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Chen X, Lu Z, Xiao J, Xia W, Pan Y, Xia H, Chen YH, Zhang H. Small-Molecule Inhibitors of TIPE3 Protein Identified through Deep Learning Suppress Cancer Cell Growth In Vitro. Cells 2024; 13:771. [PMID: 38727307 PMCID: PMC11082981 DOI: 10.3390/cells13090771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 04/17/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024] Open
Abstract
Tumor necrosis factor-α-induced protein 8-like 3 (TNFAIP8L3 or TIPE3) functions as a transfer protein for lipid second messengers. TIPE3 is highly upregulated in several human cancers and has been established to significantly promote tumor cell proliferation, migration, and invasion and inhibit the apoptosis of cancer cells. Thus, inhibiting the function of TIPE3 is expected to be an effective strategy against cancer. The advancement of artificial intelligence (AI)-driven drug development has recently invigorated research in anti-cancer drug development. In this work, we incorporated DFCNN, Autodock Vina docking, DeepBindBC, MD, and metadynamics to efficiently identify inhibitors of TIPE3 from a ZINC compound dataset. Six potential candidates were selected for further experimental study to validate their anti-tumor activity. Among these, three small-molecule compounds (K784-8160, E745-0011, and 7238-1516) showed significant anti-tumor activity in vitro, leading to reduced tumor cell viability, proliferation, and migration and enhanced apoptotic tumor cell death. Notably, E745-0011 and 7238-1516 exhibited selective cytotoxicity toward tumor cells with high TIPE3 expression while having little or no effect on normal human cells or tumor cells with low TIPE3 expression. A molecular docking analysis further supported their interactions with TIPE3, highlighting hydrophobic interactions and their shared interaction residues and offering insights for designing more effective inhibitors. Taken together, this work demonstrates the feasibility of incorporating deep learning and MD simulations in virtual drug screening and provides inhibitors with significant potential for anti-cancer drug development against TIPE3-.
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Affiliation(s)
- Xiaodie Chen
- Center for Cancer Immunology, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (X.C.); (Z.L.); (H.X.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhen Lu
- Center for Cancer Immunology, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (X.C.); (Z.L.); (H.X.)
| | - Jin Xiao
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (J.X.); (W.X.); (Y.P.)
| | - Wei Xia
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (J.X.); (W.X.); (Y.P.)
| | - Yi Pan
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (J.X.); (W.X.); (Y.P.)
| | - Houjun Xia
- Center for Cancer Immunology, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (X.C.); (Z.L.); (H.X.)
- Faculty of Pharmaceutical Sciences, Shenzhen University of Advanced Technology, Shenzhen 518055, China
| | - Youhai H. Chen
- Center for Cancer Immunology, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (X.C.); (Z.L.); (H.X.)
- University of Chinese Academy of Sciences, Beijing 100049, China
- Faculty of Pharmaceutical Sciences, Shenzhen University of Advanced Technology, Shenzhen 518055, China
| | - Haiping Zhang
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (J.X.); (W.X.); (Y.P.)
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2
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Dutta S, Shukla D. Characterization of binding kinetics and intracellular signaling of new psychoactive substances targeting cannabinoid receptor using transition-based reweighting method. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.29.560261. [PMID: 37873328 PMCID: PMC10592854 DOI: 10.1101/2023.09.29.560261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
New psychoactive substances (NPS) targeting cannabinoid receptor 1 pose a significant threat to society as recreational abusive drugs that have pronounced physiological side effects. These greater adverse effects compared to classical cannabinoids have been linked to the higher downstream β-arrestin signaling. Thus, understanding the mechanism of differential signaling will reveal important structure-activity relationship essential for identifying and potentially regulating NPS molecules. In this study, we simulate the slow (un)binding process of NPS MDMB-Fubinaca and classical cannabinoid HU-210 from CB1 using multi-ensemble simulation to decipher the effects of ligand binding dynamics on downstream signaling. The transition-based reweighing method is used for the estimation of transition rates and underlying thermodynamics of (un)binding processes of ligands with nanomolar affinities. Our analyses reveal major interaction differences with transmembrane TM7 between NPS and classical cannabinoids. A variational autoencoder-based approach, neural relational inference (NRI), is applied to assess the allosteric effects on intracellular regions attributable to variations in binding pocket interactions. NRI analysis indicate a heightened level of allosteric control of NPxxY motif for NPS-bound receptors, which contributes to the higher probability of formation of a crucial triad interaction (Y7.53-Y5.58-T3.46) necessary for stronger β-arrestin signaling. Hence, in this work, MD simulation, data-driven statistical methods, and deep learning point out the structural basis for the heightened physiological side effects associated with NPS, contributing to efforts aimed at mitigating their public health impact.
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Affiliation(s)
- Soumajit Dutta
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801
| | - Diwakar Shukla
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, 61801
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3
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Stampelou M, Ladds G, Kolocouris A. Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A 3 Receptor. J Phys Chem B 2024; 128:914-936. [PMID: 38236582 DOI: 10.1021/acs.jpcb.3c05986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
A structure-based drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational design of improved drug molecules. For unresolved GPCR-ligand complexes, a workflow that can apply both thermodynamic and kinetic binding data in combination with alpha-fold (AF)-derived or other homology models and experimentally resolved binding modes of relevant ligands in GPCR-homologs needs to be tested. Here, as test case, we studied a congeneric set of ligands that bind to a structurally unresolved G protein-coupled receptor (GPCR), the inactive human adenosine A3 receptor (hA3R). We tested three available homology models from which two have been generated from experimental structures of hA1R or hA2AR and one model was a multistate alphafold 2 (AF2)-derived model. We applied alchemical calculations with thermodynamic integration coupled with molecular dynamics (TI/MD) simulations to calculate the experimental relative binding free energies and residence time (τ)-random accelerated MD (τ-RAMD) simulations to calculate the relative residence times (RTs) for antagonists. While the TI/MD calculations produced, for the three homology models, good Pearson correlation coefficients, correspondingly, r = 0.74, 0.62, and 0.67 and mean unsigned error (mue) values of 0.94, 1.31, and 0.81 kcal mol-1, the τ-RAMD method showed r = 0.92 and 0.52 for the first two models but failed to produce accurate results for the multistate AF2-derived model. With subsequent optimization of the AF2-derived model by reorientation of the side chain of R1735.34 located in the extracellular loop 2 (EL2) that blocked ligand's unbinding, the computational model showed r = 0.84 for kinetic data and improved performance for thermodynamic data (r = 0.81, mue = 0.56 kcal mol-1). Overall, after refining the multistate AF2 model with physics-based tools, we were able to show a strong correlation between predicted and experimental ligand relative residence times and affinities, achieving a level of accuracy comparable to an experimental structure. The computational workflow used can be applied to other receptors, helping to rank candidate drugs in a congeneric series and enabling the prioritization of leads with stronger binding affinities and longer residence times.
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Affiliation(s)
- Margarita Stampelou
- Laboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou, 15771 Athens, Greece
| | - Graham Ladds
- Department of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge CB2 1PD, U.K
| | - Antonios Kolocouris
- Laboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou, 15771 Athens, Greece
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4
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Calderón JC, Plut E, Keller M, Cabrele C, Reiser O, Gervasio FL, Clark T. Extended Metadynamics Protocol for Binding/Unbinding Free Energies of Peptide Ligands to Class A G-Protein-Coupled Receptors. J Chem Inf Model 2024; 64:205-218. [PMID: 38150388 DOI: 10.1021/acs.jcim.3c01574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
A metadynamics protocol is presented to characterize the binding and unbinding of peptide ligands to class A G-protein-coupled receptors (GPCRs). The protocol expands on the one previously presented for binding and unbinding small-molecule ligands to class A GPCRs and accounts for the more demanding nature of the peptide binding-unbinding process. It applies to almost all class A GPCRs. Exemplary simulations are described for subtypes Y1R, Y2R, and Y4R of the neuropeptide Y receptor family, vasopressin binding to the vasopressin V2 receptor (V2R), and oxytocin binding to the oxytocin receptor (OTR). Binding free energies and the positions of alternative binding sites are presented and, where possible, compared with the experiment.
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Affiliation(s)
- Jacqueline C Calderón
- Computer-Chemistry-Center, Department of Chemistry and Pharmacy, Friedrich-Alexander-University Erlangen-Nuernberg, Naegelsbachstr. 25, Erlangen 91052, Germany
| | - Eva Plut
- Institute of Organic Chemistry, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg 93040, Germany
| | - Max Keller
- Institute of Pharmacy, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg D-93040, Germany
| | - Chiara Cabrele
- Institute of Organic Chemistry, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg 93040, Germany
| | - Oliver Reiser
- Institute of Organic Chemistry, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg 93040, Germany
| | | | - Timothy Clark
- Computer-Chemistry-Center, Department of Chemistry and Pharmacy, Friedrich-Alexander-University Erlangen-Nuernberg, Naegelsbachstr. 25, Erlangen 91052, Germany
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5
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Park JH, Kawakami K, Ishimoto N, Ikuta T, Ohki M, Ekimoto T, Ikeguchi M, Lee DS, Lee YH, Tame JRH, Inoue A, Park SY. Structural basis for ligand recognition and signaling of hydroxy-carboxylic acid receptor 2. Nat Commun 2023; 14:7150. [PMID: 37932263 PMCID: PMC10628104 DOI: 10.1038/s41467-023-42764-8] [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: 03/17/2023] [Accepted: 10/19/2023] [Indexed: 11/08/2023] Open
Abstract
Hydroxycarboxylic acid receptors (HCAR1, HCAR2, and HCAR3) transduce Gi/o signaling upon biding to molecules such as lactic acid, butyric acid and 3-hydroxyoctanoic acid, which are associated with lipolytic and atherogenic activity, and neuroinflammation. Although many reports have elucidated the function of HCAR2 and its potential as a therapeutic target for treating not only dyslipidemia but also neuroimmune disorders such as multiple sclerosis and Parkinson's disease, the structural basis of ligand recognition and ligand-induced Gi-coupling remains unclear. Here we report three cryo-EM structures of the human HCAR2-Gi signaling complex, each bound with different ligands: niacin, acipimox or GSK256073. All three agonists are held in a deep pocket lined by residues that are not conserved in HCAR1 and HCAR3. A distinct hairpin loop at the HCAR2 N-terminus and extra-cellular loop 2 (ECL2) completely enclose the ligand. These structures also reveal the agonist-induced conformational changes propagated to the G-protein-coupling interface during activation. Collectively, the structures presented here are expected to help in the design of ligands specific for HCAR2, leading to new drugs for the treatment of various diseases such as dyslipidemia and inflammation.
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Affiliation(s)
- Jae-Hyun Park
- Drug Design Laboratory, Graduate School of Medical Life Science, Yokohama City University, Tsurumi, Yokohama, 230-0045, Japan
| | - Kouki Kawakami
- Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, 980-8578, Japan
| | - Naito Ishimoto
- Drug Design Laboratory, Graduate School of Medical Life Science, Yokohama City University, Tsurumi, Yokohama, 230-0045, Japan
| | - Tatsuya Ikuta
- Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, 980-8578, Japan
| | - Mio Ohki
- Drug Design Laboratory, Graduate School of Medical Life Science, Yokohama City University, Tsurumi, Yokohama, 230-0045, Japan
| | - Toru Ekimoto
- Computational Life Science Laboratory, Graduate School of Medical Life Science, Yokohama City University, Yokohama City University, Tsurumi, Yokohama, 230-0045, Japan
| | - Mitsunori Ikeguchi
- Computational Life Science Laboratory, Graduate School of Medical Life Science, Yokohama City University, Yokohama City University, Tsurumi, Yokohama, 230-0045, Japan
- HPC- and AI-driven Drug Development Platform Division, Center for Computational Science, RIKEN, Yokohama, 230-0045, Japan
| | - Dong-Sun Lee
- Bio-Health Materials Core-Facility Center and Interdisciplinary Graduate Program in Advanced Convergence Technology and Science, Jeju National University, Jeju, 63243, Republic of Korea
| | - Young-Ho Lee
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Ochang, Chungbuk, 28119, Republic of Korea
- Bio-Analytical Science, University of Science and Technology, Daejeon, 34113, Republic of Korea
- Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Republic of Korea
- Department of Systems Biotechnology, Chung-Ang University, Gyeonggi, 17546, Republic of Korea
- Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, Miyagi, 980-8578, Japan
| | - Jeremy R H Tame
- Drug Design Laboratory, Graduate School of Medical Life Science, Yokohama City University, Tsurumi, Yokohama, 230-0045, Japan
| | - Asuka Inoue
- Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, 980-8578, Japan.
| | - Sam-Yong Park
- Drug Design Laboratory, Graduate School of Medical Life Science, Yokohama City University, Tsurumi, Yokohama, 230-0045, Japan.
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6
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Calderón JC, Ibrahim P, Gobbo D, Gervasio FL, Clark T. Activation/Deactivation Free-Energy Profiles for the β 2-Adrenergic Receptor: Ligand Modes of Action. J Chem Inf Model 2023; 63:6332-6343. [PMID: 37824365 DOI: 10.1021/acs.jcim.3c00805] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
We use enhanced-sampling simulations with an effective collective variable to study the activation of the β2-adrenergic receptor in the presence of ligands with different efficacy. The free-energy profiles are computed for the ligand-free (apo) receptor and binary (apo-receptor + G-protein α-subunit and receptor + ligand) and ternary complexes. The results are not only compatible with available experiments but also allow unprecedented structural insight into the nature of GPCR conformations along the activation pathway and their role in the activation mechanism. In particular, the simulations reveal an unexpected mode of action of partial agonists such as salmeterol and salbutamol that arises already in the binary complex without the G-protein. Specific differences in the polar interactions with residues in TM5, which are required to stabilize an optimal TM6 conformation that facilitates G-protein binding and receptor activation, play a major role in differentiating them from full agonists.
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Affiliation(s)
- Jacqueline C Calderón
- Computer-Chemistry-Center, Department of Chemistry and Pharmacy, Friedrich-Alexander-University Erlangen-Nuernberg, Naegelsbachstraße 25, 91052 Erlangen, Germany
| | - Passainte Ibrahim
- Institute of Medical Physics and Biophysics, Faculty of Medicine, University of Leipzig, 04109 Leipzig, Germany
| | - Dorothea Gobbo
- Pharmaceutical Sciences, University of Geneva, CH1206 Geneva, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, CH1206 Geneva, Switzerland
| | - Francesco Luigi Gervasio
- Pharmaceutical Sciences, University of Geneva, CH1206 Geneva, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, CH1206 Geneva, Switzerland
- Chemistry Department, University College London, WC1H 0AJ London, United Kingdom
- Swiss Bioinformatics Institute, CH1206 Geneva, Switzerland
| | - Timothy Clark
- Computer-Chemistry-Center, Department of Chemistry and Pharmacy, Friedrich-Alexander-University Erlangen-Nuernberg, Naegelsbachstraße 25, 91052 Erlangen, Germany
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7
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Zantza I, Pyrris Y, Raniolo S, Papadaki GF, Lambrinidis G, Limongelli V, Diallinas G, Mikros E. Uracil/H + Symport by FurE Refines Aspects of the Rocking-bundle Mechanism of APC-type Transporters. J Mol Biol 2023; 435:168226. [PMID: 37544358 DOI: 10.1016/j.jmb.2023.168226] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 07/22/2023] [Accepted: 07/31/2023] [Indexed: 08/08/2023]
Abstract
Transporters mediate the uptake of solutes, metabolites and drugs across the cell membrane. The eukaryotic FurE nucleobase/H+ symporter of Aspergillus nidulans has been used as a model protein to address structure-function relationships in the APC transporter superfamily, members of which are characterized by the LeuT-fold and seem to operate by the so-called 'rocking-bundle' mechanism. In this study, we reveal the binding mode, translocation and release pathway of uracil/H+ by FurE using path collective variable, funnel metadynamics and rational mutational analysis. Our study reveals a stepwise, induced-fit, mechanism of ordered sequential transport of proton and uracil, which in turn suggests that FurE, functions as a multi-step gated pore, rather than employing 'rocking' of compact domains, as often proposed for APC transporters. Finally, our work supports that specific residues of the cytoplasmic N-tail are involved in substrate translocation, in line with their essentiality for FurE function.
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Affiliation(s)
- Iliana Zantza
- Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15771, Greece.
| | - Yiannis Pyrris
- Department of Biology, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15781, Greece.
| | - Stefano Raniolo
- Faculty of Biomedical Sciences, Euler Institute, Università della Svizzera italiana (USI), Lugano 6900, Switzerland.
| | - Georgia F Papadaki
- Department of Biology, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15781, Greece
| | - George Lambrinidis
- Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15771, Greece.
| | - Vittorio Limongelli
- Faculty of Biomedical Sciences, Euler Institute, Università della Svizzera italiana (USI), Lugano 6900, Switzerland; Department of Pharmacy, University of Naples "Federico II", Naples 80131, Italy.
| | - George Diallinas
- Department of Biology, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15781, Greece; Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology, Heraklion 70013, Greece.
| | - Emmanuel Mikros
- Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15771, Greece; Athena Research and Innovation Center in Information Communication & Knowledge Technologies, Marousi 15125, Greece.
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8
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Zhang H, Saravanan KM, Zhang JZH. DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Protein-Ligand Interaction Prediction. Molecules 2023; 28:4691. [PMID: 37375246 DOI: 10.3390/molecules28124691] [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: 04/27/2023] [Revised: 06/08/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
The core of large-scale drug virtual screening is to select the binders accurately and efficiently with high affinity from large libraries of small molecules in which non-binders are usually dominant. The binding affinity is significantly influenced by the protein pocket, ligand spatial information, and residue types/atom types. Here, we used the pocket residues or ligand atoms as the nodes and constructed edges with the neighboring information to comprehensively represent the protein pocket or ligand information. Moreover, the model with pre-trained molecular vectors performed better than the one-hot representation. The main advantage of DeepBindGCN is that it is independent of docking conformation, and concisely keeps the spatial information and physical-chemical features. Using TIPE3 and PD-L1 dimer as proof-of-concept examples, we proposed a screening pipeline integrating DeepBindGCN and other methods to identify strong-binding-affinity compounds. It is the first time a non-complex-dependent model has achieved a root mean square error (RMSE) value of 1.4190 and Pearson r value of 0.7584 in the PDBbind v.2016 core set, respectively, thereby showing a comparable prediction power with the state-of-the-art affinity prediction models that rely upon the 3D complex. DeepBindGCN provides a powerful tool to predict the protein-ligand interaction and can be used in many important large-scale virtual screening application scenarios.
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Affiliation(s)
- Haiping Zhang
- Shenzhen Institute of Synthetic Biology, Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Konda Mani Saravanan
- Department of Biotechnology, Bharath Institute of Higher Education and Research, Chennai 600073, Tamil Nadu, India
| | - John Z H Zhang
- Shenzhen Institute of Synthetic Biology, Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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9
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Matos IDA, Goes Pinto AC, Ferraz MVF, Adan WCS, Rodrigues RP, Dos Santos JX, Kitagawa RR, Lins RD, Oliveira TB, Costa Junior NBD. Identification of potential Staphylococcus aureus dihydrofolate reductase inhibitors using QSAR, molecular docking, dynamics simulations and free energy calculation. J Biomol Struct Dyn 2023; 41:3835-3846. [PMID: 35356863 DOI: 10.1080/07391102.2022.2057361] [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: 08/01/2021] [Accepted: 03/19/2022] [Indexed: 10/18/2022]
Abstract
Herein we describe the use of molecular docking simulations, quantitative structure-activity relationships studies and ADMETox predictions to analyse the molecular recognition of a series of 7-aryl-2,4-diaminoquinazoline derivatives on the inhibition of Staphylococcus aureus dihydrofolate reductase and conducted a virtual screening to discover new potential inhibitors. A quantitative structure-activity relationship model was developed using 40 compounds and two selected descriptors. These descriptors indicated the importance of pKa and molar refractivity for the inhibitory activity against SaDHFR. The values of R2train, CVLOO and R2test generated by the model were 0.808, 0.766, and 0.785, respectively. The integration between QSAR, molecular docking, ADMETox analysis and molecular dynamics simulations with binding free energies calculation, yielded the compounds PC-124127620, PC-124127795 and PC-124127805 as promising candidates to SaDHFR inhibitors. These compounds presented high potency, good pharmacokinetics and toxicological profile. Thus, these molecules are good potential antimicrobial agent to treatment of infect disease caused by S. aureus.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Isaac de Araujo Matos
- Department of Chemistry, Graduate Program in Chemistry, Federal University of Sergipe-UFS, São Cristóvão-SE, Brazil
| | - Ana Carolina Goes Pinto
- Department of Chemistry, Graduate Program in Chemistry, Federal University of Sergipe-UFS, São Cristóvão-SE, Brazil
| | | | - Wenny Camilla Santos Adan
- Department of Pharmaceutical Sciences, Postgraduate Program in Pharmaceutical Sciences, Federal University of Espírito Santo-UFES, Vitória-ES, Brazil
| | - Ricardo Pereira Rodrigues
- Department of Pharmacy, Graduate Program in Chemistry, Federal University of Sergipe-UFS, São Cristóvão-SE, Brazil
| | - Juliane Xavier Dos Santos
- Department of Chemistry, Graduate Program in Chemistry, Federal University of Sergipe-UFS, São Cristóvão-SE, Brazil
| | - Rodrigo Rezende Kitagawa
- Department of Pharmaceutical Sciences, Postgraduate Program in Pharmaceutical Sciences, Federal University of Espírito Santo-UFES, Vitória-ES, Brazil
| | | | - Tiago Branquinho Oliveira
- Department of Pharmacy, Graduate Program in Chemistry, Federal University of Sergipe-UFS, São Cristóvão-SE, Brazil
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10
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Calderón JC, Ibrahim P, Gobbo D, Gervasio FL, Clark T. General Metadynamics Protocol To Simulate Activation/Deactivation of Class A GPCRs: Proof of Principle for the Serotonin Receptor. J Chem Inf Model 2023; 63:3105-3117. [PMID: 37161278 DOI: 10.1021/acs.jcim.3c00208] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
We present a generally applicable metadynamics protocol for characterizing the activation free-energy profiles of class A G-protein coupled receptors and a proof-of-principle study for the 5HT1A-receptor. The almost universal A100 activation index, which depends on five inter-helix distances, is used as the single collective variable in well-tempered multiple-walker metadynamics simulations. Here, we show free-energy profiles for the serotonin receptor as binary (apo-receptor + G-protein-α-subunit and receptor + ligand) and ternary complexes with two prototypical orthosteric ligands: the full agonist serotonin and the partial agonist aripiprazole. Our results are not only compatible with previously reported experimental and computational data, but they also allow differences between active and inactive conformations to be determined in unprecedented atomic detail, and with respect to the so-called microswitches that have been suggested as determinants of activation, giving insight into their role in the activation mechanism.
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Affiliation(s)
- Jacqueline C Calderón
- Computer-Chemistry-Center, Department of Chemistry and Pharmacy, Friedrich-Alexander-University Erlangen-Nuernberg, Naegelsbachstr. 25, 91052 Erlangen, Germany
| | - Passainte Ibrahim
- Institute of Medical Physics and Biophysics, Faculty of Medicine, University of Leipzig, Leipzig 04107, Germany
| | - Dorothea Gobbo
- Pharmaceutical Sciences, University of Geneva, CH1206 Geneva, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, CH1206 Geneva, Switzerland
| | - Francesco Luigi Gervasio
- Pharmaceutical Sciences, University of Geneva, CH1206 Geneva, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, CH1206 Geneva, Switzerland
- Chemistry Department, University College London, WC1H 0AJ London, U.K
| | - Timothy Clark
- Computer-Chemistry-Center, Department of Chemistry and Pharmacy, Friedrich-Alexander-University Erlangen-Nuernberg, Naegelsbachstr. 25, 91052 Erlangen, Germany
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11
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Motta S, Siani P, Donadoni E, Frigerio G, Bonati L, Di Valentin C. Metadynamics simulations for the investigation of drug loading on functionalized inorganic nanoparticles. NANOSCALE 2023; 15:7909-7919. [PMID: 37066796 DOI: 10.1039/d3nr00397c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Inorganic nanoparticles show promising properties that allow them to be efficiently used as drug carriers. The main limitation in this type of application is currently the drug loading capacity, which can be overcome with a proper functionalization of the nanoparticle surface. In this study, we present, for the first time, a computational approach based on metadynamics to estimate the binding free energy of the doxorubicin drug (DOX) to a functionalized TiO2 nanoparticle under different pH conditions. On a thermodynamic basis, we demonstrate the robustness of our approach to capture the overall mechanism behind the pH-triggered release of DOX due to environmental pH changes. Notably, binding free energy estimations align well with what is expected for a pH-sensitive drug delivery system. Based on our results, we envision the use of metadynamics as a promising computational tool for the rational design and in silico optimization of organic ligands with improved drug carrier properties.
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Affiliation(s)
- Stefano Motta
- Dipartimento di Scienze dell'Ambiente e del Territorio, Università di Milano Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Paulo Siani
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
| | - Edoardo Donadoni
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
| | - Giulia Frigerio
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
| | - Laura Bonati
- Dipartimento di Scienze dell'Ambiente e del Territorio, Università di Milano Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Cristiana Di Valentin
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
- BioNanoMedicine Center NANOMIB, University of Milano-Bicocca, Italy
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12
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Zhao Y, Zhang J, Zhang H, Gu S, Deng Y, Tu Y, Hou T, Kang Y. Sigmoid Accelerated Molecular Dynamics: An Efficient Enhanced Sampling Method for Biosystems. J Phys Chem Lett 2023; 14:1103-1112. [PMID: 36700836 DOI: 10.1021/acs.jpclett.2c03688] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Gaussian accelerated molecular dynamics (GaMD) is recognized as a popular enhanced sampling method for tackling long-standing challenges in biomolecular simulations. Inspired by GaMD, Sigmoid accelerated molecular dynamics (SaMD) is proposed in this work by adding a Sigmoid boost potential to improve the balance between the highest acceleration and accurate reweighting. Compared with GaMD, SaMD extends the accessible time scale and improves the computational efficiency as tested in three tasks. In the alanine dipeptide task, SaMD can produce the free energy landscape with better accuracy and efficiency. In the chignolin folding task, the estimated Gibbs free energy difference can converge to the experimental value ∼30% faster. In the protein-ligand binding task, the bound conformations are closer to the crystal structure with a minimal ligand root-mean-square deviation of 1.7 Å. The binding of the ligand XK263 to the HIV protease is reproduced by SaMD in ∼60% less simulation time.
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Affiliation(s)
- Yihao Zhao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou310058, Zhejiang, China
| | - Jintu Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou310058, Zhejiang, China
- CarbonSilicon AI Technology Company, Ltd., Hangzhou310018, Zhejiang, China
| | - Haotian Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou310058, Zhejiang, China
- CarbonSilicon AI Technology Company, Ltd., Hangzhou310018, Zhejiang, China
| | - Shukai Gu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou310058, Zhejiang, China
| | - Yafeng Deng
- CarbonSilicon AI Technology Company, Ltd., Hangzhou310018, Zhejiang, China
| | - Yaoquan Tu
- Division of Theoretical Chemistry and Biology, Department of Chemistry, KTH Royal Institute of Technology, 114 28Stockholm, Sweden
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou310058, Zhejiang, China
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou310058, Zhejiang, China
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13
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Kaczor AA, Wróbel TM, Bartuzi D. Allosteric Modulators of Dopamine D 2 Receptors for Fine-Tuning of Dopaminergic Neurotransmission in CNS Diseases: Overview, Pharmacology, Structural Aspects and Synthesis. Molecules 2022; 28:molecules28010178. [PMID: 36615372 PMCID: PMC9822192 DOI: 10.3390/molecules28010178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/19/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Allosteric modulation of G protein-coupled receptors (GPCRs) is nowadays a hot topic in medicinal chemistry. Allosteric modulators, i.e., compounds which bind in a receptor site topologically distinct from orthosteric sites, exhibit a number of advantages. They are more selective, safer and display a ceiling effect which prevents overdosing. Allosteric modulators of dopamine D2 receptor are potential drugs against a number of psychiatric and neurological diseases, such as schizophrenia and Parkinson's disease. In this review, an insightful summary of current research on D2 receptor modulators is presented, ranging from their pharmacology and structural aspects of ligand-receptor interactions to their synthesis.
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Affiliation(s)
- Agnieszka A. Kaczor
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modeling Laboratory, Faculty of Pharmacy, Medical University of Lublin, 4A Chodźki St., PL-20093 Lublin, Poland
- School of Pharmacy, University of Eastern Finland, Yliopistonranta 1, P.O. Box 1627, FI-70211 Kuopio, Finland
- Correspondence: ; Tel.: +48-81-448-72-73
| | - Tomasz M. Wróbel
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modeling Laboratory, Faculty of Pharmacy, Medical University of Lublin, 4A Chodźki St., PL-20093 Lublin, Poland
| | - Damian Bartuzi
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modeling Laboratory, Faculty of Pharmacy, Medical University of Lublin, 4A Chodźki St., PL-20093 Lublin, Poland
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, SE-75124 Uppsala, Sweden
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14
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Lukauskis D, Samways ML, Aureli S, Cossins BP, Taylor RD, Gervasio FL. Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein-Ligand Binding Poses. J Chem Inf Model 2022; 62:6209-6216. [PMID: 36401553 DOI: 10.1021/acs.jcim.2c01142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Predicting the correct pose of a ligand binding to a protein and its associated binding affinity is of great importance in computer-aided drug discovery. A number of approaches have been developed to these ends, ranging from the widely used fast molecular docking to the computationally expensive enhanced sampling molecular simulations. In this context, methods such as coarse-grained metadynamics and binding pose metadynamics (BPMD) use simulations with metadynamics biasing to probe the binding affinity without trying to fully converge the binding free energy landscape in order to decrease the computational cost. In BPMD, the metadynamics bias perturbs the ligand away from the initial pose. The resistance of the ligand to this bias is used to calculate a stability score. The method has been shown to be useful in reranking predicted binding poses from docking. Here, we present OpenBPMD, an open-source Python reimplementation and reinterpretation of BPMD. OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. We also investigated the role of accurate water positioning on the performance of the algorithm and showed how the combination with a grand-canonical Monte Carlo algorithm improves the accuracy of the predictions.
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Affiliation(s)
- Dominykas Lukauskis
- Department of Chemistry, University College London, LondonWC1E 6BT, United Kingdom
| | | | - Simone Aureli
- Biomolecular and Pharmaceutical Modelling Group, School of Pharmaceutical Sciences, University of Geneva, CH1211Geneva, Switzerland.,Institute of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, CH1211Geneva, Switzerland
| | - Benjamin P Cossins
- UCB, 216 Bath Road, SloughSL1 3WE, United Kingdom.,Exscientia Ltd., The Schrödinger Building, Oxford Science Park, OxfordOX4 4GE, United Kingdom
| | | | - Francesco Luigi Gervasio
- Department of Chemistry, University College London, LondonWC1E 6BT, United Kingdom.,Biomolecular and Pharmaceutical Modelling Group, School of Pharmaceutical Sciences, University of Geneva, CH1211Geneva, Switzerland.,Institute of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, CH1211Geneva, Switzerland.,UCB, 216 Bath Road, SloughSL1 3WE, United Kingdom
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15
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Funnel metadynamics and behavioral studies reveal complex effect of D2AAK1 ligand on anxiety-like processes. Sci Rep 2022; 12:21192. [PMID: 36476619 PMCID: PMC9729218 DOI: 10.1038/s41598-022-25478-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022] Open
Abstract
Anxiety is a troublesome symptom for many patients, especially those suffering from schizophrenia. Its regulation involves serotonin receptors, targeted e.g. by antipsychotics or psychedelics such as LSD. 5-HT2A receptors are known for an extremely long LSD residence time, enabling minute doses to exert a long-lasting effect. In this work, we explore the changes in anxiety-like processes induced by the previously reported antipsychotic, D2AAK1. In vivo studies revealed that the effect of D2AAK1 on the anxiety is mediated through serotonin 5-HT1A and 5-HT2A receptors, and that it is time-dependent (anxiogenic after 30 min, anxiolytic after 60 min) and dose-dependent. The funnel metadynamics simulations suggest complicated ligand-5HT2AR interactions, involving an allosteric site located under the third extracellular loop, which is a possible explanation of the time-dependency. The binding of D2AAK1 at the allosteric site results in a broader opening of the extracellular receptor entry, possibly altering the binding kinetics of orthosteric ligands.
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16
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Fu H, Zhou Y, Jing X, Shao X, Cai W. Meta-Analysis Reveals That Absolute Binding Free-Energy Calculations Approach Chemical Accuracy. J Med Chem 2022; 65:12970-12978. [PMID: 36179112 DOI: 10.1021/acs.jmedchem.2c00796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Systematic and quantitative analysis of the reliability of formally exact methods that in silico calculate absolute protein-ligand binding free energies remains lacking. Here, we provide, for the first time, evidence-based information on the reliability of these methods by statistically studying 853 cases from 34 different research groups through meta-analysis. The results show that formally exact methods approach chemical accuracy (error = 1.58 kcal/mol), even if people are challenging difficult tasks such as blind drug screening in recent years. The geometrical-pathway-based methods prove to possess a better convergence ability than the alchemical ones, while the latter have a larger application range. We also reveal the importance of always using the latest force fields to guarantee reliability and discuss the pros and cons of turning to an implicit solvent model in absolute binding free-energy calculations. Moreover, based on the meta-analysis, an evidence-based guideline for in silico binding free-energy calculations is provided.
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Affiliation(s)
- Haohao Fu
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin300071, China.,Haihe Laboratory of Sustainable Chemical Transformations, Tianjin300192, China
| | - Yan Zhou
- School of Medicine, Nankai University, Tianjin300071, China.,Department of Ultrasound, Tianjin Third Central Hospital, Tianjin300170, China
| | - Xiang Jing
- Department of Ultrasound, Tianjin Third Central Hospital, Tianjin300170, China
| | - Xueguang Shao
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin300071, China.,Haihe Laboratory of Sustainable Chemical Transformations, Tianjin300192, China
| | - Wensheng Cai
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin300071, China.,Haihe Laboratory of Sustainable Chemical Transformations, Tianjin300192, China
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17
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Zargari F, Nikfarjam Z, Nakhaei E, Ghorbanipour M, Nowroozi A, Amiri A. Study of tyramine-binding mechanism and insecticidal activity of oil extracted from Eucalyptus against Sitophilus oryzae. Front Chem 2022; 10:964700. [PMID: 36212071 PMCID: PMC9538504 DOI: 10.3389/fchem.2022.964700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 08/18/2022] [Indexed: 12/02/2022] Open
Abstract
The rice weevil, Sitophilus oryzae (L.), is a major pest of stored grains throughout the world, which causes quantitative and qualitative losses of food commodities. Eucalyptus essential oils (EOs) possess insecticidal and repellent properties, which make them a potential option for insect control in stored grains with environmentally friendly properties. In the current study, the binding mechanism of tyramine (TA) as a control compound has been investigated by funnel metadynamics (FM) simulation toward the homology model of tyramine1 receptor (TyrR) to explore its binding mode and key residues involved in the binding mechanism. EO compounds have been extracted from the leaf and flower part of Eucalyptus camaldulensis and characterized by GC/MS, and their effectiveness has been evaluated by molecular docking and conventional molecular dynamic (CMD) simulation toward the TyrR model. The FM results suggested that Asp114 followed by Asp80, Asn91, and Asn427 are crucial residues in the binding and the functioning of TA toward TyrR in Sitophilus Oryzae. The GC/MS analysis confirmed a total of 54 and 31 constituents in leaf and flower, respectively, where most of the components (29) are common in both groups. This analysis also revealed the significant concentration of Eucalyptus and α-pinene in leaves and flower EOs. The docking followed by CMD was performed to find the most effective compound in Eucalyptus EOs. In this regard, butanoic acid, 3-methyl-, 3-methyl butyl ester (B12) and 2-Octen-1-ol, 3,7-dimethyl- (B23) from leaf and trans- β-Ocimene (G04) from flower showed the maximum dock score and binding free energy, making them the leading candidates to replace tyramine in TyrR. The MM-PB/GBSA and MD analysis proved that the B12 structure is the most effective compound in inhibition of TyrR.
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Affiliation(s)
- Farshid Zargari
- Pharmacology Research Center, Zahedan University of Medical Sciences, Zahedan, Iran
- Department of Chemistry, Faculty of Science, University of Sistan and Baluchestan (USB), Zahedan, Iran
| | - Zahra Nikfarjam
- Department of Physical & Computational Chemistry, Chemistry and Chemical Engineering Research Center of Iran, Tehran, Iran
- *Correspondence: Zahra Nikfarjam,
| | - Ebrahim Nakhaei
- Department of Chemistry, Faculty of Science, University of Sistan and Baluchestan (USB), Zahedan, Iran
| | - Masoumeh Ghorbanipour
- Department of Physical Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran
| | - Alireza Nowroozi
- Department of Chemistry, Faculty of Science, University of Sistan and Baluchestan (USB), Zahedan, Iran
| | - Azam Amiri
- College of Geography and Environmental Planning, University of Sistan and Baluchestan, Zahedan, Iran
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18
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Siemons N, Pearce D, Cendra C, Yu H, Tuladhar SM, Hallani RK, Sheelamanthula R, LeCroy GS, Siemons L, White AJP, McCulloch I, Salleo A, Frost JM, Giovannitti A, Nelson J. Impact of Side-Chain Hydrophilicity on Packing, Swelling, and Ion Interactions in Oxy-Bithiophene Semiconductors. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2204258. [PMID: 35946142 DOI: 10.1002/adma.202204258] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 07/28/2022] [Indexed: 06/15/2023]
Abstract
Exchanging hydrophobic alkyl-based side chains to hydrophilic glycol-based side chains is a widely adopted method for improving mixed-transport device performance, despite the impact on solid-state packing and polymer-electrolyte interactions being poorly understood. Presented here is a molecular dynamics (MD) force field for modeling alkoxylated and glycolated polythiophenes. The force field is validated against known packing motifs for their monomer crystals. MD simulations, coupled with X-ray diffraction (XRD), show that alkoxylated polythiophenes will pack with a "tilted stack" and straight interdigitating side chains, whilst their glycolated counterpart will pack with a "deflected stack" and an s-bend side-chain configuration. MD simulations reveal water penetration pathways into the alkoxylated and glycolated crystals-through the π-stack and through the lamellar stack respectively. Finally, the two distinct ways triethylene glycol polymers can bind to cations are revealed, showing the formation of a metastable single bound state, or an energetically deep double bound state, both with a strong side-chain length dependence. The minimum energy pathways for the formation of the chelates are identified, showing the physical process through which cations can bind to one or two side chains of a glycolated polythiophene, with consequences for ion transport in bithiophene semiconductors.
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Affiliation(s)
- Nicholas Siemons
- Department of Physics, Imperial College, London, Exhibition Rd, South Kensington, London, SW7 2AZ, UK
| | - Drew Pearce
- Department of Physics, Imperial College, London, Exhibition Rd, South Kensington, London, SW7 2AZ, UK
| | - Camila Cendra
- Department of Materials Science and Engineering, Stanford University, 450 Serra Mall, Stanford, CA, 94305, USA
| | - Hang Yu
- Department of Physics, Imperial College, London, Exhibition Rd, South Kensington, London, SW7 2AZ, UK
| | - Sachetan M Tuladhar
- Department of Physics, Imperial College, London, Exhibition Rd, South Kensington, London, SW7 2AZ, UK
| | - Rawad K Hallani
- Physical Sciences and Engineering Division, KAUST Solar Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
| | - Rajendar Sheelamanthula
- Physical Sciences and Engineering Division, KAUST Solar Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
| | - Garrett S LeCroy
- Department of Materials Science and Engineering, Stanford University, 450 Serra Mall, Stanford, CA, 94305, USA
| | - Lucas Siemons
- Structural biology of cells and viruses laboratory, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Andrew J P White
- Chemical Crystallography Laboratory, Department of Chemistry, Imperial College London White City Campus, 82 Wood Lane, London, W12 0BZ, UK
| | - Iain McCulloch
- Department of Chemistry, University of Oxford, Oxford, OX1 2JD, UK
| | - Alberto Salleo
- Department of Materials Science and Engineering, Stanford University, 450 Serra Mall, Stanford, CA, 94305, USA
| | - Jarvist M Frost
- Department of Physics, Imperial College, London, Exhibition Rd, South Kensington, London, SW7 2AZ, UK
| | - Alexander Giovannitti
- Department of Materials Science and Engineering, Stanford University, 450 Serra Mall, Stanford, CA, 94305, USA
| | - Jenny Nelson
- Department of Physics, Imperial College, London, Exhibition Rd, South Kensington, London, SW7 2AZ, UK
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19
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Zhang H, Gong X, Peng Y, Saravanan KM, Bian H, Zhang JZH, Wei Y, Pan Y, Yang Y. An Efficient Modern Strategy to Screen Drug Candidates Targeting RdRp of SARS-CoV-2 With Potentially High Selectivity and Specificity. Front Chem 2022; 10:933102. [PMID: 35903186 PMCID: PMC9315156 DOI: 10.3389/fchem.2022.933102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 06/06/2022] [Indexed: 01/18/2023] Open
Abstract
Desired drug candidates should have both a high potential binding chance and high specificity. Recently, many drug screening strategies have been developed to screen compounds with high possible binding chances or high binding affinity. However, there is still no good solution to detect whether those selected compounds possess high specificity. Here, we developed a reverse DFCNN (Dense Fully Connected Neural Network) and a reverse docking protocol to check a given compound’s ability to bind diversified targets and estimate its specificity with homemade formulas. We used the RNA-dependent RNA polymerase (RdRp) target as a proof-of-concept example to identify drug candidates with high selectivity and high specificity. We first used a previously developed hybrid screening method to find drug candidates from an 8888-size compound database. The hybrid screening method takes advantage of the deep learning-based method, traditional molecular docking, molecular dynamics simulation, and binding free energy calculated by metadynamics, which should be powerful in selecting high binding affinity candidates. Also, we integrated the reverse DFCNN and reversed docking against a diversified 102 proteins to the pipeline for assessing the specificity of those selected candidates, and finally got compounds that have both predicted selectivity and specificity. Among the eight selected candidates, Platycodin D and Tubeimoside III were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC50 values of 619.5 and 265.5 nM, respectively. Our study discovered that Tubeimoside III could inhibit SARS-CoV-2 replication potently for the first time. Furthermore, the underlying mechanisms of Platycodin D and Tubeimoside III inhibiting SARS-CoV-2 are highly possible by blocking the RdRp cavity according to our screening procedure. In addition, the careful analysis predicted common critical residues involved in the binding with active inhibitors Platycodin D and Tubeimoside III, Azithromycin, and Pralatrexate, which hopefully promote the development of non-covalent binding inhibitors against RdRp.
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Affiliation(s)
- Haiping Zhang
- Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- *Correspondence: Yang Yang, ; Haiping Zhang,
| | - Xiaohua Gong
- Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for Infectious Disease, State Key Discipline of Infectious Disease, Shenzhen Third People’s Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
| | - Yun Peng
- Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for Infectious Disease, State Key Discipline of Infectious Disease, Shenzhen Third People’s Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
| | - Konda Mani Saravanan
- Department of Biotechnology, Bharath Institute of Higher Education and Research, Chennai, , India
| | - Hengwei Bian
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry and Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, China
| | - John Z. H. Zhang
- Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yanjie Wei
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yi Pan
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yang Yang
- Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for Infectious Disease, State Key Discipline of Infectious Disease, Shenzhen Third People’s Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
- *Correspondence: Yang Yang, ; Haiping Zhang,
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20
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Frimann TM, Ko SK, Harris P, Bukrinski JT, Peters GHJ. In-silico study of the interactions between acylated glucagon like-peptide-1 analogues and the native receptor. J Biomol Struct Dyn 2022:1-15. [PMID: 35612899 DOI: 10.1080/07391102.2022.2078409] [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: 10/18/2022]
Abstract
We have performed a series of multiple molecular dynamics (MD) simulations of glucagon-like peptide-1 (GLP-1) and acylated GLP-1 analogues in complex with the endogenous receptor (GLP-1R) to obtain a molecular understanding of how fatty acid (FA) chain structure, acylation position on the peptide, and presence of a linker affect the binding. MD simulations were analysed to extract heatmaps of receptor-peptide interaction patterns and to determine the free energy of binding using the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) approach. The extracted free energies from MM-PBSA calculations are in qualitative agreement with experimentally determined potencies. Furthermore, the interaction patterns seen in the receptor-GLP-1 complex simulations resemble previously reported binding interactions validating the simulations. Analysing the receptor-GLP-1 analogue complex simulations, we found that the major differences between the systems stem from FA interactions and positioning of acylation in the peptide. Hydrophobic interactions between the FA chain and a hydrophobic patch on the extracellular domain contribute significantly to the binding affinity. Acylation on Lys26 resulted in noticeably more interactions between the FA chain and the extracellular domain hydrophobic patch than found for acylation on Lys34 and Lys38, respectively. The presence of a charged linker between the peptide and FA chain can potentially stabilise the complex by forming hydrogen bonds to arginine residues in the linker region between the extracellular domain and the transmembrane domain. A molecular understanding of the fatty acid structure and its effect on binding provides important insights into designing acylated agonists for GLP-1R.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Tine Maja Frimann
- Department of Chemistry, Technical University of Denmark, Lyngby, Denmark
| | - Suk Kyu Ko
- Department of Chemistry, Technical University of Denmark, Lyngby, Denmark
| | - Pernille Harris
- Department of Chemistry, Technical University of Denmark, Lyngby, Denmark.,Department of Chemistry, H.C. Ørsted Institute, University of Copenhagen, Copenhagen, Denmark
| | | | - Günther H J Peters
- Department of Chemistry, Technical University of Denmark, Lyngby, Denmark
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21
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Djokovic N, Ruzic D, Rahnasto-Rilla M, Srdic-Rajic T, Lahtela-Kakkonen M, Nikolic K. Expanding the Accessible Chemical Space of SIRT2 Inhibitors through Exploration of Binding Pocket Dynamics. J Chem Inf Model 2022; 62:2571-2585. [PMID: 35467856 DOI: 10.1021/acs.jcim.2c00241] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Considerations of binding pocket dynamics are one of the crucial aspects of the rational design of binders. Identification of alternative conformational states or cryptic subpockets could lead to the discovery of completely novel groups of the ligands. However, experimental characterization of pocket dynamics, besides being expensive, may not be able to elucidate all of the conformational states relevant for drug discovery projects. In this study, we propose the protocol for computational simulations of sirtuin 2 (SIRT2) binding pocket dynamics and its integration into the structure-based virtual screening (SBVS) pipeline. Initially, unbiased molecular dynamics simulations of SIRT2:inhibitor complexes were performed using optimized force field parameters of SIRT2 inhibitors. Time-lagged independent component analysis (tICA) was used to design pocket-related collective variables (CVs) for enhanced sampling of SIRT2 pocket dynamics. Metadynamics simulations in the tICA eigenvector space revealed alternative conformational states of the SIRT2 binding pocket and the existence of a cryptic subpocket. Newly identified SIRT2 conformational states outperformed experimentally resolved states in retrospective SBVS validation. After performing prospective SBVS, compounds from the under-represented portions of the SIRT2 inhibitor chemical space were selected for in vitro evaluation. Two compounds, NDJ18 and NDJ85, were identified as potent and selective SIRT2 inhibitors, which validated the in silico protocol and opened up the possibility for generalization and broadening of its application. The anticancer effects of the most potent compound NDJ18 were examined on the triple-negative breast cancer cell line. Results indicated that NDJ18 represents a promising structure suitable for further evaluation.
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Affiliation(s)
- Nemanja Djokovic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Dusan Ruzic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Minna Rahnasto-Rilla
- School of Pharmacy, University of Eastern Finland, P.O. Box 1627, 70210 Kuopio, Finland
| | - Tatjana Srdic-Rajic
- Department of Experimental Oncology, Institute for Oncology and Radiology of Serbia, Pasterova 14, 11000 Belgrade, Serbia
| | | | - Katarina Nikolic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
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22
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Nicoli A, Dunkel A, Giorgino T, de Graaf C, Di Pizio A. Classification Model for the Second Extracellular Loop of Class A GPCRs. J Chem Inf Model 2022; 62:511-522. [PMID: 35113559 DOI: 10.1021/acs.jcim.1c01056] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The extracellular loop 2 (ECL2) is the longest and the most diverse loop among class A G protein-coupled receptors (GPCRs). It connects the transmembrane (TM) helices 4 and 5 and contains a highly conserved cysteine through which it is bridged with TM3. In this paper, experimental ECL2 structures were analyzed based on their sequences, shapes, and intramolecular contacts. To take into account the flexibility, we incorporated into our analyses information from the molecular dynamics trajectories available on the GPCRmd website. Despite the high sequence variability, shapes of the analyzed structures, defined by the backbone volume overlaps, can be clustered into seven main groups. Conformational differences within the clusters can be then identified by intramolecular interactions with other GPCR structural domains. Overall, our work provides a reorganization of the structural information of the ECL2 of class A GPCR subfamilies, highlighting differences and similarities on sequence and conformation levels.
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Affiliation(s)
- Alessandro Nicoli
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Andreas Dunkel
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Toni Giorgino
- Biophysics Institute, National Research Council (CNR-IBF), 20133 Milan, Italy
| | - Chris de Graaf
- Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge CB21 6DG, U.K
| | - Antonella Di Pizio
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
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23
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Conrad M, Söldner CA, Sticht H. Effect of Ions and Sequence Variants on the Antagonist Binding Properties of the Histamine H 1 Receptor. Int J Mol Sci 2022; 23:ijms23031420. [PMID: 35163341 PMCID: PMC8836275 DOI: 10.3390/ijms23031420] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/14/2022] [Accepted: 01/18/2022] [Indexed: 11/16/2022] Open
Abstract
The histamine H1 receptor (H1R) is a G protein-coupled receptor (GPCR) and represents a main target in the treatment of allergic reactions as well as inflammatory reactions and depressions. Although the overall effect of antagonists on H1 function has been extensively investigated, rather little is known about the potential modulatory effect of ions or sequence variants on antagonist binding. We investigated the dynamics of a phosphate ion present in the crystal structure and of a sodium ion, for which we determined the position in the allosteric pocket by metadynamics simulations. Both types of ions exhibit significant dynamics within their binding site; however, some key contacts remain stable over the simulation time, which might be exploited to develop more potent drugs targeting these sites. The dynamics of the ions is almost unaffected by the presence or absence of doxepin, as also reflected in their small effect (less than 1 kcal·mol-1) on doxepin binding affinity. We also examined the effect of four H1R sequence variants observed in the human population on doxepin binding. These variants cause a reduction in doxepin affinity of up to 2.5 kcal·mol-1, indicating that personalized medical treatments that take into account individual mutation patterns could increase precision in the dosage of GPCR-targeting drugs.
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Affiliation(s)
- Marcus Conrad
- Division of Bioinformatics, Institute of Biochemistry, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany; (M.C.); (C.A.S.)
| | - Christian A. Söldner
- Division of Bioinformatics, Institute of Biochemistry, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany; (M.C.); (C.A.S.)
| | - Heinrich Sticht
- Division of Bioinformatics, Institute of Biochemistry, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany; (M.C.); (C.A.S.)
- Erlangen National High Performance Computing Center (NHR@FAU), Friedrich-Alexander-University Erlangen-Nürnberg (FAU), 91058 Erlangen, Germany
- Correspondence:
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24
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Tao E, Corry B. Characterizing fenestration size in sodium channel subtypes and their accessibility to inhibitors. Biophys J 2022; 121:193-206. [PMID: 34958776 PMCID: PMC8790208 DOI: 10.1016/j.bpj.2021.12.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 12/07/2021] [Accepted: 12/16/2021] [Indexed: 01/21/2023] Open
Abstract
Voltage-gated sodium channels (Nav) underlie the electrical activity of nerve and muscle cells. Humans have nine different subtypes of these channels, which are the target of small-molecule inhibitors commonly used to treat a range of conditions. Structural studies have identified four lateral fenestrations within the Nav pore module that have been shown to influence Nav pore blocker access during resting-state inhibition. However, the structural differences among the nine subtypes are still unclear. In particular, the dimensions of the four individual fenestrations across the Nav subtypes and their differential accessibility to pore blockers is yet to be characterized. To address this, we applied classical molecular dynamics simulations to study the recently published structures of Nav1.1, Nav1.2, Nav1.4, Nav1.5, and Nav1.7. Although there is significant variability in the bottleneck sizes of the Nav fenestrations, the subtypes follow a common pattern, with wider DI-II and DIII-IV fenestrations, a more restricted DII-III fenestration, and the most restricted DI-IV fenestration. We further identify the key bottleneck residues in each fenestration and show that the motions of aromatic residue sidechains govern the bottleneck radii. Well-tempered metadynamics simulations of Nav1.4 and Nav1.5 in the presence of the pore blocker lidocaine also support the DI-II fenestration being the most likely access route for drugs. Our computational results provide a foundation for future in vitro experiments examining the route of drug access to sodium channels. Understanding the fenestrations and their accessibility to drugs is critical for future analyses of diseases mutations across different sodium channel subtypes, with the potential to inform pharmacological development of resting-state inhibitors and subtype-selective drug design.
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Affiliation(s)
- Elaine Tao
- Research School of Biology, Australian National University, Canberra, Australia
| | - Ben Corry
- Research School of Biology, Australian National University, Canberra, Australia.
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25
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Pawnikar S, Bhattarai A, Wang J, Miao Y. Binding Analysis Using Accelerated Molecular Dynamics Simulations and Future Perspectives. Adv Appl Bioinform Chem 2022; 15:1-19. [PMID: 35023931 PMCID: PMC8747661 DOI: 10.2147/aabc.s247950] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 02/20/2021] [Indexed: 12/12/2022] Open
Abstract
Biomolecular recognition such as binding of small molecules, nucleic acids, peptides and proteins to their target receptors plays key roles in cellular function and has been targeted for therapeutic drug design. Molecular dynamics (MD) is a computational approach to analyze these binding processes at an atomistic level, which provides valuable understandings of the mechanisms of biomolecular recognition. However, the rather slow biomolecular binding events often present challenges for conventional MD (cMD), due to limited simulation timescales (typically over hundreds of nanoseconds to tens of microseconds). In this regard, enhanced sampling methods, particularly accelerated MD (aMD), have proven useful to bridge the gap and enable all-atom simulations of biomolecular binding events. Here, we will review the recent method developments of Gaussian aMD (GaMD), ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD), which have greatly expanded our capabilities to simulate biomolecular binding processes. Spontaneous binding of various biomolecules to their receptors has been successfully simulated by GaMD. Microsecond LiGaMD and Pep-GaMD simulations have captured repetitive binding and dissociation of small-molecule ligands and highly flexible peptides, and thus enabled ligand/peptide binding thermodynamics and kinetics calculations. We will also present relevant application studies in simulations of important drug targets and future perspectives for rational computer-aided drug design.
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Affiliation(s)
- Shristi Pawnikar
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, 66047, USA
| | - Apurba Bhattarai
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, 66047, USA
| | - Jinan Wang
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, 66047, USA
| | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, 66047, USA
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26
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Challenges and frontiers of computational modelling of biomolecular recognition. QRB DISCOVERY 2022. [DOI: 10.1017/qrd.2022.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Abstract
Biomolecular recognition including binding of small molecules, peptides and proteins to their target receptors plays a key role in cellular function and has been targeted for therapeutic drug design. However, the high flexibility of biomolecules and slow binding and dissociation processes have presented challenges for computational modelling. Here, we review the challenges and computational approaches developed to characterise biomolecular binding, including molecular docking, molecular dynamics simulations (especially enhanced sampling) and machine learning. Further improvements are still needed in order to accurately and efficiently characterise binding structures, mechanisms, thermodynamics and kinetics of biomolecules in the future.
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27
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Salas-Estrada L, Fiorillo B, Filizola M. Metadynamics simulations leveraged by statistical analyses and artificial intelligence-based tools to inform the discovery of G protein-coupled receptor ligands. Front Endocrinol (Lausanne) 2022; 13:1099715. [PMID: 36619585 PMCID: PMC9816996 DOI: 10.3389/fendo.2022.1099715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 12/12/2022] [Indexed: 12/25/2022] Open
Abstract
G Protein-Coupled Receptors (GPCRs) are a large family of membrane proteins with pluridimensional signaling profiles. They undergo ligand-specific conformational changes, which in turn lead to the differential activation of intracellular signaling proteins and the consequent triggering of a variety of biological responses. This conformational plasticity directly impacts our understanding of GPCR signaling and therapeutic implications, as do ligand-specific kinetic differences in GPCR-induced transducer activation/coupling or GPCR-transducer complex stability. High-resolution experimental structures of ligand-bound GPCRs in the presence or absence of interacting transducers provide important, yet limited, insights into the highly dynamic process of ligand-induced activation or inhibition of these receptors. We and others have complemented these studies with computational strategies aimed at characterizing increasingly accurate metastable conformations of GPCRs using a combination of metadynamics simulations, state-of-the-art algorithms for statistical analyses of simulation data, and artificial intelligence-based tools. This minireview provides an overview of these approaches as well as lessons learned from them towards the identification of conformational states that may be difficult or even impossible to characterize experimentally and yet important to discover new GPCR ligands.
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28
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Zhao Q, Capelli R, Carloni P, Lüscher B, Li J, Rossetti G. Enhanced Sampling Approach to the Induced-Fit Docking Problem in Protein-Ligand Binding: The Case of Mono-ADP-Ribosylation Hydrolase Inhibitors. J Chem Theory Comput 2021; 17:7899-7911. [PMID: 34813698 DOI: 10.1021/acs.jctc.1c00649] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Enhanced sampling methods can predict free-energy landscapes associated with protein/ligand binding, characterizing the involved intermolecular interactions in a precise way. However, these in silico approaches can be challenged by induced-fit effects. Here, we present a variant of volume-based metadynamics tailored to tackle this problem in a general and efficient way. The validity of the approach is established by applying it to substrate/enzyme complexes of pharmacological relevance: mono-ADP-ribose (ADPr) in complex with mono-ADP-ribosylation hydrolases (MacroD1 and MacroD2), where induced-fit phenomena are known to be significant. The calculated binding free energies are consistent with experiments, with an absolute error smaller than 0.5 kcal/mol. Our simulations reveal that in all circumstances, the active loops, delimiting the boundaries of the binding site, undergo significant conformation rearrangements upon ligand binding. The calculations further provide, for the first time, the molecular basis of ADPr specificity and the relative changes in its experimental binding affinity on passing from MacroD1 to MacroD2 and all its mutants. Our study paves the way to the quantitative description of induced-fit events in molecular recognition.
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Affiliation(s)
- Qianqian Zhao
- Institute for Advanced Simulations (IAS)-5/Institute for Neuroscience and Medicine (INM)-9, Forschungszentrum Jülich, 52428 Jülich, Germany.,College of Chemistry, Fuzhou University, Fuzhou 350002, China
| | - Riccardo Capelli
- Institute for Advanced Simulations (IAS)-5/Institute for Neuroscience and Medicine (INM)-9, Forschungszentrum Jülich, 52428 Jülich, Germany.,Department of Applied Science and Technology, Politecnico di Torino, Torino 10129, Italy
| | - Paolo Carloni
- Institute for Advanced Simulations (IAS)-5/Institute for Neuroscience and Medicine (INM)-9, Forschungszentrum Jülich, 52428 Jülich, Germany.,Institute for Neuroscience and Medicine (INM)-11, Forschungszentrum Jülich, 52428 Jülich, Germany.,Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, 52062 Aachen, Germany.,Department of Neurology, RWTH Aachen University, 52062 Aachen, Germany
| | - Bernhard Lüscher
- Institute of Biochemistry and Molecular Biology, RWTH Aachen University, 52062 Aachen, Germany
| | - Jinyu Li
- College of Chemistry, Fuzhou University, Fuzhou 350002, China
| | - Giulia Rossetti
- Institute for Advanced Simulations (IAS)-5/Institute for Neuroscience and Medicine (INM)-9, Forschungszentrum Jülich, 52428 Jülich, Germany.,Jülich Supercomputing Center (JSC), Forschungszentrum Jülich, 52428 Jülich, Germany.,Department of Neurology, RWTH Aachen University, 52062 Aachen, Germany
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29
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Zhang H, Li J, Saravanan KM, Wu H, Wang Z, Wu D, Wei Y, Lu Z, Chen YH, Wan X, Pan Y. An Integrated Deep Learning and Molecular Dynamics Simulation-Based Screening Pipeline Identifies Inhibitors of a New Cancer Drug Target TIPE2. Front Pharmacol 2021; 12:772296. [PMID: 34887765 PMCID: PMC8650684 DOI: 10.3389/fphar.2021.772296] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 11/02/2021] [Indexed: 12/31/2022] Open
Abstract
The TIPE2 (tumor necrosis factor-alpha-induced protein 8-like 2) protein is a major regulator of cancer and inflammatory diseases. The availability of its sequence and structure, as well as the critical amino acids involved in its ligand binding, provides insights into its function and helps greatly identify novel drug candidates against TIPE2 protein. With the current advances in deep learning and molecular dynamics simulation-based drug screening, large-scale exploration of inhibitory candidates for TIPE2 becomes possible. In this work, we apply deep learning-based methods to perform a preliminary screening against TIPE2 over several commercially available compound datasets. Then, we carried a fine screening by molecular dynamics simulations, followed by metadynamics simulations. Finally, four compounds were selected for experimental validation from 64 candidates obtained from the screening. With surprising accuracy, three compounds out of four can bind to TIPE2. Among them, UM-164 exhibited the strongest binding affinity of 4.97 µM and was able to interfere with the binding of TIPE2 and PIP2 according to competitive bio-layer interferometry (BLI), which indicates that UM-164 is a potential inhibitor against TIPE2 function. The work demonstrates the feasibility of incorporating deep learning and MD simulation in virtual drug screening and provides high potential inhibitors against TIPE2 for drug development.
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Affiliation(s)
- Haiping Zhang
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Junxin Li
- Shenzhen Laboratory of Human Antibody Engineering, Institute of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, University City of Shenzhen, Shenzhen, China
| | - Konda Mani Saravanan
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hao Wu
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhichao Wang
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Du Wu
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yanjie Wei
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhen Lu
- Center for Cancer Immunology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, University City of Shenzhen, Shenzhen, China
| | - Youhai H Chen
- Center for Cancer Immunology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, University City of Shenzhen, Shenzhen, China
| | - Xiaochun Wan
- Shenzhen Laboratory of Human Antibody Engineering, Institute of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, University City of Shenzhen, Shenzhen, China
| | - Yi Pan
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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30
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Fiorillo B, Sepe V, Conflitti P, Roselli R, Biagioli M, Marchianò S, De Luca P, Baronissi G, Rapacciuolo P, Cassiano C, Catalanotti B, Zampella A, Limongelli V, Fiorucci S. Structural Basis for Developing Multitarget Compounds Acting on Cysteinyl Leukotriene Receptor 1 and G-Protein-Coupled Bile Acid Receptor 1. J Med Chem 2021; 64:16512-16529. [PMID: 34767347 DOI: 10.1021/acs.jmedchem.1c01078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
G-protein-coupled receptors (GPCRs) are the molecular target of 40% of marketed drugs and the most investigated structures to develop novel therapeutics. Different members of the GPCRs superfamily can modulate the same cellular process acting on diverse pathways, thus representing an attractive opportunity to achieve multitarget drugs with synergic pharmacological effects. Here, we present a series of compounds with dual activity toward cysteinyl leukotriene receptor 1 (CysLT1R) and G-protein-coupled bile acid receptor 1 (GPBAR1). They are derivatives of REV5901─the first reported dual compound─with therapeutic potential in the treatment of colitis and other inflammatory processes. We report the binding mode of the most active compounds in the two GPCRs, revealing unprecedented structural basis for future drug design studies, including the presence of a polar group opportunely spaced from an aromatic ring in the ligand to interact with Arg792.60 of CysLT1R and achieve dual activity.
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Affiliation(s)
- Bianca Fiorillo
- Department of Pharmacy, University of Naples "Federico II", Via D. Montesano, 49, I-80131 Naples, Italy
| | - Valentina Sepe
- Department of Pharmacy, University of Naples "Federico II", Via D. Montesano, 49, I-80131 Naples, Italy
| | - Paolo Conflitti
- Università della Svizzera italiana (USI), Faculty of Biomedical Sciences, Euler Institute, via G. Buffi 13, CH-6900 Lugano, Switzerland
| | - Rosalinda Roselli
- Department of Pharmacy, University of Naples "Federico II", Via D. Montesano, 49, I-80131 Naples, Italy
| | - Michele Biagioli
- Department of Medicine and Surgery, University of Perugia, Piazza L. Severi 1, 06132 Perugia, Italy
| | - Silvia Marchianò
- Department of Medicine and Surgery, University of Perugia, Piazza L. Severi 1, 06132 Perugia, Italy
| | - Pasquale De Luca
- Head─Sequencing and Molecular Analyses Center, RIMAR Stazione Zoologica, Villa Comunale, 80121 Naples, Italy
| | - Giuliana Baronissi
- Department of Pharmacy, University of Naples "Federico II", Via D. Montesano, 49, I-80131 Naples, Italy
| | - Pasquale Rapacciuolo
- Department of Pharmacy, University of Naples "Federico II", Via D. Montesano, 49, I-80131 Naples, Italy
| | - Chiara Cassiano
- Department of Pharmacy, University of Naples "Federico II", Via D. Montesano, 49, I-80131 Naples, Italy
| | - Bruno Catalanotti
- Department of Pharmacy, University of Naples "Federico II", Via D. Montesano, 49, I-80131 Naples, Italy
| | - Angela Zampella
- Department of Pharmacy, University of Naples "Federico II", Via D. Montesano, 49, I-80131 Naples, Italy
| | - Vittorio Limongelli
- Department of Pharmacy, University of Naples "Federico II", Via D. Montesano, 49, I-80131 Naples, Italy.,Università della Svizzera italiana (USI), Faculty of Biomedical Sciences, Euler Institute, via G. Buffi 13, CH-6900 Lugano, Switzerland
| | - Stefano Fiorucci
- Department of Medicine and Surgery, University of Perugia, Piazza L. Severi 1, 06132 Perugia, Italy
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31
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Zhang Q, Zhao N, Meng X, Yu F, Yao X, Liu H. The prediction of protein-ligand unbinding for modern drug discovery. Expert Opin Drug Discov 2021; 17:191-205. [PMID: 34731059 DOI: 10.1080/17460441.2022.2002298] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Drug-target thermodynamic and kinetic information have perennially important roles in drug design. The prediction of protein-ligand unbinding, which can provide important kinetic information, in experiments continues to face great challenges. Uncovering protein-ligand unbinding through molecular dynamics simulations has become efficient and inexpensive with the progress and enhancement of computing power and sampling methods. AREAS COVERED In this review, various sampling methods for protein-ligand unbinding and their basic principles are firstly briefly introduced. Then, their applications in predicting aspects of protein-ligand unbinding, including unbinding pathways, dissociation rate constants, residence time and binding affinity, are discussed. EXPERT OPINION Although various sampling methods have been successfully applied in numerous systems, they still have shortcomings and deficiencies. Most enhanced sampling methods require researchers to possess a wealth of prior knowledge of collective variables or reaction coordinates. In addition, most systems studied at present are relatively simple, and the study of complex systems in real drug research remains greatly challenging. Through the combination of machine learning and enhanced sampling methods, prediction accuracy can be further improved, and some problems encountered in complex systems also may be solved.
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Affiliation(s)
| | - Nannan Zhao
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Xiaoxiao Meng
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Fansen Yu
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Xiaojun Yao
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, China.,Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau, China
| | - Huanxiang Liu
- School of Pharmacy, Lanzhou University, Lanzhou, China
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32
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Cecchini M, Changeux JP. Nicotinic receptors: From protein allostery to computational neuropharmacology. Mol Aspects Med 2021; 84:101044. [PMID: 34656371 DOI: 10.1016/j.mam.2021.101044] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 09/28/2021] [Accepted: 09/30/2021] [Indexed: 11/15/2022]
Abstract
We propose an extension and further development of the Monod-Wyman-Changeux model for allosteric transitions of regulatory proteins to brain communications and specifically to neurotransmitters receptors, with the nicotinic acetylcholine receptor (nAChR) as a model of ligand-gated ion channels. The present development offers an expression of the change of the gating isomerization constant caused by pharmacological ligand binding in terms of its value in the absence of ligands and several "modulation factors", which vary with orthosteric ligand binding (agonists/antagonists), allosteric ligand binding (positive allosteric modulators/negative allosteric modulators) and receptor desensitization. The new - explicit - formulation of such "modulation factors", provides expressions for the pharmacological attributes of potency, efficacy, and selectivity for the modulatory ligands (including endogenous neurotransmitters) in terms of their binding affinity for the active, resting, and desensitized states of the receptor. The current formulation provides ways to design neuroactive compounds with a controlled pharmacological profile, opening the field of computational neuro-pharmacology.
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Affiliation(s)
- Marco Cecchini
- Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, F-67083, Strasbourg Cedex, France.
| | - Jean-Pierre Changeux
- Kavli Institute for Brain & Mind University of California, San Diego La Jolla, CA, 92093, USA; Institut Pasteur, URA 2182, CNRS, F-75015, France; Collège de France, F-75005 Paris, France.
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33
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Chatzigoulas A, Cournia Z. Rational design of allosteric modulators: Challenges and successes. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1529] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Alexios Chatzigoulas
- Biomedical Research Foundation Academy of Athens Athens Greece
- Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece
| | - Zoe Cournia
- Biomedical Research Foundation Academy of Athens Athens Greece
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34
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Chini MG, Lauro G, Bifulco G. Addressing the Target Identification and Accelerating the Repositioning of Anti‐Inflammatory/Anti‐Cancer Organic Compounds by Computational Approaches. European J Org Chem 2021. [DOI: 10.1002/ejoc.202100245] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Maria Giovanna Chini
- Department of Biosciences and Territory University of Molise C.da Fonte Lappone 86090 Pesche (IS) Italy
| | - Gianluigi Lauro
- Department of Pharmacy University of Salerno Via Giovanni Paolo II 132 84084 Fisciano (SA) Italy
| | - Giuseppe Bifulco
- Department of Pharmacy University of Salerno Via Giovanni Paolo II 132 84084 Fisciano (SA) Italy
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35
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Callea L, Bonati L, Motta S. Metadynamics-Based Approaches for Modeling the Hypoxia-Inducible Factor 2α Ligand Binding Process. J Chem Theory Comput 2021; 17:3841-3851. [PMID: 34082524 PMCID: PMC8280741 DOI: 10.1021/acs.jctc.1c00114] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
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Several methods based
on enhanced-sampling molecular dynamics have
been proposed for studying ligand binding processes. Here, we developed
a protocol that combines the advantages of steered molecular dynamics
(SMD) and metadynamics. While SMD is proposed for investigating possible
unbinding pathways of the ligand and identifying the preferred one,
metadynamics, with the path collective variable (PCV) formalism, is
suggested to explore the binding processes along the pathway defined
on the basis of SMD, by using only two CVs. We applied our approach
to the study of binding of two known ligands to the hypoxia-inducible
factor 2α, where the buried binding cavity makes simulation
of the process a challenging task. Our approach allowed identification
of the preferred entrance pathway for each ligand, highlighted the
features of the bound and intermediate states in the free-energy surface,
and provided a binding affinity scale in agreement with experimental
data. Therefore, it seems to be a suitable tool for elucidating ligand
binding processes of similar complex systems.
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Affiliation(s)
- Lara Callea
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milan, Italy
| | - Laura Bonati
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milan, Italy
| | - Stefano Motta
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milan, Italy
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36
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Xu X, Kaindl J, Clark MJ, Hübner H, Hirata K, Sunahara RK, Gmeiner P, Kobilka BK, Liu X. Binding pathway determines norepinephrine selectivity for the human β 1AR over β 2AR. Cell Res 2021; 31:569-579. [PMID: 33093660 PMCID: PMC8089101 DOI: 10.1038/s41422-020-00424-2] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 09/28/2020] [Indexed: 01/29/2023] Open
Abstract
Beta adrenergic receptors (βARs) mediate physiologic responses to the catecholamines epinephrine and norepinephrine released by the sympathetic nervous system. While the hormone epinephrine binds β1AR and β2AR with similar affinity, the smaller neurotransmitter norepinephrine is approximately tenfold selective for the β1AR. To understand the structural basis for this physiologically important selectivity, we solved the crystal structures of the human β1AR bound to an antagonist carazolol and different agonists including norepinephrine, epinephrine and BI-167107. Structural comparison revealed that the catecholamine-binding pockets are identical between β1AR and β2AR, but the extracellular vestibules have different shapes and electrostatic properties. Metadynamics simulations and mutagenesis studies revealed that these differences influence the path norepinephrine takes to the orthosteric pocket and contribute to the different association rates and thus different affinities.
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Affiliation(s)
- Xinyu Xu
- Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing, 100084 China ,School of Medicine, Tsinghua University, Beijing, 100084 China
| | - Jonas Kaindl
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich–Alexander University Erlangen–Nürnberg, Nikolaus-Fiebiger-Straße 10, Erlangen, 91058 Germany
| | - Mary J. Clark
- Department of Pharmacology, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093 USA
| | - Harald Hübner
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich–Alexander University Erlangen–Nürnberg, Nikolaus-Fiebiger-Straße 10, Erlangen, 91058 Germany
| | - Kunio Hirata
- Advanced Photon Technology Division, Research Infrastructure Group, SR Life Science Instrumentation Unit, RIKEN/SPring-8 Center, 1-1-1 Kouto Sayo-cho Sayo-gun, Hyogo, 679-5148 Japan ,Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012 Japan
| | - Roger K. Sunahara
- Department of Pharmacology, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093 USA
| | - Peter Gmeiner
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich–Alexander University Erlangen–Nürnberg, Nikolaus-Fiebiger-Straße 10, Erlangen, 91058 Germany
| | - Brian K. Kobilka
- Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing, 100084 China ,School of Medicine, Tsinghua University, Beijing, 100084 China ,Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305 USA
| | - Xiangyu Liu
- Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing, 100084 China ,School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084 China
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37
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Jhaveri A, Maisuria D, Varga M, Mohammadyani D, Johnson ME. Thermodynamics and Free Energy Landscape of BAR-Domain Dimerization from Molecular Simulations. J Phys Chem B 2021; 125:3739-3751. [PMID: 33826319 DOI: 10.1021/acs.jpcb.0c10992] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Proteins with BAR domains function to bind to and remodel biological membranes, where the dimerization of BAR domains is a key step in this function. These domains can dimerize in solution or after localizing to the membrane surface. Here, we characterize the binding thermodynamics of homodimerization between the LSP1 BAR domain proteins in solution, using molecular dynamics (MD) simulations. By combining the MARTINI coarse-grained protein models with enhanced sampling through metadynamics, we construct a two-dimensional free energy surface quantifying the bound versus unbound ensembles as a function of two distance variables. With this methodology, our simulations can simultaneously characterize the structures and relative stabilities of a range of sampled dimers, portraying a heterogeneous and extraordinarily stable bound ensemble, where the proper crystal structure dimer is the most stable in a 100 mM NaCl solution. Nonspecific dimers that are sampled involve contacts that are consistent with experimental structures of higher-order oligomers formed by the LSP1 BAR domain. Because the BAR dimers and oligomers can assemble on membranes, we characterize the relative alignment of the known membrane binding patches, finding that only the specific dimer is aligned to form strong interactions with the membrane. Hence, we would predict a strong selection of the specific dimer in binding to or assembling when on the membrane. Establishing the pairwise stabilities of homodimer contacts is difficult experimentally when the proteins form stable oligomers, but through the method used here, we can isolate these contacts, providing a foundation to study the same interactions on the membrane.
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Affiliation(s)
- Adip Jhaveri
- TC Jenkins Department of Biophysics, Johns Hopkins University, 3400 N Charles St, Baltimore, Maryland 21218, United States
| | - Dhruw Maisuria
- TC Jenkins Department of Biophysics, Johns Hopkins University, 3400 N Charles St, Baltimore, Maryland 21218, United States
| | - Matthew Varga
- TC Jenkins Department of Biophysics, Johns Hopkins University, 3400 N Charles St, Baltimore, Maryland 21218, United States
| | - Dariush Mohammadyani
- TC Jenkins Department of Biophysics, Johns Hopkins University, 3400 N Charles St, Baltimore, Maryland 21218, United States
| | - Margaret E Johnson
- TC Jenkins Department of Biophysics, Johns Hopkins University, 3400 N Charles St, Baltimore, Maryland 21218, United States
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38
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Souza PCT, Limongelli V, Wu S, Marrink SJ, Monticelli L. Perspectives on High-Throughput Ligand/Protein Docking With Martini MD Simulations. Front Mol Biosci 2021; 8:657222. [PMID: 33855050 PMCID: PMC8039319 DOI: 10.3389/fmolb.2021.657222] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 03/05/2021] [Indexed: 01/12/2023] Open
Abstract
Molecular docking is central to rational drug design. Current docking techniques suffer, however, from limitations in protein flexibility and solvation models and by the use of simplified scoring functions. All-atom molecular dynamics simulations, on the other hand, feature a realistic representation of protein flexibility and solvent, but require knowledge of the binding site. Recently we showed that coarse-grained molecular dynamics simulations, based on the most recent version of the Martini force field, can be used to predict protein/ligand binding sites and pathways, without requiring any a priori information, and offer a level of accuracy approaching all-atom simulations. Given the excellent computational efficiency of Martini, this opens the way to high-throughput drug screening based on dynamic docking pipelines. In this opinion article, we sketch the roadmap to achieve this goal.
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Affiliation(s)
- Paulo C. T. Souza
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Groningen, Netherlands
- PharmCADD, Busan, South Korea
- Molecular Microbiology and Structural Biochemistry (MMSB, UMR 5086), CNRS, University of Lyon, Lyon, France
| | - Vittorio Limongelli
- Faculty of Biomedical Sciences, Institute of Computational Science, Università della Svizzera Italiana (USI), Lugano, Switzerland
- Department of Pharmacy, University of Naples “Federico II”, Naples, Italy
| | - Sangwook Wu
- PharmCADD, Busan, South Korea
- Department of Physics, Pukyong National University, Busan, South Korea
| | - Siewert J. Marrink
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Groningen, Netherlands
| | - Luca Monticelli
- Molecular Microbiology and Structural Biochemistry (MMSB, UMR 5086), CNRS, University of Lyon, Lyon, France
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39
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Zhang H, Yang Y, Li J, Wang M, Saravanan KM, Wei J, Tze-Yang Ng J, Tofazzal Hossain M, Liu M, Zhang H, Ren X, Pan Y, Peng Y, Shi Y, Wan X, Liu Y, Wei Y. A novel virtual screening procedure identifies Pralatrexate as inhibitor of SARS-CoV-2 RdRp and it reduces viral replication in vitro. PLoS Comput Biol 2020; 16:e1008489. [PMID: 33382685 PMCID: PMC7774833 DOI: 10.1371/journal.pcbi.1008489] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 11/03/2020] [Indexed: 01/18/2023] Open
Abstract
The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus poses serious threats to the global public health and leads to worldwide crisis. No effective drug or vaccine is readily available. The viral RNA-dependent RNA polymerase (RdRp) is a promising therapeutic target. A hybrid drug screening procedure was proposed and applied to identify potential drug candidates targeting RdRp from 1906 approved drugs. Among the four selected market available drug candidates, Pralatrexate and Azithromycin were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC50 values of 0.008μM and 9.453 μM, respectively. For the first time, our study discovered that Pralatrexate is able to potently inhibit SARS-CoV-2 replication with a stronger inhibitory activity than Remdesivir within the same experimental conditions. The paper demonstrates the feasibility of fast and accurate anti-viral drug screening for inhibitors of SARS-CoV-2 and provides potential therapeutic agents against COVID-19.
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Affiliation(s)
- Haiping Zhang
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Yang Yang
- Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for infectious disease, State Key Discipline of Infectious Disease, Shenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
| | - Junxin Li
- Shenzhen Laboratory of Human Antibody Engineering, Institute of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, University City of Shenzhen, Shenzhen, China
| | - Min Wang
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Konda Mani Saravanan
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Jinli Wei
- Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for infectious disease, State Key Discipline of Infectious Disease, Shenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
| | - Justin Tze-Yang Ng
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Md. Tofazzal Hossain
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- University of Chinese Academy of Sciences, Shijingshan District, Beijing, China
| | - Maoxuan Liu
- Shenzhen Laboratory of Human Antibody Engineering, Institute of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, University City of Shenzhen, Shenzhen, China
| | - Huiling Zhang
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Xiaohu Ren
- Institute of Toxicology, Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Yi Pan
- Department of Computer Science, Georgia State University, Atlanta, Georgia, United States of America
| | - Yin Peng
- Department of Pathology, School of Medicine, Shenzhen University, Shenzhen, China
| | - Yi Shi
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Xiaochun Wan
- Shenzhen Laboratory of Human Antibody Engineering, Institute of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, University City of Shenzhen, Shenzhen, China
- * E-mail: (XW); (YL); (YW)
| | - Yingxia Liu
- Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for infectious disease, State Key Discipline of Infectious Disease, Shenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
- * E-mail: (XW); (YL); (YW)
| | - Yanjie Wei
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- * E-mail: (XW); (YL); (YW)
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40
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Juárez-Jiménez J, Tew P, O Connor M, Llabrés S, Sage R, Glowacki D, Michel J. Combining Virtual Reality Visualization with Ensemble Molecular Dynamics to Study Complex Protein Conformational Changes. J Chem Inf Model 2020; 60:6344-6354. [PMID: 33180485 DOI: 10.1021/acs.jcim.0c00221] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Molecular dynamics (MD) simulations are increasingly used to elucidate relationships between protein structure, dynamics, and their biological function. Currently, it is extremely challenging to perform MD simulations of large-scale structural rearrangements in proteins that occur on millisecond timescales or beyond, as this requires very significant computational resources, or the use of cumbersome "collective variable" enhanced sampling protocols. Here, we describe a framework that combines ensemble MD simulations and virtual reality visualization (eMD-VR) to enable users to interactively generate realistic descriptions of large amplitude, millisecond timescale protein conformational changes in proteins. Detailed tests demonstrate that eMD-VR substantially decreases the computational cost of folding simulations of a WW domain, without the need to define collective variables a priori. We further show that eMD-VR generated pathways can be combined with Markov state models to describe the thermodynamics and kinetics of large-scale loop motions in the enzyme cyclophilin A. Our results suggest eMD-VR is a powerful tool for exploring protein energy landscapes in bioengineering efforts.
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Affiliation(s)
- Jordi Juárez-Jiménez
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
| | - Philip Tew
- Interactive Scientific, Engine Shed, Station Approach, Bristol BS1 6QH, United Kingdom
| | - Michael O Connor
- Intangible Realities Laboratory, University of Bristol, Cantock's Close, Bristol BS8 1TS, United Kingdom.,Department of Computer Science, University of Bristol, Merchant Venture's Building, Bristol BS8 1UB, United Kingdom.,Centre for Computational Chemistry, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, United Kingdom
| | - Salomé Llabrés
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
| | - Rebecca Sage
- Interactive Scientific, Engine Shed, Station Approach, Bristol BS1 6QH, United Kingdom
| | - David Glowacki
- Intangible Realities Laboratory, University of Bristol, Cantock's Close, Bristol BS8 1TS, United Kingdom.,Department of Computer Science, University of Bristol, Merchant Venture's Building, Bristol BS8 1UB, United Kingdom.,Centre for Computational Chemistry, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, United Kingdom
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
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41
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Peters BL, Deng J, Ferguson AL. Free energy calculations of the functional selectivity of 5-HT2B G protein-coupled receptor. PLoS One 2020; 15:e0243313. [PMID: 33296400 PMCID: PMC7725398 DOI: 10.1371/journal.pone.0243313] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 11/18/2020] [Indexed: 12/16/2022] Open
Abstract
G Protein-Coupled Receptors (GPCRs) mediate intracellular signaling in response to extracellular ligand binding and are the target of one-third of approved drugs. Ligand binding modulates the GPCR molecular free energy landscape by preferentially stabilizing active or inactive conformations that dictate intracellular protein recruitment and downstream signaling. We perform enhanced sampling molecular dynamics simulations to recover the free energy surfaces of a thermostable mutant of the GPCR serotonin receptor 5-HT2B in the unliganded form and bound to a lysergic acid diethylamide (LSD) agonist and lisuride antagonist. LSD binding imparts a ∼110 kJ/mol driving force for conformational rearrangement into an active state. The lisuride-bound form is structurally similar to the apo form and only ∼24 kJ/mol more stable. This work quantifies ligand-induced conformational specificity and functional selectivity of 5-HT2B and presents a platform for high-throughput virtual screening of ligands and rational engineering of the ligand-bound molecular free energy landscape.
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Affiliation(s)
- Brandon L. Peters
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois, United States of America
| | - Jinxia Deng
- Zoetis Inc, Kalamazoo, Michigan, United States of America
| | - Andrew L. Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois, United States of America
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42
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Decherchi S, Cavalli A. Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation. Chem Rev 2020; 120:12788-12833. [PMID: 33006893 PMCID: PMC8011912 DOI: 10.1021/acs.chemrev.0c00534] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Indexed: 12/19/2022]
Abstract
Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.
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Affiliation(s)
- Sergio Decherchi
- Computational
and Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, 16163 Genoa, Italy
| | - Andrea Cavalli
- Computational
and Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, 16163 Genoa, Italy
- Department
of Pharmacy and Biotechnology, University
of Bologna, 40126 Bologna, Italy
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43
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Wan S, Potterton A, Husseini FS, Wright DW, Heifetz A, Malawski M, Townsend-Nicholson A, Coveney PV. Hit-to-lead and lead optimization binding free energy calculations for G protein-coupled receptors. Interface Focus 2020; 10:20190128. [PMID: 33178414 PMCID: PMC7653344 DOI: 10.1098/rsfs.2019.0128] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/06/2020] [Indexed: 12/13/2022] Open
Abstract
We apply the hit-to-lead ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and lead-optimization TIES (thermodynamic integration with enhanced sampling) methods to compute the binding free energies of a series of ligands at the A1 and A2A adenosine receptors, members of a subclass of the GPCR (G protein-coupled receptor) superfamily. Our predicted binding free energies, calculated using ESMACS, show a good correlation with previously reported experimental values of the ligands studied. Relative binding free energies, calculated using TIES, accurately predict experimentally determined values within a mean absolute error of approximately 1 kcal mol-1. Our methodology may be applied widely within the GPCR superfamily and to other small molecule-receptor protein systems.
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Affiliation(s)
- Shunzhou Wan
- Centre for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, UK
| | - Andrew Potterton
- Institute of Structural and Molecular Biology, Research Department of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, UK
| | - Fouad S. Husseini
- Centre for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, UK
| | - David W. Wright
- Centre for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, UK
| | - Alexander Heifetz
- Institute of Structural and Molecular Biology, Research Department of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, UK
- Evotec (UK) Ltd, 114 Innovation Drive, Milton Park, Abingdon OX14 4RZ, UK
| | - Maciej Malawski
- ACK Cyfronet, AGH University of Science and Technology, Nawojki 11, 30-950, Kraków, Poland
| | - Andrea Townsend-Nicholson
- Institute of Structural and Molecular Biology, Research Department of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, UK
| | - Peter V. Coveney
- Centre for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, UK
- Computational Science Laboratory, Institute for Informatics, Faculty of Science, University of Amsterdam, 1098XH Amsterdam, The Netherlands
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44
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Mondal D, Kolev V, Warshel A. Exploring the activation pathway and G i-coupling specificity of the μ-opioid receptor. Proc Natl Acad Sci U S A 2020; 117:26218-26225. [PMID: 33020275 PMCID: PMC7585030 DOI: 10.1073/pnas.2013364117] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Understanding the activation mechanism of the μ-opioid receptor (μ-OR) and its selective coupling to the inhibitory G protein (Gi) is vital for pharmaceutical research aimed at finding treatments for the opioid overdose crisis. Many attempts have been made to understand the mechanism of the μ-OR activation, following the elucidation of new crystal structures such as the antagonist- and agonist-bound μ-OR. However, the focus has not been placed on the underlying energetics and specificity of the activation process. An energy-based picture would not only help to explain this coupling but also help to explore why other possible options are not common. For example, one would like to understand why μ-OR is more selective to Gi than a stimulatory G protein (Gs). Our study used homology modeling and a coarse-grained model to generate all of the possible "end states" of the thermodynamic cycle of the activation of μ-OR. The end points were further used to generate reasonable intermediate structures of the receptor and the Gi to calculate two-dimensional free energy landscapes. The results of the landscape calculations helped to propose a plausible sequence of conformational changes in the μ-OR and Gi system and for exploring the path that leads to its activation. Furthermore, in silico alanine scanning calculations of the last 21 residues of the C terminals of Gi and Gs were performed to shed light on the selective binding of Gi to μ-OR. Overall, the present work appears to demonstrate the potential of multiscale modeling in exploring the action of G protein-coupled receptors.
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Affiliation(s)
- Dibyendu Mondal
- Department of Chemistry, University of Southern California, Los Angeles, CA 90089
| | - Vesselin Kolev
- Department of Chemistry, University of Southern California, Los Angeles, CA 90089
| | - Arieh Warshel
- Department of Chemistry, University of Southern California, Los Angeles, CA 90089
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45
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Cutrona KJ, Newton AS, Krimmer SG, Tirado-Rives J, Jorgensen WL. Metadynamics as a Postprocessing Method for Virtual Screening with Application to the Pseudokinase Domain of JAK2. J Chem Inf Model 2020; 60:4403-4415. [PMID: 32383599 DOI: 10.1021/acs.jcim.0c00276] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
With standard scoring methods, top-ranked compounds from virtual screening by docking often turn out to be inactive. For this reason, metadynamics, a method used to sample rare events, was studied to further evaluate docking poses with the aim of reducing false positives. Specifically, virtual screening was performed with Glide SP to seek potential molecules to bind to the ATP site in the pseudokinase domain of JAK2 kinase, and promising compounds were selected from the top-ranked 1000 based on visualization. Rescoring with Glide XP, GOLD, and MM/GBSA was unable to differentiate well between active and inactive compounds. Metadynamics was then used to gauge the relative binding affinity from the required time or the potential of mean force needed to dissociate the ligand from the bound complex. With consideration of previously known binders of varying affinities, metadynamics was able to differentiate between the most active compounds and inactive or weakly active ones, and it could identify correctly most of the selected virtual screening compounds as false positives. Thus, metadynamics has the potential to be a viable postprocessing method for virtual screening, minimizing the expense of buying or synthesizing inactive compounds.
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Affiliation(s)
- Kara J Cutrona
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Ana S Newton
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Stefan G Krimmer
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Julian Tirado-Rives
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - William L Jorgensen
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
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46
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Conrad M, Söldner CA, Miao Y, Sticht H. Agonist Binding and G Protein Coupling in Histamine H 2 Receptor: A Molecular Dynamics Study. Int J Mol Sci 2020; 21:ijms21186693. [PMID: 32932742 PMCID: PMC7554837 DOI: 10.3390/ijms21186693] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 09/04/2020] [Accepted: 09/08/2020] [Indexed: 02/06/2023] Open
Abstract
The histamine H2 receptor (H2R) plays an important role in the regulation of gastric acid secretion. Therefore, it is a main drug target for the treatment of gastroesophageal reflux or peptic ulcer disease. However, there is as of yet no 3D-structural information available hampering a mechanistic understanding of H2R. Therefore, we created a model of the histamine-H2R-Gs complex based on the structure of the ternary complex of the β2-adrenoceptor and investigated the conformational stability of this active GPCR conformation. Since the physiologically relevant motions with respect to ligand binding and conformational changes of GPCRs can only partly be assessed on the timescale of conventional MD (cMD) simulations, we also applied metadynamics and Gaussian accelerated molecular dynamics (GaMD) simulations. A multiple walker metadynamics simulation in combination with cMD was applied for the determination of the histamine binding mode. The preferential binding pose detected is in good agreement with previous data from site directed mutagenesis and provides a basis for rational ligand design. Inspection of the H2R-Gs interface reveals a network of polar interactions that may contribute to H2R coupling selectivity. The cMD and GaMD simulations demonstrate that the active conformation is retained on a μs-timescale in the ternary histamine-H2R-Gs complex and in a truncated complex that contains only Gs helix α5 instead of the entire G protein. In contrast, histamine alone is unable to stabilize the active conformation, which is in line with previous studies of other GPCRs.
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Affiliation(s)
- Marcus Conrad
- Bioinformatik, Institut für Biochemie, Emil-Fischer-Centrum, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Fahrstraße 17, 91054 Erlangen, Germany; (M.C.); (C.A.S.)
| | - Christian A. Söldner
- Bioinformatik, Institut für Biochemie, Emil-Fischer-Centrum, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Fahrstraße 17, 91054 Erlangen, Germany; (M.C.); (C.A.S.)
| | - Yinglong Miao
- Department of Computational Biology and Molecular Biosciences, University of Kansas, Lawrence, KS 66047, USA;
| | - Heinrich Sticht
- Bioinformatik, Institut für Biochemie, Emil-Fischer-Centrum, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Fahrstraße 17, 91054 Erlangen, Germany; (M.C.); (C.A.S.)
- Correspondence:
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Ligand binding free-energy calculations with funnel metadynamics. Nat Protoc 2020; 15:2837-2866. [PMID: 32814837 DOI: 10.1038/s41596-020-0342-4] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2018] [Accepted: 04/17/2020] [Indexed: 11/09/2022]
Abstract
The accurate resolution of the binding mechanism of a ligand to its molecular target is fundamental to develop a successful drug design campaign. Free-energy calculations, which provide the energy value of the ligand-protein binding complex, are essential for resolving the binding mode of the ligand. The accuracy of free-energy calculation methods is counteracted by their poor user-friendliness, which hampers their broad application. Here we present the Funnel-Metadynamics Advanced Protocol (FMAP), which is a flexible and user-friendly graphical user interface (GUI)-based protocol to perform funnel metadynamics, a binding free-energy method that employs a funnel-shape restraint potential to reveal the ligand binding mode and accurately calculate the absolute ligand-protein binding free energy. FMAP guides the user through all phases of the free-energy calculation process, from preparation of the input files, to production simulation, to analysis of the results. FMAP delivers the ligand binding mode and the absolute protein-ligand binding free energy as outputs. Alternative binding modes and the role of waters are also elucidated, providing a detailed description of the ligand binding mechanism. The entire protocol on the paradigmatic system benzamidine-trypsin, composed of ~105 k atoms, took ~2.8 d using the Cray XC50 piz Daint cluster at the Swiss National Supercomputing Centre.
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Torrens-Fontanals M, Stepniewski TM, Aranda-García D, Morales-Pastor A, Medel-Lacruz B, Selent J. How Do Molecular Dynamics Data Complement Static Structural Data of GPCRs. Int J Mol Sci 2020; 21:E5933. [PMID: 32824756 PMCID: PMC7460635 DOI: 10.3390/ijms21165933] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 08/11/2020] [Accepted: 08/15/2020] [Indexed: 01/08/2023] Open
Abstract
G protein-coupled receptors (GPCRs) are implicated in nearly every physiological process in the human body and therefore represent an important drug targeting class. Advances in X-ray crystallography and cryo-electron microscopy (cryo-EM) have provided multiple static structures of GPCRs in complex with various signaling partners. However, GPCR functionality is largely determined by their flexibility and ability to transition between distinct structural conformations. Due to this dynamic nature, a static snapshot does not fully explain the complexity of GPCR signal transduction. Molecular dynamics (MD) simulations offer the opportunity to simulate the structural motions of biological processes at atomic resolution. Thus, this technique can incorporate the missing information on protein flexibility into experimentally solved structures. Here, we review the contribution of MD simulations to complement static structural data and to improve our understanding of GPCR physiology and pharmacology, as well as the challenges that still need to be overcome to reach the full potential of this technique.
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Affiliation(s)
- Mariona Torrens-Fontanals
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
| | - Tomasz Maciej Stepniewski
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
- InterAx Biotech AG, PARK innovAARE, 5234 Villigen, Switzerland
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, 02-093 Warsaw, Poland
| | - David Aranda-García
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
| | - Adrián Morales-Pastor
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
| | - Brian Medel-Lacruz
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
| | - Jana Selent
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
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Capelli R, Lyu W, Bolnykh V, Meloni S, Olsen JMH, Rothlisberger U, Parrinello M, Carloni P. Accuracy of Molecular Simulation-Based Predictions of koff Values: A Metadynamics Study. J Phys Chem Lett 2020; 11:6373-6381. [PMID: 32672983 DOI: 10.1021/acs.jpclett.0c00999] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
The koff values of ligands unbinding to proteins are key parameters for drug discovery. Their predictions based on molecular simulation may under- or overestimate experiment in a system- and/or technique-dependent way. Here we use an established method-infrequent metadynamics, based on the AMBER force field-to compute the koff of the ligand iperoxo (in clinical use) targeting the muscarinic receptor M2. The ligand charges are calculated by either (i) the Amber standard procedure or (ii) B3LYP-DFT. The calculations using (i) turn out not to provide a reasonable estimation of the transition-state free energy. Those using (ii) differ from experiment by 2 orders of magnitude. On the basis of B3LYP DFT QM/MM simulations, we suggest that the observed discrepancy in (ii) arises, at least in part, from the lack of electronic polarization and/or charge transfer in biomolecular force fields. These issues might be present in other systems, such as DNA-protein complexes.
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Affiliation(s)
- Riccardo Capelli
- Computational Biomedicine Section, IAS-5/INM-9, Forschungzentrum Jülich, Wilhelm-Johnen-straße, D-52425 Jülich, Germany
- JARA-HPC, Forschungszentrum Jülich, D-54245 Jülich, Germany
| | - Wenping Lyu
- Computational Biomedicine Section, IAS-5/INM-9, Forschungzentrum Jülich, Wilhelm-Johnen-straße, D-52425 Jülich, Germany
| | - Viacheslav Bolnykh
- Computational Biomedicine Section, IAS-5/INM-9, Forschungzentrum Jülich, Wilhelm-Johnen-straße, D-52425 Jülich, Germany
- Laboratory of Computational Chemistry and Biochemistry, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Simone Meloni
- Dipartimento di Scienze Chimiche e Farmaceutiche, Università degli Studi di Ferrara, Via Luigi Borsari 46, I-44121, Ferrara, Italy
| | - Jógvan Magnus Haugaard Olsen
- Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, UiT The Arctic University of Norway, N-9037 Tromsø, Norway
- Department of Chemistry, Aarhus University, DK-8000 Aarhus C, Denmark
| | - Ursula Rothlisberger
- Laboratory of Computational Chemistry and Biochemistry, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Michele Parrinello
- Department of Chemistry and Applied Biosciences, ETH Zürich, c/o USI Campus, Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland
- Facoltà di Informatica, Istituto di Scienze Computazionali, Università della Svizzera Italiana (USI), Via Giuseppe Buffi 13, CH-6900, Lugano, Ticino, Switzerland
- Istituto Italiano di Tecnologia, Via Morego 30, I-16163 Genova, Italy
| | - Paolo Carloni
- Computational Biomedicine Section, IAS-5/INM-9, Forschungzentrum Jülich, Wilhelm-Johnen-straße, D-52425 Jülich, Germany
- JARA-Institute INM-11: Molecular Neuroscience and Neuroimaging, Forschungzentrum Jülich, Wilhelm-Johnen-straße, D-52425 Jülich, Germany
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50
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Abstract
The way chemists represent chemical structures as two-dimensional sketches made up of atoms and bonds, simplifying the complex three-dimensional molecules comprising nuclei and electrons of the quantum mechanical description, is the everyday language of chemistry. This language uses models, particularly of bonding, that are not contained in the quantum mechanical description of chemical systems, but has been used to derive machine-readable formats for storing and manipulating chemical structures in digital computers. This language is fuzzy and varies from chemist to chemist but has been astonishingly successful and perhaps contributes with its fuzziness to the success of chemistry. It is this creative imagination of chemical structures that has been fundamental to the cognition of chemistry and has allowed thought experiments to take place. Within the everyday language, the model nature of these concepts is not always clear to practicing chemists, so that controversial discussions about the merits of alternative models often arise. However, the extensive use of artificial intelligence (AI) and machine learning (ML) in chemistry, with the aim of being able to make reliable predictions, will require that these models be extended to cover all relevant properties and characteristics of chemical systems. This, in turn, imposes conditions such as completeness, compactness, computational efficiency and non-redundancy on the extensions to the almost universal Lewis and VSEPR bonding models. Thus, AI and ML are likely to be important in rationalizing, extending and standardizing chemical bonding models. This will not affect the everyday language of chemistry but may help to understand the unique basis of chemical language.
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Affiliation(s)
- Timothy Clark
- Computer-Chemistry-Center, Department of Chemistry and Pharmacy, Friedrich-Alexander-University Erlangen-Nürnberg, Nägelsbachstr. 25, 91052 Erlangen, Germany
| | - Martin G Hicks
- Beilstein-Institut, Trakehner Str. 7–9, 60487 Frankfurt am Main, Germany
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