1
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Verma J, Vashisth H. Molecular basis for differential recognition of an allosteric inhibitor by receptor tyrosine kinases. Proteins 2024; 92:905-922. [PMID: 38506327 PMCID: PMC11222054 DOI: 10.1002/prot.26685] [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: 11/13/2023] [Revised: 02/08/2024] [Accepted: 03/06/2024] [Indexed: 03/21/2024]
Abstract
Understanding kinase-inhibitor selectivity continues to be a major objective in kinase drug discovery. We probe the molecular basis of selectivity of an allosteric inhibitor (MSC1609119A-1) of the insulin-like growth factor-I receptor kinase (IGF1RK), which has been shown to be ineffective for the homologous insulin receptor kinase (IRK). Specifically, we investigated the structural and energetic basis of the allosteric binding of this inhibitor to each kinase by combining molecular modeling, molecular dynamics (MD) simulations, and thermodynamic calculations. We predict the inhibitor conformation in the binding pocket of IRK and highlight that the charged residues in the histidine-arginine-aspartic acid (HRD) and aspartic acid-phenylalanine-glycine (DFG) motifs and the nonpolar residues in the binding pocket govern inhibitor interactions in the allosteric pocket of each kinase. We suggest that the conformational changes in the IGF1RK residues M1054 and M1079, movement of the ⍺C-helix, and the conformational stabilization of the DFG motif favor the selectivity of the inhibitor toward IGF1RK. Our thermodynamic calculations reveal that the observed selectivity can be rationalized through differences observed in the electrostatic interaction energy of the inhibitor in each inhibitor/kinase complex and the hydrogen bonding interactions of the inhibitor with the residue V1063 in IGF1RK that are not attained with the corresponding residue V1060 in IRK. Overall, our study provides a rationale for the molecular basis of recognition of this allosteric inhibitor by IGF1RK and IRK, which is potentially useful in developing novel inhibitors with improved affinity and selectivity.
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Affiliation(s)
- Jyoti Verma
- Department of Chemical Engineering and Bioengineering, University of New Hampshire, Durham, NH 03824
| | - Harish Vashisth
- Department of Chemical Engineering and Bioengineering, University of New Hampshire, Durham, NH 03824
- Department of Chemistry, University of New Hampshire, Durham, NH 03824
- Integrated Applied Mathematics Program, University of New Hampshire, Durham, NH 03824
- Molecular and Cellular Biotechnology Program, University of New Hampshire, Durham, NH 03824
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2
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Singh H, Kumar R, Mazumder A. Protein kinase inhibitors in the management of cancer: therapeutic opportunities from natural compounds. JOURNAL OF ASIAN NATURAL PRODUCTS RESEARCH 2024; 26:663-680. [PMID: 38373215 DOI: 10.1080/10286020.2024.2313546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 01/28/2024] [Indexed: 02/21/2024]
Abstract
Kinase is an enzyme that helps in the phosphorylation of the targeted molecules and can affect their ability to react with other molecules. So, kinase influences metabolic reactions like cell signaling, secretory processes, transport of molecules, etc. The increased activity of certain kinases may cause various types of cancer, i.e. leukemia, glioblastoma, and neuroblastomas. So, the growth of particular cancer cells can be prevented by the inhibition of the kinase responsible for those cancers. Natural products are the key resources for the development of new drugs where approximately 60% of anti-tumor drugs are being developed with the same including specific kinase dwellers. This study comprised molecular interactions of various molecules (obtained from natural sources) as kinase inhibitors for the treatment of cancer. It is expected that by analyzing the skeleton behavior, the process of action, and the body-related activity of these organic products, new cancer-avoiding molecules can be developed.
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Affiliation(s)
- Himanshu Singh
- Pharmaceutical Chemistry, Noida Institute of Engineering and Technology (Pharmacy Institute), Greater Noida, India
| | - Rajnish Kumar
- Pharmaceutical Chemistry, Noida Institute of Engineering and Technology (Pharmacy Institute), Greater Noida, India
| | - Avijit Mazumder
- Pharmaceutical Chemistry, Noida Institute of Engineering and Technology (Pharmacy Institute), Greater Noida, India
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3
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Jiang W. Studying the Collective Functional Response of a Receptor in Alchemical Ligand Binding Free Energy Simulations with Accelerated Solvation Layer Dynamics. J Chem Theory Comput 2024; 20:3085-3095. [PMID: 38568961 DOI: 10.1021/acs.jctc.4c00191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
Ligand binding free energy simulations (LB-FES) that involve sampling of protein functional conformations have been longstanding challenges in research on molecular recognition. Particularly, modeling of the conformational transition pathway and design of the heuristic biasing mechanism are severe bottlenecks for the existing enhanced configurational sampling (ECS) methods. Inspired by the key role of hydration in regulating conformational dynamics of macromolecules, this report proposes a novel ECS approach that facilitates binding-associated structural dynamics by accelerated hydration transitions in combination with the λ-exchange of free energy perturbation (FEP). Two challenging protein-ligand binding processes involving large configurational transitions of the receptor are studied, with hydration transitions at binding sites accelerated by Hamiltonian-simulated annealing of the hydration layer. Without the need for pathway analysis or ad hoc barrier flattening potential, LB-FES were performed with FEP/λ-exchange molecular dynamics simulation at a minor overhead for annealing of the hydration layer. The LB-FES studies showed that the accelerated rehydration significantly enhances the collective conformational transitions of the receptor, and convergence of binding affinity calculations is obtained at a sweet-spot simulation time scale. Alchemical LB-FES with the proposed ECS strategy is free from the effort of trial and error for the setup and realizes efficient on-the-fly sampling for the collective functional response of the receptor and bound water and therefore presents a practical approach to high-throughput screening in drug discovery.
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Affiliation(s)
- Wei Jiang
- Computational Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Building 240, Argonne, Illinois 60439, United States
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4
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Barragan AM, Ghaby K, Pond MP, Roux B. Computational Investigation of the Covalent Inhibition Mechanism of Bruton's Tyrosine Kinase by Ibrutinib. J Chem Inf Model 2024; 64:3488-3502. [PMID: 38546820 DOI: 10.1021/acs.jcim.4c00023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Covalent inhibitors represent a promising class of therapeutic compounds. Nonetheless, rationally designing covalent inhibitors to achieve a right balance between selectivity and reactivity remains extremely challenging. To better understand the covalent binding mechanism, a computational study is carried out using the irreversible covalent inhibitor of Bruton tyrosine kinase (BTK) ibrutinib as an example. A multi-μs classical molecular dynamics trajectory of the unlinked inhibitor is generated to explore the fluctuations of the compound associated with the kinase binding pocket. Then, the reaction pathway leading to the formation of the covalent bond with the cysteine residue at position 481 via a Michael addition is determined using the string method in collective variables on the basis of hybrid quantum mechanical-molecular mechanical (QM/MM) simulations. The reaction pathway shows a strong correlation between the covalent bond formation and the protonation/deprotonation events taking place sequentially in the covalent inhibition reaction, consistent with a 3-step reaction with transient thiolate and enolates intermediate states. Two possible atomistic mechanisms affecting deprotonation/protonation events from the thiolate to the enolate intermediate were observed: a highly correlated direct pathway involving proton transfer to the Cα of the acrylamide warhead from the cysteine involving one or a few water molecules and a more indirect pathway involving a long-lived enolate intermediate state following the escape of the proton to the bulk solution. The results are compared with experiments by simulating the long-time kinetics of the reaction using kinetic modeling.
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Affiliation(s)
- Angela M Barragan
- Department of Biochemistry and Molecular Biology, The University of Chicago, 929 E 57th Street, Chicago, Illinois 60637, United States
| | - Kyle Ghaby
- Department of Biochemistry and Molecular Biology, The University of Chicago, 929 E 57th Street, Chicago, Illinois 60637, United States
| | - Matthew P Pond
- Department of Biochemistry and Molecular Biology, The University of Chicago, 929 E 57th Street, Chicago, Illinois 60637, United States
| | - Benoît Roux
- Department of Biochemistry and Molecular Biology, The University of Chicago, 929 E 57th Street, Chicago, Illinois 60637, United States
- Department of Chemistry, The University of Chicago, 5735 S Ellis Avenue, Chicago, Illinois 60637, United States
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5
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Yu L, Gao Y, Aaron N, Qiang L. A glimpse of the connection between PPARγ and macrophage. Front Pharmacol 2023; 14:1254317. [PMID: 37701041 PMCID: PMC10493289 DOI: 10.3389/fphar.2023.1254317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 07/31/2023] [Indexed: 09/14/2023] Open
Abstract
Nuclear receptors are ligand-regulated transcription factors that regulate vast cellular activities and serve as an important class of drug targets. Among them, peroxisome proliferator-activated receptors (PPARs) are members of the nuclear receptor family and have been extensively studied for their roles in metabolism, differentiation, development, and cancer, among others. Recently, there has been considerable interest in understanding and defining the function of PPARs and their agonists in regulating innate and adaptive immune responses and their pharmacological potential in combating chronic inflammatory diseases. In this review, we focus on emerging evidence for the potential role of PPARγ in macrophage biology, which is the prior innate immune executive in metabolic and tissue homeostasis. We also discuss the role of PPARγ as a regulator of macrophage function in inflammatory diseases. Lastly, we discuss the possible application of PPARγ antagonists in metabolic pathologies.
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Affiliation(s)
- Lexiang Yu
- Naomi Berrie Diabetes Center, Columbia University, New York, NY, United States
- Department of Pathology and Cell Biology, Columbia University, New York, NY, United States
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Yuen Gao
- Department of Physiology, Michigan State University, East Lansing, MI, United States
| | - Nicole Aaron
- Naomi Berrie Diabetes Center, Columbia University, New York, NY, United States
- Department of Molecular Pharmacology and Therapeutics, Columbia University, New York, NY, United States
| | - Li Qiang
- Naomi Berrie Diabetes Center, Columbia University, New York, NY, United States
- Department of Pathology and Cell Biology, Columbia University, New York, NY, United States
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States
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6
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Yuan Y, Cui Q. Accurate and Efficient Multilevel Free Energy Simulations with Neural Network-Assisted Enhanced Sampling. J Chem Theory Comput 2023; 19:5394-5406. [PMID: 37527495 PMCID: PMC10810721 DOI: 10.1021/acs.jctc.3c00591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/03/2023]
Abstract
Free energy differences (ΔF) are essential to quantitative characterization and understanding of chemical and biological processes. Their direct estimation with an accurate quantum mechanical potential is of great interest and yet impractical due to high computational cost and incompatibility with typical alchemical free energy protocols. One promising solution is the multilevel free energy simulation in which the estimate of ΔF at an inexpensive low level of theory is combined with the correction toward a higher level of theory. The poor configurational overlap generally expected between the two levels of theory, however, presents a major challenge. We overcome this challenge by using a deep neural network model and enhanced sampling simulations. An adversarial autoencoder is used to identify a low-dimensional (latent) space that compactly represents the degrees of freedom that encode the distinct distributions at the two levels of theory. Enhanced sampling in this latent space is then used to drive the sampling of configurations that predominantly contribute to the free energy correction. Results for both gas phase and condensed phase systems demonstrate that this data-driven approach offers high accuracy and efficiency with great potential for scalability to complex systems.
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Affiliation(s)
- Yuchen Yuan
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Qiang Cui
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, Massachusetts 02215, United States
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7
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Gizzio J, Thakur A, Haldane A, Levy RM. Evolutionary divergence in the conformational landscapes of tyrosine vs serine/threonine kinases. eLife 2022; 11:83368. [PMID: 36562610 PMCID: PMC9822262 DOI: 10.7554/elife.83368] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/22/2022] [Indexed: 12/24/2022] Open
Abstract
Inactive conformations of protein kinase catalytic domains where the DFG motif has a "DFG-out" orientation and the activation loop is folded present a druggable binding pocket that is targeted by FDA-approved 'type-II inhibitors' in the treatment of cancers. Tyrosine kinases (TKs) typically show strong binding affinity with a wide spectrum of type-II inhibitors while serine/threonine kinases (STKs) usually bind more weakly which we suggest here is due to differences in the folded to extended conformational equilibrium of the activation loop between TKs vs. STKs. To investigate this, we use sequence covariation analysis with a Potts Hamiltonian statistical energy model to guide absolute binding free-energy molecular dynamics simulations of 74 protein-ligand complexes. Using the calculated binding free energies together with experimental values, we estimated free-energy costs for the large-scale (~17-20 Å) conformational change of the activation loop by an indirect approach, circumventing the very challenging problem of simulating the conformational change directly. We also used the Potts statistical potential to thread large sequence ensembles over active and inactive kinase states. The structure-based and sequence-based analyses are consistent; together they suggest TKs evolved to have free-energy penalties for the classical 'folded activation loop' DFG-out conformation relative to the active conformation, that is, on average, 4-6 kcal/mol smaller than the corresponding values for STKs. Potts statistical energy analysis suggests a molecular basis for this observation, wherein the activation loops of TKs are more weakly 'anchored' against the catalytic loop motif in the active conformation and form more stable substrate-mimicking interactions in the inactive conformation. These results provide insights into the molecular basis for the divergent functional properties of TKs and STKs, and have pharmacological implications for the target selectivity of type-II inhibitors.
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Affiliation(s)
- Joan Gizzio
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, United States.,Department of Chemistry, Temple University, Philadelphia, United States
| | - Abhishek Thakur
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, United States.,Department of Chemistry, Temple University, Philadelphia, United States
| | - Allan Haldane
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, United States.,Department of Physics, Temple University, Philadelphia, United States
| | - Ronald M Levy
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, United States.,Department of Chemistry, Temple University, Philadelphia, United States
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8
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Awoonor-Williams E. Estimating the binding energetics of reversible covalent inhibitors of the SARS-CoV-2 main protease: an in silico study. Phys Chem Chem Phys 2022; 24:23391-23401. [PMID: 36128834 DOI: 10.1039/d2cp03080b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The main protease (Mpro) of the SARS-CoV-2 virus is an attractive therapeutic target for developing antivirals to combat COVID-19. Mpro is essential for the replication cycle of the SARS-CoV-2 virus, so inhibiting Mpro blocks a vital piece of the cell replication machinery of the virus. A promising strategy to disrupt the viral replication cycle is to design inhibitors that bind to the active site cysteine (Cys145) of the Mpro. Cysteine targeted covalent inhibitors are gaining traction in drug discovery owing to the benefits of improved potency and extended drug-target engagement. An interesting aspect of these inhibitors is that they can be chemically tuned to form a covalent, but reversible bond, with their targets of interest. Several small-molecule cysteine-targeting covalent inhibitors of the Mpro have been discovered-some of which are currently undergoing evaluation in early phase human clinical trials. Understanding the binding energetics of these inhibitors could provide new insights to facilitate the design of potential drug candidates against COVID-19. Motivated by this, we employed rigorous absolute binding free energy calculations and hybrid quantum mechanical/molecular mechanical (QM/MM) calculations to estimate the energetics of binding of some promising reversible covalent inhibitors of the Mpro. We find that the inclusion of enhanced sampling techniques such as replica-exchange algorithm in binding free energy calculations can improve the convergence of predicted non-covalent binding free energy estimates of inhibitors binding to the Mpro target. In addition, our results indicate that binding free energy calculations coupled with multiscale simulations can be a useful approach to employ in ranking covalent inhibitors to their targets. This approach may be valuable in prioritizing and refining covalent inhibitor compounds for lead discovery efforts against COVID-19 and other coronavirus infections.
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Affiliation(s)
- Ernest Awoonor-Williams
- Department of Chemistry, Memorial University of Newfoundland, St. John's, NL, A1B 3X9, Canada.
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9
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Novel Tyrosine Kinase Inhibitors to Target Chronic Myeloid Leukemia. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27103220. [PMID: 35630697 PMCID: PMC9143943 DOI: 10.3390/molecules27103220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/13/2022] [Accepted: 05/16/2022] [Indexed: 11/19/2022]
Abstract
This paper reports on a novel series of tyrosine kinase inhibitors (TKIs) potentially useful for the treatment of chronic myeloid leukemia (CML). The newly designed and synthesized compounds are structurally related to nilotinib (NIL), a second-generation oral TKI, and to a series of imatinib (IM)-based TKIs, previously reported by our research group, these latter characterized by a hybrid structure between TKIs and heme oxygenase-1 (HO-1) inhibitors. The enzyme HO-1 was selected as an additional target since it is overexpressed in many cases of drug resistance, including CML. The new derivatives 1a–j correctly tackle the chimeric protein BCR-ABL. Therefore, the inhibition of TK was comparable to or higher than NIL and IM for many novel compounds, while most of the new analogs showed only moderate potency against HO-1. Molecular docking studies revealed insights into the binding mode with BCR-ABL and HO-1, providing a structural explanation for the differential activity. Cytotoxicity on K562 CML cells, both NIL-sensitive and -resistant, was evaluated. Notably, some new compounds strongly reduced the viability of K562 sensitive cells.
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10
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Shinobu A, Re S, Sugita Y. Practical Protocols for Efficient Sampling of Kinase-Inhibitor Binding Pathways Using Two-Dimensional Replica-Exchange Molecular Dynamics. Front Mol Biosci 2022; 9:878830. [PMID: 35573746 PMCID: PMC9099257 DOI: 10.3389/fmolb.2022.878830] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
Molecular dynamics (MD) simulations are increasingly used to study various biological processes such as protein folding, conformational changes, and ligand binding. These processes generally involve slow dynamics that occur on the millisecond or longer timescale, which are difficult to simulate by conventional atomistic MD. Recently, we applied a two-dimensional (2D) replica-exchange MD (REMD) method, which combines the generalized replica exchange with solute tempering (gREST) with the replica-exchange umbrella sampling (REUS) in kinase-inhibitor binding simulations, and successfully observed multiple ligand binding/unbinding events. To efficiently apply the gREST/REUS method to other kinase-inhibitor systems, we establish modified, practical protocols with non-trivial simulation parameter tuning. The current gREST/REUS simulation protocols are tested for three kinase-inhibitor systems: c-Src kinase with PP1, c-Src kinase with Dasatinib, and c-Abl kinase with Imatinib. We optimized the definition of kinase-ligand distance as a collective variable (CV), the solute temperatures in gREST, and replica distributions and umbrella forces in the REUS simulations. Also, the initial structures of each replica in the 2D replica space were prepared carefully by pulling each ligand from and toward the protein binding sites for keeping stable kinase conformations. These optimizations were carried out individually in multiple short MD simulations. The current gREST/REUS simulation protocol ensures good random walks in 2D replica spaces, which are required for enhanced sampling of inhibitor dynamics around a target kinase.
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Affiliation(s)
- Ai Shinobu
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Suyong Re
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health, and Nutrition, Ibaraki, Japan
| | - Yuji Sugita
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Saitama, Japan
- RIKEN Center for Computational Science, Kobe, Japan
- *Correspondence: Yuji Sugita,
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11
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CrkII/Abl phosphorylation cascade is critical for NLRC4 inflammasome activity and is blocked by Pseudomonas aeruginosa ExoT. Nat Commun 2022; 13:1295. [PMID: 35277504 PMCID: PMC8917168 DOI: 10.1038/s41467-022-28967-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 02/22/2022] [Indexed: 11/17/2022] Open
Abstract
Type 3 Secretion System (T3SS) is a highly conserved virulence structure that plays an essential role in the pathogenesis of many Gram-negative pathogenic bacteria, including Pseudomonas aeruginosa. Exotoxin T (ExoT) is the only T3SS effector protein that is expressed in all T3SS-expressing P. aeruginosa strains. Here we show that T3SS recognition leads to a rapid phosphorylation cascade involving Abl / PKCδ / NLRC4, which results in NLRC4 inflammasome activation, culminating in inflammatory responses that limit P. aeruginosa infection in wounds. We further show that ExoT functions as the main anti-inflammatory agent for P. aeruginosa in that it blocks the phosphorylation cascade through Abl / PKCδ / NLRC4 by targeting CrkII, which we further demonstrate to be important for Abl transactivation and NLRC4 inflammasome activation in response to T3SS and P. aeruginosa infection. Pseudomonas aeruginosa secretes the toxin ExoT, which is important for pathogenesis. Here, the authors show that ExoT inhibits NLRC4-dependent inflammatory responses during wound infection.
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12
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Bagchee-Clark AJ, Mucaki EJ, Whitehead T, Rogan PK. Pathway-extended gene expression signatures integrate novel biomarkers that improve predictions of patient responses to kinase inhibitors. MedComm (Beijing) 2021; 1:311-327. [PMID: 34766125 PMCID: PMC8491218 DOI: 10.1002/mco2.46] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 11/11/2020] [Accepted: 11/15/2020] [Indexed: 12/11/2022] Open
Abstract
Cancer chemotherapy responses have been related to multiple pharmacogenetic biomarkers, often for the same drug. This study utilizes machine learning to derive multi‐gene expression signatures that predict individual patient responses to specific tyrosine kinase inhibitors, including erlotinib, gefitinib, sorafenib, sunitinib, lapatinib and imatinib. Support vector machine (SVM) learning was used to train mathematical models that distinguished sensitivity from resistance to these drugs using a novel systems biology‐based approach. This began with expression of genes previously implicated in specific drug responses, then expanded to evaluate genes whose products were related through biochemical pathways and interactions. Optimal pathway‐extended SVMs predicted responses in patients at accuracies of 70% (imatinib), 71% (lapatinib), 83% (sunitinib), 83% (erlotinib), 88% (sorafenib) and 91% (gefitinib). These best performing pathway‐extended models demonstrated improved balance predicting both sensitive and resistant patient categories, with many of these genes having a known role in cancer aetiology. Ensemble machine learning‐based averaging of multiple pathway‐extended models derived for an individual drug increased accuracy to >70% for erlotinib, gefitinib, lapatinib and sorafenib. Through incorporation of novel cancer biomarkers, machine learning‐based pathway‐extended signatures display strong efficacy predicting both sensitive and resistant patient responses to chemotherapy.
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Affiliation(s)
- Ashis J Bagchee-Clark
- Department of Biochemistry, Schulich School of Medicine and Dentistry University of Western Ontario, London, Canada N6A 2C8 Canada
| | - Eliseos J Mucaki
- Department of Biochemistry, Schulich School of Medicine and Dentistry University of Western Ontario, London, Canada N6A 2C8 Canada
| | - Tyson Whitehead
- SHARCNET University of Western Ontario London Ontario N6A 5B7 Canada
| | - Peter K Rogan
- Department of Biochemistry, Schulich School of Medicine and Dentistry University of Western Ontario, London, Canada N6A 2C8 Canada.,Cytognomix Inc., 60 North Centre Road, Box 27052, London, Canada N5X 3X5 Canada
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13
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Patterson JR, Graves AP, Stoy P, Cheung M, Desai TA, Fries H, Gatto GJ, Holt DA, Shewchuk L, Totoritis R, Wang L, Kallander LS. Identification of Diarylurea Inhibitors of the Cardiac-Specific Kinase TNNI3K by Designing Selectivity Against VEGFR2, p38α, and B-Raf. J Med Chem 2021; 64:15651-15670. [PMID: 34699203 DOI: 10.1021/acs.jmedchem.1c00700] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
A series of diarylurea inhibitors of the cardiac-specific kinase TNNI3K were developed to elucidate the biological function of TNNI3K and evaluate TNNI3K as a therapeutic target for the treatment of cardiovascular diseases. Utilizing a structure-based design, enhancements in kinase selectivity were engineered into the series, capitalizing on the established X-ray crystal structures of TNNI3K, VEGFR2, p38α, and B-Raf. Our efforts culminated in the discovery of an in vivo tool compound 47 (GSK329), which exhibited desirable TNNI3K potency and rat pharmacokinetic properties as well as promising kinase selectivity against VEGFR2 (40-fold), p38α (80-fold), and B-Raf (>200-fold). Compound 47 demonstrated positive cardioprotective outcomes in a mouse model of ischemia/reperfusion cardiac injury, indicating that optimized exemplars from this series, such as 47, are favorable leads for discovering novel medicines for cardiac diseases.
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Affiliation(s)
- Jaclyn R Patterson
- Heart Failure Discovery Performance Unit, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, Pennsylvania 19426, United States
| | - Alan P Graves
- Platform Technology and Sciences, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, Pennsylvania 19426, United States
| | - Patrick Stoy
- Heart Failure Discovery Performance Unit, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, Pennsylvania 19426, United States
| | - Mui Cheung
- Heart Failure Discovery Performance Unit, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, Pennsylvania 19426, United States
| | - Tina A Desai
- Heart Failure Discovery Performance Unit, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, Pennsylvania 19426, United States
| | - Harvey Fries
- Heart Failure Discovery Performance Unit, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, Pennsylvania 19426, United States
| | - Gregory J Gatto
- Heart Failure Discovery Performance Unit, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, Pennsylvania 19426, United States
| | - Dennis A Holt
- Heart Failure Discovery Performance Unit, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, Pennsylvania 19426, United States
| | - Lisa Shewchuk
- Platform Technology and Sciences, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, Pennsylvania 19426, United States
| | - Rachel Totoritis
- Platform Technology and Sciences, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, Pennsylvania 19426, United States
| | - Liping Wang
- Platform Technology and Sciences, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, Pennsylvania 19426, United States
| | - Lara S Kallander
- Heart Failure Discovery Performance Unit, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, Pennsylvania 19426, United States
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14
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Awoonor-Williams E, Rowley CN. Modeling the Binding and Conformational Energetics of a Targeted Covalent Inhibitor to Bruton's Tyrosine Kinase. J Chem Inf Model 2021; 61:5234-5242. [PMID: 34590480 DOI: 10.1021/acs.jcim.1c00897] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Targeted covalent inhibitors (TCIs) bind to their targets in both covalent and noncovalent modes, providing exceptionally high affinity and selectivity. These inhibitors have been effectively employed as inhibitors of protein kinases, with Taunton and coworkers (Nat. Chem. Biol. 2015, 11, 525-531) reporting a notable example of a TCI with a cyanoacrylamide warhead that forms a covalent thioether linkage to an active-site cysteine (Cys481) of Bruton's tyrosine kinase (BTK). The specific mechanism of the binding and the relative importance of the covalent and noncovalent interactions is difficult to determine experimentally, and established simulation methods for calculating the absolute binding affinity of an inhibitor cannot describe the covalent bond-forming steps. Here, an integrated approach using alchemical free-energy perturbation and QM/MM molecular dynamics methods was employed to model the complete Gibbs energy profile for the covalent inhibition of BTK by a cyanoacrylamide TCI. These calculations provide a rigorous and complete absolute Gibbs energy profile of the covalent modification binding process. Following a classic thiol-Michael addition mechanism, the target cysteine is deprotonated to form a nucleophilic thiolate, which then undergoes a facile conjugate addition to the electrophilic functional group to form a bond with the noncovalently bound ligand. This model predicts that the formation of the covalent linkage is highly exergonic relative to the noncovalent binding alone. Nevertheless, noncovalent interactions between the ligand and individual amino acid residues in the binding pocket of the enzyme are also essential for ligand binding, particularly van der Waals dispersion forces, which have a larger contribution to the binding energy than the covalent component in absolute terms. This model also shows that the mechanism of covalent modification of a protein occurs through a complex series of steps and that entropy, conformational flexibility, noncovalent interactions, and the formation of covalent linkage are all significant factors in the ultimate binding affinity of a covalent drug to its target.
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Affiliation(s)
- Ernest Awoonor-Williams
- Department of Chemistry, Memorial University of Newfoundland, St. John's, Newfoundland A1B 3X9, Canada
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Thomas T, Roux B. TYROSINE KINASES: COMPLEX MOLECULAR SYSTEMS CHALLENGING COMPUTATIONAL METHODOLOGIES. THE EUROPEAN PHYSICAL JOURNAL. B 2021; 94:203. [PMID: 36524055 PMCID: PMC9749240 DOI: 10.1140/epjb/s10051-021-00207-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 09/14/2021] [Indexed: 05/28/2023]
Abstract
Classical molecular dynamics (MD) simulations based on atomic models play an increasingly important role in a wide range of applications in physics, biology, and chemistry. Nonetheless, generating genuine knowledge about biological systems using MD simulations remains challenging. Protein tyrosine kinases are important cellular signaling enzymes that regulate cell growth, proliferation, metabolism, differentiation, and migration. Due to the large conformational changes and long timescales involved in their function, these kinases present particularly challenging problems to modern computational and theoretical frameworks aimed at elucidating the dynamics of complex biomolecular systems. Markov state models have achieved limited success in tackling the broader conformational ensemble and biased methods are often employed to examine specific long timescale events. Recent advances in machine learning continue to push the limitations of current methodologies and provide notable improvements when integrated with the existing frameworks. A broad perspective is drawn from a critical review of recent studies.
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16
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Computational studies of anaplastic lymphoma kinase mutations reveal common mechanisms of oncogenic activation. Proc Natl Acad Sci U S A 2021; 118:2019132118. [PMID: 33674381 PMCID: PMC7958353 DOI: 10.1073/pnas.2019132118] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
High-risk tumors are genomically heterogeneous, harboring gene amplifications and mutations. The activation status of mutated proteins in cancer can profoundly impact disease progression, patient response, and drug sensitivity. Yet, outside of a few hotspot mutations, functional studies of clinically observed mutations are not commonly pursued. We report a combined experimental profiling and computational analysis of the effects of clinically observed and “test” mutations in the kinase domain of anaplastic lymphoma kinase (ALK), a known oncogenic driver in pediatric neuroblastoma. We find that the activation status of the mutated protein is a good indicator of the transforming ability in NIH 3T3 cells. We also report biophysical as well as data-driven models with predictive power to profile these mutant kinases in silico. Kinases play important roles in diverse cellular processes, including signaling, differentiation, proliferation, and metabolism. They are frequently mutated in cancer and are the targets of a large number of specific inhibitors. Surveys of cancer genome atlases reveal that kinase domains, which consist of 300 amino acids, can harbor numerous (150 to 200) single-point mutations across different patients in the same disease. This preponderance of mutations—some activating, some silent—in a known target protein make clinical decisions for enrolling patients in drug trials challenging since the relevance of the target and its drug sensitivity often depend on the mutational status in a given patient. We show through computational studies using molecular dynamics (MD) as well as enhanced sampling simulations that the experimentally determined activation status of a mutated kinase can be predicted effectively by identifying a hydrogen bonding fingerprint in the activation loop and the αC-helix regions, despite the fact that mutations in cancer patients occur throughout the kinase domain. In our study, we find that the predictive power of MD is superior to a purely data-driven machine learning model involving biochemical features that we implemented, even though MD utilized far fewer features (in fact, just one) in an unsupervised setting. Moreover, the MD results provide key insights into convergent mechanisms of activation, primarily involving differential stabilization of a hydrogen bond network that engages residues of the activation loop and αC-helix in the active-like conformation (in >70% of the mutations studied, regardless of the location of the mutation).
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17
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Beeston HS, Klein T, Norman RA, Tucker JA, Anderson M, Ashcroft AE, Holdgate GA. Validation of ion mobility spectrometry - mass spectrometry as a screening tool to identify type II kinase inhibitors of FGFR1 kinase. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2021:e9130. [PMID: 34038603 DOI: 10.1002/rcm.9130] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/10/2021] [Accepted: 05/24/2021] [Indexed: 05/22/2023]
Abstract
RATIONALE The protein kinase FGFR1 regulates cellular processes in human development. As over-activity of FGFR1 is implicated with cancer, effective inhibitors are in demand. Type I inhibitors, which bind to the active form of FGFR1, are less effective than type II inhibitors, which bind to the inactive form. Screening to distinguish between type I and type II inhibitors is required. METHODS X-ray crystallography was used to indicate whether a range of potential inhibitors bind to the active or inactive FGFR1 kinase conformation. The binding affinity of each ligand to FGFR1 was measured using biochemical methods. Electrospray ionisation - ion mobility spectrometry - mass spectrometry (ESI-IMS-MS) in conjunction with collision-induced protein unfolding generated a conformational profile of each FGFR1-ligand complex. The results indicate that the protein's conformational profile depends on whether the inhibitor is type I or type II. RESULTS X-ray crystallography confirmed which of the kinase inhibitors bind to the active or inactive form of FGFR1 kinase. Collision-induced unfolding combined with ESI-IMS-MS showed distinct differences in the FGFR1 folding landscape for type I and type II inhibitors. Biochemical studies indicated a similar range of FGFR1 affinities for both types of inhibitors, thus providing confidence that the conformational variations detected using ESI-IMS-MS can be interpretated unequivocally and that this is an effective screening method. CONCLUSIONS A robust ESI-IMS-MS method has been implemented to distinguish between the binding mode of type I and type II inhibitors by monitoring the conformational unfolding profile of FGFR1. This rapid method requires low sample concentrations and could be used as a high-throughput screening technique for the characterisation of novel kinase inhibitors.
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Affiliation(s)
- Helen S Beeston
- Astbury Centre for Structural Molecular Biology & Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK
| | - Tobias Klein
- Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Alderley Park, Macclesfield, SK10 4TG, UK
| | - Richard A Norman
- Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Alderley Park, Macclesfield, SK10 4TG, UK
| | - Julie A Tucker
- Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Alderley Park, Macclesfield, SK10 4TG, UK
| | - Malcolm Anderson
- Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Alderley Park, Macclesfield, SK10 4TG, UK
| | - Alison E Ashcroft
- Astbury Centre for Structural Molecular Biology & Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK
| | - Geoffrey A Holdgate
- Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Alderley Park, Macclesfield, SK10 4TG, UK
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18
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Arasteh S, Zhang BW, Levy RM. Protein Loop Conformational Free Energy Changes via an Alchemical Path without Reaction Coordinates. J Phys Chem Lett 2021; 12:4368-4377. [PMID: 33938761 PMCID: PMC8170697 DOI: 10.1021/acs.jpclett.1c00778] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
We introduce a method called restrain-free energy perturbation-release 2.0 (R-FEP-R 2.0) to estimate conformational free energy changes of protein loops via an alchemical path. R-FEP-R 2.0 is a generalization of the method called restrain-free energy perturbation-release (R-FEP-R) that can only estimate conformational free energy changes of protein side chains but not loops. The reorganization of protein loops is a central feature of many biological processes. Unlike other advanced sampling algorithms such as umbrella sampling and metadynamics, R-FEP-R and R-FEP-R 2.0 do not require predetermined collective coordinates and transition pathways that connect the two endpoint conformational states. The R-FEP-R 2.0 method was applied to estimate the conformational free energy change of a β-turn flip in the protein ubiquitin. The result obtained by R-FEP-R 2.0 agrees with the benchmarks very well. We also comment on problems commonly encountered when applying umbrella sampling to calculate protein conformational free energy changes.
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Affiliation(s)
- Shima Arasteh
- Center for Biophysics and Computational Biology and Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Bin W Zhang
- Center for Biophysics and Computational Biology and Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Ronald M Levy
- Center for Biophysics and Computational Biology and Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
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Narayan B, Buchete NV, Elber R. Computer Simulations of the Dissociation Mechanism of Gleevec from Abl Kinase with Milestoning. J Phys Chem B 2021; 125:5706-5715. [PMID: 33930271 DOI: 10.1021/acs.jpcb.1c00264] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Gleevec (a.k.a., imatinib) is an important anticancer (e.g., chronic myeloid leukemia) chemotherapeutic drug due to its inhibitory interaction with the Abl kinase. Here, we use atomically detailed simulations within the Milestoning framework to study the molecular dissociation mechanism of Gleevec from Abl kinase. We compute the dissociation free energy profile, the mean first passage time for unbinding, and explore the transition state ensemble of conformations. The milestones form a multidimensional network with average connectivity of about 2.93, which is significantly higher than the connectivity for a one-dimensional reaction coordinate. The free energy barrier for Gleevec dissociation is estimated to be ∼10 kcal/mol, and the exit time is ∼55 ms. We examined the transition state conformations using both, the committor and transition function. We show that near the transition state the highly conserved salt bridge K217 and E286 is transiently broken. Together with the calculated free energy profile, these calculations can advance the understanding of the molecular interaction mechanisms between Gleevec and Abl kinase and play a role in future drug design and optimization studies.
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Affiliation(s)
- Brajesh Narayan
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland.,Institute for Discovery, University College Dublin, Belfield, Dublin 4, Ireland
| | - Nicolae-Viorel Buchete
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland.,Institute for Discovery, University College Dublin, Belfield, Dublin 4, Ireland
| | - Ron Elber
- Oden Institute for Computational Engineering and Science, Department of Chemistry, University of Texas at Austin, Austin Texas 78712, United States
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20
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Awoonor-Williams E, Abu-Saleh AAAA. Covalent and non-covalent binding free energy calculations for peptidomimetic inhibitors of SARS-CoV-2 main protease. Phys Chem Chem Phys 2021; 23:6746-6757. [PMID: 33711090 DOI: 10.1039/d1cp00266j] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
COVID-19, the disease caused by the newly discovered coronavirus-SARS-CoV-2, has created a global health, social, and economic crisis. As of mid-January 2021, there are over 90 million confirmed cases and more than 2 million reported deaths due to COVID-19. Currently, there are very limited therapeutics for the treatment or prevention of COVID-19. For this reason, it is important to find drug targets that will lead to the development of safe and effective therapeutics against the disease. The main protease (Mpro) of the virus is an attractive target for the development of effective antiviral therapeutics because it is required for proteolytic cleavage of viral polyproteins. Furthermore, the Mpro has no human homologues, so drugs designed to bind to this target directly have less risk for off-target effects. Recently, several high-resolution crystallographic structures of the Mpro in complex with inhibitors have been determined-to guide drug development and to spur efforts in structure-based drug design. One of the primary objectives of modern structure-based drug design is the accurate prediction of receptor-ligand binding affinities for rational drug design and discovery. Here, we perform rigorous alchemical absolute binding free energy calculations and QM/MM calculations to give insight into the total binding energy of two recently crystallized inhibitors of SARS-CoV-2 Mpro, namely, N3 and α-ketoamide 13b. The total binding energy consists of both covalent and non-covalent binding components since both compounds are covalent inhibitors of the Mpro. Our results indicate that the covalent and non-covalent binding free energy contributions of both inhibitors to the Mpro target differ significantly. The N3 inhibitor has more favourable non-covalent interactions, particularly hydrogen bonding, in the binding site of the Mpro than the α-ketoamide inhibitor. Also, the Gibbs energy of reaction for the Mpro-N3 covalent adduct is greater than the Gibbs reaction energy for the Mpro-α-ketoamide covalent adduct. These differences in the covalent and non-covalent binding free energy contributions for both inhibitors could be a plausible explanation for their in vitro differences in antiviral activity. Our findings are consistent with the reversible and irreversible character of both inhibitors as reported by experiment and highlight the importance of both covalent and non-covalent binding free energy contributions to the absolute binding affinity of a covalent inhibitor towards its target. This information could prove useful in the rational design, discovery, and evaluation of potent SARS-CoV-2 Mpro inhibitors for targeted antiviral therapy.
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Affiliation(s)
- Ernest Awoonor-Williams
- Department of Chemistry, Memorial University of Newfoundland, St. John's, NL A1B 3X9, Canada.
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21
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Ma Z, Huang SY, Cheng F, Zou X. Rapid Identification of Inhibitors and Prediction of Ligand Selectivity for Multiple Proteins: Application to Protein Kinases. J Phys Chem B 2021; 125:2288-2298. [PMID: 33651624 DOI: 10.1021/acs.jpcb.1c00016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Rapid identification of inhibitors for a family of proteins and prediction of ligand specificity are highly desirable for structure-based drug design. However, sequentially docking ligands into each protein target with conventional single-target docking methods is too computationally expensive to achieve these two goals, especially when the number of the targets is large. In this work, we use an efficient ensemble docking algorithm for simultaneous docking of ligands against multiple protein targets. We use protein kinases, a family of proteins that are highly important for many cellular processes and for rational drug design, as an example to demonstrate the feasibility of investigating ligand selectivity with this algorithm. Specifically, 14 human protein kinases were selected. First, native docking calculations were performed to test the ability of our energy scoring function to reproduce the experimentally determined structures of the ligand-protein kinase complexes. Next, cross-docking calculations were conducted using our ensemble docking algorithm to study ligand selectivity, based on the assumption that the native target of an inhibitor should have a more negative (i.e., favorable) energy score than the non-native targets. Staurosporine and Gleevec were studied as examples of nonselective and selective binding, respectively. Virtual ligand screening was also performed against five protein kinases that have at least seven known inhibitors. Our quantitative analysis of the results showed that the ensemble algorithm can be effective on screening for inhibitors and investigating their selectivities for multiple target proteins.
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Affiliation(s)
- Zhiwei Ma
- Dalton Cardiovascular Research Center, Department of Physics and Astronomy, Department of Biochemistry, Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Sheng-You Huang
- Dalton Cardiovascular Research Center, Department of Physics and Astronomy, Department of Biochemistry, Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Fei Cheng
- McCombs School of Business, University of Texas, Austin, Texas 78712, United States
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, Department of Physics and Astronomy, Department of Biochemistry, Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
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22
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Sohraby F, Javaheri Moghadam M, Aliyar M, Aryapour H. A boosted unbiased molecular dynamics method for predicting ligands binding mechanisms: probing the binding pathway of dasatinib to Src-kinase. Bioinformatics 2021; 36:4714-4720. [PMID: 32525544 DOI: 10.1093/bioinformatics/btaa565] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 06/03/2020] [Accepted: 06/05/2020] [Indexed: 11/14/2022] Open
Abstract
SUMMARY Small molecules such as metabolites and drugs play essential roles in biological processes and pharmaceutical industry. Knowing their interactions with biomacromolecular targets demands a deep understanding of binding mechanisms. Dozens of papers have suggested that discovering of the binding event by means of conventional unbiased molecular dynamics (MD) simulation urges considerable amount of computational resources, therefore, only one who holds a cluster or a supercomputer can afford such extensive simulations. Thus, many researchers who do not own such resources are reluctant to take the benefits of running unbiased MD simulation, in full atomistic details, when studying a ligand binding pathway. Many researchers are impelled to be content with biased MD simulations which seek its validation due to its intrinsic preconceived framework. In this work, we have presented a workable stratagem to encourage everyone to perform unbiased (unguided) MD simulations, in this case a protein-ligand binding process, by typical desktop computers and so achieve valuable results in nanosecond time scale. Here, we have described a dynamical binding's process of an anticancer drug, the dasatinib, to the c-Src kinase in full atomistic details for the first time, without applying any biasing force or potential which may lead the drug to artificial interactions with the protein. We have attained multiple independent binding events which occurred in the nanosecond time scales, surprisingly as little as ∼30 ns. Both the protonated and deprotonated forms of the dasatinib reached the crystallographic binding mode without having any major intermediate state during induction. AVAILABILITY AND IMPLEMENTATION The links of the tutorial and technical documents are accessible in the article. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Farzin Sohraby
- Department of Biology, Faculty of Science, Golestan University, Gorgan 4913815759, Iran
| | | | - Masoud Aliyar
- Department of Biology, Faculty of Science, Golestan University, Gorgan 4913815759, Iran
| | - Hassan Aryapour
- Department of Biology, Faculty of Science, Golestan University, Gorgan 4913815759, Iran
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23
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Albanese SK, Chodera JD, Volkamer A, Keng S, Abel R, Wang L. Is Structure-Based Drug Design Ready for Selectivity Optimization? J Chem Inf Model 2020; 60:6211-6227. [PMID: 33119284 PMCID: PMC8310368 DOI: 10.1021/acs.jcim.0c00815] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Alchemical free-energy calculations are now widely used to drive or maintain potency in small-molecule lead optimization with a roughly 1 kcal/mol accuracy. Despite this, the potential to use free-energy calculations to drive optimization of compound selectivity among two similar targets has been relatively unexplored in published studies. In the most optimistic scenario, the similarity of binding sites might lead to a fortuitous cancellation of errors and allow selectivity to be predicted more accurately than affinity. Here, we assess the accuracy with which selectivity can be predicted in the context of small-molecule kinase inhibitors, considering the very similar binding sites of human kinases CDK2 and CDK9 as well as another series of ligands attempting to achieve selectivity between the more distantly related kinases CDK2 and ERK2. Using a Bayesian analysis approach, we separate systematic from statistical errors and quantify the correlation in systematic errors between selectivity targets. We find that, in the CDK2/CDK9 case, a high correlation in systematic errors suggests that free-energy calculations can have significant impact in aiding chemists in achieving selectivity, while in more distantly related kinases (CDK2/ERK2), the correlation in systematic error suggests that fortuitous cancellation may even occur between systems that are not as closely related. In both cases, the correlation in systematic error suggests that longer simulations are beneficial to properly balance statistical error with systematic error to take full advantage of the increase in apparent free-energy calculation accuracy in selectivity prediction.
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Affiliation(s)
- Steven K. Albanese
- Louis V. Gerstner, Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Andrea Volkamer
- Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin
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24
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Lahey SLJ, Thien Phuc TN, Rowley CN. Benchmarking Force Field and the ANI Neural Network Potentials for the Torsional Potential Energy Surface of Biaryl Drug Fragments. J Chem Inf Model 2020; 60:6258-6268. [PMID: 33263401 DOI: 10.1021/acs.jcim.0c00904] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Many drug molecules contain biaryl fragments, resulting in a torsional barrier corresponding to rotation around the bond linking the aryls. The potential energy surfaces of these torsions vary significantly because of steric and electronic effects, ultimately affecting the relative stability of the molecular conformations in the protein-bound and solution states. Simulations of protein-ligand binding require accurate computational models to represent the intramolecular interactions to provide accurate predictions of the structure and dynamics of binding. In this article, we compare four force fields [generalized AMBER force field (GAFF), open force field (OpenFF), CHARMM general force field (CGenFF), optimized potentials for liquid simulations (OPLS)] and two neural network potentials (ANI-2x and ANI-1ccx) for their ability to predict the torsional potential energy surfaces of 88 biaryls extracted from drug fragments. The root mean square deviation (rmsd) over the full potential energy surface and the mean absolute deviation of the torsion rotational barrier height (MADB) relative to high-level ab initio reference data (CCSD(T1)*) were used as the measure of accuracy. Uncertainties in these metrics due to the composition of the data set were estimated using bootstrap analysis. In comparison to high-level ab initio data, ANI-1ccx was most accurate for predicting the barrier height (rmsd: 0.5 ± 0.0 kcal/mol, MADB: 0.8 ± 0.1 kcal/mol), followed closely by ANI-2x (rmsd: 0.5 ± 0.0 kcal/mol, MADB: 1.0 ± 0.2 kcal/mol), then CGenFF (rmsd: 0.8 ± 0.1 kcal/mol, MADB: 1.3 ± 0.1 kcal/mol) and OpenFF (rmsd: 0.7 ± 0.1 kcal/mol, MADB: 1.3 ± 0.1 kcal/mol), then GAFF (rmsd: 1.2 ± 0.2 kcal/mol, MADB: 2.6 ± 0.5 kcal/mol), and finally OPLS (rmsd: 3.6 ± 0.3 kcal/mol, MADB: 3.6 ± 0.3 kcal/mol). Significantly, the neural network potentials (NNPs) are systematically more accurate and more reliable than any of the force fields. As a practical example, the NNP/molecular mechanics method was used to simulate the isomerization of ozanimod, a drug used for multiple sclerosis. Multinanosecond molecular dynamics (MD) simulations in an explicit aqueous solvent were performed, as well as umbrella sampling and adaptive biasing force-enhanced sampling techniques. The rate constant for this isomerization calculated using transition state theory was 4.30 × 10-1 ns-1, which is consistent with direct MD simulations.
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Affiliation(s)
- Shae-Lynn J Lahey
- Department of Chemistry, Memorial University of Newfoundland, St. John's, Newfoundland and Labrador A1B 3T4, Canada
| | - Tu Nguyen Thien Phuc
- Department of Chemistry, Memorial University of Newfoundland, St. John's, Newfoundland and Labrador A1B 3T4, Canada
| | - Christopher N Rowley
- Department of Chemistry, Memorial University of Newfoundland, St. John's, Newfoundland and Labrador A1B 3T4, Canada
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25
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Paul F, Thomas T, Roux B. Diversity of Long-Lived Intermediates along the Binding Pathway of Imatinib to Abl Kinase Revealed by MD Simulations. J Chem Theory Comput 2020; 16:7852-7865. [PMID: 33147951 DOI: 10.1021/acs.jctc.0c00739] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Imatinib, a drug used for the treatment of chronic myeloid leukemia and other cancers, works by blocking the catalytic site of pathological constitutively active Abl kinase. While the binding pose is known from X-ray crystallography, the different steps leading to the formation of the complex are not well understood. The results from extensive molecular dynamics simulations show that imatinib can primarily exit the known crystallographic binding pose through the cleft of the binding site or by sliding under the αC helix. Once displaced from the crystallographic binding pose, imatinib becomes trapped in intermediate states. These intermediates are characterized by a high diversity of ligand orientations and conformations, and relaxation timescales within this region may exceed 3-4 ms. Analysis indicates that the metastable intermediate states should be spectroscopically indistinguishable from the crystallographic binding pose, in agreement with tryptophan stopped-flow fluorescence experiments.
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Affiliation(s)
- Fabian Paul
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, Illinois 60637, United States
| | - Trayder Thomas
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, Illinois 60637, United States
| | - Benoît Roux
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, Illinois 60637, United States
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26
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Xie T, Saleh T, Rossi P, Kalodimos CG. Conformational states dynamically populated by a kinase determine its function. Science 2020; 370:science.abc2754. [PMID: 33004676 DOI: 10.1126/science.abc2754] [Citation(s) in RCA: 99] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 08/12/2020] [Indexed: 12/12/2022]
Abstract
Protein kinases intrinsically sample a number of conformational states with distinct catalytic and binding activities. We used nuclear magnetic resonance spectroscopy to describe in atomic-level detail how Abl kinase interconverts between an active and two discrete inactive structures. Extensive differences in key structural elements between the conformational states give rise to multiple intrinsic regulatory mechanisms. The findings explain how oncogenic mutants can counteract inhibitory mechanisms to constitutively activate the kinase. Energetic dissection revealed the contributions of the activation loop, the Asp-Phe-Gly (DFG) motif, the regulatory spine, and the gatekeeper residue to kinase regulation. Characterization of the transient conformation to which the drug imatinib binds enabled the elucidation of drug-resistance mechanisms. Structural insight into inactive states highlights how they can be leveraged for the design of selective inhibitors.
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Affiliation(s)
- Tao Xie
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Tamjeed Saleh
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Paolo Rossi
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
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27
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Verkhivker GM, Agajanian S, Hu G, Tao P. Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning. Front Mol Biosci 2020; 7:136. [PMID: 32733918 PMCID: PMC7363947 DOI: 10.3389/fmolb.2020.00136] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 06/08/2020] [Indexed: 12/12/2022] Open
Abstract
Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the "second secret of life." The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of allosteric modulators. The unifying theme and overarching goal of allosteric regulation studies in recent years have been integration between emerging experiment and computational approaches and technologies to advance quantitative characterization of allosteric mechanisms in proteins. Despite significant advances, the quantitative characterization and reliable prediction of functional allosteric states, interactions, and mechanisms continue to present highly challenging problems in the field. In this review, we discuss simulation-based multiscale approaches, experiment-informed Markovian models, and network modeling of allostery and information-theoretical approaches that can describe the thermodynamics and hierarchy allosteric states and the molecular basis of allosteric mechanisms. The wealth of structural and functional information along with diversity and complexity of allosteric mechanisms in therapeutically important protein families have provided a well-suited platform for development of data-driven research strategies. Data-centric integration of chemistry, biology and computer science using artificial intelligence technologies has gained a significant momentum and at the forefront of many cross-disciplinary efforts. We discuss new developments in the machine learning field and the emergence of deep learning and deep reinforcement learning applications in modeling of molecular mechanisms and allosteric proteins. The experiment-guided integrated approaches empowered by recent advances in multiscale modeling, network science, and machine learning can lead to more reliable prediction of allosteric regulatory mechanisms and discovery of allosteric modulators for therapeutically important protein targets.
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Affiliation(s)
- Gennady M. Verkhivker
- Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA, United States
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, United States
| | - Steve Agajanian
- Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA, United States
| | - Guang Hu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Peng Tao
- Department of Chemistry, Center for Drug Discovery, Design, and Delivery (CD4), Center for Scientific Computation, Southern Methodist University, Dallas, TX, United States
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Singh N, Li W. Absolute Binding Free Energy Calculations for Highly Flexible Protein MDM2 and Its Inhibitors. Int J Mol Sci 2020; 21:ijms21134765. [PMID: 32635537 PMCID: PMC7369993 DOI: 10.3390/ijms21134765] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Revised: 06/26/2020] [Accepted: 07/02/2020] [Indexed: 01/16/2023] Open
Abstract
Reliable prediction of binding affinities for ligand-receptor complex has been the primary goal of a structure-based drug design process. In this respect, alchemical methods are evolving as a popular choice to predict the binding affinities for biomolecular complexes. However, the highly flexible protein-ligand systems pose a challenge to the accuracy of binding free energy calculations mostly due to insufficient sampling. Herein, integrated computational protocol combining free energy perturbation based absolute binding free energy calculation with free energy landscape method was proposed for improved prediction of binding free energy for flexible protein-ligand complexes. The proposed method is applied to the dataset of various classes of p53-MDM2 (murine double minute 2) inhibitors. The absolute binding free energy calculations for MDMX (murine double minute X) resulted in a mean absolute error value of 0.816 kcal/mol while it is 3.08 kcal/mol for MDM2, a highly flexible protein compared to MDMX. With the integration of the free energy landscape method, the mean absolute error for MDM2 is improved to 1.95 kcal/mol.
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Affiliation(s)
- Nidhi Singh
- Institute for Advanced Study, Shenzhen University, Shenzhen 518060, China;
- College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
| | - Wenjin Li
- Institute for Advanced Study, Shenzhen University, Shenzhen 518060, China;
- Correspondence: ; Tel.: +86-755-2694-2336
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29
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Finkbeiner P, Hehn JP, Gnamm C. Phosphine Oxides from a Medicinal Chemist's Perspective: Physicochemical and in Vitro Parameters Relevant for Drug Discovery. J Med Chem 2020; 63:7081-7107. [PMID: 32479078 DOI: 10.1021/acs.jmedchem.0c00407] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Phosphine oxides and related phosphorus-containing functional groups such as phosphonates and phosphinates are established structural motifs that are still underrepresented in today's drug discovery projects, and only few examples can be found among approved drugs. In this account, the physicochemical and in vitro properties of phosphine oxides and related phosphorus-containing functional groups are reported and compared to more commonly used structural motifs in drug discovery. Furthermore, the impact on the physicochemical properties of a real drug scaffold is exemplified by a series of phosphorus-containing analogs of imatinib. We demonstrate that phosphine oxides are highly polar functional groups leading to high solubility and metabolic stability but occasionally at the cost of reduced permeability. We conclude that phosphine oxides and related phosphorus-containing functional groups are valuable polar structural elements and that they deserve to be considered as a routine part of every medicinal chemist's toolbox.
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Affiliation(s)
- Peter Finkbeiner
- Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach an der Riß, Germany
| | - Jörg P Hehn
- Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach an der Riß, Germany
| | - Christian Gnamm
- Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach an der Riß, Germany
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30
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Tahereh Valizadeh, Kiani F, Gharib F, Zabihi F, Koohyar F. Solvent Effects on Protonation Constants of Imatinib in Different Aqueous Solutions of Methanol at T = 298.15 K. RUSSIAN JOURNAL OF PHYSICAL CHEMISTRY A 2020. [DOI: 10.1134/s0036024420010331] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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31
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Menzer WM, Xie B, Minh DDL. On Restraints in End-Point Protein-Ligand Binding Free Energy Calculations. J Comput Chem 2020; 41:573-586. [PMID: 31821590 PMCID: PMC7311925 DOI: 10.1002/jcc.26119] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 10/26/2019] [Accepted: 11/08/2019] [Indexed: 12/14/2022]
Abstract
The impact of harmonic restraints on protein heavy atoms and ligand atoms on end-point free energy calculations is systematically characterized for 54 protein-ligand complexes. We observe that stronger restraints reduce the equilibration time and statistical inefficiency, suppress conformational sampling, influence correlation with experiment, and monotonically decrease the estimated loss of entropy upon binding, leading to stronger estimated binding free energies in most systems. A statistical estimator that reweights for the biasing potential and includes data prior to the estimated equilibration time has the highest correlation with experiment. A spring constant of 20 cal mol-1 Å-2 maintains a near-native energy landscape and suppresses artifactual energy minima while minimally limiting thermal fluctuations about the crystal structure. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- William M Menzer
- Department of Biology, Illinois Institute of Technology, Chicago, Illinois, 60616
| | - Bing Xie
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois, 60616
| | - David D L Minh
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois, 60616
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32
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Fedarkevich AN, Sharko OL, Shmanai VV. Computer Modeling and Synthesis of Potential Inhibitors of Tyrosine Kinase BCR-ABL with the T315I Mutation. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2020. [DOI: 10.1134/s1068162020020089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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33
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Xu JH, Eberhardt J, Hill-Payne B, González-Páez GE, Castellón JO, Cravatt BF, Forli S, Wolan DW, Backus KM. Integrative X-ray Structure and Molecular Modeling for the Rationalization of Procaspase-8 Inhibitor Potency and Selectivity. ACS Chem Biol 2020; 15:575-586. [PMID: 31927936 PMCID: PMC7370820 DOI: 10.1021/acschembio.0c00019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Caspases are a critical class of proteases involved in regulating programmed cell death and other biological processes. Selective inhibitors of individual caspases, however, are lacking, due in large part to the high structural similarity found in the active sites of these enzymes. We recently discovered a small-molecule inhibitor, 63-R, that covalently binds the zymogen, or inactive precursor (pro-form), of caspase-8, but not other caspases, pointing to an untapped potential of procaspases as targets for chemical probes. Realizing this goal would benefit from a structural understanding of how small molecules bind to and inhibit caspase zymogens. There have, however, been very few reported procaspase structures. Here, we employ X-ray crystallography to elucidate a procaspase-8 crystal structure in complex with 63-R, which reveals large conformational changes in active-site loops that accommodate the intramolecular cleavage events required for protease activation. Combining these structural insights with molecular modeling and mutagenesis-based biochemical assays, we elucidate key interactions required for 63-R inhibition of procaspase-8. Our findings inform the mechanism of caspase activation and its disruption by small molecules and, more generally, have implications for the development of small molecule inhibitors and/or activators that target alternative (e.g., inactive precursor) protein states to ultimately expand the druggable proteome.
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Affiliation(s)
- Janice H. Xu
- Department of Molecular Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037
- Department of Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037
| | - Jerome Eberhardt
- Department of Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037
| | - Brianna Hill-Payne
- Departments of Biological Chemistry and Chemistry and Biochemistry, David Geffen School of Medicine, University of California, Los Angeles, 405 Hilgard Avenue, Los Angeles, CA 90095
| | - Gonzalo E. González-Páez
- Department of Molecular Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037
- Department of Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037
| | - José Omar Castellón
- Departments of Biological Chemistry and Chemistry and Biochemistry, David Geffen School of Medicine, University of California, Los Angeles, 405 Hilgard Avenue, Los Angeles, CA 90095
| | - Benjamin F. Cravatt
- Department of Chemistry, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037
| | - Stefano Forli
- Department of Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037
| | - Dennis W. Wolan
- Department of Molecular Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037
- Department of Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037
| | - Keriann M. Backus
- Departments of Biological Chemistry and Chemistry and Biochemistry, David Geffen School of Medicine, University of California, Los Angeles, 405 Hilgard Avenue, Los Angeles, CA 90095
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Ghanta MK, Elango P, L V K S B. Current Therapeutic Strategies and Perspectives for Neuroprotection in Parkinson's Disease. Curr Pharm Des 2020; 26:4738-4746. [PMID: 32065086 DOI: 10.2174/1381612826666200217114658] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Accepted: 02/10/2020] [Indexed: 02/04/2023]
Abstract
Parkinson's disease is a progressive neurodegenerative disorder of dopaminergic striatal neurons in basal ganglia. Treatment of Parkinson's disease (PD) through dopamine replacement strategies may provide improvement in early stages and this treatment response is related to dopaminergic neuronal mass which decreases in advanced stages. This treatment failure was revealed by many studies and levodopa treatment became ineffective or toxic in chronic stages of PD. Early diagnosis and neuroprotective agents may be a suitable approach for the treatment of PD. The essentials required for early diagnosis are biomarkers. Characterising the striatal neurons, understanding the status of dopaminergic pathways in different PD stages may reveal the effects of the drugs used in the treatment. This review updates on characterisation of striatal neurons, electrophysiology of dopaminergic pathways in PD, biomarkers of PD, approaches for success of neuroprotective agents in clinical trials. The literature was collected from the articles in database of PubMed, MedLine and other available literature resources.
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Affiliation(s)
- Mohan K Ghanta
- Department of Pharmacology, Sri Ramachandra Medical College and Research Institute, Sri Ramachandra Institute of Higher Education and Research (DU), Porur, Chennai-600116, Tamil Nadu, India
| | - P Elango
- Department of Pharmacology, Panimalar Medical College Hospital & Research Institute, Poonamallee, Chennai-600123, Tamil Nadu, India
| | - Bhaskar L V K S
- Department of Zoology, Guru Ghasidas University, Bilaspur, 495009 (CG), India
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35
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Lahey SLJ, Rowley CN. Simulating protein-ligand binding with neural network potentials. Chem Sci 2020; 11:2362-2368. [PMID: 34084397 PMCID: PMC8157423 DOI: 10.1039/c9sc06017k] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 01/22/2020] [Indexed: 01/14/2023] Open
Abstract
Drug molecules adopt a range of conformations both in solution and in their protein-bound state. The strain and reduced flexibility of bound drugs can partially counter the intermolecular interactions that drive protein-ligand binding. To make accurate computational predictions of drug binding affinities, computational chemists have attempted to develop efficient empirical models of these interactions, although these methods are not always reliable. Machine learning has allowed the development of highly-accurate neural-network potentials (NNPs), which are capable of predicting the stability of molecular conformations with accuracy comparable to state-of-the-art quantum chemical calculations but at a billionth of the computational cost. Here, we demonstrate that these methods can be used to represent the intramolecular forces of protein-bound drugs within molecular dynamics simulations. These simulations are shown to be capable of predicting the protein-ligand binding pose and conformational component of the absolute Gibbs energy of binding for a set of drug molecules. Notably, the conformational energy for anti-cancer drug erlotinib binding to its target was found to be considerably overestimated by a molecular mechanical model, while the NNP predicts a more moderate value. Although the ANI-1ccX NNP was not trained to describe ionic molecules, reasonable binding poses are predicted for charged ligands, but this method is not suitable for modeling charged ligands in solution.
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Affiliation(s)
- Shae-Lynn J Lahey
- Memorial University of Newfoundland St. John's Newfoundland and Labrador Canada
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36
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Abl family tyrosine kinases govern IgG extravasation in the skin in a murine pemphigus model. Nat Commun 2019; 10:4432. [PMID: 31570755 PMCID: PMC6769004 DOI: 10.1038/s41467-019-12232-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 08/28/2019] [Indexed: 01/02/2023] Open
Abstract
The pathway of homeostatic IgG extravasation is not fully understood, in spite of its importance for the maintenance of host immunity, the management of autoantibody-mediated disorders, and the use of antibody-based biologics. Here we show in a murine model of pemphigus, a prototypic cutaneous autoantibody-mediated disorder, that blood-circulating IgG extravasates into the skin in a time- and dose-dependent manner under homeostatic conditions. This IgG extravasation is unaffected by depletion of Fcγ receptors, but is largely attenuated by specific ablation of dynamin-dependent endocytic vesicle formation in blood endothelial cells (BECs). Among dynamin-dependent endocytic vesicles, IgG co-localizes well with caveolae in cultured BECs. An Abl family tyrosine kinase inhibitor imatinib, which reduces caveolae-mediated endocytosis, impairs IgG extravasation in the skin and attenuates the murine pemphigus manifestations. Our study highlights the kinetics of IgG extravasation in vivo, which might be a clue to understand the pathological mechanism of autoantibody-mediated autoimmune disorders. How antibody reaches tissues from circulation is critical for understanding antibody-mediated immunity. Here the authors show that IgG extravasation in the skin is mediated by endothelial caveolin transport independently of FcR, and is targetable by imatinib, which reduces IgG-dependent pathology in a mouse model of pemphigus.
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37
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Malkhasian AY, Howlin BJ. Automated drug design of kinase inhibitors to treat Chronic Myeloid Leukemia. J Mol Graph Model 2019; 91:52-60. [DOI: 10.1016/j.jmgm.2019.05.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 05/17/2019] [Accepted: 05/17/2019] [Indexed: 11/25/2022]
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38
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Encounter complexes and hidden poses of kinase-inhibitor binding on the free-energy landscape. Proc Natl Acad Sci U S A 2019; 116:18404-18409. [PMID: 31451651 PMCID: PMC6744929 DOI: 10.1073/pnas.1904707116] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Modern drug discovery increasingly focuses on the drug-target binding kinetics which depend on drug (un)binding pathways. The conventional molecular dynamics simulation can observe only a few binding events even using the fastest supercomputer. Here, we develop 2D gREST/REUS simulation with enhanced flexibility of the ligand and the protein binding site. Simulation (43 μs in total) applied to an inhibitor binding to c-Src kinase covers 100 binding and unbinding events. On the statistically converged free-energy landscapes, we succeed in predicting the X-ray binding structure, including water positions. Furthermore, we characterize hidden semibound poses and transient encounter complexes on the free-energy landscapes. Regulatory residues distant from the catalytic core are responsible for the initial inhibitor uptake and regulation of subsequent bindings, which was unresolved by experiments. Stabilizing/blocking of either the semibound poses or the encounter complexes can be an effective strategy to optimize drug-target residence time.
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39
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Jordan EJ, Patil K, Suresh K, Park JH, Mosse YP, Lemmon MA, Radhakrishnan R. Computational algorithms for in silico profiling of activating mutations in cancer. Cell Mol Life Sci 2019; 76:2663-2679. [PMID: 30982079 PMCID: PMC6589134 DOI: 10.1007/s00018-019-03097-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 04/01/2019] [Accepted: 04/08/2019] [Indexed: 12/17/2022]
Abstract
Methods to catalog and computationally assess the mutational landscape of proteins in human cancers are desirable. One approach is to adapt evolutionary or data-driven methods developed for predicting whether a single-nucleotide polymorphism (SNP) is deleterious to protein structure and function. In cases where understanding the mechanism of protein activation and regulation is desired, an alternative approach is to employ structure-based computational approaches to predict the effects of point mutations. Through a case study of mutations in kinase domains of three proteins, namely, the anaplastic lymphoma kinase (ALK) in pediatric neuroblastoma patients, serine/threonine-protein kinase B-Raf (BRAF) in melanoma patients, and erythroblastic oncogene B 2 (ErbB2 or HER2) in breast cancer patients, we compare the two approaches above. We find that the structure-based method is most appropriate for developing a binary classification of several different mutations, especially infrequently occurring ones, concerning the activation status of the given target protein. This approach is especially useful if the effects of mutations on the interactions of inhibitors with the target proteins are being sought. However, many patients will present with mutations spread across different target proteins, making structure-based models computationally demanding to implement and execute. In this situation, data-driven methods-including those based on machine learning techniques and evolutionary methods-are most appropriate for recognizing and illuminate mutational patterns. We show, however, that, in the present status of the field, the two methods have very different accuracies and confidence values, and hence, the optimal choice of their deployment is context-dependent.
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Affiliation(s)
- E Joseph Jordan
- Graduate Group in Biochemistry and Molecular Biophysics, University of Pennsylvania, Philadelphia, PA, USA
| | - Keshav Patil
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Krishna Suresh
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Jin H Park
- Department of Pharmacology, Yale University, New Haven, CT, USA
- Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Yael P Mosse
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Mark A Lemmon
- Department of Pharmacology, Yale University, New Haven, CT, USA
- Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Ravi Radhakrishnan
- Graduate Group in Biochemistry and Molecular Biophysics, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
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40
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Haldane A, Flynn WF, He P, Levy RM. Coevolutionary Landscape of Kinase Family Proteins: Sequence Probabilities and Functional Motifs. Biophys J 2019; 114:21-31. [PMID: 29320688 DOI: 10.1016/j.bpj.2017.10.028] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 09/11/2017] [Accepted: 10/17/2017] [Indexed: 01/25/2023] Open
Abstract
The protein kinase catalytic domain is one of the most abundant domains across all branches of life. Although kinases share a common core function of phosphoryl-transfer, they also have wide functional diversity and play varied roles in cell signaling networks, and for this reason are implicated in a number of human diseases. This functional diversity is primarily achieved through sequence variation, and uncovering the sequence-function relationships for the kinase family is a major challenge. In this study we use a statistical inference technique inspired by statistical physics, which builds a coevolutionary "Potts" Hamiltonian model of sequence variation in a protein family. We show how this model has sufficient power to predict the probability of specific subsequences in the highly diverged kinase family, which we verify by comparing the model's predictions with experimental observations in the Uniprot database. We show that the pairwise (residue-residue) interaction terms of the statistical model are necessary and sufficient to capture higher-than-pairwise mutation patterns of natural kinase sequences. We observe that previously identified functional sets of residues have much stronger correlated interaction scores than are typical.
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Affiliation(s)
- Allan Haldane
- Center for Biophysics and Computational Biology, Department of Chemistry, and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania
| | - William F Flynn
- Center for Biophysics and Computational Biology, Department of Chemistry, and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania; Department of Physics and Astronomy, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Peng He
- Center for Biophysics and Computational Biology, Department of Chemistry, and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania
| | - Ronald M Levy
- Center for Biophysics and Computational Biology, Department of Chemistry, and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania.
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41
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Liu R, Yue Z, Tsai CC, Shen J. Assessing Lysine and Cysteine Reactivities for Designing Targeted Covalent Kinase Inhibitors. J Am Chem Soc 2019; 141:6553-6560. [PMID: 30945531 DOI: 10.1021/jacs.8b13248] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Targeted covalent inhibitor design is gaining increasing interest and acceptance. A typical covalent kinase inhibitor design targets a reactive cysteine; however, this strategy is limited by the low abundance of cysteine and acquired drug resistance from point mutations. Inspired by the recent development of lysine-targeted chemical probes, we asked if nucleophilic (reactive) catalytic lysines are common on the basis of the published crystal structures of the human kinome. Using a newly developed p Ka prediction tool based on continuous constant pH molecular dynamics, the catalytic lysines of eight unique kinases from various human kinase groups were retrospectively and prospectively predicted to be nucleophilic, when kinase is in the rare DFG-out/αC-out type of conformation. Importantly, other reactive lysines as well as cysteines at various locations were also identified. On the basis of the findings, we proposed a new strategy in which selective type II reversible kinase inhibitors are modified to design highly selective, lysine-targeted covalent inhibitors. Traditional covalent drugs were discovered serendipitously; the presented tool, which can assess the reactivities of any potentially targetable residues, may accelerate the rational discovery of new covalent inhibitors. Another significant finding of the work is that lysines and cysteines in kinases may adopt neutral and charged states at physiological pH, respectively. This finding may shift the current paradigm of computational studies of kinases, which assume fixed solution protonation states.
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Affiliation(s)
- Ruibin Liu
- Department of Pharmaceutical Sciences , School of Pharmacy, University of Maryland , Baltimore , Maryland 21201 , United States
| | - Zhi Yue
- Department of Pharmaceutical Sciences , School of Pharmacy, University of Maryland , Baltimore , Maryland 21201 , United States
| | - Cheng-Chieh Tsai
- Department of Pharmaceutical Sciences , School of Pharmacy, University of Maryland , Baltimore , Maryland 21201 , United States
| | - Jana Shen
- Department of Pharmaceutical Sciences , School of Pharmacy, University of Maryland , Baltimore , Maryland 21201 , United States
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42
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Solvents to Fragments to Drugs: MD Applications in Drug Design. Molecules 2018; 23:molecules23123269. [PMID: 30544890 PMCID: PMC6321499 DOI: 10.3390/molecules23123269] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 12/02/2018] [Accepted: 12/03/2018] [Indexed: 01/24/2023] Open
Abstract
Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates.
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43
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Aleksandrov A, Myllykallio H. Advances and challenges in drug design against tuberculosis: application of in silico approaches. Expert Opin Drug Discov 2018; 14:35-46. [PMID: 30477360 DOI: 10.1080/17460441.2019.1550482] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
INTRODUCTION Tuberculosis (TB) caused by Mycobacterium tuberculosis (Mtb) remains the deadliest infectious disease in the world with one-third of the world's population thought to be infected. Over the years, TB mortality rate has been largely reduced; however, this progress has been threatened by the increasing appearance of multidrug-resistant Mtb. Considerable recent efforts have been undertaken to develop new generation antituberculosis drugs. Many of these attempts have relied on in silico approaches, which have emerged recently as powerful tools complementary to biochemical attempts. Areas covered: The authors review the status of pharmaceutical drug development against TB with a special emphasis on computational work. They focus on those studies that have been validated by in vitro and/or in vivo experiments, and thus, that can be considered as successful. The major goals of this review are to present target protein systems, to highlight how in silico efforts compliment experiments, and to aid future drug design endeavors. Expert opinion: Despite having access to all of the gene and protein sequences of Mtb, the search for new optimal treatments against this deadly pathogen are still ongoing. Together with the geometric growth of protein structural and sequence databases, computational methods have become a powerful technique accelerating the successful identification of new ligands.
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Affiliation(s)
- Alexey Aleksandrov
- a Laboratoire d'Optique et Biosciences (CNRS UMR7645, INSERM U1182) , Ecole Polytechnique , Palaiseau , France
| | - Hannu Myllykallio
- a Laboratoire d'Optique et Biosciences (CNRS UMR7645, INSERM U1182) , Ecole Polytechnique , Palaiseau , France
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Haldar S, Comitani F, Saladino G, Woods C, van der Kamp MW, Mulholland AJ, Gervasio FL. A Multiscale Simulation Approach to Modeling Drug-Protein Binding Kinetics. J Chem Theory Comput 2018; 14:6093-6101. [PMID: 30208708 DOI: 10.1021/acs.jctc.8b00687] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Drug-target binding kinetics has recently emerged as a sometimes critical determinant of in vivo efficacy and toxicity. Its rational optimization to improve potency or reduce side effects of drugs is, however, extremely difficult. Molecular simulations can play a crucial role in identifying features and properties of small ligands and their protein targets affecting the binding kinetics, but significant challenges include the long time scales involved in (un)binding events and the limited accuracy of empirical atomistic force fields (lacking, e.g., changes in electronic polarization). In an effort to overcome these hurdles, we propose a method that combines state-of-the-art enhanced sampling simulations and quantum mechanics/molecular mechanics (QM/MM) calculations at the BLYP/VDZ level to compute association free energy profiles and characterize the binding kinetics in terms of structure and dynamics of the transition state ensemble. We test our combined approach on the binding of the anticancer drug Imatinib to Src kinase, a well-characterized target for cancer therapy with a complex binding mechanism involving significant conformational changes. The results indicate significant changes in polarization along the binding pathways, which affect the predicted binding kinetics. This is likely to be of widespread importance in binding of ligands to protein targets.
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Affiliation(s)
- Susanta Haldar
- Centre for Computational Chemistry, School of Chemistry , University of Bristol , Bristol , BS8 1TS , United Kingdom
| | | | | | - Christopher Woods
- Centre for Computational Chemistry, School of Chemistry , University of Bristol , Bristol , BS8 1TS , United Kingdom
| | - Marc W van der Kamp
- Centre for Computational Chemistry, School of Chemistry , University of Bristol , Bristol , BS8 1TS , United Kingdom
- School of Biochemistry , University of Bristol , Bristol , BS8 1TD , United Kingdom
| | - Adrian J Mulholland
- Centre for Computational Chemistry, School of Chemistry , University of Bristol , Bristol , BS8 1TS , United Kingdom
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From bench to bedside, via desktop. Recent advances in the application of cutting-edge in silico tools in the research of drugs targeting bromodomain modules. Biochem Pharmacol 2018; 159:40-51. [PMID: 30414936 DOI: 10.1016/j.bcp.2018.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2018] [Accepted: 11/07/2018] [Indexed: 11/22/2022]
Abstract
The discipline of drug discovery has greatly benefited by computational tools and in silico algorithms aiming at rationalization of many related processes, from the stage of early hit identification to the preclinical phases of drug candidate validation. The various methodologies referred to as molecular modeling tools span a broad spectrum of applications, from straightforward approaches such as virtual screening of compound libraries to more advanced techniques involving the precise estimation of free energy upon binding of the candidate drug to its macromolecular target. To this end, we report an overview of specific studies where implementation of such sophisticated modeling algorithms has shown to be indispensable for addressing challenging systems and biological questions otherwise difficult to answer. We focus our attention on the emerging field of bromodomain inhibitors. Bromodomains are small modules involved in epigenetic signaling and currently comprise high-priority targets for developing both drug candidates and chemical probes for basic biomedical research. We attempt a critical presentation of selected cases utilizing cutting-edge in silico methodologies, with our main emphasis being on absolute or relative free energy simulations, on implementation of quantum-mechanics level calculations and on characterization of solvent thermodynamics. We discuss the advantages and strengths as well as the drawbacks and weaknesses of computational tools utilized in those works and we attempt to comment on specific issues related to their integration into the regular medicinal chemistry practice. Our conclusion is that while such methods indeed represent highly promising resources for further advancing the discipline, their application is not always trivial.
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Wang L, Zheng G, Liu X, Ni D, He X, Cheng J, Lu S. Molecular dynamics simulations provide insights into the origin of gleevec's selectivity toward human tyrosine kinases. J Biomol Struct Dyn 2018; 37:2733-2744. [PMID: 30052122 DOI: 10.1080/07391102.2018.1496139] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Protein kinases are critical drug targets against cancer. Since the discovery of Gleevec, a specific inhibitor of Abl kinase, the capability of this drug to distinguish between Abl and other tyrosine kinases, such as Src, has been intensely investigated but the origin of Gleevec's selectivity to Abl against Src is less studied. Here, we performed molecular dynamics (MD) simulations, dynamical cross-correlation matrices (DCCM), dynamical network analysis, and binding free energy calculations to explore Gleevec's selectivity based on the crystal structures of Abl, Src, and their common ancestors (ANC-AS) and the two constructed mutation systems (AS→Abl and AS→Src). MD simulations revealed that the conformation of the phosphate-binding loop (P-loop) was altered significantly in the AS→Abl system. DCCM results unraveled that mutations increased anticorrelated motions in the AS→Abl system. Community network analysis suggested that the P-loop established special contacts in the AS→Abl system that are devoid in the AS→Src system. The binding free energy calculations unveiled that the affinity of Gleevec to AS→Abl increased to near the Abl level, whereas its affinity to AS→Src decreased to near the Src level. Analysis of individual residue contributions showed that the differences were located mainly at the P-loop. This study is valuable for understanding the sensitivity of Gleevec to human tyrosine kinases. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Lulu Wang
- a Department of Critical Care Medicine , Binzhou Medical University Hospital , Binzhou , Shandong , China
| | - Guodong Zheng
- b Department of VIP clinic , Changhai Hospital, Naval Military Medical University , Shanghai , China
| | - Xianxian Liu
- c Department of Infectious Diseases , Binzhou Medical University Hospital , Binzhou , Shandong , China
| | - Duan Ni
- d Department of Pathophysiology Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education , Shanghai Jiao Tong University, School of Medicine , Shanghai , China
| | - Xinheng He
- d Department of Pathophysiology Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education , Shanghai Jiao Tong University, School of Medicine , Shanghai , China
| | - Jinying Cheng
- c Department of Infectious Diseases , Binzhou Medical University Hospital , Binzhou , Shandong , China
| | - Shaoyong Lu
- d Department of Pathophysiology Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education , Shanghai Jiao Tong University, School of Medicine , Shanghai , China
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Targeting heme Oxygenase-1 with hybrid compounds to overcome Imatinib resistance in chronic myeloid leukemia cell lines. Eur J Med Chem 2018; 158:937-950. [DOI: 10.1016/j.ejmech.2018.09.048] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 09/14/2018] [Accepted: 09/15/2018] [Indexed: 10/28/2022]
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Shah NH, Amacher JF, Nocka LM, Kuriyan J. The Src module: an ancient scaffold in the evolution of cytoplasmic tyrosine kinases. Crit Rev Biochem Mol Biol 2018; 53:535-563. [PMID: 30183386 PMCID: PMC6328253 DOI: 10.1080/10409238.2018.1495173] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Tyrosine kinases were first discovered as the protein products of viral oncogenes. We now know that this large family of metazoan enzymes includes nearly one hundred structurally diverse members. Tyrosine kinases are broadly classified into two groups: the transmembrane receptor tyrosine kinases, which sense extracellular stimuli, and the cytoplasmic tyrosine kinases, which contain modular ligand-binding domains and propagate intracellular signals. Several families of cytoplasmic tyrosine kinases have in common a core architecture, the "Src module," composed of a Src-homology 3 (SH3) domain, a Src-homology 2 (SH2) domain, and a kinase domain. Each of these families is defined by additional elaborations on this core architecture. Structural, functional, and evolutionary studies have revealed a unifying set of principles underlying the activity and regulation of tyrosine kinases built on the Src module. The discovery of these conserved properties has shaped our knowledge of the workings of protein kinases in general, and it has had important implications for our understanding of kinase dysregulation in disease and the development of effective kinase-targeted therapies.
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Affiliation(s)
- Neel H. Shah
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
- Department of Chemistry, University of California, Berkeley, CA, USA
- California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA
- Howard Hughes Medical Institute, University of California, Berkeley, CA, USA
| | - Jeanine F. Amacher
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
- Department of Chemistry, University of California, Berkeley, CA, USA
- California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA
- Howard Hughes Medical Institute, University of California, Berkeley, CA, USA
| | - Laura M. Nocka
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
- Department of Chemistry, University of California, Berkeley, CA, USA
- California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA
- Howard Hughes Medical Institute, University of California, Berkeley, CA, USA
| | - John Kuriyan
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
- Department of Chemistry, University of California, Berkeley, CA, USA
- California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA
- Howard Hughes Medical Institute, University of California, Berkeley, CA, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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Shamsi Z, Cheng KJ, Shukla D. Reinforcement Learning Based Adaptive Sampling: REAPing Rewards by Exploring Protein Conformational Landscapes. J Phys Chem B 2018; 122:8386-8395. [DOI: 10.1021/acs.jpcb.8b06521] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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50
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He P, Zhang BW, Arasteh S, Wang L, Abel R, Levy RM. Conformational Free Energy Changes via an Alchemical Path without Reaction Coordinates. J Phys Chem Lett 2018; 9:4428-4435. [PMID: 30024165 PMCID: PMC6092130 DOI: 10.1021/acs.jpclett.8b01851] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
We introduce a novel method called restrain-free energy perturbation-release (R-FEP-R) to estimate conformational free energy changes via an alchemical path, which for some conformational landscapes like those associated with cellular signaling proteins in the kinase family is more direct and readily converged than the corresponding free energy changes along the physical path. The R-FEP-R method was developed from the dual topology free energy perturbation method that is widely applied to estimate the binding free energy difference between two ligands. In R-FEP-R, the free energy change between two conformational basins is calculated by free energy perturbations that remove those atoms involved in the conformational change from their initial conformational basin while simultaneously growing them back according to the final conformational basin. Both the initial and final dual topology states are unphysical, but they are designed in a way such that the unphysical contributions to the initial and final partition functions cancel. Compared with other advanced sampling algorithms such as umbrella sampling and metadynamics, the R-FEP-R method does not require predetermined transition pathways or reaction coordinates that connect the two conformational states. As a first illustration, the R-FEP-R method was applied to calculate the free energy change between conformational basins for alanine dipeptide in solution and for a side chain in the binding pocket of T4 lysozyme. The results obtained by R-FEP-R agree with the benchmarks very well.
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Affiliation(s)
- Peng He
- Center for Biophysics and Computational Biology, Department of Chemistry and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Bin W. Zhang
- Center for Biophysics and Computational Biology, Department of Chemistry and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Shima Arasteh
- Center for Biophysics and Computational Biology, Department of Chemistry and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Lingle Wang
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Robert Abel
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Ronald M. Levy
- Center for Biophysics and Computational Biology, Department of Chemistry and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
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