DOCSLIB.ORG

Study of a Highly Accurate and Fast Protein-Ligand Docking Algorithm Based on Molecular Dynamics

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Study of a Highly Accurate and Fast Protein-Ligand Docking Algorithm Based on Molecular Dynamics Study of a Highly Accurate and Fast Protein-Ligand Docking Algorithm Based on Molecular Dynamics M. Taufer1,2,3, M. Crowley2, D. Price2, A.A. Chien 1, C.L. Brooks III2,3 1 Dept. of Computer Science and Engineering 2 Dept. of Molecular Biology (TPC6) University of California at San Diego The Scripps Research Institute La Jolla, California 92093, U.S. La Jolla, California 92037, U.S. [email protected] taufer,crowley,priced,[email protected] 3 Center for Theoretical Biological Physics La Jolla, California 92093, U.S. Abstract plexes are time and resource intensive. As a result, much research effort has been focused on computational meth- Few methods use molecular dynamics simulations based ods for the prediction of this difficult-to-obtain structural on atomically detailed force fields to study the protein- information. In general, this process is called docking. ligand docking process because they are considered too Current docking algorithms typically use a fast, simpli- time demanding despite their accuracy. In this paper fied scoring function to direct the conformational search we present a docking algorithm based on molecular dy- and select the best structures. However, recent work has namics simulations which has a highly flexible compu- demonstrated that there are significant inaccuracies as- tational granularity. We compare the accuracy and the sociated with these algorithms [1]. Furthermore, there time required with well-known, commonly used dock- are indications that inaccuracies can be reduced by us- ing methods like AutoDock, DOCK, FlexX, ICM, and ing algorithms that use more sophisticated physics. For GOLD. We show that our algorithm is accurate, fast example, CDOCKER, a docking algorithm based on and, because of its flexibility, applicable even to loosely molecular dynamics (MD) and a conventional molecular coupled distributed systems like desktop grids for dock- mechanics force field, indeed provides better accuracy ing. than other methods. However, it is still among the more Keywords: Force field based methods, docking accu- compute-intensive methods. Our aim is to adapt the racy, desktop grid computing. CDOCKER method to improve its performance with- out sacrificing the accuracy. In particular, we would like to take advantage of advances in computer technologies 1 Introduction and the establishment of new distributed architectures, A vast number of the essential roles that proteins play such as desktop grids. require small molecules to bind to specific spots in the Desktop grids provide a viable and inexpensive solu- protein structure. For instance, the small molecules can tion to the hitherto uncompetitive computational cost act as switches to turn on or off a protein function, or and time for the force field methods. New algorithms are the substrates for the particular chemical reaction with finer computational granularity need to be devel- that a protein enzyme catalyzes. Obtaining the atomic oped, especially algorithms more suitable for the highly level details of the protein-ligand interactions is a valu- volatile nodes of desktop grids. In this paper we present able tool in the development of novel pharmaceuticals. an algorithm for the docking process based on MD simu- Conventional experimental techniques for obtaining de- lations as in [2], but characterized by a higher flexibility tailed structural information about protein-ligand com- that makes it adaptable to any computing platform, even 1 to very challenging desktop grids. in which the Newtonian equations of motion are dis- Docking many ligands to the same protein followed by cretized and solved numerically by an integration pro- scoring them for their relative strength of interaction has cedure such as the Verlet algorithm. The force on the been proposed as a procedure to identify candidates for atoms is the negative gradient of the CHARMM poten- drug development. Screening large databases of com- tial energy function [11]. pounds in this manner can potentially provide an alter- native to conventional high-throughput screening, but it 2.2 Modeling Protein-Ligand Interactions is not cost-effective unless the docking algorithm is fast and accurate. Advances in energy calculation techniques make it vi- Most of the well-known, commonly used docking meth- able to use a grid-based representation of the protein- ods that are not based on MD were compared and an- ligand potential interactions to calculate our scoring alyzed in detail in [3]. We validate the accuracy of function. A grid potential allows us to represent a rigid our algorithm by applying the tests defined in [3] to protein binding site as a potential field magnitude at our method, and compare to the published results for each grid point. Protein interactions with the ligand the following other methods: AutoDock [4], DOCK [5], in the binding site are interpolated from the interaction FlexX [6], ICM [7], and GOLD [8]. We show that our strength of the grid points near each atom of the ligand, algorithm is indeed more accurate than all other meth- rather than from computing the interactions of all ligand ods except ICM. We reach a docking success rate of atoms with all protein atoms individually, resulting in over 70%, confirming the accuracy reported in [2]. The orders of magnitude fewer floating-point computations time required for running a complete docking attempt is than in the traditional molecular mechanics method. longer but comparable with the time of the other meth- In a preliminary phase of the docking simulations, we ods. The fine computational granularity of our algorithm calculate three dimensional grid maps for each of the 20 is trivially parallel and each simulation attempt is de- potential atom types composing the ligands under inves- composable into independent sub-jobs. This flexibility tigation. Each grid map consists of a three dimensional makes our accurate docking simulations fast when there lattice of regularly spaced points surrounding and cen- are many independent compute nodes, and thus, appli- tered on the active site of a protein. Each point within the cable to a wide range of platforms from traditional su- grid map stores the potential energy of a ’probe’ atom percomputers to loosely coupled distributed systems like due to its interaction with the macromolecule. For ex- desktop grids. ample, in a carbon grid map, the value associated with In Section 2 we present our docking protocol as well a grid point represents the potential energy of a carbon as some well-known and commonly used docking algo- atom at that location due to its interactions with all atoms rithms that we will compare with our method. In Sec- of the protein receptor. We have chosen a grid spacing tion 3 we define the metrics of accuracy and time that of 1Å based on previous work that showed no signifi- we use to validate our method while in Section 4 we cant differences in docking accuracy for grid spacings compare our method with the other docking algorithms between 0.25 Å and 1 Å [2]. based on those metrics. Finally, in Section 5, we dis- To facilitate the penetration of small ligands into the pro- cuss the applicability of our docking protocol to desktop tein sites and allow larger configurational changes, van grids, and future work coming out of this research. der Waals (vdW) and electrostatic potentials with soft core repulsions [12] were utilized instead of the tradi- 2 The MD-based Docking Algorithm tional potentials. A soft core repulsion reduces the po- tential barrier at vanishing interatomic distances to a fi- 2.1 The CHARMM Scientific Computation nite limit. In this case, ligands can pass between confor- Code mational minima with a relatively small potential barrier that would normally be infinite and impassible with an We use CHARMM to perform MD simulations unmodified potential. and investigate the protein-ligand docking process. CHARMM is a program for simulating biologically rel- 2.3 MD Docking Protocol evant macromolecules (proteins, DNA, RNA) and com- plexes thereof [9]. It allows the investigation of the As in most of the existing methods, we model the structure and dynamics of large molecules in solvent or protein-ligand complex as composed of a rigid protein crystals. CHARMM can be used to calculate free energy structure and a flexible ligand. A flexible ligand has differences upon mutations or ligand binding [10]. One three translational degrees of freedom, three rotational of the most common applications of CHARMM is MD, degrees of freedom and one dihedral rotation for each 2 rotable bond. The docking search is computed over a Model Protein Ligand Interactions 6+n dimensional space where n is the number of rotable bonds in the ligand. Figure 1 shows the MD-based algo- Alter Ligand Configuration and Orientations rithm used for our docking simulations. One loop con- stitutes a docking trial. Given a protein and a ligand to dock into its binding site (a so-called protein-ligand Dock Ligand into Active Protein Site complex), a docking attempt consists of a sequence of m independent trials. For each trial, a random configura- MD Simulation tion for the ligand is generated by running 1000 steps of MD at the constant temperature of 1000K in vac- Docking Trial Energy Minimization uum, starting from a reasonable structure with random initial velocities on each ligand atom. We have ana- lyzed the distribution of torsional angles generated by Calucate Score this method, and found that they indeed vary randomly over the physically reasonable range for each rotable Evaluate Candidate Solutions bond. We are confident that our initial configurations randomly sample the available configurational space of Figure 1. Our MD-based protein-ligand docking the ligand. algorithm. Starting from the new ligand configuration, a set of 10 different orientations are randomly generated and docked into the receptor, that is, moved into the cen- terms, some of which reflect the short-range vdW in- ter of the grid.
Load more

Recommended publications
  • Computer-Aided Drug Design: a Practical Guide
    Computer-Aided Drug Design: A Practical Guide Forrest Smith, Ph.D. Department of Drug Discovery and Development Harrison School of Pharmacy Auburn, University History of Drug Design • Natural Products-Ebers Papyrus, 1500 B.C. documents over 700 plant based products used to treat a variety of illnesses • The rise of Organic Chemistry, middle or the 20th Century, semi-synthetic and synthetic drugs • Computers emerge in the late 1980s, CADD with minimal impact • Automation in the 1990s, High Through-put Screening and Robotics, Compound Libraries • CADD has continued to advance since its introduction with improving capabilities Computational Chemistry • Ab initio calculations • Semi-empirical calculations • Molecular Mechanics Molecular Mechanics Global Minimum Force Fields • MM2, MM3, MM4 • MMFF • AMBER • CHARM • OPLS Finding the Global Minimum • Systematic Search • Monte Carlo Methods • Simulated Annealing • Quenched Dynamics Minor Groove Binders Computer-Aided Drug Design • Structure Based Design – Docking – Molecular Dynamics – Free Energy Perturbation • Ligand Based Design-QSAR – COMFA – Pharmacophore Modeling – Shape Based Methods Docking-The Receptor • X-Ray Crystal Structures – RCSB Protein Data Bank (https://www.rcsb.org/) – Private Data • Homology Modeling • Nuclear Magnetic Resonance Docking- The Ligand • Proprietary Ligands • Databases qReal Databases • FDA approved drugs- (http://chemoinfo.ipmc.cnrs.fr/MOLDB/index.html) • Purchasable compounds Zinc 15, currently 100 million compounds (http://zinc15.docking.org/) q Virtual Databases-
    [Show full text]
  • Small Molecule and Protein Docking Introduction
    Small Molecule and Protein Docking Introduction • A significant portion of biology is built on the paradigm sequence structure function • As we sequence more genomes and get more structural information, the next challenge will be to predict interactions and binding for two or more biomolecules (nucleic acids, proteins, peptides, drugs or other small molecules) Introduction • The questions we are interested in are: – Do two biomolecules bind each other? – If so, how do they bind? – What is the binding free energy or affinity? • The goals we have are: – Searching for lead compounds – Estimating effect of modifications – General understanding of binding – … Rationale • The ability to predict the binding site and binding affinity of a drug or compound is immensely valuable in the area or pharmaceutical design • Most (if not all) drug companies use computational methods as one of the first methods of screening or development • Computer-aided drug design is a more daunting task, but there are several examples of drugs developed with a significant contribution from computational methods Examples • Tacrine – inhibits acetylcholinesterase and boost acetylcholine levels (for treating Alzheimer’s disease) • Relenza – targets influenza • Invirase, Norvir, Crixivan – Various HIV protease inhibitors • Celebrex – inhibits Cox-2 enzyme which causes inflammation (not our fault) Docking • Docking refers to a computational scheme that tries to find the best binding orientation between two biomolecules where the starting point is the atomic coordinates of the two molecules • Additional data may be provided (biochemical, mutational, conservation, etc.) and this can significantly improve the performance, however this extra information is not required Bound vs. Unbound Docking • The simplest problem is the “bound” docking problem.
    [Show full text]
  • Structure Based Pharmacophore Modeling, Virtual Screening
    www.nature.com/scientificreports OPEN Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identifcation of natural anti‑cancer agents targeting XIAP protein Firoz A. Dain Md Opo1,2, Mohammed M. Rahman3*, Foysal Ahammad4, Istiak Ahmed5, Mohiuddin Ahmed Bhuiyan2 & Abdullah M. Asiri3 X‑linked inhibitor of apoptosis protein (XIAP) is a member of inhibitor of apoptosis protein (IAP) family responsible for neutralizing the caspases‑3, caspases‑7, and caspases‑9. Overexpression of the protein decreased the apoptosis process in the cell and resulting development of cancer. Diferent types of XIAP antagonists are generally used to repair the defective apoptosis process that can eliminate carcinoma from living bodies. The chemically synthesis compounds discovered till now as XIAP inhibitors exhibiting side efects, which is making difculties during the treatment of chemotherapy. So, the study has design to identifying new natural compounds that are able to induce apoptosis by freeing up caspases and will be low toxic. To identify natural compound, a structure‑ based pharmacophore model to the protein active site cavity was generated following by virtual screening, molecular docking and molecular dynamics (MD) simulation. Initially, seven hit compounds were retrieved and based on molecular docking approach four compounds has chosen for further evaluation. To confrm stability of the selected drug candidate to the target protein the MD simulation approach were employed, which confrmed stability of the three compounds. Based on the fnding, three newly obtained compounds namely Caucasicoside A (ZINC77257307), Polygalaxanthone III (ZINC247950187), and MCULE‑9896837409 (ZINC107434573) may serve as lead compounds to fght against the treatment of XIAP related cancer, although further evaluation through wet lab is necessary to measure the efcacy of the compounds.
    [Show full text]
  • BUDE: GPU-Accelerated Molecular Docking for Drug Discovery
    BUDE A General Purpose Molecular Docking Program Using OpenCL Richard B Sessions 1 The molecular docking problem Proteins typically O(1000) atoms ligand Ligands typically O(100) atoms predicted complex receptor 1 Sampling (6-degrees of freedom) EMC 2 Binding affinity prediction EFE-FF 2 An atom-atom based forcefield parameterised according to atom type, analagous to standard molecular mechanics 3 Empirical Free Energy Forcefield McIntosh-Smith, S., et al., Benchmarking Energy Efficiency, Power Costs and Carbon Emissions on Heterogeneous Systems. soft core Computer Journal, 2012. 55(2): p. 192-205. Re-docking a ligand into the Xray Structure (good prediction == low RMSD) 1CIL (Human carbonic anhydrase II) RMSD ~ 0.2 Å 5 Another example 1EZQ (Human Factor XA) RMSD ~ 1.2 Å 6 Accuracy of Pose Prediction (re-docking the BindingDB validation set, 84 complexes) www.bindingdb.org 7 Binding Energy Prediction: is BUDE any better? Mike Hann’s 2006 test of docking software Yes – better but not perfect! 8 BUDE Simplified Flow Diagram (C++/OpenCL) Start BUDE Enter Initial End BUDE Data Yes Data Error(s)? Reading Print Help Yes No Error(s)? Write Control File Prepare Data for Docking No Docking Info Type Docking End BUDE Act on No Option Small Yes Large Error(s)? Site Docking Surface Docking Generate Surface Pairs Calculate Energies Do Docking Print Results No Do Generation Rank Energies Host Job Parallel EMC Code? Score Results Accelerated Job Yes Yes Last No Generation 9 BUDE’s heterogeneous approach 1. Discover all OpenCL platforms/devices, inc. both CPUs and GPUs 2. Run a micro benchmark on each device, ideally a short piece of real work 3.
    [Show full text]
  • Homology Modeling and Molecular Docking of Antagonists to Class B G
    University of Vermont ScholarWorks @ UVM Graduate College Dissertations and Theses Dissertations and Theses 2016 Homology Modeling and Molecular Docking of Antagonists to Class B G-Protein Coupled Receptor Pituitary Adenylate Cyclase Type 1 (PAC1R) Suzanne Louise Stanton University of Vermont Follow this and additional works at: https://scholarworks.uvm.edu/graddis Part of the Chemistry Commons Recommended Citation Stanton, Suzanne Louise, "Homology Modeling and Molecular Docking of Antagonists to Class B G-Protein Coupled Receptor Pituitary Adenylate Cyclase Type 1 (PAC1R)" (2016). Graduate College Dissertations and Theses. 624. https://scholarworks.uvm.edu/graddis/624 This Thesis is brought to you for free and open access by the Dissertations and Theses at ScholarWorks @ UVM. It has been accepted for inclusion in Graduate College Dissertations and Theses by an authorized administrator of ScholarWorks @ UVM. For more information, please contact [email protected]. HOMOLOGY MODELING AND MOLECULAR DOCKING OF ANTAGONISTS TO CLASS B G-PROTEIN COUPLED RECEPTOR PITUITARY ADENYLATE CYLCASE TYPE 1 (PAC1R) A Thesis Presented by Suzanne Louise Stanton to The Faculty of the Graduate College of The University of Vermont In Partial Fulfillment of the Requirements for the Degree of Master of Science Specializing in Organic Chemistry/Chemistry October, 2016 Defense Date: May 9, 2016 Thesis Examination Committee: Matthias Brewer, Ph.D., Advisor Jose Madalengoitia, Ph.D. Victor May, Ph.D. Rory Waterman, Ph.D. Cynthia J. Forehand, PhD., Dean of the Graduate College ABSTRACT Recent studies have identified the Class B g-protein coupled receptor (GPCR) pituitary adenylate cyclase activating polypeptide type 1 (PAC1R) as a key component in physiological stress management.
    [Show full text]
  • NEURAL CREST in DEVELOPMENT and REPAIR NEURAL CREST in DEVELOPMENTNEURAL and REPAIR Jorge Aquino Benjamín Jorge Benjamín Aquino
    Thesis for doctoral degree (Ph.D.) 2007 Thesis for doctoral degree (Ph.D.) 2007 NEURAL CREST IN DEVELOPMENT AND REPAIR NEURAL CREST IN DEVELOPMENT AND REPAIR NEURAL CREST IN DEVELOPMENT Jorge Benjamín Aquino Jorge Jorge Benjamín Aquino Division of Molecular Neurobiology Department of Medical Biochemistry and Biophysics Karolinska Institutet, Stockholm, Sweden NEURAL CREST IN DEVELOPMENT AND REPAIR Jorge Benjamín Aquino Stockholm 2007 All previously published papers were reproduced with permission from the publisher. Published by Karolinska Institutet. Printed by Larserics Digital Print AB © Jorge Benjamín Aquino, 2007 ISBN 978-91-7357-367-2 In memory of my father and showing gratitude to my mother “In summary, there are no small problems. Problems that appear small are large problems that are not understood. Nature is a harmonious mechanism where all parts, including those appearing to play a secondary role, cooperate in the functional whole. No one can predict their importance in the future. All natural arrangements, however capricious they may seem, have a function.” -Santiago Ramón y Cajal, 1897 ABSTRACT This thesis is focused on several aspects of neural crest biology, largely related to two main issues: 1) understanding its mechanisms of migration and 2) analyzing the potential of neural crest stem cells or their derivatives for nervous system repair. If repair is the aim, knowledge of both timing and events leading to axonal degeneration is required in order to block it and/or increase regeneration outcome. In these studies I have made use of mouse, rat and chicken in vivo and in vitro models to address specific questions regarding the scope of this thesis.
    [Show full text]
  • Combining Molecular Dynamics and Docking Simulations to Develop Targeted Protocols for Performing Optimized Virtual Screening Campaigns on the Htrpm8 Channel
    International Journal of Molecular Sciences Article Combining Molecular Dynamics and Docking Simulations to Develop Targeted Protocols for Performing Optimized Virtual Screening Campaigns on the hTRPM8 Channel Carmine Talarico 1 , Silvia Gervasoni 2 , Candida Manelfi 1, Alessandro Pedretti 2 , Giulio Vistoli 2 and Andrea R. Beccari 1,* 1 Dompé Farmaceutici SpA, Via Campo di Pile, 67100 L’Aquila, Italy; [email protected] (C.T.); candida.manelfi@dompe.com (C.M.) 2 Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Mangiagalli, 25, I-20133 Milano, Italy; [email protected] (S.G.); [email protected] (A.P.); [email protected] (G.V.) * Correspondence: [email protected]; Tel.: +39 081 6132339 Received: 17 January 2020; Accepted: 20 March 2020; Published: 25 March 2020 Abstract: Background: There is an increasing interest in TRPM8 ligands of medicinal interest, the rational design of which can be nowadays supported by structure-based in silico studies based on the recently resolved TRPM8 structures. Methods: The study involves the generation of a reliable hTRPM8 homology model, the reliability of which was assessed by a 1.0 µs MD simulation which was also used to generate multiple receptor conformations for the following structure-based virtual screening (VS) campaigns; docking simulations utilized different programs and involved all monomers of the selected frames; the so computed docking scores were combined by consensus approaches based on the EFO algorithm. Results: The obtained models revealed very satisfactory performances; LiGen™ provided the best results among the tested docking programs; the combination of docking results from the four monomers elicited a markedly beneficial effect on the computed consensus models.
    [Show full text]
  • Intuitive, Reproducible High-Throughput Molecular Dynamics in Galaxy: a Tutorial
    bioRxiv preprint doi: https://doi.org/10.1101/2020.05.08.084780; this version posted May 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Intuitive, reproducible high-throughput molecular dynamics in Galaxy: a tutorial Simon A. Bray1, Tharindu Senapathi2, Christopher B. Barnett2, , and Björn A. Grüning1, 1Department of Computer Science, University of Freiburg, Georges-Kohler-Allee 106, Freiburg, Germany 2Scientific Computing Research Unit, University of Cape Town, 7700 Cape Town, South Africa This paper is a tutorial developed for the data analysis platform references for further reading. It presumes that the user has Galaxy. The purpose of Galaxy is to make high-throughput a basic understanding of the Galaxy platform. The aim is computational data analysis, such as molecular dynamics, a to guide the user through the various steps of a molecular dy- structured, reproducible and transparent process. In this tu- namics study, from accessing publicly available crystal struc- torial we focus on 3 questions: How are protein-ligand systems tures, to performing MD simulation (leveraging the popular parameterized for molecular dynamics simulation? What kind GROMACS (6, 7) engine), to analysis of the results. of analysis can be carried out on molecular trajectories? How can high-throughput MD be used to study multiple ligands? Af- The entire analysis described in this article can be con- ter finishing you will have learned about force-fields and MD pa- ducted efficiently on any Galaxy server which has the rameterization, how to conduct MD simulation and analysis for needed tools.
    [Show full text]
  • 15 Structure Prediction of Protein Complexes
    15 Structure Prediction of Protein Complexes Brian Pierce, Andrew T. Phillips, and Zhiping Weng 15.1 Introduction Protein–protein interactions are critical for biological function. They directly and indirectly influence the biological systems of which they are a part. Antibodies bind with antigens to detect and stop viruses and other infectious agents. Cell signaling is performed in many cases through the interactions between proteins. Many dis- eases involve protein–protein interactions on some level, including cancer and prion diseases. A very useful source of information about the interaction between two proteins is the 3D structure of their macromolecular complex. Using this, it is possible to easily identify such details as the residues that are directly involved with binding, the nature of the interface itself, and the conformational change undergone by the protein partners. X-ray crystallography has provided us with many structures of protein– protein complexes, but time and experimental limitations have left many protein complex structures yet unsolved. While techniques such as homology modeling and mutational analysis yield some information about binding sites, more information is needed to understand the nature of protein–protein interactions. Thus, it is useful to employ protein docking to predict the complex structure. 15.1.1 Protein Docking: Definition Protein–protein docking can be defined as the determination of the complex struc- ture between two proteins, given the coordinates of the individual proteins. Protein– protein docking can be further classified as bound docking, which uses the structures from the complex structure as input, and unbound docking, which uses the struc- tures from the individually crystallized subunits as input.
    [Show full text]
  • Molecular Docking: Shifting Paradigms in Drug Discovery
    International Journal of Molecular Sciences Review Molecular Docking: Shifting Paradigms in Drug Discovery Luca Pinzi * and Giulio Rastelli Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125 Modena, Italy * Correspondence: [email protected] Received: 6 August 2019; Accepted: 2 September 2019; Published: 4 September 2019 Abstract: Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence. Keywords: molecular docking; drug discovery; drug repurposing; reverse screening; target fishing; polypharmacology; adverse drug reactions 1. Introduction The experimental screening of large libraries of compounds against panels of molecular targets, i.e., High-Throughput Screening (HTS), has represented the gold standard for discovering biologically active hits. However, the high costs required to establish and maintain these screening platforms often hamper their use for drug discovery [1].
    [Show full text]
  • Molecular Simulations of G Protein-Coupled Receptors
    Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 2011 Molecular simulations of G protein-coupled receptors A journey into structure-based ligand design and receptor function PIERRE MATRICON ACTA UNIVERSITATIS UPSALIENSIS ISSN 1651-6214 ISBN 978-91-513-1134-0 UPPSALA urn:nbn:se:uu:diva-434103 2021 Dissertation presented at Uppsala University to be publicly examined in A1:111a, Uppsala Biomedical Centre (BMC), Husargatan 3, Uppsala, Friday, 26 March 2021 at 13:15 for the degree of Doctor of Philosophy. The examination will be conducted in English. Faculty examiner: Professor Bjørn Olav Brandsdal (The Arctic University of Norway). Abstract Matricon, P. 2021. Molecular simulations of G protein-coupled receptors: A journey into structure-based ligand design and receptor function. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 2011. 82 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-513-1134-0. The superfamily of G protein-coupled receptors (GPCRs) contains a large number of important drug targets. These cell surface receptors recognize extracellular signaling molecules, which stimulates intracellular pathways that play major roles in human physiology. Breakthroughs in structural biology have led to an exponentially increasing number of atomic resolution GPCR structures, which have provided insights into the molecular basis of ligand binding and receptor activation. However, in order to use these structures in rational drug design, computational methods able to predict ligand binding modes and affinities are required. In the first part of this thesis, molecular simulations were used to explore the potential of using structure- based approaches to discover and optimize GPCR ligands.
    [Show full text]
  • Multiscale Molecular Modeling in G Protein-Coupled Receptor (GPCR)-Ligand Studies
    biomolecules Review Multiscale Molecular Modeling in G Protein-Coupled Receptor (GPCR)-Ligand Studies Pratanphorn Nakliang, Raudah Lazim, Hyerim Chang and Sun Choi * College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea; [email protected] (P.N.); [email protected] (R.L.); [email protected] (H.C.) * Correspondence: [email protected]; Tel.: +82-2-3277-4503; Fax: +82-2-3277-2851 Received: 5 March 2020; Accepted: 17 April 2020; Published: 19 April 2020 Abstract: G protein-coupled receptors (GPCRs) are major drug targets due to their ability to facilitate signal transduction across cell membranes, a process that is vital for many physiological functions to occur. The development of computational technology provides modern tools that permit accurate studies of the structures and properties of large chemical systems, such as enzymes and GPCRs, at the molecular level. The advent of multiscale molecular modeling permits the implementation of multiple levels of theories on a system of interest, for instance, assigning chemically relevant regions to high quantum mechanics (QM) level of theory while treating the rest of the system using classical force field (molecular mechanics (MM) potential). Multiscale QM/MM molecular modeling have far-reaching applications in the rational design of GPCR drugs/ligands by affording precise ligand binding configurations through the consideration of conformational plasticity. This enables the identification of key binding site residues that could be targeted to manipulate GPCR function. This review will focus on recent applications of multiscale QM/MM molecular simulations in GPCR studies that could boost the efficiency of future structure-based drug design (SBDD) strategies.
    [Show full text]