1
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Ivashchenko SD, Shulga DA, Ivashchenko VD, Zinovev EV, Vlasov AV. In silico studies of the open form of human tissue transglutaminase. Sci Rep 2024; 14:15981. [PMID: 38987418 PMCID: PMC11236986 DOI: 10.1038/s41598-024-66348-8] [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: 02/07/2024] [Accepted: 07/01/2024] [Indexed: 07/12/2024] Open
Abstract
Human tissue transglutaminase (tTG) is an intriguing multifunctional enzyme involved in various diseases, including celiac disease and neurological disorders. Although a number of tTG inhibitors have been developed, the molecular determinants governing ligand binding remain incomplete due to the lack of high-resolution structural data in the vicinity of its active site. In this study, we obtained the complete high-resolution model of tTG by in silico methods based on available PDB structures. We discovered significant differences in the active site architecture between our and known tTG models, revealing an additional loop which affects the ligand binding affinity. We assembled a library of new potential tTG inhibitors based on the obtained complete model of the enzyme. Our library substantially expands the spectrum of possible drug candidates targeting tTG and encompasses twelve molecular scaffolds, eleven of which are novel and exhibit higher binding affinity then already known ones, according to our in silico studies. The results of this study open new directions for structure-based drug design of tTG inhibitors, offering the complete protein model and suggesting a wide range of new compounds for further experimental validation.
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Affiliation(s)
- S D Ivashchenko
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia, 141701
- Laboratory of Microbiology, BIOTECH University, Moscow, Russia, 125080
| | - D A Shulga
- Department of Chemistry, Moscow State University, Moscow, Russia, 119991
| | - V D Ivashchenko
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia, 141701
| | - E V Zinovev
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia, 141701
| | - A V Vlasov
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia, 141701.
- Laboratory of Microbiology, BIOTECH University, Moscow, Russia, 125080.
- Joint Institute for Nuclear Research, Dubna, Russia, 141980.
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2
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McCone JAJ, Teesdale-Spittle PH, Flanagan JU, Harvey JE. A Structure-Activity Investigation of the Fungal Metabolite (-)-TAN-2483B: Inhibition of Bruton's Tyrosine Kinase. Chemistry 2024; 30:e202401051. [PMID: 38629656 DOI: 10.1002/chem.202401051] [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: 03/14/2024] [Indexed: 06/01/2024]
Abstract
The natural product (-)-TAN-2483B is a fungal secondary metabolite which displays promising anti-cancer and immunomodulatory activity. Our previous syntheses of (-)-TAN-2483B and sidechain analogues uncovered inhibitory activity against Bruton's tyrosine kinase (Btk), an established drug target for various leukaemia and immunological diseases. A structure-based computational study using ensemble docking and molecular dynamics was performed to determine plausible binding modes for (-)-TAN-2483B and analogues in the Btk binding site. These hypotheses guided the design of new analogues which were synthesised and their inhibitory activities determined, providing insights into the structural determinants of the furopyranone scaffold that confer both activity and selectivity for Btk. These findings offer new perspectives for generating optimised (-)-TAN-2483B-based kinase inhibitors for the treatment of leukaemia and immunological diseases.
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Affiliation(s)
- Jordan A J McCone
- School of Chemical and Physical Sciences, Centre for Biodiscovery, Victoria University of Wellington, Wellington, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
| | - Paul H Teesdale-Spittle
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
- School of Biological Sciences, Centre for Biodiscovery, Victoria University of Wellington, Wellington, New Zealand
| | - Jack U Flanagan
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
- Auckland Cancer Society Research Centre, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
- Department of Pharmacology and Clinical Pharmacology, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
| | - Joanne E Harvey
- School of Chemical and Physical Sciences, Centre for Biodiscovery, Victoria University of Wellington, Wellington, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
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3
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Smith L, Novak B, Osato M, Mobley DL, Bowman GR. PopShift: A Thermodynamically Sound Approach to Estimate Binding Free Energies by Accounting for Ligand-Induced Population Shifts from a Ligand-Free Markov State Model. J Chem Theory Comput 2024; 20:1036-1050. [PMID: 38291966 PMCID: PMC10867841 DOI: 10.1021/acs.jctc.3c00870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 02/01/2024]
Abstract
Obtaining accurate binding free energies from in silico screens has been a long-standing goal for the computational chemistry community. However, accuracy and computational cost are at odds with one another, limiting the utility of methods that perform this type of calculation. Many methods achieve massive scale by explicitly or implicitly assuming that the target protein adopts a single structure, or undergoes limited fluctuations around that structure, to minimize computational cost. Others simulate each protein-ligand complex of interest, accepting lower throughput in exchange for better predictions of binding affinities. Here, we present the PopShift framework for accounting for the ensemble of structures a protein adopts and their relative probabilities. Protein degrees of freedom are enumerated once, and then arbitrarily many molecules can be screened against this ensemble. Specifically, we use Markov state models (MSMs) as a compressed representation of a protein's thermodynamic ensemble. We start with a ligand-free MSM and then calculate how addition of a ligand shifts the populations of each protein conformational state based on the strength of the interaction between that protein conformation and the ligand. In this work we use docking to estimate the affinity between a given protein structure and ligand, but any estimator of binding affinities could be used in the PopShift framework. We test PopShift on the classic benchmark pocket T4 Lysozyme L99A. We find that PopShift is more accurate than common strategies, such as docking to a single structure and traditional ensemble docking─producing results that compare favorably with alchemical binding free energy calculations in terms of RMSE but not correlation─and may have a more favorable computational cost profile in some applications. In addition to predicting binding free energies and ligand poses, PopShift also provides insight into how the probability of different protein structures is shifted upon addition of various concentrations of ligand, providing a platform for predicting affinities and allosteric effects of ligand binding. Therefore, we expect PopShift will be valuable for hit finding and for providing insight into phenomena like allostery.
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Affiliation(s)
- Louis
G. Smith
- Departments
of Biochemistry & Biophysics and Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Borna Novak
- Department
of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Medical
Scientist Training Program, Washington University
in St. Louis, St. Louis, Missouri 63130, United
States
| | - Meghan Osato
- School
of Pharmacy and Pharmaceutical Sciences, University of California, Irvine, Irvine, California 92697, United States
| | - David L. Mobley
- School
of Pharmacy and Pharmaceutical Sciences, University of California, Irvine, Irvine, California 92697, United States
| | - Gregory R. Bowman
- Departments
of Biochemistry & Biophysics and Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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4
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Kotev M, Diaz Gonzalez C. Molecular Dynamics and Other HPC Simulations for Drug Discovery. Methods Mol Biol 2024; 2716:265-291. [PMID: 37702944 DOI: 10.1007/978-1-0716-3449-3_12] [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] [Indexed: 09/14/2023]
Abstract
High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs. Herein, we report how computational performance has evolved over the past years, and then we detail three domains where HPC is essential. Molecular dynamics (MD) is commonly used to explore the flexibility of proteins, thus generating a better understanding of different possible approaches to modulate their activity. Modeling and simulation of biopolymer complexes enables the study of protein-protein interactions (PPI) in healthy and disease states, thus helping the identification of targets of pharmacological interest. Virtual screening (VS) also benefits from HPC to predict in a short time, among millions or billions of virtual chemical compounds, the best potential ligands that will be tested in relevant assays to start a rational drug design process.
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Affiliation(s)
- Martin Kotev
- Evotec SE, Integrated Drug Discovery, Molecular Architects, Campus Curie, Toulouse, France
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5
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Harnish MT, Lopez D, Morrison CT, Narayanan R, Fernandez EJ, Shen T. Novel Covalent Modifier-Induced Local Conformational Changes within the Intrinsically Disordered Region of the Androgen Receptor. BIOLOGY 2023; 12:1442. [PMID: 37998041 PMCID: PMC10669190 DOI: 10.3390/biology12111442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 10/18/2023] [Accepted: 11/05/2023] [Indexed: 11/25/2023]
Abstract
Intrinsically disordered regions (IDRs) of transcription factors play an important biological role in liquid condensate formation and gene regulation. It is thus desirable to investigate the druggability of IDRs and how small-molecule binders can alter their conformational stability. For the androgen receptor (AR), certain covalent ligands induce important changes, such as the neutralization of the condensate. To understand the specificity of ligand-IDR interaction and potential implications for the mechanism of neutralizing liquid-liquid phase separation (LLPS), we modeled and performed computer simulations of ligand-bound peptide segments obtained from the human AR. We analyzed how different covalent ligands affect local secondary structure, protein contact map, and protein-ligand contacts for these protein systems. We find that effective neutralizers make specific interactions (such as those between cyanopyrazole and tryptophan) that alter the helical propensity of the peptide segments. These findings on the mechanism of action can be useful for designing molecules that influence IDR structure and condensate of the AR in the future.
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Affiliation(s)
- Michael T. Harnish
- Department of Biochemistry & Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA; (M.T.H.); (D.L.); (C.T.M.); (E.J.F.)
| | - Daniel Lopez
- Department of Biochemistry & Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA; (M.T.H.); (D.L.); (C.T.M.); (E.J.F.)
| | - Corbin T. Morrison
- Department of Biochemistry & Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA; (M.T.H.); (D.L.); (C.T.M.); (E.J.F.)
| | - Ramesh Narayanan
- Department of Medicine, College of Medicine, University of Tennessee Health Science Center, Memphis, TN 38103, USA;
| | - Elias J. Fernandez
- Department of Biochemistry & Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA; (M.T.H.); (D.L.); (C.T.M.); (E.J.F.)
| | - Tongye Shen
- Department of Biochemistry & Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA; (M.T.H.); (D.L.); (C.T.M.); (E.J.F.)
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6
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Chattopadhyay S, Do NP, Flower DR, Chattopadhyay AK. Extracting prime protein targets as possible drug candidates: machine learning evaluation. Med Biol Eng Comput 2023; 61:3035-3048. [PMID: 37608081 PMCID: PMC10582137 DOI: 10.1007/s11517-023-02893-0] [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: 01/23/2023] [Accepted: 07/19/2023] [Indexed: 08/24/2023]
Abstract
Extracting "high ranking" or "prime protein targets" (PPTs) as potent MRSA drug candidates from a given set of ligands is a key challenge in efficient molecular docking. This study combines protein-versus-ligand matching molecular docking (MD) data extracted from 10 independent molecular docking (MD) evaluations - ADFR, DOCK, Gemdock, Ledock, Plants, Psovina, Quickvina2, smina, vina, and vinaxb to identify top MRSA drug candidates. Twenty-nine active protein targets (APT) from the enhanced DUD-E repository ( http://DUD-E.decoys.org ) are matched against 1040 ligands using "forward modeling" machine learning for initial "data mining and modeling" (DDM) to extract PPTs and the corresponding high affinity ligands (HALs). K-means clustering (KMC) is then performed on 400 ligands matched against 29 PTs, with each cluster accommodating HALs, and the corresponding PPTs. Performance of KMC is then validated against randomly chosen head, tail, and middle active ligands (ALs). KMC outcomes have been validated against two other clustering methods, namely, Gaussian mixture model (GMM) and density based spatial clustering of applications with noise (DBSCAN). While GMM shows similar results as with KMC, DBSCAN has failed to yield more than one cluster and handle the noise (outliers), thus affirming the choice of KMC or GMM. Databases obtained from ADFR to mine PPTs are then ranked according to the number of the corresponding HAL-PPT combinations (HPC) inside the derived clusters, an approach called "reverse modeling" (RM). From the set of 29 PTs studied, RM predicts high fidelity of 5 PPTs (17%) that bind with 76 out of 400, i.e., 19% ligands leading to a prediction of next-generation MRSA drug candidates: PPT2 (average HPC is 41.1%) is the top choice, followed by PPT14 (average HPC 25.46%), and then PPT15 (average HPC 23.12%). This algorithm can be generically implemented irrespective of pathogenic forms and is particularly effective for sparse data.
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Affiliation(s)
- Subhagata Chattopadhyay
- Dept. of Computer Science and Engineering, GITAM School of Technology, Gandhi Institute of Technology And Management (GITAM) deemed to be University, Bengaluru, Karnataka, 561203, India
| | - Nhat Phuong Do
- Department of Applied Mathematics and Data Science, College of Engineering and Physical Sciences, Aston University, Birmingham, B4 7ET, UK
| | - Darren R Flower
- School of Life and Health Sciences, Aston University, Birmingham, B4 7ET, UK
| | - Amit K Chattopadhyay
- Department of Applied Mathematics and Data Science, College of Engineering and Physical Sciences, Aston University, Birmingham, B4 7ET, UK.
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7
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Smith LG, Novak B, Osato M, Mobley DL, Bowman GR. PopShift: A thermodynamically sound approach to estimate binding free energies by accounting for ligand-induced population shifts from a ligand-free MSM. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.14.549110. [PMID: 37503302 PMCID: PMC10370083 DOI: 10.1101/2023.07.14.549110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Obtaining accurate binding free energies from in silico screens has been a longstanding goal for the computational chemistry community. However, accuracy and computational cost are at odds with one another, limiting the utility of methods that perform this type of calculation. Many methods achieve massive scale by explicitly or implicitly assuming that the target protein adopts a single structure, or undergoes limited fluctuations around that structure, to minimize computational cost. Others simulate each protein-ligand complex of interest, accepting lower throughput in exchange for better predictions of binding affinities. Here, we present the PopShift framework for accounting for the ensemble of structures a protein adopts and their relative probabilities. Protein degrees of freedom are enumerated once, and then arbitrarily many molecules can be screened against this ensemble. Specifically, we use Markov state models (MSMs) as a compressed representation of a protein's thermodynamic ensemble. We start with a ligand-free MSM and then calculate how addition of a ligand shifts the populations of each protein conformational state based on the strength of the interaction between that protein conformation and the ligand. In this work we use docking to estimate the affinity between a given protein structure and ligand, but any estimator of binding affinities could be used in the PopShift framework. We test PopShift on the classic benchmark pocket T4 Lysozyme L99A. We find that PopShift is more accurate than common strategies, such as docking to a single structure and traditional ensemble docking-producing results that compare favorably with alchemical binding free energy calculations in terms of RMSE but not correlation - and may have a more favorable computational cost profile in some applications. In addition to predicting binding free energies and ligand poses, PopShift also provides insight into how the probability of different protein structures is shifted upon addition of various concentrations of ligand, providing a platform for predicting affinities and allosteric effects of ligand binding. Therefore, we expect PopShift will be valuable for hit finding and for providing insight into phenomena like allostery.
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Affiliation(s)
- Louis G Smith
- University of Pennsylvania, Depts. of Biochemistry & Biophysics and Bioengineering
| | - Borna Novak
- Washington University in St. Louis, Department of Biochemistry and Molecular Biophysics
- Medical Scientist Training Program, Washington University in St. Louis
| | - Meghan Osato
- University of California Irvine, School of Pharmacy and Pharmaceutical Sciences
| | - David L Mobley
- University of California Irvine, School of Pharmacy and Pharmaceutical Sciences
| | - Gregory R Bowman
- University of Pennsylvania, Depts. of Biochemistry & Biophysics and Bioengineering
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8
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Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics. Int J Mol Sci 2023; 24:ijms24054401. [PMID: 36901832 PMCID: PMC10003049 DOI: 10.3390/ijms24054401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/19/2023] [Accepted: 02/21/2023] [Indexed: 02/25/2023] Open
Abstract
Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.
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9
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Hough MA, Prischi F, Worrall JAR. Perspective: Structure determination of protein-ligand complexes at room temperature using X-ray diffraction approaches. Front Mol Biosci 2023; 10:1113762. [PMID: 36756363 PMCID: PMC9899996 DOI: 10.3389/fmolb.2023.1113762] [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: 12/01/2022] [Accepted: 01/12/2023] [Indexed: 01/24/2023] Open
Abstract
The interaction between macromolecular proteins and small molecule ligands is an essential component of cellular function. Such ligands may include enzyme substrates, molecules involved in cellular signalling or pharmaceutical drugs. Together with biophysical techniques used to assess the thermodynamic and kinetic properties of ligand binding to proteins, methodology to determine high-resolution structures that enable atomic level interactions between protein and ligand(s) to be directly visualised is required. Whilst such structural approaches are well established with high throughput X-ray crystallography routinely used in the pharmaceutical sector, they provide only a static view of the complex. Recent advances in X-ray structural biology methods offer several new possibilities that can examine protein-ligand complexes at ambient temperature rather than under cryogenic conditions, enable transient binding sites and interactions to be characterised using time-resolved approaches and combine spectroscopic measurements from the same crystal that the structures themselves are determined. This Perspective reviews several recent developments in these areas and discusses new possibilities for applications of these advanced methodologies to transform our understanding of protein-ligand interactions.
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Affiliation(s)
- Michael A. Hough
- School of Life Sciences, University of Essex, Colchester, United Kingdom,Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot, United Kingdom,*Correspondence: Michael A. Hough, ; Jonathan A. R. Worrall,
| | - Filippo Prischi
- School of Life Sciences, University of Essex, Colchester, United Kingdom
| | - Jonathan A. R. Worrall
- School of Life Sciences, University of Essex, Colchester, United Kingdom,*Correspondence: Michael A. Hough, ; Jonathan A. R. Worrall,
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10
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Nhat Phuong D, Flower DR, Chattopadhyay S, Chattopadhyay AK. Towards Effective Consensus Scoring in Structure-Based Virtual Screening. Interdiscip Sci 2023; 15:131-145. [PMID: 36550341 PMCID: PMC9941253 DOI: 10.1007/s12539-022-00546-8] [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: 05/21/2022] [Revised: 12/11/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022]
Abstract
Virtual screening (VS) is a computational strategy that uses in silico automated protein docking inter alia to rank potential ligands, or by extension rank protein-ligand pairs, identifying potential drug candidates. Most docking methods use preferred sets of physicochemical descriptors (PCDs) to model the interactions between host and guest molecules. Thus, conventional VS is often data-specific, method-dependent and with demonstrably differing utility in identifying candidate drugs. This study proposes four universality classes of novel consensus scoring (CS) algorithms that combine docking scores, derived from ten docking programs (ADFR, DOCK, Gemdock, Ledock, PLANTS, PSOVina, QuickVina2, Smina, Autodock Vina and VinaXB), using decoys from the DUD-E repository ( http://dude.docking.org/ ) against 29 MRSA-oriented targets to create a general VS formulation that can identify active ligands for any suitable protein target. Our results demonstrate that CS provides improved ligand-protein docking fidelity when compared to individual docking platforms. This approach requires only a small number of docking combinations and can serve as a viable and parsimonious alternative to more computationally expensive docking approaches. Predictions from our CS algorithm are compared against independent machine learning evaluations using the same docking data, complementing the CS outcomes. Our method is a reliable approach for identifying protein targets and high-affinity ligands that can be tested as high-probability candidates for drug repositioning.
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Affiliation(s)
- Do Nhat Phuong
- grid.7273.10000 0004 0376 4727Department of Mathematics, College of Engineering and Physical Sciences, Aston University, Birmingham, B4 7ET UK
| | - Darren R. Flower
- grid.7273.10000 0004 0376 4727Life and Health Sciences, Aston University, Birmingham, B4 7ET UK
| | | | - Amit K. Chattopadhyay
- grid.7273.10000 0004 0376 4727Department of Mathematics, College of Engineering and Physical Sciences, Aston University, Birmingham, B4 7ET UK
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11
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Blanes-Mira C, Fernández-Aguado P, de Andrés-López J, Fernández-Carvajal A, Ferrer-Montiel A, Fernández-Ballester G. Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening. Molecules 2022; 28:molecules28010175. [PMID: 36615367 PMCID: PMC9821981 DOI: 10.3390/molecules28010175] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/19/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.
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12
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Kognole AA, Hazel A, MacKerell AD. SILCS-RNA: Toward a Structure-Based Drug Design Approach for Targeting RNAs with Small Molecules. J Chem Theory Comput 2022; 18:5672-5691. [PMID: 35913731 PMCID: PMC9474704 DOI: 10.1021/acs.jctc.2c00381] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
RNA molecules can act as potential drug targets in different diseases, as their dysregulated expression or misfolding can alter various cellular processes. Noncoding RNAs account for ∼70% of the human genome, and these molecules can have complex tertiary structures that present a great opportunity for targeting by small molecules. In the present study, the site identification by ligand competitive saturation (SILCS) computational approach is extended to target RNA, termed SILCS-RNA. Extensions to the method include an enhanced oscillating excess chemical potential protocol for the grand canonical Monte Carlo calculations and individual simulations of the neutral and charged solutes from which the SILCS functional group affinity maps (FragMaps) are calculated for subsequent binding site identification and docking calculations. The method is developed and evaluated against seven RNA targets and their reported small molecule ligands. SILCS-RNA provides a detailed characterization of the functional group affinity pattern in the small molecule binding sites, recapitulating the types of functional groups present in the ligands. The developed method is also shown to be useful for identification of new potential binding sites and identifying ligand moieties that contribute to binding, granular information that can facilitate ligand design. However, limitations in the method are evident including the ability to map the regions of binding sites occupied by ligand phosphate moieties and to fully account for the wide range of conformational heterogeneity in RNA associated with binding of different small molecules, emphasizing inherent challenges associated with applying computer-aided drug design methods to RNA. While limitations are present, the current study indicates how the SILCS-RNA approach may enhance drug discovery efforts targeting RNAs with small molecules.
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Affiliation(s)
- Abhishek A Kognole
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
| | - Anthony Hazel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
| | - Alexander D MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
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13
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Yang C, Chen EA, Zhang Y. Protein-Ligand Docking in the Machine-Learning Era. Molecules 2022; 27:4568. [PMID: 35889440 PMCID: PMC9323102 DOI: 10.3390/molecules27144568] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 07/14/2022] [Indexed: 11/16/2022] Open
Abstract
Molecular docking plays a significant role in early-stage drug discovery, from structure-based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive power is critically dependent on the protein-ligand scoring function. In this review, we give a broad overview of recent scoring function development, as well as the docking-based applications in drug discovery. We outline the strategies and resources available for structure-based VS and discuss the assessment and development of classical and machine learning protein-ligand scoring functions. In particular, we highlight the recent progress of machine learning scoring function ranging from descriptor-based models to deep learning approaches. We also discuss the general workflow and docking protocols of structure-based VS, such as structure preparation, binding site detection, docking strategies, and post-docking filter/re-scoring, as well as a case study on the large-scale docking-based VS test on the LIT-PCBA data set.
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Affiliation(s)
- Chao Yang
- Department of Chemistry, New York University, New York, NY 10003, USA; (C.Y.); (E.A.C.)
| | - Eric Anthony Chen
- Department of Chemistry, New York University, New York, NY 10003, USA; (C.Y.); (E.A.C.)
| | - Yingkai Zhang
- Department of Chemistry, New York University, New York, NY 10003, USA; (C.Y.); (E.A.C.)
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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14
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Stafford KA, Anderson BM, Sorenson J, van den Bedem H. AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High-Throughput Screens. J Chem Inf Model 2022; 62:1178-1189. [PMID: 35235748 PMCID: PMC8924924 DOI: 10.1021/acs.jcim.1c01250] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Structure-based, virtual High-Throughput Screening (vHTS) methods for predicting ligand activity in drug discovery are important when there are no or relatively few known compounds that interact with a therapeutic target of interest. State-of-the-art computational vHTS necessarily relies on effective methods for pose sampling and docking and generating an accurate affinity score from the docked poses. However, proteins are dynamic; in vivo ligands bind to a conformational ensemble. In silico docking to the single conformation represented by a crystal structure can adversely affect the pose quality. Here, we introduce AtomNet PoseRanker (ANPR), a graph convolutional network trained to identify and rerank crystal-like ligand poses from a sampled ensemble of protein conformations and ligand poses. In contrast to conventional vHTS methods that incorporate receptor flexibility, a deep learning approach can internalize valid cognate and noncognate binding modes corresponding to distinct receptor conformations, thereby learning to infer and account for receptor flexibility even on single conformations. ANPR significantly enriched pose quality in docking to cognate and noncognate receptors of the PDBbind v2019 data set. Improved pose rankings that better represent experimentally observed ligand binding modes improve hit rates in vHTS campaigns and thereby advance computational drug discovery, especially for novel therapeutic targets or novel binding sites.
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Affiliation(s)
- Kate A Stafford
- Atomwise, Inc., 717 Market Street, Suite 800, San Francisco, California 94103, United States
| | - Brandon M Anderson
- Atomwise, Inc., 717 Market Street, Suite 800, San Francisco, California 94103, United States
| | - Jon Sorenson
- Atomwise, Inc., 717 Market Street, Suite 800, San Francisco, California 94103, United States
| | - Henry van den Bedem
- Atomwise, Inc., 717 Market Street, Suite 800, San Francisco, California 94103, United States.,Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California 94158, United States
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15
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Mohammadi S, Narimani Z, Ashouri M, Firouzi R, Karimi-Jafari MH. Ensemble learning from ensemble docking: revisiting the optimum ensemble size problem. Sci Rep 2022; 12:410. [PMID: 35013496 PMCID: PMC8748946 DOI: 10.1038/s41598-021-04448-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 12/21/2021] [Indexed: 11/09/2022] Open
Abstract
Despite considerable advances obtained by applying machine learning approaches in protein–ligand affinity predictions, the incorporation of receptor flexibility has remained an important bottleneck. While ensemble docking has been used widely as a solution to this problem, the optimum choice of receptor conformations is still an open question considering the issues related to the computational cost and false positive pose predictions. Here, a combination of ensemble learning and ensemble docking is suggested to rank different conformations of the target protein in light of their importance for the final accuracy of the model. Available X-ray structures of cyclin-dependent kinase 2 (CDK2) in complex with different ligands are used as an initial receptor ensemble, and its redundancy is removed through a graph-based redundancy removal, which is shown to be more efficient and less subjective than clustering-based representative selection methods. A set of ligands with available experimental affinity are docked to this nonredundant receptor ensemble, and the energetic features of the best scored poses are used in an ensemble learning procedure based on the random forest method. The importance of receptors is obtained through feature selection measures, and it is shown that a few of the most important conformations are sufficient to reach 1 kcal/mol accuracy in affinity prediction with considerable improvement of the early enrichment power of the models compared to the different ensemble docking without learning strategies. A clear strategy has been provided in which machine learning selects the most important experimental conformers of the receptor among a large set of protein–ligand complexes while simultaneously maintaining the final accuracy of affinity predictions at the highest level possible for available data. Our results could be informative for future attempts to design receptor-specific docking-rescoring strategies.
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Affiliation(s)
- Sara Mohammadi
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Zahra Narimani
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), 45137-66731, Zanjan, Iran
| | - Mitra Ashouri
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Rohoullah Firouzi
- Department of Physical Chemistry, Chemistry and Chemical Engineering Research Center of Iran, Tehran, Iran
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16
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17
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Adelusi TI, Oyedele AQK, Boyenle ID, Ogunlana AT, Adeyemi RO, Ukachi CD, Idris MO, Olaoba OT, Adedotun IO, Kolawole OE, Xiaoxing Y, Abdul-Hammed M. Molecular modeling in drug discovery. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100880] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
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18
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Ricci-Lopez J, Aguila SA, Gilson MK, Brizuela CA. Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning. J Chem Inf Model 2021; 61:5362-5376. [PMID: 34652141 DOI: 10.1021/acs.jcim.1c00511] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
One of the main challenges of structure-based virtual screening (SBVS) is the incorporation of the receptor's flexibility, as its explicit representation in every docking run implies a high computational cost. Therefore, a common alternative to include the receptor's flexibility is the approach known as ensemble docking. Ensemble docking consists of using a set of receptor conformations and performing the docking assays over each of them. However, there is still no agreement on how to combine the ensemble docking results to obtain the final ligand ranking. A common choice is to use consensus strategies to aggregate the ensemble docking scores, but these strategies exhibit slight improvement regarding the single-structure approach. Here, we claim that using machine learning (ML) methodologies over the ensemble docking results could improve the predictive power of SBVS. To test this hypothesis, four proteins were selected as study cases: CDK2, FXa, EGFR, and HSP90. Protein conformational ensembles were built from crystallographic structures, whereas the evaluated compound library comprised up to three benchmarking data sets (DUD, DEKOIS 2.0, and CSAR-2012) and cocrystallized molecules. Ensemble docking results were processed through 30 repetitions of 4-fold cross-validation to train and validate two ML classifiers: logistic regression and gradient boosting trees. Our results indicate that the ML classifiers significantly outperform traditional consensus strategies and even the best performance case achieved with single-structure docking. We provide statistical evidence that supports the effectiveness of ML to improve the ensemble docking performance.
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Affiliation(s)
- Joel Ricci-Lopez
- Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Ensenada, Baja California C.P. 22860, Mexico.,Centro de Nanociencias y Nanotecnología, Universidad Nacional Autónoma de México (UNAM), Ensenada, Baja California C.P. 22860, Mexico
| | - Sergio A Aguila
- Centro de Nanociencias y Nanotecnología, Universidad Nacional Autónoma de México (UNAM), Ensenada, Baja California C.P. 22860, Mexico
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, La Jolla, San Diego, California 92093, United States
| | - Carlos A Brizuela
- Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Ensenada, Baja California C.P. 22860, Mexico
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19
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Sarma H, Upadhyaya M, Gogoi B, Phukan M, Kashyap P, Das B, Devi R, Sharma HK. Cardiovascular Drugs: an Insight of In Silico Drug Design Tools. J Pharm Innov 2021. [DOI: 10.1007/s12247-021-09587-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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20
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Abstract
Molecular docking is one of the most widely used computational tools in structure-based drug design and is critically dependent on accuracy and robustness of the scoring function. In this work, we introduce a new scoring function Lin_F9, which is a linear combination of nine empirical terms, including a unified metal bond term to specifically describe metal-ligand interactions. Parameters in Lin_F9 are obtained with a multistage fitting protocol using explicit water-included structures. For the CASF-2016 benchmark test set, Lin_F9 achieves the top scoring power among all 34 classical scoring functions for both original crystal poses and locally optimized poses with Pearson correlation coefficients (R) of 0.680 and 0.687, respectively. Meanwhile, in comparison with Vina, Lin_F9 achieves consistently better scoring power and ranking power with various types of protein-ligand complex structures that mimic real docking applications, including end-to-end flexible docking for the CASF-2016 benchmark test set using a single or an ensemble of protein receptor structures, as well as for D3R Grand Challenge (GC4) test sets. Lin_F9 has been implemented in a fork of Smina as an optional built-in scoring function that can be used for docking applications as well as for further improvement of scoring functions and docking protocols. Lin_F9 is accessible through https://yzhang.hpc.nyu.edu/Lin_F9/.
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Affiliation(s)
- Chao Yang
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Yingkai Zhang
- Department of Chemistry, New York University, New York, New York 10003, United States
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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21
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Osman DA, Macías MA, Al-Wahaibi LH, Al-Shaalan NH, Zondagh LS, Joubert J, Garcia-Granda S, El-Emam AA. Structural Insights and Docking Analysis of Adamantane-Linked 1,2,4-Triazole Derivatives as Potential 11β-HSD1 Inhibitors. Molecules 2021; 26:5335. [PMID: 34500764 PMCID: PMC8433897 DOI: 10.3390/molecules26175335] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 08/26/2021] [Accepted: 08/27/2021] [Indexed: 11/21/2022] Open
Abstract
The solid-state structural analysis and docking studies of three adamantane-linked 1,2,4-triazole derivatives are presented. Crystal structure analyses revealed that compound 2 crystallizes in the triclinic P-1 space group, while compounds 1 and 3 crystallize in the same monoclinic P21/c space group. Since the only difference between them is the para substitution on the aryl group, the electronic nature of these NO2 and halogen groups seems to have no influence over the formation of the solid. However, a probable correlation with the size of the groups is not discarded due to the similar intermolecular disposition between the NO2/Cl substituted molecules. Despite the similarities, CE-B3LYP energy model calculations show that pairwise interaction energies vary between them, and therefore the total packing energy is affected. HOMO-LUMO calculated energies show that the NO2 group influences the reactivity properties characterizing the molecule as soft and with the best disposition to accept electrons. Further, in silico studies predicted that the compounds might be able to inhibit the 11β-HSD1 enzyme, which is implicated in obesity and diabetes. Self- and cross-docking experiments revealed that a number of non-native 11β-HSD1 inhibitors were able to accurately dock within the 11β-HSD1 X-ray structure 4C7J. The molecular docking of the adamantane-linked 1,2,4-triazoles have similar predicted binding affinity scores compared to the 4C7J native ligand 4YQ. However, they were unable to form interactions with key active site residues. Based on these docking results, a series of potentially improved compounds were designed using computer aided drug design tools. The docking results of the new compounds showed similar predicted 11β-HSD1 binding affinity scores as well as interactions to a known potent 11β-HSD1 inhibitor.
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Affiliation(s)
- Doaa A. Osman
- Department of Physical and Analytical Chemistry, Faculty of Chemistry, Oviedo University-CINN, 33006 Oviedo, Spain; (D.A.O.); (S.G.-G.)
| | - Mario A. Macías
- Crystallography and Chemistry of Materials, CrisQuimMat, Department of Chemistry, Universidad de Los Andes, Carrera 1 No. 18A-10, Bogotá 111711, Colombia;
| | - Lamya H. Al-Wahaibi
- Department of Chemistry, College of Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia;
| | - Nora H. Al-Shaalan
- Department of Chemistry, College of Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia;
| | - Luke S. Zondagh
- Pharmaceutical Chemistry, School of Pharmacy, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa; (L.S.Z.); (J.J.)
| | - Jacques Joubert
- Pharmaceutical Chemistry, School of Pharmacy, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa; (L.S.Z.); (J.J.)
| | - Santiago Garcia-Granda
- Department of Physical and Analytical Chemistry, Faculty of Chemistry, Oviedo University-CINN, 33006 Oviedo, Spain; (D.A.O.); (S.G.-G.)
| | - Ali A. El-Emam
- Department of Medicinal Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt
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22
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Moghadam B, Ashouri M, Roohi H, Karimi-Jafari MH. Computational evidence of new putative allosteric sites in the acetylcholinesterase receptor. J Mol Graph Model 2021; 107:107981. [PMID: 34246109 DOI: 10.1016/j.jmgm.2021.107981] [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] [Received: 05/23/2021] [Revised: 06/27/2021] [Accepted: 06/29/2021] [Indexed: 10/21/2022]
Abstract
Acetylcholinesterase (AChE), with a rigid structure and buried active site at the end of a deep narrow gorge, is interesting enough to solve the paradox between high catalytic activity and unavailability of the active site in treatment of Alzheimer's disease (AD). In this way, the blind docking process is performed on an ensemble of AChE structures created with molecular dynamics (MD) simulations to survey the whole space of AChE to find multiple access pathways to the active site and ranking them based on their affinity scores. Our results show that there are other allosteric binding sites in the protein structure whose inhibition, can affect protein function by disrupting the release of the Acetylcholine (AC) degradation products. In this study, inhibitory activities of Hybride14 and two natural compounds (Papaverine and Palmatine) were evaluated for all possible allosteric sites via docking method. The results confirmed the non-competitive inhibition mechanism. The best binding mode for these inhibitors and efficacy of hydrogen bonds and hydrophobic interactions on inhibitory activities of ligands were also disclosed. Furthermore, our studies provide significant molecular insight for AChE inhibition that could aid in the development of new drugs for AD's treatment.
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Affiliation(s)
- Behnaz Moghadam
- Department of Chemistry, Faculty of Science, University of Guilan, Iran
| | - Mitra Ashouri
- Department of Physical Chemistry, School of Chemistry, College of Science, University of Tehran, Tehran, Iran.
| | - Hossein Roohi
- Department of Chemistry, Faculty of Science, University of Guilan, Iran.
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23
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Stanzione F, Giangreco I, Cole JC. Use of molecular docking computational tools in drug discovery. PROGRESS IN MEDICINAL CHEMISTRY 2021; 60:273-343. [PMID: 34147204 DOI: 10.1016/bs.pmch.2021.01.004] [Citation(s) in RCA: 109] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.
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Affiliation(s)
| | - Ilenia Giangreco
- Cambridge Crystallographic Data Centre, Cambridge, United Kingdom
| | - Jason C Cole
- Cambridge Crystallographic Data Centre, Cambridge, United Kingdom
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24
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Djokovic N, Ruzic D, Djikic T, Cvijic S, Ignjatovic J, Ibric S, Baralic K, Buha Djordjevic A, Curcic M, Djukic‐Cosic D, Nikolic K. An Integrative in silico Drug Repurposing Approach for Identification of Potential Inhibitors of SARS-CoV-2 Main Protease. Mol Inform 2021; 40:e2000187. [PMID: 33787066 PMCID: PMC8250230 DOI: 10.1002/minf.202000187] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 02/15/2021] [Indexed: 12/25/2022]
Abstract
Considering the urgent need for novel therapeutics in ongoing COVID-19 pandemic, drug repurposing approach might offer rapid solutions comparing to de novo drug design. In this study, we designed an integrative in silico drug repurposing approach for rapid selection of potential candidates against SARS-CoV-2 Main Protease (Mpro ). To screen FDA-approved drugs, we implemented structure-based molecular modelling techniques, physiologically-based pharmacokinetic (PBPK) modelling of drugs disposition and data mining analysis of drug-gene-COVID-19 association. Through presented approach, we selected the most promising FDA approved drugs for further COVID-19 drug development campaigns and analysed them in context of available experimental data. To the best of our knowledge, this is unique in silico study which integrates structure-based molecular modeling of Mpro inhibitors with predictions of their tissue disposition, drug-gene-COVID-19 associations and prediction of pleiotropic effects of selected candidates.
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Affiliation(s)
- Nemanja Djokovic
- Department of Pharmaceutical ChemistryFaculty of PharmacyUniversity of BelgradeVojvode Stepe 45011221BelgradeSerbia
| | - Dusan Ruzic
- Department of Pharmaceutical ChemistryFaculty of PharmacyUniversity of BelgradeVojvode Stepe 45011221BelgradeSerbia
| | - Teodora Djikic
- Department of Pharmaceutical ChemistryFaculty of PharmacyUniversity of BelgradeVojvode Stepe 45011221BelgradeSerbia
| | - Sandra Cvijic
- Department of Pharmaceutical Technology and CosmetologyUniversity of BelgradeFaculty of PharmacyVojvode Stepe 45011221BelgradeSerbia
| | - Jelisaveta Ignjatovic
- Department of Pharmaceutical Technology and CosmetologyUniversity of BelgradeFaculty of PharmacyVojvode Stepe 45011221BelgradeSerbia
| | - Svetlana Ibric
- Department of Pharmaceutical Technology and CosmetologyUniversity of BelgradeFaculty of PharmacyVojvode Stepe 45011221BelgradeSerbia
| | - Katarina Baralic
- Department of Toxicology “Akademik Danilo Soldatovic”Faculty of PharmacyUniversity of BelgradeVojvode Stepe 45011221BelgradeSerbia
| | - Aleksandra Buha Djordjevic
- Department of Toxicology “Akademik Danilo Soldatovic”Faculty of PharmacyUniversity of BelgradeVojvode Stepe 45011221BelgradeSerbia
| | - Marijana Curcic
- Department of Toxicology “Akademik Danilo Soldatovic”Faculty of PharmacyUniversity of BelgradeVojvode Stepe 45011221BelgradeSerbia
| | - Danijela Djukic‐Cosic
- Department of Toxicology “Akademik Danilo Soldatovic”Faculty of PharmacyUniversity of BelgradeVojvode Stepe 45011221BelgradeSerbia
| | - Katarina Nikolic
- Department of Pharmaceutical ChemistryFaculty of PharmacyUniversity of BelgradeVojvode Stepe 45011221BelgradeSerbia
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25
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Salman MM, Al-Obaidi Z, Kitchen P, Loreto A, Bill RM, Wade-Martins R. Advances in Applying Computer-Aided Drug Design for Neurodegenerative Diseases. Int J Mol Sci 2021; 22:4688. [PMID: 33925236 PMCID: PMC8124449 DOI: 10.3390/ijms22094688] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/26/2021] [Accepted: 04/26/2021] [Indexed: 12/11/2022] Open
Abstract
Neurodegenerative diseases (NDs) including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease are incurable and affect millions of people worldwide. The development of treatments for this unmet clinical need is a major global research challenge. Computer-aided drug design (CADD) methods minimize the huge number of ligands that could be screened in biological assays, reducing the cost, time, and effort required to develop new drugs. In this review, we provide an introduction to CADD and examine the progress in applying CADD and other molecular docking studies to NDs. We provide an updated overview of potential therapeutic targets for various NDs and discuss some of the advantages and disadvantages of these tools.
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Affiliation(s)
- Mootaz M. Salman
- Department of Physiology, Anatomy and Genetics, University of Oxford, Parks Road, Oxford OX1 3QX, UK;
- Oxford Parkinson’s Disease Centre, University of Oxford, South Parks Road, Oxford OX1 3QX, UK
| | - Zaid Al-Obaidi
- Department of Pharmaceutical Chemistry, College of Pharmacy, University of Alkafeel, Najaf 54001, Iraq;
- Department of Chemistry and Biochemistry, College of Medicine, University of Kerbala, Karbala 56001, Iraq
| | - Philip Kitchen
- School of Biosciences, College of Health and Life Sciences, Aston University, Aston Triangle, Birmingham B4 7ET, UK; (P.K.); (R.M.B.)
| | - Andrea Loreto
- Department of Physiology, Anatomy and Genetics, University of Oxford, Parks Road, Oxford OX1 3QX, UK;
- John Van Geest Centre for Brain Repair, University of Cambridge, Cambridge CB2 0PY, UK
| | - Roslyn M. Bill
- School of Biosciences, College of Health and Life Sciences, Aston University, Aston Triangle, Birmingham B4 7ET, UK; (P.K.); (R.M.B.)
| | - Richard Wade-Martins
- Department of Physiology, Anatomy and Genetics, University of Oxford, Parks Road, Oxford OX1 3QX, UK;
- Oxford Parkinson’s Disease Centre, University of Oxford, South Parks Road, Oxford OX1 3QX, UK
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26
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Targeting multiple conformations of SARS-CoV2 Papain-Like Protease for drug repositioning: An in-silico study. Comput Biol Med 2021; 131:104295. [PMID: 33662683 PMCID: PMC7902231 DOI: 10.1016/j.compbiomed.2021.104295] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 02/15/2021] [Accepted: 02/19/2021] [Indexed: 12/16/2022]
Abstract
Papain-Like Protease (PLpro) is a key protein for SARS-CoV-2 viral replication which is the cause of the emerging COVID-19 pandemic. Targeting PLpro can suppress viral replication and provide treatment options for COVID-19. Due to the dynamic nature of its binding site loop, PLpro multiple conformations were generated through a long-range 1 micro-second molecular dynamics (MD) simulation. Clustering the MD trajectory enabled us to extract representative structures for the conformational space generated. Adding to the MD representative structures, X-ray structures were involved in an ensemble docking approach to screen the FDA approved drugs for a drug repositioning endeavor. Guided by our recent benchmarking study of SARS-CoV-2 PLpro, FRED docking software was selected for such a virtual screening task. The results highlighted potential consensus binders to many of the MD clusters as well as the newly introduced X-ray structure of PLpro complexed with a small molecule. For instance, three drugs Benserazide, Dobutamine and Masoprocol showed a superior consensus enrichment against the PLpro conformations. Further MD simulations for these drugs complexed with PLpro suggested the superior stability and binding of dobutamine and masoprocol inside the binding site compared to Benserazide. Generally, this approach can facilitate identifying drugs for repositioning via targeting multiple conformations of a crucial target for the rapidly emerging COVID-19 pandemic.
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27
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Lemfack MC, Brandt W, Krüger K, Gurowietz A, Djifack J, Jung JP, Hopf M, Noack H, Junker B, von Reuß S, Piechulla B. Reaction mechanism of the farnesyl pyrophosphate C-methyltransferase towards the biosynthesis of pre-sodorifen pyrophosphate by Serratia plymuthica 4Rx13. Sci Rep 2021; 11:3182. [PMID: 33542330 PMCID: PMC7862628 DOI: 10.1038/s41598-021-82521-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 01/18/2021] [Indexed: 11/25/2022] Open
Abstract
Classical terpenoid biosynthesis involves the cyclization of the linear prenyl pyrophosphate precursors geranyl-, farnesyl-, or geranylgeranyl pyrophosphate (GPP, FPP, GGPP) and their isomers, to produce a huge number of natural compounds. Recently, it was shown for the first time that the biosynthesis of the unique homo-sesquiterpene sodorifen by Serratia plymuthica 4Rx13 involves a methylated and cyclized intermediate as the substrate of the sodorifen synthase. To further support the proposed biosynthetic pathway, we now identified the cyclic prenyl pyrophosphate intermediate pre-sodorifen pyrophosphate (PSPP). Its absolute configuration (6R,7S,9S) was determined by comparison of calculated and experimental CD-spectra of its hydrolysis product and matches with those predicted by semi-empirical quantum calculations of the reaction mechanism. In silico modeling of the reaction mechanism of the FPP C-methyltransferase (FPPMT) revealed a SN2 mechanism for the methyl transfer followed by a cyclization cascade. The cyclization of FPP to PSPP is guided by a catalytic dyad of H191 and Y39 and involves an unprecedented cyclopropyl intermediate. W46, W306, F56, and L239 form the hydrophobic binding pocket and E42 and H45 complex a magnesium cation that interacts with the diphosphate moiety of FPP. Six additional amino acids turned out to be essential for product formation and the importance of these amino acids was subsequently confirmed by site-directed mutagenesis. Our results reveal the reaction mechanism involved in methyltransferase-catalyzed cyclization and demonstrate that this coupling of C-methylation and cyclization of FPP by the FPPMT represents an alternative route of terpene biosynthesis that could increase the terpenoid diversity and structural space.
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Affiliation(s)
- Marie Chantal Lemfack
- Institute of Biological Sciences, University of Rostock, Albert-Einstein-Straße 3, 18059, Rostock, Germany.
| | - Wolfgang Brandt
- Department of Bioorganic Chemistry, Leibniz-Institute of Plant Biochemistry, Weinberg 3, 06120, Halle, Germany.
| | - Katja Krüger
- Institute of Biological Sciences, University of Rostock, Albert-Einstein-Straße 3, 18059, Rostock, Germany.,Department of Internal Medicine I, University Hospital RWTH Aachen, 52074, Aachen, Germany
| | - Alexandra Gurowietz
- Department of Bioorganic Chemistry, Leibniz-Institute of Plant Biochemistry, Weinberg 3, 06120, Halle, Germany.,Institute of Biology, Martin-Luther-Universität Halle-Wittenberg, Weinberg 10, 06120, Halle (Saale), Germany
| | - Jacky Djifack
- Institute of Biological Sciences, University of Rostock, Albert-Einstein-Straße 3, 18059, Rostock, Germany.,PIMAN Consultants, 12 Rue Barthelemy Danjou, 92100, Boulogne-Billancourt, France
| | - Jan-Philip Jung
- Institute of Biological Sciences, University of Rostock, Albert-Einstein-Straße 3, 18059, Rostock, Germany
| | - Marius Hopf
- Institute of Biological Sciences, University of Rostock, Albert-Einstein-Straße 3, 18059, Rostock, Germany.,Duale Hochschule Gera-Eisenach, Weg der Freundschaft 4, 07546, Gera, Germany
| | - Heiko Noack
- Institute of Pharmacy/Biosynthesis of Active Substances, Hoher Weg 8, 06120, Halle (Saale), Germany
| | - Björn Junker
- Institute of Pharmacy/Biosynthesis of Active Substances, Hoher Weg 8, 06120, Halle (Saale), Germany
| | - Stephan von Reuß
- Laboratory of Bioanalytical Chemistry, Institute of Chemistry, University of Neuchatel, Avenue de Bellevaux 51, 2000, Neuchâtel, Switzerland
| | - Birgit Piechulla
- Institute of Biological Sciences, University of Rostock, Albert-Einstein-Straße 3, 18059, Rostock, Germany
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28
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Maddah M, Bahramsoltani R, Yekta NH, Rahimi R, Aliabadi R, Pourfath M. Proposing high-affinity inhibitors from Glycyrrhiza glabra L. against SARS-CoV-2 infection: virtual screening and computational analysis. NEW J CHEM 2021. [DOI: 10.1039/d1nj02031e] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Licorice as a traditional medicine introduces promising antiviral phytochemicals against SARS-CoV-2.
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Affiliation(s)
- Mina Maddah
- School of Electrical and Computer Engineering, University College of Engineering, University of Tehran, Tehran, Iran
- Super Computing Institute, University of Tehran, Tehran, Iran
| | - Roodabeh Bahramsoltani
- Department of Traditional Pharmacy, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Clinical Virology, Tehran University of Medical Sciences, Tehran, Iran
| | - Nafiseh Hoseini Yekta
- Department of Persian Medicine, Faculty of Medicine, AJA University of Medical Sciences, Tehran, Iran
| | - Roja Rahimi
- Department of Traditional Pharmacy, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Phytopharmacology Interest Group (PPIG), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Rasoul Aliabadi
- School of Electrical and Computer Engineering, University College of Engineering, University of Tehran, Tehran, Iran
| | - Mahdi Pourfath
- School of Electrical and Computer Engineering, University College of Engineering, University of Tehran, Tehran, Iran
- Super Computing Institute, University of Tehran, Tehran, Iran
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29
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Liu C, Brini E, Perez A, Dill KA. Computing Ligands Bound to Proteins Using MELD-Accelerated MD. J Chem Theory Comput 2020; 16:6377-6382. [PMID: 32910647 DOI: 10.1021/acs.jctc.0c00543] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Predicting the poses of small-molecule ligands in protein binding sites is often done by virtual screening algorithms such as DOCK. In principle, molecular dynamics (MD) using atomistic force fields could give better free-energy-based pose selection, but MD is computationally expensive. Here, we ask if modeling employing limited data (MELD)-accelerated MD (MELD × MD) can pick out the best DOCK poses taken as input. We study 30 different ligand-protein pairs. MELD × MD finds native poses, based on best free energies, in 23 out of the 30 cases, 20 of which were previously known DOCK failures. We conclude that MELD × MD can add value for predicting accurate poses of small molecules bound to proteins.
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Affiliation(s)
- Cong Liu
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794-5252, United States.,Department of Chemistry, Stony Brook University, Stony Brook, New York 11790-3400, United States
| | - Emiliano Brini
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794-5252, United States
| | - Alberto Perez
- Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
| | - Ken A Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794-5252, United States.,Department of Chemistry, Stony Brook University, Stony Brook, New York 11790-3400, United States.,Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794-3800, United States
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30
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Rasouli H, Hosseini Ghazvini SMB, Yarani R, Altıntaş A, Jooneghani SGN, Ramalho TC. Deciphering inhibitory activity of flavonoids against tau protein kinases: a coupled molecular docking and quantum chemical study. J Biomol Struct Dyn 2020; 40:411-424. [PMID: 32897165 DOI: 10.1080/07391102.2020.1814868] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Today, Alzheimer's disease (AD) is one of the most important neurodegenerative disorders that affected millions of people worldwide. Hundreds of academic investigations highlighted the potential roles of natural metabolites in the cornerstone of AD prevention. Nevertheless, alkaloids are only metabolites that successfully showed promising clinical therapeutic effects on the prevention of AD. In this regard, other plant metabolites such as flavonoids are also considered as promising substances in the improvement of AD complications. The lack of data on molecular mode of action of flavonoids inside brain tissues, and their potential to transport across the blood-brain barrier, a physical hindrance between bloodstream and brain tissues, limited the large-scale application of these compounds for AD therapy programs. Herein, a coupled docking and quantum study was applied to determine the binding mode of flavonoids and three protein kinases involved in the pathogenesis of AD. The results suggested that all docked metabolites showed considerable binding affinity to interact with target receptors, but some compounds possessed higher binding energy values. Because docking simulation cannot entirely reveal the potential roles of ligand substructures in the interaction with target residues, quantum chemical analyses (QCAs) were performed to cover this drawback. Accordingly, QCAs determined that distribution of molecular orbitals have a pivotal function in the determination of the type of reaction between ligands and receptors; therefore, using such quantum chemical descriptors may correct the results of virtual docking outcomes to highlight promising backbones for further developments.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Hassan Rasouli
- National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
| | | | - Reza Yarani
- T1D Biology, Department of Clinical Research, Steno Diabetes Center Copenhagen, Denmark
| | - Ali Altıntaş
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health & Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Saber Ghafari Nikoo Jooneghani
- Department of Chemistry, Faculty of Science, Arak University, Arak, Iran.,Quantum Chemistry Group, Department of Chemistry, Faculty of Sciences, Arak University, Arak, Iran
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31
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Rodrigues DA, Pinheiro PDSM, Sagrillo FS, Bolognesi ML, Fraga CAM. Histone deacetylases as targets for the treatment of neurodegenerative disorders: Challenges and future opportunities. Med Res Rev 2020; 40:2177-2211. [DOI: 10.1002/med.21701] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Revised: 06/02/2020] [Accepted: 06/09/2020] [Indexed: 12/15/2022]
Affiliation(s)
- Daniel A. Rodrigues
- Laboratório de Avaliação e Síntese de Substâncias Bioativas (LASSBio), Instituto de Ciências Biomédicas Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil
- Programa de Pós‐Graduação em Química, Instituto de Química Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil
| | - Pedro de S. M. Pinheiro
- Laboratório de Avaliação e Síntese de Substâncias Bioativas (LASSBio), Instituto de Ciências Biomédicas Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil
- Programa de Pós‐Graduação em Farmacologia e Química Medicinal, Instituto de Ciências Biomédicas Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil
- Department of Pharmacy and Biotechnology Alma Mater Studiorum‐University of Bologna Bologna Italy
| | - Fernanda S. Sagrillo
- Laboratório de Avaliação e Síntese de Substâncias Bioativas (LASSBio), Instituto de Ciências Biomédicas Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil
| | - Maria L. Bolognesi
- Department of Pharmacy and Biotechnology Alma Mater Studiorum‐University of Bologna Bologna Italy
| | - Carlos A. M. Fraga
- Laboratório de Avaliação e Síntese de Substâncias Bioativas (LASSBio), Instituto de Ciências Biomédicas Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil
- Programa de Pós‐Graduação em Química, Instituto de Química Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil
- Programa de Pós‐Graduação em Farmacologia e Química Medicinal, Instituto de Ciências Biomédicas Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil
- Department of Pharmacy and Biotechnology Alma Mater Studiorum‐University of Bologna Bologna Italy
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32
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Gazgalis D, Zaka M, Abbasi BH, Logothetis DE, Mezei M, Cui M. Protein Binding Pocket Optimization for Virtual High-Throughput Screening (vHTS) Drug Discovery. ACS OMEGA 2020; 5:14297-14307. [PMID: 32596567 PMCID: PMC7315428 DOI: 10.1021/acsomega.0c00522] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 05/28/2020] [Indexed: 06/11/2023]
Abstract
The virtual high-throughput screening (vHTS) approach has been widely used for large database screening to identify potential lead compounds for drug discovery. Due to its high computational demands, docking that allows receptor flexibility has been a challenging problem for virtual screening. Therefore, the selection of protein target conformations is crucial to produce useful vHTS results. Since only a single protein structure is used to screen large databases in most vHTS studies, the main challenge is to reduce false negative rates in selecting compounds for in vitro tests. False negatives are most likely to occur when using apo structures or homology models of protein targets due to the small volume of the binding pocket formed by incorrect side-chain conformations. Even holo protein structures can exhibit high false negative rates due to ligand-induced fit effects, since the shape of the binding pocket highly depends on its bound ligand. To reduce false negative rates and improve success rates for vHTS in drug discovery, we have developed a new Monte Carlo-based approach that optimizes the binding pocket of protein targets. This newly developed Monte Carlo pocket optimization (MCPO) approach was assessed on several datasets showing promising results. The binding pocket optimization approach could be a useful tool for vHTS-based drug discovery, especially in cases when only apo structures or homology models are available.
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Affiliation(s)
- Dimitris Gazgalis
- Department
of Pharmaceutical Sciences, Northeastern
University School of Pharmacy, Boston, Massachusetts 02115, United States
| | - Mehreen Zaka
- Department
of Pharmaceutical Sciences, Northeastern
University School of Pharmacy, Boston, Massachusetts 02115, United States
- Department
of Biotechnology, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Bilal Haider Abbasi
- Department
of Biotechnology, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Diomedes E. Logothetis
- Department
of Pharmaceutical Sciences, Northeastern
University School of Pharmacy, Boston, Massachusetts 02115, United States
| | - Mihaly Mezei
- Department
of Pharmacological Sciences, Icahn School
of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Meng Cui
- Department
of Pharmaceutical Sciences, Northeastern
University School of Pharmacy, Boston, Massachusetts 02115, United States
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33
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Bera I, Payghan PV. Use of Molecular Dynamics Simulations in Structure-Based Drug Discovery. Curr Pharm Des 2020; 25:3339-3349. [PMID: 31480998 DOI: 10.2174/1381612825666190903153043] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 09/01/2019] [Indexed: 12/31/2022]
Abstract
BACKGROUND Traditional drug discovery is a lengthy process which involves a huge amount of resources. Modern-day drug discovers various multidisciplinary approaches amongst which, computational ligand and structure-based drug designing methods contribute significantly. Structure-based drug designing techniques require the knowledge of structural information of drug target and drug-target complexes. Proper understanding of drug-target binding requires the flexibility of both ligand and receptor to be incorporated. Molecular docking refers to the static picture of the drug-target complex(es). Molecular dynamics, on the other hand, introduces flexibility to understand the drug binding process. OBJECTIVE The aim of the present study is to provide a systematic review on the usage of molecular dynamics simulations to aid the process of structure-based drug design. METHOD This review discussed findings from various research articles and review papers on the use of molecular dynamics in drug discovery. All efforts highlight the practical grounds for which molecular dynamics simulations are used in drug designing program. In summary, various aspects of the use of molecular dynamics simulations that underline the basis of studying drug-target complexes were thoroughly explained. RESULTS This review is the result of reviewing more than a hundred papers. It summarizes various problems that use molecular dynamics simulations. CONCLUSION The findings of this review highlight how molecular dynamics simulations have been successfully implemented to study the structure-function details of specific drug-target complexes. It also identifies the key areas such as stability of drug-target complexes, ligand binding kinetics and identification of allosteric sites which have been elucidated using molecular dynamics simulations.
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Affiliation(s)
- Indrani Bera
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, United States
| | - Pavan V Payghan
- Structural Biology and Bioinformatics Department, CSIR-IICB, Kolkata, India.,Department of Pharmaceutical Sciences, Washington State University College of Pharmacy and Pharmaceutical Sciences, Spokane, WA, United States
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34
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Saikia S, Bordoloi M. Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective. Curr Drug Targets 2020; 20:501-521. [PMID: 30360733 DOI: 10.2174/1389450119666181022153016] [Citation(s) in RCA: 203] [Impact Index Per Article: 50.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 06/08/2018] [Accepted: 08/28/2018] [Indexed: 01/21/2023]
Abstract
Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don't always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, comparison of performance for existing tools and algorithms, state of art in docking, recent trends of diseases and current drug industries, evidence from clinical trials and post-marketing surveillance are discussed. These aspects of the molecular drug designing paradigm are quite controversial and challenging and this review would be an asset to the bioinformatics and drug designing communities.
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Affiliation(s)
- Surovi Saikia
- Natural Products Chemistry Group, CSIR North East Institute of Science & Technology, Jorhat-785006, Assam, India
| | - Manobjyoti Bordoloi
- Natural Products Chemistry Group, CSIR North East Institute of Science & Technology, Jorhat-785006, Assam, India
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35
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Chandak T, Mayginnes JP, Mayes H, Wong CF. Using machine learning to improve ensemble docking for drug discovery. Proteins 2020; 88:1263-1270. [PMID: 32401384 DOI: 10.1002/prot.25899] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 04/09/2020] [Accepted: 05/07/2020] [Indexed: 01/26/2023]
Abstract
Ensemble docking has provided an inexpensive method to account for receptor flexibility in molecular docking for virtual screening. Unfortunately, as there is no rigorous theory to connect the docking scores from multiple structures to measured activity, researchers have not yet come up with effective ways to use these scores to classify compounds into actives and inactives. This shortcoming has led to the decrease, rather than an increase in the performance of classifying compounds when more structures are added to the ensemble. Previously, we suggested machine learning, implemented in the form of a naïve Bayesian model could alleviate this problem. However, the naïve Bayesian model assumed that the probabilities of observing the docking scores to different structures to be independent. This approximation might prevent it from achieving even higher performance. In the work presented in this paper, we have relaxed this approximation when using several other machine learning methods-k nearest neighbor, logistic regression, support vector machine, and random forest-to improve ensemble docking. We found significant improvement.
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Affiliation(s)
- Tanay Chandak
- Department of Chemistry and Biochemistry and Center for Nanoscience, University of Missouri-St. Louis, Saint Louis, Missouri, USA
| | - John P Mayginnes
- Department of Chemistry and Biochemistry and Center for Nanoscience, University of Missouri-St. Louis, Saint Louis, Missouri, USA
| | - Howard Mayes
- Department of Chemistry and Biochemistry and Center for Nanoscience, University of Missouri-St. Louis, Saint Louis, Missouri, USA
| | - Chung F Wong
- Department of Chemistry and Biochemistry and Center for Nanoscience, University of Missouri-St. Louis, Saint Louis, Missouri, USA
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36
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Schaller D, Šribar D, Noonan T, Deng L, Nguyen TN, Pach S, Machalz D, Bermudez M, Wolber G. Next generation 3D pharmacophore modeling. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1468] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- David Schaller
- Pharmaceutical and Medicinal Chemistry Freie Universität Berlin Berlin Germany
| | - Dora Šribar
- Pharmaceutical and Medicinal Chemistry Freie Universität Berlin Berlin Germany
| | - Theresa Noonan
- Pharmaceutical and Medicinal Chemistry Freie Universität Berlin Berlin Germany
| | - Lihua Deng
- Pharmaceutical and Medicinal Chemistry Freie Universität Berlin Berlin Germany
| | - Trung Ngoc Nguyen
- Pharmaceutical and Medicinal Chemistry Freie Universität Berlin Berlin Germany
| | - Szymon Pach
- Pharmaceutical and Medicinal Chemistry Freie Universität Berlin Berlin Germany
| | - David Machalz
- Pharmaceutical and Medicinal Chemistry Freie Universität Berlin Berlin Germany
| | - Marcel Bermudez
- Pharmaceutical and Medicinal Chemistry Freie Universität Berlin Berlin Germany
| | - Gerhard Wolber
- Pharmaceutical and Medicinal Chemistry Freie Universität Berlin Berlin Germany
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37
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Liang J, Mantelos A, Toh ZQ, Tortorella SM, Ververis K, Vongsvivut J, Bambery KR, Licciardi PV, Hung A, Karagiannis TC. Investigation of potential anti-pneumococcal effects of l-sulforaphane and metabolites: Insights from synchrotron-FTIR microspectroscopy and molecular docking studies. J Mol Graph Model 2020; 97:107568. [PMID: 32097886 DOI: 10.1016/j.jmgm.2020.107568] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 02/10/2020] [Indexed: 01/06/2023]
Abstract
Streptococcus pneumoniae infection can lead to pneumococcal disease, a major cause of mortality in children under the age of five years. In low- and middle-income country settings where pneumococcal disease burden is high, vaccine use is low and widespread antibiotic use has led to increased rates of multi-drug resistant pneumococci. l-sulforaphane (LSF), derived from broccoli and other cruciferous vegetables, has established anti-inflammatory, antioxidant, and anti-microbial properties. Hence, we sought to investigate the potential role of LSF against pneumococcal infection. Using a combination of in vitro and computational methods, the results showed that LSF and relevant metabolites had a potential to reduce pneumococcal adherence through modulation of host receptors, regulation of inflammation, or through direct modification of bacterial factors. Treatment with LSF and metabolites reduced pneumococcal adherence to respiratory epithelial cells. Synchrotron-Fourier transform infrared microspectroscopy (S-FTIR) revealed biochemical changes in protein and lipid profiles of lung epithelial cells following treatment with LSF or metabolites. Molecular docking studies of 116 pneumococcal and 89 host factors revealed a potent effect for the metabolite LSF-glutathione (GSH). A comprehensive list of factors involved in interactions between S. pneumoniae and host cells was compiled to construct a bacterium and host interaction network. Network analysis revealed plasminogen, fibronectin, and RrgA as key factors involved in pneumococcal-host interactions. Therefore, we propose that these constitute critical targets for direct inhibition by LSF and/or metabolites, which may disrupt pneumococcal-host adherence. Overall, our findings further enhance understanding of the potential role of LSF to modulate pneumococcal-host dynamics.
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Affiliation(s)
- Julia Liang
- Epigenomic Medicine, Department of Diabetes, Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia; School of Science, RMIT University, VIC, 3001, Australia
| | - Anita Mantelos
- Epigenomic Medicine, Department of Diabetes, Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia; Murdoch Children's Research Institute, Melbourne, Parkville, VIC, 3052, Australia; Department of Paediatrics, University of Melbourne, Parkville, VIC, 3052, Australia
| | - Zheng Quan Toh
- Murdoch Children's Research Institute, Melbourne, Parkville, VIC, 3052, Australia
| | - Stephanie M Tortorella
- Epigenomic Medicine, Department of Diabetes, Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia
| | - Katherine Ververis
- Epigenomic Medicine, Department of Diabetes, Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia
| | | | - Keith R Bambery
- ANSTO Australian Synchrotron, 800 Blackburn Road, Clayton, VIC, 3168, Australia
| | - Paul V Licciardi
- Murdoch Children's Research Institute, Melbourne, Parkville, VIC, 3052, Australia; Department of Paediatrics, University of Melbourne, Parkville, VIC, 3052, Australia
| | - Andrew Hung
- School of Science, RMIT University, VIC, 3001, Australia
| | - Tom C Karagiannis
- Epigenomic Medicine, Department of Diabetes, Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia; Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3052, Australia.
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38
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Parks CD, Gaieb Z, Chiu M, Yang H, Shao C, Walters WP, Jansen JM, McGaughey G, Lewis RA, Bembenek SD, Ameriks MK, Mirzadegan T, Burley SK, Amaro RE, Gilson MK. D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies. J Comput Aided Mol Des 2020; 34:99-119. [PMID: 31974851 PMCID: PMC7261493 DOI: 10.1007/s10822-020-00289-y] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 01/13/2020] [Indexed: 12/11/2022]
Abstract
The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.
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Affiliation(s)
- Conor D Parks
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Zied Gaieb
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Michael Chiu
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Huanwang Yang
- RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08903, USA
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Chenghua Shao
- RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08903, USA
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, 92093, USA
| | | | - Johanna M Jansen
- Novartis Institutes for BioMedical Research, Emeryville, CA, 94608, USA
| | | | - Richard A Lewis
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, 4002, Basel, Switzerland
| | | | | | | | - Stephen K Burley
- RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08903, USA
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Rommie E Amaro
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA.
- Department of Chemistry and Biochemistry, UC San Diego, La Jolla, CA, 92093-0340, USA.
| | - Michael K Gilson
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA.
- Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, 9500 Gilman Drive, MC0751, La Jolla, CA, 92093, USA.
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39
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Tetrahydroquinoline-Isoxazole/Isoxazoline Hybrid Compounds as Potential Cholinesterases Inhibitors: Synthesis, Enzyme Inhibition Assays, and Molecular Modeling Studies. Int J Mol Sci 2019; 21:ijms21010005. [PMID: 31861333 PMCID: PMC6981637 DOI: 10.3390/ijms21010005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 12/04/2019] [Indexed: 01/18/2023] Open
Abstract
A series of 44 hybrid compounds that included in their structure tetrahydroquinoline (THQ) and isoxazole/isoxazoline moieties were synthesized through the 1,3-dipolar cycloaddition reaction (1,3-DC) from the corresponding N-allyl/propargyl THQs, previously obtained via cationic Povarov reaction. In vitro cholinergic enzymes inhibition potential of all compounds was tested. Enzyme inhibition assays showed that some hybrids exhibited significant potency to inhibit acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). Especially, the hybrid compound 5n presented the more effective inhibition against AChE (4.24 µM) with an acceptable selectivity index versus BChE (SI: 5.19), while compound 6aa exhibited the greatest inhibition activity on BChE (3.97 µM) and a significant selectivity index against AChE (SI: 0.04). Kinetic studies were carried out for compounds with greater inhibitory activity of cholinesterases. Structure–activity relationships of the molecular hybrids were analyzed, through computational models using a molecular cross-docking algorithm and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) binding free energy approach, which indicated a good correlation between the experimental inhibition values and the predicted free binding energy.
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40
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Tosstorff A, Svilenov H, Peters GH, Harris P, Winter G. Structure-based discovery of a new protein-aggregation breaking excipient. Eur J Pharm Biopharm 2019; 144:207-216. [DOI: 10.1016/j.ejpb.2019.09.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Revised: 07/28/2019] [Accepted: 09/11/2019] [Indexed: 01/06/2023]
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41
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Schneider M, Pons JL, Labesse G, Bourguet W. In Silico Predictions of Endocrine Disruptors Properties. Endocrinology 2019; 160:2709-2716. [PMID: 31265055 PMCID: PMC6804484 DOI: 10.1210/en.2019-00382] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 06/26/2019] [Indexed: 01/12/2023]
Abstract
Endocrine-disrupting chemicals (EDCs) are a broad class of molecules present in our environment that are suspected to cause adverse effects in the endocrine system by interfering with the synthesis, transport, degradation, or action of endogenous ligands. The characterization of the harmful interaction between environmental compounds and their potential cellular targets and the development of robust in vivo, in vitro, and in silico screening methods are important for assessment of the toxic potential of large numbers of chemicals. In this context, computer-aided technologies that will allow for activity prediction of endocrine disruptors and environmental risk assessments are being developed. These technologies must be able to cope with diverse data and connect chemistry at the atomic level with the biological activity at the cellular, organ, and organism levels. Quantitative structure-activity relationship methods became popular for toxicity issues. They correlate the chemical structure of compounds with biological activity through a number of molecular descriptors (e.g., molecular weight and parameters to account for hydrophobicity, topology, or electronic properties). Chemical structure analysis is a first step; however, modeling intermolecular interactions and cellular behavior will also be essential. The increasing number of three-dimensional crystal structures of EDCs' targets has provided a wealth of structural information that can be used to predict their interactions with EDCs using docking and scoring procedures. In the present review, we have described the various computer-assisted approaches that use ligands and targets properties to predict endocrine disruptor activities.
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Affiliation(s)
- Melanie Schneider
- Centre de Biochimie Structurale, CNRS, INSERM, Université de Montpellier, Montpellier, France
| | - Jean-Luc Pons
- Centre de Biochimie Structurale, CNRS, INSERM, Université de Montpellier, Montpellier, France
| | - Gilles Labesse
- Centre de Biochimie Structurale, CNRS, INSERM, Université de Montpellier, Montpellier, France
- Correspondence: Gilles Labesse, PhD, or William Bourguet, PhD, Centre de Biochimie Structurale, 29 rue de Navacelles, 34090 Montpellier, France. E-mail: or
| | - William Bourguet
- Centre de Biochimie Structurale, CNRS, INSERM, Université de Montpellier, Montpellier, France
- Correspondence: Gilles Labesse, PhD, or William Bourguet, PhD, Centre de Biochimie Structurale, 29 rue de Navacelles, 34090 Montpellier, France. E-mail: or
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42
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Fan N, Bauer CA, Stork C, de Bruyn Kops C, Kirchmair J. ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance. Mol Inform 2019; 39:e1900103. [PMID: 31663691 PMCID: PMC7187304 DOI: 10.1002/minf.201900103] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 10/28/2019] [Indexed: 01/16/2023]
Abstract
Protein flexibility and solvation pose major challenges to docking algorithms and scoring functions. One established strategy for addressing these challenges is to use multiple protein conformations for docking (all-against-all ensemble docking). Recent studies have shown that the performance of ensemble docking can be improved by selecting the most relevant protein structures for docking. In search for a robust approach to protein structure selection, we have come up with an integrated mAchine Learning AnD DockINg approach (ALADDIN). ALADDIN employs a battery of random forest classifiers to select, individually for each compound of interest, from an ensemble of protein structures, the single most suitable protein structure for docking. ALADDIN outperformed the best single-structure docking runs, ensemble docking and a similarity-based docking approach on three out of four investigated targets, with up to 0.15, 0.11 and 0.16 higher area under the receiver operating characteristic curve (AUC) values, respectively. Only in the case of cytochrome P450 3A4, ALADDIN, like any of the other tested approaches, failed to obtain decent performance. ALADDIN can be particularly useful for structure-based virtual screening of malleable proteins, including kinases, some viral enzymes and anti-targets.
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Affiliation(s)
- Ningning Fan
- Universität Hamburg, Faculty of Mathematics, Informatics and Natural Sciences, Department of Informatics, Center for Bioinformatics, 20146, Hamburg, Germany
| | - Christoph A Bauer
- University of Bergen, Department of Chemistry, N-5020, Bergen, Norway.,University of Bergen, Computational Biology Unit (CBU), N-5020, Bergen, Norway
| | - Conrad Stork
- Universität Hamburg, Faculty of Mathematics, Informatics and Natural Sciences, Department of Informatics, Center for Bioinformatics, 20146, Hamburg, Germany
| | - Christina de Bruyn Kops
- Universität Hamburg, Faculty of Mathematics, Informatics and Natural Sciences, Department of Informatics, Center for Bioinformatics, 20146, Hamburg, Germany
| | - Johannes Kirchmair
- Universität Hamburg, Faculty of Mathematics, Informatics and Natural Sciences, Department of Informatics, Center for Bioinformatics, 20146, Hamburg, Germany.,University of Bergen, Department of Chemistry, N-5020, Bergen, Norway.,University of Bergen, Computational Biology Unit (CBU), N-5020, Bergen, Norway
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43
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Bruno A, Costantino G, Sartori L, Radi M. The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization. Curr Med Chem 2019; 26:3838-3873. [PMID: 29110597 DOI: 10.2174/0929867324666171107101035] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Revised: 09/27/2017] [Accepted: 09/28/2017] [Indexed: 01/04/2023]
Abstract
BACKGROUND Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug. METHODS In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan. RESULTS A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given. CONCLUSION The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.
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Affiliation(s)
- Agostino Bruno
- Experimental Therapeutics Unit, IFOM - The FIRC Institute for Molecular Oncology Foundation, Via Adamello 16 - 20139 Milano, Italy
| | - Gabriele Costantino
- Dipartimento di Scienze degli Alimenti e del Farmaco, Universita degli Studi di Parma, Viale delle Scienze, 27/A, 43124 Parma, Italy
| | - Luca Sartori
- Experimental Therapeutics Unit, IFOM - The FIRC Institute for Molecular Oncology Foundation, Via Adamello 16 - 20139 Milano, Italy
| | - Marco Radi
- Dipartimento di Scienze degli Alimenti e del Farmaco, Universita degli Studi di Parma, Viale delle Scienze, 27/A, 43124 Parma, Italy
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44
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Bresso E, Fernandez D, Amora DX, Noel P, Petitot AS, de Sa MEL, Albuquerque EVS, Danchin EGJ, Maigret B, Martins NF. A Chemosensory GPCR as a Potential Target to Control the Root-Knot Nematode Meloidogyne incognita Parasitism in Plants. Molecules 2019; 24:E3798. [PMID: 31652525 PMCID: PMC6832152 DOI: 10.3390/molecules24203798] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 01/31/2019] [Accepted: 02/01/2019] [Indexed: 01/10/2023] Open
Abstract
Root-knot nematodes (RKN), from the Meloidogyne genus, have a worldwide distribution and cause severe economic damage to many life-sustaining crops. Because of their lack of specificity and danger to the environment, most chemical nematicides have been banned from use. Thus, there is a great need for new and safe compounds to control RKN. Such research involves identifying beforehand the nematode proteins essential to the invasion. Since G protein-coupled receptors GPCRs are the target of a large number of drugs, we have focused our research on the identification of putative nematode GPCRs such as those capable of controlling the movement of the parasite towards (or within) its host. A datamining procedure applied to the genome of Meloidogyne incognita allowed us to identify a GPCR, belonging to the neuropeptide GPCR family that can serve as a target to carry out a virtual screening campaign. We reconstructed a 3D model of this receptor by homology modeling and validated it through extensive molecular dynamics simulations. This model was used for large scale molecular dockings which produced a filtered limited set of putative antagonists for this GPCR. Preliminary experiments using these selected molecules allowed the identification of an active compound, namely C260-2124, from the ChemDiv provider, which can serve as a starting point for further investigations.
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Affiliation(s)
- Emmanuel Bresso
- Université de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France.
- EMBRAPA Genetic Resources and Biotechnology, Brasilia 70770-917, DF, Brazil.
| | - Diana Fernandez
- EMBRAPA Genetic Resources and Biotechnology, Brasilia 70770-917, DF, Brazil.
- IRD, CIRAD, Université de Montpellier, IPME, F-34398 Montpellier, France.
| | - Deisy X Amora
- EMBRAPA Genetic Resources and Biotechnology, Brasilia 70770-917, DF, Brazil.
| | - Philippe Noel
- Université de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France.
| | | | | | | | - Etienne G J Danchin
- INRA, Université Côte d'Azur, CNRS, Institut Sophia Agrobiotech, F-06903 Sophia-Antipolis, France.
| | - Bernard Maigret
- Université de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France.
| | - Natália F Martins
- EMBRAPA Genetic Resources and Biotechnology, Brasilia 70770-917, DF, Brazil.
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45
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Torres PHM, Sodero ACR, Jofily P, Silva-Jr FP. Key Topics in Molecular Docking for Drug Design. Int J Mol Sci 2019; 20:E4574. [PMID: 31540192 PMCID: PMC6769580 DOI: 10.3390/ijms20184574] [Citation(s) in RCA: 176] [Impact Index Per Article: 35.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 07/09/2019] [Accepted: 07/10/2019] [Indexed: 12/18/2022] Open
Abstract
Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.
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Affiliation(s)
- Pedro H M Torres
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK.
| | - Ana C R Sodero
- Department of Drugs and Medicines; School of Pharmacy; Federal University of Rio de Janeiro, Rio de Janeiro 21949-900, RJ, Brazil.
| | - Paula Jofily
- Laboratório de Modelagem e Dinâmica Molecular, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21949-900, RJ, Brazil.
| | - Floriano P Silva-Jr
- Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro 21949-900, RJ, Brazil.
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46
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Bajusz D, Rácz A, Héberger K. Comparison of Data Fusion Methods as Consensus Scores for Ensemble Docking. Molecules 2019; 24:E2690. [PMID: 31344902 PMCID: PMC6695709 DOI: 10.3390/molecules24152690] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 07/18/2019] [Accepted: 07/22/2019] [Indexed: 12/05/2022] Open
Abstract
Ensemble docking is a widely applied concept in structure-based virtual screening-to at least partly account for protein flexibility-usually granting a significant performance gain at a modest cost of speed. From the individual, single-structure docking scores, a consensus score needs to be produced by data fusion: this is usually done by taking the best docking score from the available pool (in most cases- and in this study as well-this is the minimum score). Nonetheless, there are a number of other fusion rules that can be applied. We report here the results of a detailed statistical comparison of seven fusion rules for ensemble docking, on five case studies of current drug targets, based on four performance metrics. Sevenfold cross-validation and variance analysis (ANOVA) allowed us to highlight the best fusion rules. The results are presented in bubble plots, to unite the four performance metrics into a single, comprehensive image. Notably, we suggest the use of the geometric and harmonic means as better alternatives to the generally applied minimum fusion rule.
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Affiliation(s)
- Dávid Bajusz
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok krt. 2, H-1117 Budapest, Hungary
| | - Anita Rácz
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok krt. 2, H-1117 Budapest, Hungary.
| | - Károly Héberger
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok krt. 2, H-1117 Budapest, Hungary
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47
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Wong CF. Improving ensemble docking for drug discovery by machine learning. JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY 2019. [DOI: 10.1142/s0219633619200013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Ensemble docking has provided an inexpensive method to account for receptor flexibility in molecular docking. However, it is still unclear how best to use the docking scores from multiple structures to classify compounds into actives and inactives. Previous studies have also found that the performance of classification could decrease rather than increase with the number of structures included in the ensemble. Machine learning could help to alleviate these problems.
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Affiliation(s)
- Chung F. Wong
- Department of Chemistry and Biochemistry and Center for Nanoscience, University of Missouri-St. Louis, Saint Louis, MO 63121, USA
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48
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Slater O, Kontoyianni M. The compromise of virtual screening and its impact on drug discovery. Expert Opin Drug Discov 2019; 14:619-637. [PMID: 31025886 DOI: 10.1080/17460441.2019.1604677] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Introduction: Docking and structure-based virtual screening (VS) have been standard approaches in structure-based design for over two decades. However, our understanding of the limitations, potential, and strength of these techniques has enhanced, raising expectations. Areas covered: Based on a survey of reports in the past five years, we assess whether VS: (1) predicts binding poses in agreement with crystallographic data (when available); (2) is a superior screening tool, as often claimed; (3) is successful in identifying chemical scaffolds that can be starting points for subsequent lead optimization cycles. Data shows that knowledge of the target and its chemotypes in postprocessing lead to viable hits in early drug discovery endeavors. Expert opinion: VS is capable of accurate placements in the pocket for the most part, but does not consistently score screening collections accurately. What matters is capitalization on available resources to get closer to a viable lead or optimizable series. Integration of approaches, subjective hit selection guided by knowledge of the receptor or endogenous ligand, libraries driven by experimental guides, validation studies to identify the best docking/scoring that reproduces experimental findings, constraints regarding receptor-ligand interactions, thoroughly designed methodologies, and predefined cutoff scoring criteria strengthen VS's position in pharmaceutical research.
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Affiliation(s)
- Olivia Slater
- a Department of Pharmaceutical Sciences , Southern Illinois University Edwardsville , Edwardsville , IL , USA
| | - Maria Kontoyianni
- a Department of Pharmaceutical Sciences , Southern Illinois University Edwardsville , Edwardsville , IL , USA
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49
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Spiriti J, Subramanian SR, Palli R, Wu M, Zuckerman DM. Middle-way flexible docking: Pose prediction using mixed-resolution Monte Carlo in estrogen receptor α. PLoS One 2019; 14:e0215694. [PMID: 31013302 PMCID: PMC6478315 DOI: 10.1371/journal.pone.0215694] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 04/06/2019] [Indexed: 12/17/2022] Open
Abstract
There is a vast gulf between the two primary strategies for simulating protein-ligand interactions. Docking methods significantly limit or eliminate protein flexibility to gain great speed at the price of uncontrolled inaccuracy, whereas fully flexible atomistic molecular dynamics simulations are expensive and often suffer from limited sampling. We have developed a flexible docking approach geared especially for highly flexible or poorly resolved targets based on mixed-resolution Monte Carlo (MRMC), which is intended to offer a balance among speed, protein flexibility, and sampling power. The binding region of the protein is treated with a standard atomistic force field, while the remainder of the protein is modeled at the residue level with a Gō model that permits protein flexibility while saving computational cost. Implicit solvation is used. Here we assess three facets of the MRMC approach with implications for other docking studies: (i) the role of receptor flexibility in cross-docking pose prediction; (ii) the use of non-equilibrium candidate Monte Carlo (NCMC) and (iii) the use of pose-clustering in scoring. We examine 61 co-crystallized ligands of estrogen receptor α, an important cancer target known for its flexibility. We also compare the performance of the MRMC approach with Autodock smina. Adding protein flexibility, not surprisingly, leads to significantly lower total energies and stronger interactions between protein and ligand, but notably we document the important role of backbone flexibility in the improvement. The improved backbone flexibility also leads to improved performance relative to smina. Somewhat unexpectedly, our implementation of NCMC leads to only modestly improved sampling of ligand poses. Overall, the addition of protein flexibility improves the performance of docking, as measured by energy-ranked poses, but we do not find significant improvements based on cluster information or the use of NCMC. We discuss possible improvements for the model including alternative coarse-grained force fields, improvements to the treatment of solvation, and adding additional types of NCMC moves.
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Affiliation(s)
- Justin Spiriti
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, United States of America
| | - Sundar Raman Subramanian
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, United States of America
| | - Rohith Palli
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, United States of America
| | - Maria Wu
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, United States of America
| | - Daniel M. Zuckerman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, United States of America
- * E-mail:
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50
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Basciu A, Malloci G, Pietrucci F, Bonvin AMJJ, Vargiu AV. Holo-like and Druggable Protein Conformations from Enhanced Sampling of Binding Pocket Volume and Shape. J Chem Inf Model 2019; 59:1515-1528. [PMID: 30883122 DOI: 10.1021/acs.jcim.8b00730] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Understanding molecular recognition of small molecules by proteins in atomistic detail is key for drug design. Molecular docking is a widely used computational method to mimic ligand-protein association in silico. However, predicting conformational changes occurring in proteins upon ligand binding is still a major challenge. Ensemble docking approaches address this issue by considering a set of different conformations of the protein obtained either experimentally or from computer simulations, e.g., molecular dynamics. However, holo structures prone to host (the correct) ligands are generally poorly sampled by standard molecular dynamics simulations of the apo protein. In order to address this limitation, we introduce a computational approach based on metadynamics simulations called ensemble docking with enhanced sampling of pocket shape (EDES) that allows holo-like conformations of proteins to be generated by exploiting only their apo structures. This is achieved by defining a set of collective variables that effectively sample different shapes of the binding site, ultimately mimicking the steric effect due to the ligand. We assessed the method on three challenging proteins undergoing different extents of conformational changes upon ligand binding. In all cases our protocol generates a significant fraction of structures featuring a low RMSD from the experimental holo geometry. Moreover, ensemble docking calculations using those conformations yielded in all cases native-like poses among the top-ranked ones.
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Affiliation(s)
- Andrea Basciu
- Dipartimento di Fisica , Università di Cagliari, Cittadella Universitaria , I- 09042 Monserrato (CA) , Italy
| | - Giuliano Malloci
- Dipartimento di Fisica , Università di Cagliari, Cittadella Universitaria , I- 09042 Monserrato (CA) , Italy
| | - Fabio Pietrucci
- Sorbonne Université , Muséum National d'Histoire Naturelle, UMR CNRS 7590, IRD, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC , F-75005 Paris , France
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry , Utrecht University , Padualaan 8 , 3584 CH Utrecht , The Netherlands
| | - Attilio V Vargiu
- Dipartimento di Fisica , Università di Cagliari, Cittadella Universitaria , I- 09042 Monserrato (CA) , Italy.,Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry , Utrecht University , Padualaan 8 , 3584 CH Utrecht , The Netherlands
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