diverse epidemiological studies have reported associations between exposure to BASs and neural diseases. In this study, we developed in silico models to predict the blood-brain barrier (BBB) permeation of organic and inorganic BASs. Due to a lack of BBB data for BASs, the chemical space of BASs and BBB dataset were compared in order to select BBB data that were structurally similar to BASs. In silico models to predict log-scaled BBB penetration were developed using support vector regression for organic BASs and multiple linear regression for inorganic BASs. The model for organic BASs was developed with 231 compounds (training set: 153 and test set: 78) and achieved good prediction accuracy on an external test set (R2 = 0.64), and the model outperformed the model for pharmaceuticals. The model for inorganic BASs was developed with 11 compounds (R2 = 0.51). Applicability domain (AD) analysis of the models clarified molecular structures reliably predicted by the models. Therefore, the models developed in this study can be used for predicting BBB permeable BASs in human. These models were developed according to the Quantitative Structure-Activity Relationship validation principles proposed by the Organization for Economic Cooperation and Development.
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Affiliation(s)
- Hyun Kil Shin
- Toxicoinformatics group, Department of predictive toxicology, Korea institute of toxicology, Daejeon 34114, Republic of Korea
| | - Sangwoo Lee
- Bio-system Research Group, Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon, 34114, Republic of Korea
| | - Ha-Na Oh
- Bio-system Research Group, Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon, 34114, Republic of Korea
| | - Donggon Yoo
- Bio-system Research Group, Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon, 34114, Republic of Korea; Human and Environmental Toxicology, University of Science and Technology, Daejeon, 34113, Republic of Korea
| | - Seungmin Park
- Bio-system Research Group, Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon, 34114, Republic of Korea; Human and Environmental Toxicology, University of Science and Technology, Daejeon, 34113, Republic of Korea
| | - Woo-Keun Kim
- Bio-system Research Group, Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon, 34114, Republic of Korea; Human and Environmental Toxicology, University of Science and Technology, Daejeon, 34113, Republic of Korea
| | - Myung-Gyun Kang
- Toxicoinformatics group, Department of predictive toxicology, Korea institute of toxicology, Daejeon 34114, Republic of Korea.
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Ganeshpurkar A, Singh R, Shivhare S, Divya, Kumar D, Gutti G, Singh R, Kumar A, Singh SK. Improved machine learning scoring functions for identification of Electrophorus electricus's acetylcholinesterase inhibitors. Mol Divers 2021; 26:1455-1479. [PMID: 34328603 DOI: 10.1007/s11030-021-10280-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 07/17/2021] [Indexed: 10/20/2022]
Abstract
Structure-based drug design (SBDD) is an important in silico technique, used for the identification of enzyme inhibitors. Acetylcholinesterase (AChE), obtained from Electrophorus electricus (ee), is widely used for the screening of AChE inhibitors. It shares structural homology with the AChE of human and other organisms. Till date, the three-dimensional crystal structure of enzyme from ee is not available that makes it challenging to use the SBDD approach for the identification of inhibitors. A homology model was developed for eeAChE in the present study, followed by its structural refinement through energy minimisation. The docking protocol was developed using a grid dimension of 84 × 66 × 72 and grid point spacing of 0.375 Å for eeAChE. The protocol was validated by redocking a set of co-crystallised inhibitors obtained from mouse AChE, and their interaction profiles were compared. The results indicated a poor performance of the Autodock scoring function. Hence, a batch of machine learning-based scoring functions were developed. The validation results displayed an accuracy of 81.68 ± 1.73% and 82.92 ± 3.05% for binary and multiclass classification scoring function, respectively. The regression-based scoring function produced [Formula: see text] and [Formula: see text] values of 0.94, 0.635 and 0.634, respectively.
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Affiliation(s)
- Ankit Ganeshpurkar
- Pharmaceutical Chemistry Research Laboratory, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India
| | - Ravi Singh
- Pharmaceutical Chemistry Research Laboratory, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India
| | - Shalini Shivhare
- Pharmaceutical Chemistry Research Laboratory, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India
| | - Divya
- Pharmaceutical Chemistry Research Laboratory, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India
| | - Devendra Kumar
- Pharmaceutical Chemistry Research Laboratory, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India
| | - Gopichand Gutti
- Pharmaceutical Chemistry Research Laboratory, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India
| | | | - Ashok Kumar
- Pharmaceutical Chemistry Research Laboratory, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India
| | - Sushil Kumar Singh
- Pharmaceutical Chemistry Research Laboratory, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India.
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