1
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Zhao M, Yu W, MacKerell AD. Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms. J Phys Chem B 2024; 128:7362-7375. [PMID: 39031121 PMCID: PMC11294009 DOI: 10.1021/acs.jpcb.4c03045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2024]
Abstract
In the domain of computer-aided drug design, achieving precise and accurate estimates of ligand-protein binding is paramount in the context of screening extensive drug libraries and performing ligand optimization. A fundamental aspect of the SILCS (site identification by ligand competitive saturation) methodology lies in the generation of comprehensive 3D free-energy functional group affinity maps (FragMaps), encompassing the entirety of the target molecule structure. These FragMaps offer an intricate landscape of functional group affinities across the protein, bilayer, or RNA, acting as the basis for subsequent SILCS-Monte Carlo (MC) simulations wherein ligands are docked to the target molecule. To augment the efficiency and breadth of ligand sampling capabilities, we implemented an improved SILCS-MC methodology. By harnessing the parallel computing capability of GPUs, our approach facilitates concurrent calculations over multiple ligands and binding sites, markedly enhancing the computational efficiency. Moreover, the integration of a genetic algorithm (GA) with MC allows us to employ an evolutionary approach to perform ligand sampling, assuring enhanced convergence characteristics. In addition, the potential utility of parallel tempering (PT) to improve sampling was investigated. Implementation of SILCS-MC on GPU architecture is shown to accelerate the speed of SILCS-MC calculations by over 2-orders of magnitude. Use of GA and PT yield improvements over Markov-chain MC, increasing the precision of the resultant docked orientations and binding free energies, though the extent of improvements is relatively small. Accordingly, significant improvements in speed are obtained through the GPU implementation with minor improvements in the precision of the docking obtained via the tested GA and PT algorithms.
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Affiliation(s)
- Mingtian Zhao
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, 20 Penn St., Baltimore, Maryland 21201, USA
| | - Wenbo Yu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, 20 Penn St., Baltimore, Maryland 21201, USA
| | - Alexander D. MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, 20 Penn St., Baltimore, Maryland 21201, USA
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2
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Kudo G, Yanagisawa K, Yoshino R, Hirokawa T. AAp-MSMD: Amino Acid Preference Mapping on Protein-Protein Interaction Surfaces Using Mixed-Solvent Molecular Dynamics. J Chem Inf Model 2023; 63:7768-7777. [PMID: 38085669 PMCID: PMC10751795 DOI: 10.1021/acs.jcim.3c01677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 12/26/2023]
Abstract
Peptides have attracted much attention recently owing to their well-balanced properties as drugs against protein-protein interaction (PPI) surfaces. Molecular simulation-based predictions of binding sites and amino acid residues with high affinity to PPI surfaces are expected to accelerate the design of peptide drugs. Mixed-solvent molecular dynamics (MSMD), which adds probe molecules or fragments of functional groups as solutes to the hydration model, detects the binding hotspots and cryptic sites induced by small molecules. The detection results vary depending on the type of probe molecule; thus, they provide important information for drug design. For rational peptide drug design using MSMD, we proposed MSMD with amino acid residue probes, named amino acid probe-based MSMD (AAp-MSMD), to detect hotspots and identify favorable amino acid types on protein surfaces to which peptide drugs bind. We assessed our method in terms of hotspot detection at the amino acid probe level and binding free energy prediction with amino acid probes at the PPI site for the complex structure that formed the PPI. In hotspot detection, the max-spatial probability distribution map (max-PMAP) obtained from AAp-MSMD detected the PPI site, to which each type of amino acid can bind favorably. In the binding free energy prediction using amino acid probes, ΔGFE obtained from AAp-MSMD roughly estimated the experimental binding affinities from the structure-activity relationship. AAp-MSMD, with amino acid probes, provides estimated binding sites and favorable amino acid types at the PPI site of a target protein.
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Affiliation(s)
- Genki Kudo
- Physics
Department, Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8571, Ibaraki Japan
| | - Keisuke Yanagisawa
- Department
of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro 152-8550, Tokyo Japan
- Middle
Molecule IT-based Drug Discovery Laboratory, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro 152-8550, Tokyo Japan
| | - Ryunosuke Yoshino
- Faculty
of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki Japan
- Transborder
Medical Research Center, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Ibaraki Japan
| | - Takatsugu Hirokawa
- Faculty
of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki Japan
- Transborder
Medical Research Center, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Ibaraki Japan
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3
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Barrera-Téllez FJ, Prieto-Martínez FD, Hernández-Campos A, Martínez-Mayorga K, Castillo-Bocanegra R. In Silico Exploration of the Trypanothione Reductase (TryR) of L. mexicana. Int J Mol Sci 2023; 24:16046. [PMID: 38003236 PMCID: PMC10671491 DOI: 10.3390/ijms242216046] [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: 08/17/2023] [Revised: 10/23/2023] [Accepted: 10/31/2023] [Indexed: 11/26/2023] Open
Abstract
Human leishmaniasis is a neglected tropical disease which affects nearly 1.5 million people every year, with Mexico being an important endemic region. One of the major defense mechanisms of these parasites is based in the polyamine metabolic pathway, as it provides the necessary compounds for its survival. Among the enzymes in this route, trypanothione reductase (TryR), an oxidoreductase enzyme, is crucial for the Leishmania genus' survival against oxidative stress. Thus, it poses as an attractive drug target, yet due to the size and features of its catalytic pocket, modeling techniques such as molecular docking focusing on that region is not convenient. Herein, we present a computational study using several structure-based approaches to assess the druggability of TryR from L. mexicana, the predominant Leishmania species in Mexico, beyond its catalytic site. Using this consensus methodology, three relevant pockets were found, of which the one we call σ-site promises to be the most favorable one. These findings may help the design of new drugs of trypanothione-related diseases.
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Affiliation(s)
- Francisco J. Barrera-Téllez
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
| | - Fernando D. Prieto-Martínez
- Instituto de Química, Unidad Mérida, Universidad Nacional Autónoma de México, Carretera Mérida-Tetiz, Km. 4.5, Ucú 97357, Mexico
| | - Alicia Hernández-Campos
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
| | - Karina Martínez-Mayorga
- Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Unidad Mérida, Universidad Nacional Autónoma de México, Sierra Papacal, Mérida 97302, Mexico
| | - Rafael Castillo-Bocanegra
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
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4
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Mechanistic Investigation of the Androgen Receptor DNA-Binding Domain and Modulation via Direct Interactions with DNA Abasic Sites: Understanding the Mechanisms Involved in Castration-Resistant Prostate Cancer. Int J Mol Sci 2023; 24:ijms24021270. [PMID: 36674785 PMCID: PMC9864221 DOI: 10.3390/ijms24021270] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/28/2022] [Accepted: 12/30/2022] [Indexed: 01/11/2023] Open
Abstract
The androgen receptor (AR) is an important drug target in prostate cancer and a driver of castration-resistant prostate cancer (CRPC). A significant challenge in designing effective drugs lies in targeting constitutively active AR variants and, most importantly, nearly all AR variants lacking the ligand-binding domain (LBD). Recent findings show that an AR's constitutive activity may occur in the presence of somatic DNA mutations within non-coding regions, but the role of these mutations remains elusive. The discovery of new drugs targeting CRPC is hampered by the limited molecular understanding of how AR binds mutated DNA sequences, frequently observed in prostate cancer, and how mutations within the protein and DNA regulate AR-DNA interactions. Using atomistic molecular dynamics (MD) simulations and quantum mechanical calculations, we focused our efforts on (i) rationalising the role of several activating DBD mutations linked to prostate cancer, and (ii) DBD interactions in the presence of abasic DNA lesions, which frequently occur in CRPC. Our results elucidate the role of mutations within DBD through their modulation of the intrinsic dynamics of the DBD-DNA ternary complex. Furthermore, our results indicate that the DNA apurinic lesions occurring in the androgen-responsive element (ARE) enhance direct AR-DNA interactions and stabilise the DBD homodimerisation interface. Moreover, our results strongly suggest that those abasic lesions may form reversible covalent crosslinks between DNA and lysine residues of an AR via a Schiff base. In addition to providing an atomistic model explaining how protein mutations within the AR DNA-binding domain affect AR dimerisation and AR-DNA interactions, our findings provide insight into how somatic mutations occurring in DNA non-coding regions may activate ARs. These mutations are frequently observed in prostate cancer and may contribute to disease progression by enhancing direct AR-DNA interactions.
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5
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Radoux CJ, Vianello F, McGreig J, Desai N, Bradley AR. The druggable genome: Twenty years later. FRONTIERS IN BIOINFORMATICS 2022; 2:958378. [PMID: 36304325 PMCID: PMC9580872 DOI: 10.3389/fbinf.2022.958378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
The concept of the druggable genome has been with us for 20 years. During this time, researchers have developed several methods and resources to help assess a target’s druggability. In parallel, evidence for target-disease associations has been collated at scale by Open Targets. More recently, the Protein Data Bank in Europe (PDBe) have built a knowledge base matching per-residue annotations with available protein structure. While each resource is useful in isolation, we believe there is enormous potential in bringing all relevant data into a single knowledge graph, from gene-level to protein residue. Automation is vital for the processing and assessment of all available structures. We have developed scalable, automated workflows that provide hotspot-based druggability assessments for all available structures across large numbers of targets. Ultimately, we will run our method at a proteome scale, an ambition made more realistic by the arrival of AlphaFold 2. Bringing together annotations from the residue up to the gene level and building connections within the graph to represent pathways or protein-protein interactions will create complexity that mirrors the biological systems they represent. Such complexity is difficult for the human mind to utilise effectively, particularly at scale. We believe that graph-based AI methods will be able to expertly navigate such a knowledge graph, selecting the targets of the future.
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6
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Wakefield AE, Kozakov D, Vajda S. Mapping the binding sites of challenging drug targets. Curr Opin Struct Biol 2022; 75:102396. [PMID: 35636004 PMCID: PMC9790766 DOI: 10.1016/j.sbi.2022.102396] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/20/2022] [Accepted: 04/25/2022] [Indexed: 02/03/2023]
Abstract
An increasing number of medically important proteins are challenging drug targets because their binding sites are too shallow or too polar, are cryptic and thus not detectable without a bound ligand or located in a protein-protein interface. While such proteins may not bind druglike small molecules with sufficiently high affinity, they are frequently druggable using novel therapeutic modalities. The need for such modalities can be determined by experimental or computational fragment based methods. Computational mapping by mixed solvent molecular dynamics simulations or the FTMap server can be used to determine binding hot spots. The strength and location of the hot spots provide very useful information for selecting potentially successful approaches to drug discovery.
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Affiliation(s)
- Amanda E. Wakefield
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215,Department of Chemistry, Boston University, Boston, Massachusetts 02215
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA NY, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215,Department of Chemistry, Boston University, Boston, Massachusetts 02215
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7
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Inverse Mixed-Solvent Molecular Dynamics for Visualization of the Residue Interaction Profile of Molecular Probes. Int J Mol Sci 2022; 23:ijms23094749. [PMID: 35563139 PMCID: PMC9103889 DOI: 10.3390/ijms23094749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/18/2022] [Accepted: 04/23/2022] [Indexed: 02/01/2023] Open
Abstract
To ensure efficiency in discovery and development, the application of computational technology is essential. Although virtual screening techniques are widely applied in the early stages of drug discovery research, the computational methods used in lead optimization to improve activity and reduce the toxicity of compounds are still evolving. In this study, we propose a method to construct the residue interaction profile of the chemical structure used in the lead optimization by performing “inverse” mixed-solvent molecular dynamics (MSMD) simulation. Contrary to constructing a protein-based, atom interaction profile, we constructed a probe-based, protein residue interaction profile using MSMD trajectories. It provides us the profile of the preferred protein environments of probes without co-crystallized structures. We assessed the method using three probes: benzamidine, catechol, and benzene. As a result, the residue interaction profile of each probe obtained by MSMD was a reasonable physicochemical description of the general non-covalent interaction. Moreover, comparison with the X-ray structure containing each probe as a ligand shows that the map of the interaction profile matches the arrangement of amino acid residues in the X-ray structure.
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8
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Cornejo NR, Amofah B, Lipinski A, Langlais PR, Ghosh I, Jewett JC. Direct Intracellular Delivery of Benzene Diazonium Ions As Observed by Increased Tyrosine Phosphorylation. Biochemistry 2022; 61:656-664. [PMID: 35302352 PMCID: PMC9203130 DOI: 10.1021/acs.biochem.1c00820] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A challenge within the field of bioconjugation is developing probes to uncover novel information on proteins and other biomolecules. Intracellular delivery of these probes offers the promise of giving relevant context to this information, and these probes can serve as hypothesis-generating tools within complex systems. Leveraging the utility of triazabutadiene chemistry, herein, we discuss the development of a probe that undergoes reduction-mediated deprotection to rapidly deliver a benzene diazonium ion (BDI) into cells. The intracellular BDI resulted in an increase in global tyrosine phosphorylation levels. Seeing phosphatase dysregulation as a potential source of this increase, a tyrosine phosphatase (PTP1B) was tested and shown to be both inhibited and covalently modified by the BDI. In addition to the expected azobenzene formation at tyrosine side chains, key reactive histidine residues were also modified.
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Affiliation(s)
- Natasha R Cornejo
- Department of Chemistry and Biochemistry, University of Arizona, 1306 East University Boulevard, Tucson, Arizona 85721, United States
| | - Bismark Amofah
- Department of Chemistry and Biochemistry, University of Arizona, 1306 East University Boulevard, Tucson, Arizona 85721, United States
| | - Austin Lipinski
- Department of Medicine, Division of Endocrinology, University of Arizona College of Medicine, Tucson, Arizona 85721, United States
| | - Paul R Langlais
- Department of Medicine, Division of Endocrinology, University of Arizona College of Medicine, Tucson, Arizona 85721, United States
| | - Indraneel Ghosh
- Department of Chemistry and Biochemistry, University of Arizona, 1306 East University Boulevard, Tucson, Arizona 85721, United States
| | - John C Jewett
- Department of Chemistry and Biochemistry, University of Arizona, 1306 East University Boulevard, Tucson, Arizona 85721, United States
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9
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Aida H, Shigeta Y, Harada R. Ligand Binding Path Sampling Based on Parallel Cascade Selection Molecular Dynamics: LB-PaCS-MD. MATERIALS 2022; 15:ma15041490. [PMID: 35208030 PMCID: PMC8878848 DOI: 10.3390/ma15041490] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/08/2022] [Accepted: 02/10/2022] [Indexed: 01/09/2023]
Abstract
Parallel cascade selection molecular dynamics (PaCS-MD) is a rare-event sampling method that generates transition pathways between a reactant and product. To sample the transition pathways, PaCS-MD repeats short-time MD simulations from important configurations as conformational resampling cycles. In this study, PaCS-MD was extended to sample ligand binding pathways toward a target protein, which is referred to as LB-PaCS-MD. In a ligand-concentrated environment, where multiple ligand copies are randomly arranged around the target protein, LB-PaCS-MD allows for the frequent sampling of ligand binding pathways. To select the important configurations, we specified the center of mass (COM) distance between each ligand and the relevant binding site of the target protein, where snapshots generated by the short-time MD simulations were ranked by their COM distance values. From each cycle, snapshots with smaller COM distance values were selected as the important configurations to be resampled using the short-time MD simulations. By repeating conformational resampling cycles, the COM distance values gradually decreased and converged to constants, meaning that a set of ligand binding pathways toward the target protein was sampled by LB-PaCS-MD. To demonstrate relative efficiency, LB-PaCS-MD was applied to several proteins, and their ligand binding pathways were sampled more frequently than conventional MD simulations.
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Affiliation(s)
- Hayato Aida
- Graduate School of Science and Technology, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Japan;
| | - Yasuteru Shigeta
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Japan;
| | - Ryuhei Harada
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Japan;
- Correspondence:
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10
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Mi X, Shukla D. Predicting the Activities of Drug Excipients on Biological Targets using One-Shot Learning. J Phys Chem B 2022; 126:1492-1503. [PMID: 35142529 DOI: 10.1021/acs.jpcb.1c10574] [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
Excipients are major components of drugs and are used to improve drug attributes such as stability and appearance. Excipients approved by the U.S. Food and Drug Administration (FDA) are regarded as safe for humans in allowed concentrations, but their potential interactions with drug targets have not been investigated systematically, which might influence a drug's efficacy. Deep learning models have been used for the identification of ligands that could bind to the drug targets. However, due to the limited available data, it is challenging to reliably estimate the likelihood of a ligand-protein interaction. One-shot learning techniques provide a potential approach to address this low data problem as these techniques require only one or a few examples to classify the new data. In this study, we apply one-shot learning models to data sets that include ligands binding to G-protein-coupled receptors (GPCRs) and kinases. The predicted results suggest that one-shot learning could be used for predicting ligand-protein interactions, and the models attain better performance when protein targets contain conserved binding pockets. The trained models are also used to predict interactions between excipients and drug targets, which provides a potential efficient strategy to explore the activities of drug excipients. We find that a large number of drug excipients could interact with biological targets and influence their function. The results demonstrate how one-shot learning can be used to make accurate predictions for excipient-protein interactions, and these methods could be used for selecting excipients with limited drug-protein interactions.
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Affiliation(s)
- Xuenan Mi
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Diwakar Shukla
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.,Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.,Cancer Center at Illinois, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.,Center for Digital Agriculture, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Plant Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.,National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
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11
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Goel H, Hazel A, Yu W, Jo S, MacKerell AD. Application of Site-Identification by Ligand Competitive Saturation in Computer-Aided Drug Design. NEW J CHEM 2022; 46:919-932. [PMID: 35210743 PMCID: PMC8863107 DOI: 10.1039/d1nj04028f] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Site Identification by Ligand Competitive Saturation (SILCS) is a molecular simulation approach that uses diverse small solutes in aqueous solution to obtain functional group affinity patterns of a protein or other macromolecule. This involves employing a combined Grand Canonical Monte Carlo (GCMC)-molecular dynamics (MD) method to sample the full 3D space of the protein, including deep binding pockets and interior cavities from which functional group free energy maps (FragMaps) are obtained. The information content in the maps, which include contributions from protein flexibilty and both protein and functional group desolvation contributions, can be used in many aspects of the drug discovery process. These include identification of novel ligand binding pockets, including allosteric sites, pharmacophore modeling, prediction of relative protein-ligand binding affinities for database screening and lead optimization efforts, evaluation of protein-protein interactions as well as in the formulation of biologics-based drugs including monoclonal antibodies. The present article summarizes the various tools developed in the context of the SILCS methodology and their utility in computer-aided drug design (CADD) applications, showing how the SILCS toolset can improve the drug-development process on a number of fronts with respect to both accuracy and throughput representing a new avenue of CADD applications.
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Affiliation(s)
- Himanshu Goel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St. Baltimore, Maryland 21201, United States
| | - Anthony Hazel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St. Baltimore, Maryland 21201, United States
| | - Wenbo Yu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St. Baltimore, Maryland 21201, United States
| | - Sunhwan Jo
- SilcsBio LLC, 1100 Wicomico St. Suite 323, Baltimore, MD, 21230, United States
| | - Alexander D. MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St. Baltimore, Maryland 21201, United States., SilcsBio LLC, 1100 Wicomico St. Suite 323, Baltimore, MD, 21230, United States.,, Tel: 410-706-7442, Fax: 410-706-5017
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12
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Sabanés Zariquiey F, Jacoby E, Vos A, van Vlijmen HWT, Tresadern G, Harvey J. Divide and Conquer. Pocket-Opening Mixed-Solvent Simulations in the Perspective of Docking Virtual Screening Applications for Drug Discovery. J Chem Inf Model 2022; 62:533-543. [PMID: 35041430 DOI: 10.1021/acs.jcim.1c01164] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The existence of a druggable binding pocket is a prerequisite for computational drug-target interaction studies including virtual screening. Retrospective studies have shown that extended sampling methods like Markov State Modeling and mixed-solvent simulations can identify cryptic pockets relevant for drug discovery. Here, we apply a combination of mixed-solvent molecular dynamics (MD) and time-structure independent component analysis (TICA) to four retrospective case studies: NPC2, the CECR2 bromodomain, TEM-1, and MCL-1. We compare previous experimental and computational findings to our results. It is shown that the successful identification of cryptic pockets depends on the system and the cosolvent probes. We used alternative TICA internal features such as the unbiased backbone coordinates or backbone dihedrals versus biased interatomic distances. We found that in the case of NPC2, TEM-1, and MCL-1, the use of unbiased features is able to identify cryptic pockets, although in the case of the CECR2 bromodomain, more specific features are required to properly capture a pocket opening. In the perspective of virtual screening applications, it is shown how docking studies with the parent ligands depend critically on the conformational state of the targets.
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Affiliation(s)
| | - Edgar Jacoby
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Ann Vos
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Herman W T van Vlijmen
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Jeremy Harvey
- Department of Chemistry, KU Leuven, Celestijnenlaan 200F, 3001 Leuven, Belgium
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13
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Iacono R, Minopoli N, Ferrara MC, Tarallo A, Damiano C, Porto C, Strollo S, Roig-Zamboni V, Peluso G, Sulzenbacher G, Cobucci-Ponzano B, Parenti G, Moracci M. Carnitine is a pharmacological allosteric chaperone of the human lysosomal α-glucosidase. J Enzyme Inhib Med Chem 2021; 36:2068-2079. [PMID: 34565280 PMCID: PMC8477953 DOI: 10.1080/14756366.2021.1975694] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 08/23/2021] [Accepted: 08/29/2021] [Indexed: 11/22/2022] Open
Abstract
Pompe disease is an inherited metabolic disorder due to the deficiency of the lysosomal acid α-glucosidase (GAA). The only approved treatment is enzyme replacement therapy with the recombinant enzyme (rhGAA). Further approaches like pharmacological chaperone therapy, based on the stabilising effect induced by small molecules on the target enzyme, could be a promising strategy. However, most known chaperones could be limited by their potential inhibitory effects on patient's enzymes. Here we report on the discovery of novel chaperones for rhGAA, L- and D-carnitine, and the related compound acetyl-D-carnitine. These drugs stabilise the enzyme at pH and temperature without inhibiting the activity and acted synergistically with active-site directed pharmacological chaperones. Remarkably, they enhanced by 4-fold the acid α-glucosidase activity in fibroblasts from three Pompe patients with added rhGAA. This synergistic effect of L-carnitine and rhGAA has the potential to be translated into improved therapeutic efficacy of ERT in Pompe disease.
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Affiliation(s)
- Roberta Iacono
- Department of Biology, University of Naples “Federico II”, Complesso Universitario di Monte S. Angelo, Naples, Italy
- Institute of Biosciences and Bioresources – CNR, Naples, Italy
| | - Nadia Minopoli
- Telethon Institute of Genetics & Medicine, Pozzuoli, Italy
| | | | | | - Carla Damiano
- Telethon Institute of Genetics & Medicine, Pozzuoli, Italy
| | - Caterina Porto
- Telethon Institute of Genetics & Medicine, Pozzuoli, Italy
| | - Sandra Strollo
- Telethon Institute of Genetics & Medicine, Pozzuoli, Italy
| | - Véronique Roig-Zamboni
- Centre National de la Recherche Scientifique (CNRS), Aix-Marseille University, AFMB, Marseille, France
| | - Gianfranco Peluso
- Research Institute on Terrestrial Ecosystems, UOS Naples-CNR, Naples, Italy
| | - Gerlind Sulzenbacher
- Centre National de la Recherche Scientifique (CNRS), Aix-Marseille University, AFMB, Marseille, France
| | | | - Giancarlo Parenti
- Telethon Institute of Genetics & Medicine, Pozzuoli, Italy
- Department of Translational Medical Sciences, Federico II University, Naples, Italy
| | - Marco Moracci
- Department of Biology, University of Naples “Federico II”, Complesso Universitario di Monte S. Angelo, Naples, Italy
- Institute of Biosciences and Bioresources – CNR, Naples, Italy
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14
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Szabó PB, Sabanés Zariquiey F, Nogueira JJ. Cosolvent and Dynamic Effects in Binding Pocket Search by Docking Simulations. J Chem Inf Model 2021; 61:5508-5523. [PMID: 34730967 PMCID: PMC8659376 DOI: 10.1021/acs.jcim.1c00924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Indexed: 11/30/2022]
Abstract
The lack of conformational sampling in virtual screening projects can lead to inefficient results because many of the potential drugs may not be able to bind to the target protein during the static docking simulations. Here, we performed ensemble docking for around 2000 United States Food and Drug Administration (FDA)-approved drugs with the RNA-dependent RNA polymerase (RdRp) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a target. The representative protein structures were generated by clustering classical molecular dynamics trajectories, which were evolved using three solvent scenarios, namely, pure water, benzene/water and phenol/water mixtures. The introduction of dynamic effects in the theoretical model showed improvement in docking results in terms of the number of strong binders and binding sites in the protein. Some of the discovered pockets were found only for the cosolvent simulations, where the nonpolar probes induced local conformational changes in the protein that lead to the opening of transient pockets. In addition, the selection of the ligands based on a combination of the binding free energy and binding free energy gap between the best two poses for each ligand provided more suitable binders than the selection of ligands based solely on one of the criteria. The application of cosolvent molecular dynamics to enhance the sampling of the configurational space is expected to improve the efficacy of virtual screening campaigns of future drug discovery projects.
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Affiliation(s)
- P. Bernát Szabó
- Department
of Chemistry, KU Leuven, Celestijnenlaan 200F, 3001 Leuven, Belgium
- Department
of Chemistry, Universidad Autónoma
de Madrid, Calle Francisco Tomás y Valiente, 7, 28049 Madrid, Spain
| | | | - Juan J. Nogueira
- Department
of Chemistry, Universidad Autónoma
de Madrid, Calle Francisco Tomás y Valiente, 7, 28049 Madrid, Spain
- IADCHEM,
Institute for Advanced Research in Chemistry, Universidad Autónoma de Madrid, Calle Francisco Tomás y Valiente, 7, 28049 Madrid, Spain
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15
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Chatzigoulas A, Cournia Z. Rational design of allosteric modulators: Challenges and successes. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1529] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Alexios Chatzigoulas
- Biomedical Research Foundation Academy of Athens Athens Greece
- Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece
| | - Zoe Cournia
- Biomedical Research Foundation Academy of Athens Athens Greece
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16
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Yanagisawa K, Moriwaki Y, Terada T, Shimizu K. EXPRORER: Rational Cosolvent Set Construction Method for Cosolvent Molecular Dynamics Using Large-Scale Computation. J Chem Inf Model 2021; 61:2744-2753. [PMID: 34061535 DOI: 10.1021/acs.jcim.1c00134] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Cosolvent molecular dynamics (CMD) simulations involve an MD simulation of a protein in the presence of explicit water molecules mixed with cosolvent molecules to perform hotspot detection, binding site identification, and binding energy estimation, while other existing methods (e.g., MixMD, SILCS, and MDmix) utilize small molecules that represent functional groups of compounds. However, the cosolvent selections employed in these methods differ and there are only a few cosolvents that are commonly used in these methods. In this study, we proposed a systematic method for constructing a set of cosolvents for drug discovery, termed the EXtended PRObes set construction by REpresentative Retrieval (EXPRORER). First, we extracted typical substructures from FDA-approved drugs, generated 138 cosolvent structures, and for each cosolvent molecule, we conducted CMD simulations to generate a spatial probability distribution map of cosolvent atoms (PMAP). Analyses of PMAP similarity revealed that a cosolvent pair with a PMAP similarity greater than 0.70-0.75 shared similar structural features. We present a method for the construction of a cosolvent subset that satisfies a similarity threshold for all cosolvents, and we tested the constructed sets for four proteins. To our knowledge, this is the first study to include a systematic proposal for cosolvent set construction, and thus, the EXPRORER cosolvents will provide deeper insights into ligand binding sites of various proteins.
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Affiliation(s)
- Keisuke Yanagisawa
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, Tokyo 152-8550, Japan.,Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan
| | - Yoshitaka Moriwaki
- Agricultural Bioinformatics Research Unit, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan
| | - Tohru Terada
- Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan.,Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Tokyo 113-8657, Japan.,Agricultural Bioinformatics Research Unit, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan
| | - Kentaro Shimizu
- Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan.,Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Tokyo 113-8657, Japan.,Agricultural Bioinformatics Research Unit, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan
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17
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Zavala-Barrera C, Del-Río-Robles JE, García-Jiménez I, Egusquiza-Alvarez CA, Hernández-Maldonado JP, Vázquez-Prado J, Reyes-Cruz G. The calcium sensing receptor (CaSR) promotes Rab27B expression and activity to control secretion in breast cancer cells. BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR CELL RESEARCH 2021; 1868:119026. [PMID: 33845096 DOI: 10.1016/j.bbamcr.2021.119026] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 03/23/2021] [Accepted: 03/25/2021] [Indexed: 12/11/2022]
Abstract
Chemotactic and angiogenic factors secreted within the tumor microenvironment eventually facilitate the metastatic dissemination of cancer cells. Calcium-sensing receptor (CaSR) activates secretory pathways in breast cancer cells via a mechanism driven by vesicular trafficking of this receptor. However, it remains to be elucidated how endosomal proteins in secretory vesicles are controlled by CaSR. In the present study, we demonstrate that CaSR promotes expression of Rab27B and activates this secretory small GTPase via PI3K, PKA, mTOR and MADD, a guanine nucleotide exchange factor, also known as DENN/Rab3GEP. Active Rab27B leads secretion of various cytokines and chemokines, including IL-6, IL-1β, IL-8, IP-10 and RANTES. Expression of Rab27B is stimulated by CaSR in MDA-MB-231 and MCF-7 breast epithelial cancer cells, but not in non-cancerous MCF-10A cells. This regulatory mechanism also occurs in HeLa and PC3 cells. Our findings provide insightful information regarding how CaSR activates a Rab27B-dependent mechanism to control secretion of factors known to intervene in paracrine communication circuits within the tumor microenvironment.
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Affiliation(s)
- Cesar Zavala-Barrera
- Departments of Cell Biology, Centro de Investigación y Estudios Avanzados del IPN (Cinvestav-IPN), Mexico City, Mexico
| | - Jorge Eduardo Del-Río-Robles
- Departments of Cell Biology, Centro de Investigación y Estudios Avanzados del IPN (Cinvestav-IPN), Mexico City, Mexico
| | - Irving García-Jiménez
- Departments of Cell Biology, Centro de Investigación y Estudios Avanzados del IPN (Cinvestav-IPN), Mexico City, Mexico
| | | | | | - José Vázquez-Prado
- Departments of Pharmacology, Centro de Investigación y Estudios Avanzados del IPN (Cinvestav-IPN), Mexico City, Mexico
| | - Guadalupe Reyes-Cruz
- Departments of Cell Biology, Centro de Investigación y Estudios Avanzados del IPN (Cinvestav-IPN), Mexico City, Mexico.
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18
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Inflammageing in the cardiovascular system: mechanisms, emerging targets, and novel therapeutic strategies. Clin Sci (Lond) 2021; 134:2243-2262. [PMID: 32880386 DOI: 10.1042/cs20191213] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 08/18/2020] [Accepted: 08/19/2020] [Indexed: 12/13/2022]
Abstract
In the elderly population, pathological inflammation has been associated with ageing-associated diseases. The term 'inflammageing', which was used for the first time by Franceschi and co-workers in 2000, is associated with the chronic, low-grade, subclinical inflammatory processes coupled to biological ageing. The source of these inflammatory processes is debated. The senescence-associated secretory phenotype (SASP) has been proposed as the main origin of inflammageing. The SASP is characterised by the release of inflammatory cytokines, elevated activation of the NLRP3 inflammasome, altered regulation of acetylcholine (ACh) nicotinic receptors, and abnormal NAD+ metabolism. Therefore, SASP may be 'druggable' by small molecule therapeutics targeting those emerging molecular targets. It has been shown that inflammageing is a hallmark of various cardiovascular diseases, including atherosclerosis, hypertension, and adverse cardiac remodelling. Therefore, the pathomechanism involving SASP activation via the NLRP3 inflammasome; modulation of NLRP3 via α7 nicotinic ACh receptors; and modulation by senolytics targeting other proteins have gained a lot of interest within cardiovascular research and drug development communities. In this review, which offers a unique view from both clinical and preclinical target-based drug discovery perspectives, we have focused on cardiovascular inflammageing and its molecular mechanisms. We have outlined the mechanistic links between inflammageing, SASP, interleukin (IL)-1β, NLRP3 inflammasome, nicotinic ACh receptors, and molecular targets of senolytic drugs in the context of cardiovascular diseases. We have addressed the 'druggability' of NLRP3 and nicotinic α7 receptors by small molecules, as these proteins represent novel and exciting targets for therapeutic interventions targeting inflammageing in the cardiovascular system and beyond.
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19
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Lazim R, Suh D, Choi S. Advances in Molecular Dynamics Simulations and Enhanced Sampling Methods for the Study of Protein Systems. Int J Mol Sci 2020; 21:E6339. [PMID: 32882859 PMCID: PMC7504087 DOI: 10.3390/ijms21176339] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 08/10/2020] [Accepted: 08/11/2020] [Indexed: 12/12/2022] Open
Abstract
Molecular dynamics (MD) simulation is a rigorous theoretical tool that when used efficiently could provide reliable answers to questions pertaining to the structure-function relationship of proteins. Data collated from protein dynamics can be translated into useful statistics that can be exploited to sieve thermodynamics and kinetics crucial for the elucidation of mechanisms responsible for the modulation of biological processes such as protein-ligand binding and protein-protein association. Continuous modernization of simulation tools enables accurate prediction and characterization of the aforementioned mechanisms and these qualities are highly beneficial for the expedition of drug development when effectively applied to structure-based drug design (SBDD). In this review, current all-atom MD simulation methods, with focus on enhanced sampling techniques, utilized to examine protein structure, dynamics, and functions are discussed. This review will pivot around computer calculations of protein-ligand and protein-protein systems with applications to SBDD. In addition, we will also be highlighting limitations faced by current simulation tools as well as the improvements that have been made to ameliorate their efficiency.
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Affiliation(s)
- Raudah Lazim
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea
| | - Donghyuk Suh
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea
| | - Sun Choi
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea
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