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Herz AM, Kellici T, Morao I, Michel J. Alchemical Free Energy Workflows for the Computation of Protein-Ligand Binding Affinities. Methods Mol Biol 2024; 2716:241-264. [PMID: 37702943 DOI: 10.1007/978-1-0716-3449-3_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
Alchemical free energy methods can be used for the efficient computation of relative binding free energies during preclinical drug discovery stages. In recent years, this has been facilitated further by the implementation of workflows that enable non-experts to quickly and consistently set up the required simulations. Given the correct input structures, workflows handle the difficult aspects of setting up perturbations, including consistently defining the perturbable molecule, its atom mapping and topology generation, perturbation network generation, running of the simulations via different sampling methods, and analysis of the results. Different academic and commercial workflows are discussed, including FEW, FESetup, FEPrepare, CHARMM-GUI, Transformato, PMX, QLigFEP, TIES, ProFESSA, PyAutoFEP, BioSimSpace, FEP+, Flare, and Orion. These workflows differ in various aspects, such as mapping algorithms or enhanced sampling methods. Some workflows can accommodate more than one molecular dynamics (MD) engine and use external libraries for tasks. Differences between workflows can present advantages for different use cases, however a lack of interoperability of the workflows' components hinders systematic comparisons.
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Affiliation(s)
- Anna M Herz
- EaStChem School of Chemistry, Joseph Black Building, University of Edinburgh, Edinburgh, UK
| | - Tahsin Kellici
- Evotec (UK) Ltd., In Silico Research and Development, Abingdon, Oxfordshire, UK
- Merck & Co., Inc., Modelling and Informatics, West Point, PA, USA
| | - Inaki Morao
- Evotec (UK) Ltd., In Silico Research and Development, Abingdon, Oxfordshire, UK
| | - Julien Michel
- EaStChem School of Chemistry, Joseph Black Building, University of Edinburgh, Edinburgh, UK.
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Qureshi R, Zou B, Alam T, Wu J, Lee VHF, Yan H. Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:238-255. [PMID: 35007197 DOI: 10.1109/tcbb.2022.3141697] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Lung cancer is a major cause of cancer deaths worldwide, and has a very low survival rate. Non-small cell lung cancer (NSCLC) is the largest subset of lung cancers, which accounts for about 85% of all cases. It has been well established that a mutation in the epidermal growth factor receptor (EGFR) can lead to lung cancer. EGFR Tyrosine Kinase Inhibitors (TKIs) are developed to target the kinase domain of EGFR. These TKIs produce promising results at the initial stage of therapy, but the efficacy becomes limited due to the development of drug resistance. In this paper, we provide a comprehensive overview of computational methods, for understanding drug resistance mechanisms. The important EGFR mutants and the different generations of EGFR-TKIs, with the survival and response rates are discussed. Next, we evaluate the role of important EGFR parameters in drug resistance mechanism, including structural dynamics, hydrogen bonds, stability, dimerization, binding free energies, and signaling pathways. Personalized drug resistance prediction models, drug response curve, drug synergy, and other data-driven methods are also discussed. Recent advancements in deep learning; such as AlphaFold2, deep generative models, big data analytics, and the applications of statistics and permutation are also highlighted. We explore limitations in the current methodologies, and discuss strategies to overcome them. We believe this review will serve as a reference for researchers; to apply computational techniques for precision medicine, analyzing structures of protein-drug complexes, drug discovery, and understanding the drug response and resistance mechanisms in lung cancer patients.
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Kutzner C, Kniep C, Cherian A, Nordstrom L, Grubmüller H, de Groot BL, Gapsys V. GROMACS in the Cloud: A Global Supercomputer to Speed Up Alchemical Drug Design. J Chem Inf Model 2022; 62:1691-1711. [PMID: 35353508 PMCID: PMC9006219 DOI: 10.1021/acs.jcim.2c00044] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
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We assess costs and
efficiency of state-of-the-art high-performance
cloud computing and compare the results to traditional on-premises
compute clusters. Our use case is atomistic simulations carried out
with the GROMACS molecular dynamics (MD) toolkit with a particular
focus on alchemical protein–ligand binding free energy calculations.
We set up a compute cluster in the Amazon Web Services (AWS) cloud
that incorporates various different instances with Intel, AMD, and
ARM CPUs, some with GPU acceleration. Using representative biomolecular
simulation systems, we benchmark how GROMACS performs on individual
instances and across multiple instances. Thereby we assess which instances
deliver the highest performance and which are the most cost-efficient
ones for our use case. We find that, in terms of total costs, including
hardware, personnel, room, energy, and cooling, producing MD trajectories
in the cloud can be about as cost-efficient as an on-premises cluster
given that optimal cloud instances are chosen. Further, we find that
high-throughput ligand-screening can be accelerated dramatically by
using global cloud resources. For a ligand screening study consisting
of 19 872 independent simulations or ∼200 μs of
combined simulation trajectory, we made use of diverse hardware available
in the cloud at the time of the study. The computations scaled-up
to reach peak performance using more than 4 000 instances,
140 000 cores, and 3 000 GPUs simultaneously. Our simulation
ensemble finished in about 2 days in the cloud, while weeks would
be required to complete the task on a typical on-premises cluster
consisting of several hundred nodes.
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Affiliation(s)
- Carsten Kutzner
- Department of Theoretical and Computational Biophysics, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany
| | - Christian Kniep
- Amazon Development Center Germany, Amazon Web Services, Krausenstr. 38, 10117 Berlin, Germany
| | - Austin Cherian
- Amazon Web Services Singapore Pte Ltd, 23 Church St, #10-01, Singapore 049481
| | - Ludvig Nordstrom
- Amazon Web Services, 60 Holborn Viaduct, London EC1A 2FD, United Kingdom
| | - Helmut Grubmüller
- Department of Theoretical and Computational Biophysics, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany
| | - Bert L de Groot
- Computational Biomolecular Dynamics Group, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany
| | - Vytautas Gapsys
- Computational Biomolecular Dynamics Group, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany
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Faelber K, Dietrich L, Noel JK, Wollweber F, Pfitzner AK, Mühleip A, Sánchez R, Kudryashev M, Chiaruttini N, Lilie H, Schlegel J, Rosenbaum E, Hessenberger M, Matthaeus C, Kunz S, von der Malsburg A, Noé F, Roux A, van der Laan M, Kühlbrandt W, Daumke O. Structure and assembly of the mitochondrial membrane remodelling GTPase Mgm1. Nature 2019; 571:429-433. [PMID: 31292547 DOI: 10.1038/s41586-019-1372-3] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Accepted: 06/13/2019] [Indexed: 12/25/2022]
Abstract
Balanced fusion and fission are key for the proper function and physiology of mitochondria1,2. Remodelling of the mitochondrial inner membrane is mediated by the dynamin-like protein mitochondrial genome maintenance 1 (Mgm1) in fungi or the related protein optic atrophy 1 (OPA1) in animals3-5. Mgm1 is required for the preservation of mitochondrial DNA in yeast6, whereas mutations in the OPA1 gene in humans are a common cause of autosomal dominant optic atrophy-a genetic disorder that affects the optic nerve7,8. Mgm1 and OPA1 are present in mitochondria as a membrane-integral long form and a short form that is soluble in the intermembrane space. Yeast strains that express temperature-sensitive mutants of Mgm19,10 or mammalian cells that lack OPA1 display fragmented mitochondria11,12, which suggests that Mgm1 and OPA1 have an important role in inner-membrane fusion. Consistently, only the mitochondrial outer membrane-not the inner membrane-fuses in the absence of functional Mgm113. Mgm1 and OPA1 have also been shown to maintain proper cristae architecture10,14; for example, OPA1 prevents the release of pro-apoptotic factors by tightening crista junctions15. Finally, the short form of OPA1 localizes to mitochondrial constriction sites, where it presumably promotes mitochondrial fission16. How Mgm1 and OPA1 perform their diverse functions in membrane fusion, scission and cristae organization is at present unknown. Here we present crystal and electron cryo-tomography structures of Mgm1 from Chaetomium thermophilum. Mgm1 consists of a GTPase (G) domain, a bundle signalling element domain, a stalk, and a paddle domain that contains a membrane-binding site. Biochemical and cell-based experiments demonstrate that the Mgm1 stalk mediates the assembly of bent tetramers into helical filaments. Electron cryo-tomography studies of Mgm1-decorated lipid tubes and fluorescence microscopy experiments on reconstituted membrane tubes indicate how the tetramers assemble on positively or negatively curved membranes. Our findings convey how Mgm1 and OPA1 filaments dynamically remodel the mitochondrial inner membrane.
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Affiliation(s)
- Katja Faelber
- Crystallography, Max-Delbrück-Centrum for Molecular Medicine, Berlin, Germany.
| | - Lea Dietrich
- Department of Structural Biology, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
| | - Jeffrey K Noel
- Crystallography, Max-Delbrück-Centrum for Molecular Medicine, Berlin, Germany
| | - Florian Wollweber
- Medical Biochemistry & Molecular Biology, Center for Molecular Signaling, PZMS, Saarland University Medical School, Homburg, Germany
| | | | - Alexander Mühleip
- Department of Structural Biology, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
| | - Ricardo Sánchez
- Alexander von Humboldt - Sofja Kovalevskaja Research Group, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
| | - Misha Kudryashev
- Alexander von Humboldt - Sofja Kovalevskaja Research Group, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
| | | | - Hauke Lilie
- Institute of Biochemistry and Biotechnology, Section of Protein Biochemistry, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Jeanette Schlegel
- Crystallography, Max-Delbrück-Centrum for Molecular Medicine, Berlin, Germany
| | - Eva Rosenbaum
- Crystallography, Max-Delbrück-Centrum for Molecular Medicine, Berlin, Germany
| | - Manuel Hessenberger
- Crystallography, Max-Delbrück-Centrum for Molecular Medicine, Berlin, Germany
| | - Claudia Matthaeus
- Crystallography, Max-Delbrück-Centrum for Molecular Medicine, Berlin, Germany
| | - Séverine Kunz
- EM facility, Max-Delbrück-Centrum for Molecular Medicine, Berlin, Germany
| | - Alexander von der Malsburg
- Medical Biochemistry & Molecular Biology, Center for Molecular Signaling, PZMS, Saarland University Medical School, Homburg, Germany
| | - Frank Noé
- Institute for Mathematics, Freie Universität Berlin, Berlin, Germany
| | - Aurélien Roux
- Biochemistry Department, University of Geneva, Geneva, Switzerland
| | - Martin van der Laan
- Medical Biochemistry & Molecular Biology, Center for Molecular Signaling, PZMS, Saarland University Medical School, Homburg, Germany
| | - Werner Kühlbrandt
- Department of Structural Biology, Max Planck Institute of Biophysics, Frankfurt am Main, Germany.
| | - Oliver Daumke
- Crystallography, Max-Delbrück-Centrum for Molecular Medicine, Berlin, Germany. .,Institute of Chemistry and Biochemistry, Freie Universität Berlin, Berlin, Germany.
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Banegas-Luna AJ, Imbernón B, Llanes Castro A, Pérez-Garrido A, Cerón-Carrasco JP, Gesing S, Merelli I, D'Agostino D, Pérez-Sánchez H. Advances in distributed computing with modern drug discovery. Expert Opin Drug Discov 2018; 14:9-22. [PMID: 30484337 DOI: 10.1080/17460441.2019.1552936] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Computational chemistry dramatically accelerates the drug discovery process and high-performance computing (HPC) can be used to speed up the most expensive calculations. Supporting a local HPC infrastructure is both costly and time-consuming, and, therefore, many research groups are moving from in-house solutions to remote-distributed computing platforms. Areas covered: The authors focus on the use of distributed technologies, solutions, and infrastructures to gain access to HPC capabilities, software tools, and datasets to run the complex simulations required in computational drug discovery (CDD). Expert opinion: The use of computational tools can decrease the time to market of new drugs. HPC has a crucial role in handling the complex algorithms and large volumes of data required to achieve specificity and avoid undesirable side-effects. Distributed computing environments have clear advantages over in-house solutions in terms of cost and sustainability. The use of infrastructures relying on virtualization reduces set-up costs. Distributed computing resources can be difficult to access, although web-based solutions are becoming increasingly available. There is a trade-off between cost-effectiveness and accessibility in using on-demand computing resources rather than free/academic resources. Graphics processing unit computing, with its outstanding parallel computing power, is becoming increasingly important.
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Affiliation(s)
- Antonio Jesús Banegas-Luna
- a Bioinformatics and High Performance Computing Research Group (BIO-HPC) , Universidad Católica de Murcia (UCAM) , Murcia , Spain
| | - Baldomero Imbernón
- a Bioinformatics and High Performance Computing Research Group (BIO-HPC) , Universidad Católica de Murcia (UCAM) , Murcia , Spain
| | - Antonio Llanes Castro
- a Bioinformatics and High Performance Computing Research Group (BIO-HPC) , Universidad Católica de Murcia (UCAM) , Murcia , Spain
| | - Alfonso Pérez-Garrido
- a Bioinformatics and High Performance Computing Research Group (BIO-HPC) , Universidad Católica de Murcia (UCAM) , Murcia , Spain
| | - José Pedro Cerón-Carrasco
- a Bioinformatics and High Performance Computing Research Group (BIO-HPC) , Universidad Católica de Murcia (UCAM) , Murcia , Spain
| | - Sandra Gesing
- b Center for Research Computing , University of Notre Dame , Notre Dame , IN , USA
| | - Ivan Merelli
- c Institute for Biomedical Technologies , National Research Council of Italy , Segrate (Milan) , Italy
| | - Daniele D'Agostino
- d Institute for Applied Mathematics and Information Technologies "E. Magenes" , National Research Council of Italy , Genoa , Italy
| | - Horacio Pérez-Sánchez
- a Bioinformatics and High Performance Computing Research Group (BIO-HPC) , Universidad Católica de Murcia (UCAM) , Murcia , Spain
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Salmaso V, Moro S. Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview. Front Pharmacol 2018; 9:923. [PMID: 30186166 PMCID: PMC6113859 DOI: 10.3389/fphar.2018.00923] [Citation(s) in RCA: 303] [Impact Index Per Article: 50.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Accepted: 07/26/2018] [Indexed: 12/22/2022] Open
Abstract
Computational techniques have been applied in the drug discovery pipeline since the 1980s. Given the low computational resources of the time, the first molecular modeling strategies relied on a rigid view of the ligand-target binding process. During the years, the evolution of hardware technologies has gradually allowed simulating the dynamic nature of the binding event. In this work, we present an overview of the evolution of structure-based drug discovery techniques in the study of ligand-target recognition phenomenon, going from the static molecular docking toward enhanced molecular dynamics strategies.
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Affiliation(s)
- Veronica Salmaso
- Molecular Modeling Section, Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section, Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
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7
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Li Y, Yu J. Genetic engineering of inorganic functional modular materials. Chem Sci 2016; 7:3472-3481. [PMID: 29997839 PMCID: PMC6007181 DOI: 10.1039/c6sc00123h] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2016] [Accepted: 03/28/2016] [Indexed: 01/31/2023] Open
Abstract
Since the launch of the Materials Genome Initiative by the US government in 2011, many computer techniques have been developed to predict the structures and properties of advanced materials, providing important guidance for laboratory experimentation and a promising new direction for future materials innovation. However, lots of inorganic materials are difficult for computers to process because of their complex three-dimensionally extended structures. Fortunately, many of these materials are built from well-defined stacking layer modules, and the stacking sequences of their layer modules unambiguously determine their three-dimensional structures. Such one-dimensional stacking sequence representation is naturally accessible for computer processing, easing the problems not only of structure elucidation, but also in the enumeration, evaluation, and screening of a large number of unknown materials with desired properties. More importantly, with the aid of various computational methods, we may reveal the relationship between the stacking sequences and the properties of these materials, which is a key prerequisite for function-led targeted synthesis. This Minireview covers the most recent progress in this emerging area.
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Affiliation(s)
- Yi Li
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry , Jilin University , Qianjin Street 2699 , Changchun 130012 , China .
| | - Jihong Yu
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry , Jilin University , Qianjin Street 2699 , Changchun 130012 , China .
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8
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Doerr S, Harvey MJ, Noé F, De Fabritiis G. HTMD: High-Throughput Molecular Dynamics for Molecular Discovery. J Chem Theory Comput 2016; 12:1845-52. [PMID: 26949976 DOI: 10.1021/acs.jctc.6b00049] [Citation(s) in RCA: 241] [Impact Index Per Article: 30.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Recent advances in molecular simulations have allowed scientists to investigate slower biological processes than ever before. Together with these advances came an explosion of data that has transformed a traditionally computing-bound into a data-bound problem. Here, we present HTMD, a programmable, extensible platform written in Python that aims to solve the data generation and analysis problem as well as increase reproducibility by providing a complete workspace for simulation-based discovery. So far, HTMD includes system building for CHARMM and AMBER force fields, projection methods, clustering, molecular simulation production, adaptive sampling, an Amazon cloud interface, Markov state models, and visualization. As a result, a single, short HTMD script can lead from a PDB structure to useful quantities such as relaxation time scales, equilibrium populations, metastable conformations, and kinetic rates. In this paper, we focus on the adaptive sampling and Markov state modeling features.
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Affiliation(s)
- S Doerr
- Computational Biophysics Laboratory (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB) , C/Doctor Aiguader 88, 08003 Barcelona, Spain
| | - M J Harvey
- Acellera, Barcelona Biomedical Research Park (PRBB) , C/Doctor Aiguader 88, 08003 Barcelona, Spain
| | - Frank Noé
- Department of Mathematics, Computer Science and Bioinformatics, Free University of Berlin , Berlin, Germany
| | - G De Fabritiis
- Institució Catalana de Recerca i Estudis Avançats (ICREA) , Passeig Lluis Companys 23, Barcelona 08010, Spain
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Sobeslav V, Maresova P, Krejcar O, Franca TC, Kuca K. Use of cloud computing in biomedicine. J Biomol Struct Dyn 2016; 34:2688-2697. [DOI: 10.1080/07391102.2015.1127182] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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10
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Hart T, Xie L. Providing data science support for systems pharmacology and its implications to drug discovery. Expert Opin Drug Discov 2016; 11:241-56. [PMID: 26689499 DOI: 10.1517/17460441.2016.1135126] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
INTRODUCTION The conventional one-drug-one-target-one-disease drug discovery process has been less successful in tracking multi-genic, multi-faceted complex diseases. Systems pharmacology has emerged as a new discipline to tackle the current challenges in drug discovery. The goal of systems pharmacology is to transform huge, heterogeneous, and dynamic biological and clinical data into interpretable and actionable mechanistic models for decision making in drug discovery and patient treatment. Thus, big data technology and data science will play an essential role in systems pharmacology. AREAS COVERED This paper critically reviews the impact of three fundamental concepts of data science on systems pharmacology: similarity inference, overfitting avoidance, and disentangling causality from correlation. The authors then discuss recent advances and future directions in applying the three concepts of data science to drug discovery, with a focus on proteome-wide context-specific quantitative drug target deconvolution and personalized adverse drug reaction prediction. EXPERT OPINION Data science will facilitate reducing the complexity of systems pharmacology modeling, detecting hidden correlations between complex data sets, and distinguishing causation from correlation. The power of data science can only be fully realized when integrated with mechanism-based multi-scale modeling that explicitly takes into account the hierarchical organization of biological systems from nucleic acid to proteins, to molecular interaction networks, to cells, to tissues, to patients, and to populations.
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Affiliation(s)
- Thomas Hart
- a The Rockefeller University , New York , NY , USA.,b Department of Biological Sciences, Hunter College , The City University of New York , New York , NY , USA
| | - Lei Xie
- c Department of Computer Science, Hunter College , The City University of New York , New York , NY , USA.,d The Graduate Center , The City University of New York , New York , NY , USA
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Fukunishi Y, Mashimo T, Misoo K, Wakabayashi Y, Miyaki T, Ohta S, Nakamura M, Ikeda K. Miscellaneous Topics in Computer-Aided Drug Design: Synthetic Accessibility and GPU Computing, and Other Topics. Curr Pharm Des 2016; 22:3555-68. [PMID: 27075578 PMCID: PMC5080912 DOI: 10.2174/1381612822666160414142547] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Accepted: 04/12/2016] [Indexed: 01/12/2023]
Abstract
BACKGROUND Computer-aided drug design is still a state-of-the-art process in medicinal chemistry, and the main topics in this field have been extensively studied and well reviewed. These topics include compound databases, ligand-binding pocket prediction, protein-compound docking, virtual screening, target/off-target prediction, physical property prediction, molecular simulation and pharmacokinetics/pharmacodynamics (PK/PD) prediction. Message and Conclusion: However, there are also a number of secondary or miscellaneous topics that have been less well covered. For example, methods for synthesizing and predicting the synthetic accessibility (SA) of designed compounds are important in practical drug development, and hardware/software resources for performing the computations in computer-aided drug design are crucial. Cloud computing and general purpose graphics processing unit (GPGPU) computing have been used in virtual screening and molecular dynamics simulations. Not surprisingly, there is a growing demand for computer systems which combine these resources. In the present review, we summarize and discuss these various topics of drug design.
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Affiliation(s)
- Yoshifumi Fukunishi
- Molecular Profiling Research Center for Drug Discovery (molprof), National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26, Aomi, Koto-ku, Tokyo 135-0064, Japan.
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12
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Biggin PC, Aldeghi M, Bodkin MJ, Heifetz A. Beyond Membrane Protein Structure: Drug Discovery, Dynamics and Difficulties. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 922:161-181. [PMID: 27553242 DOI: 10.1007/978-3-319-35072-1_12] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Most of the previous content of this book has focused on obtaining the structures of membrane proteins. In this chapter we explore how those structures can be further used in two key ways. The first is their use in structure based drug design (SBDD) and the second is how they can be used to extend our understanding of their functional activity via the use of molecular dynamics. Both aspects now heavily rely on computations. This area is vast, and alas, too large to consider in depth in a single book chapter. Thus where appropriate we have referred the reader to recent reviews for deeper assessment of the field. We discuss progress via the use of examples from two main drug target areas; G-protein coupled receptors (GPCRs) and ion channels. We end with a discussion of some of the main challenges in the area.
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Affiliation(s)
- Philip C Biggin
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK.
| | - Matteo Aldeghi
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
| | - Michael J Bodkin
- Evotec Ltd, 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK
| | - Alexander Heifetz
- Evotec Ltd, 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK
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