Elucidating Binding Sites and Affinities of Erα Agonist and Antagonist to Human Alpha-Fetoprotein by in Silico Modeling and Point Mutagenesis Nurbubu T
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MODELLER 10.1 Manual
MODELLER A Program for Protein Structure Modeling Release 10.1, r12156 Andrej Saliˇ with help from Ben Webb, M.S. Madhusudhan, Min-Yi Shen, Guangqiang Dong, Marc A. Martı-Renom, Narayanan Eswar, Frank Alber, Maya Topf, Baldomero Oliva, Andr´as Fiser, Roberto S´anchez, Bozidar Yerkovich, Azat Badretdinov, Francisco Melo, John P. Overington, and Eric Feyfant email: modeller-care AT salilab.org URL https://salilab.org/modeller/ 2021/03/12 ii Contents Copyright notice xxi Acknowledgments xxv 1 Introduction 1 1.1 What is Modeller?............................................. 1 1.2 Modeller bibliography....................................... .... 2 1.3 Obtainingandinstallingtheprogram. .................... 3 1.4 Bugreports...................................... ............ 4 1.5 Method for comparative protein structure modeling by Modeller ................... 5 1.6 Using Modeller forcomparativemodeling. ... 8 1.6.1 Preparinginputfiles . ............. 8 1.6.2 Running Modeller ......................................... 9 2 Automated comparative modeling with AutoModel 11 2.1 Simpleusage ..................................... ............ 11 2.2 Moreadvancedusage............................... .............. 12 2.2.1 Including water molecules, HETATM residues, and hydrogenatoms .............. 12 2.2.2 Changing the default optimization and refinement protocol ................... 14 2.2.3 Getting a very fast and approximate model . ................. 14 2.2.4 Building a model from multiple templates . .................. 15 2.2.5 Buildinganallhydrogenmodel -
Phase II Study of Hormone Therapy with Tamoxifen in Patients with Well Differentiated Neuroendocrine Tumors and Hormone Receptor Positive Expression (HORMONET)
Phase II study of hormone therapy with tamoxifen in patients with well differentiated neuroendocrine tumors and hormone receptor positive expression (HORMONET) Time of researchers and respective Departments: Rachel Riechelmann (Main Investigador)1, Milton Barros 1, Marcos Camandaroba1,2, Virgilio Souza1,2, Celso Abdon Mello1, Paula Nicole3, Eduardo Nóbrega4, Ludmilla Chinen5, Marina De Brot6, Héber Salvador7. Departaments: 1- Clinical Oncology; 2- postgraduate student; 3- Radiology; 4- Nuclear Medicine; 5-International Research Center (CIPE); 6-Pathologic anatomy; 7- Abdomen Surgery AC Camargo Cancer Center - Brazil Registration Number on Institutional Research Ethics Committee: 2626/18 March,06,2019 Introduction Neuroendocrine tumors (NET) are rare neoplasms, but with increasing incidence and prevalence in the last decades. Although they may manifest in the most diverse tissues, the vast majority of cases will affect organs of the digestive tract and lung. At diagnosis, more than half of the cases present metastatic disease, and among patients with localized disease, up to one-third will have recurrence of the disease. Unfortunately, the minority of patients with metastatic disease are eligible for curative intent.1 Although there are many types of NET, they are often studied together as a group because their cells share common histological findings, have special secretory granules, and the ability to secrete bioactive amines and polypeptide hormones. Approximately 25 percent of the tumors present functional hormonal syndromes (situation of great morbidity for these patients), being the carcinoid syndrome, the most common one. From the molecular point of view, these neoplasias are largely dependent on the activation of the mTOR pathway and neoangiogenesis.2 Another striking feature of neuroendocrine cells is the expression of cell surface hormone receptors whose activation or blockade may exert an important regulatory function. -
MODELLER 10.0 Manual
MODELLER A Program for Protein Structure Modeling Release 10.0, r12005 Andrej Saliˇ with help from Ben Webb, M.S. Madhusudhan, Min-Yi Shen, Guangqiang Dong, Marc A. Martı-Renom, Narayanan Eswar, Frank Alber, Maya Topf, Baldomero Oliva, Andr´as Fiser, Roberto S´anchez, Bozidar Yerkovich, Azat Badretdinov, Francisco Melo, John P. Overington, and Eric Feyfant email: modeller-care AT salilab.org URL https://salilab.org/modeller/ 2021/01/25 ii Contents Copyright notice xxi Acknowledgments xxv 1 Introduction 1 1.1 What is Modeller?............................................. 1 1.2 Modeller bibliography....................................... .... 2 1.3 Obtainingandinstallingtheprogram. .................... 3 1.4 Bugreports...................................... ............ 4 1.5 Method for comparative protein structure modeling by Modeller ................... 5 1.6 Using Modeller forcomparativemodeling. ... 8 1.6.1 Preparinginputfiles . ............. 8 1.6.2 Running Modeller ......................................... 9 2 Automated comparative modeling with AutoModel 11 2.1 Simpleusage ..................................... ............ 11 2.2 Moreadvancedusage............................... .............. 12 2.2.1 Including water molecules, HETATM residues, and hydrogenatoms .............. 12 2.2.2 Changing the default optimization and refinement protocol ................... 14 2.2.3 Getting a very fast and approximate model . ................. 14 2.2.4 Building a model from multiple templates . .................. 15 2.2.5 Buildinganallhydrogenmodel -
Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development
processes Review Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development Outi M. H. Salo-Ahen 1,2,* , Ida Alanko 1,2, Rajendra Bhadane 1,2 , Alexandre M. J. J. Bonvin 3,* , Rodrigo Vargas Honorato 3, Shakhawath Hossain 4 , André H. Juffer 5 , Aleksei Kabedev 4, Maija Lahtela-Kakkonen 6, Anders Støttrup Larsen 7, Eveline Lescrinier 8 , Parthiban Marimuthu 1,2 , Muhammad Usman Mirza 8 , Ghulam Mustafa 9, Ariane Nunes-Alves 10,11,* , Tatu Pantsar 6,12, Atefeh Saadabadi 1,2 , Kalaimathy Singaravelu 13 and Michiel Vanmeert 8 1 Pharmaceutical Sciences Laboratory (Pharmacy), Åbo Akademi University, Tykistökatu 6 A, Biocity, FI-20520 Turku, Finland; ida.alanko@abo.fi (I.A.); rajendra.bhadane@abo.fi (R.B.); parthiban.marimuthu@abo.fi (P.M.); atefeh.saadabadi@abo.fi (A.S.) 2 Structural Bioinformatics Laboratory (Biochemistry), Åbo Akademi University, Tykistökatu 6 A, Biocity, FI-20520 Turku, Finland 3 Faculty of Science-Chemistry, Bijvoet Center for Biomolecular Research, Utrecht University, 3584 CH Utrecht, The Netherlands; [email protected] 4 Swedish Drug Delivery Forum (SDDF), Department of Pharmacy, Uppsala Biomedical Center, Uppsala University, 751 23 Uppsala, Sweden; [email protected] (S.H.); [email protected] (A.K.) 5 Biocenter Oulu & Faculty of Biochemistry and Molecular Medicine, University of Oulu, Aapistie 7 A, FI-90014 Oulu, Finland; andre.juffer@oulu.fi 6 School of Pharmacy, University of Eastern Finland, FI-70210 Kuopio, Finland; maija.lahtela-kakkonen@uef.fi (M.L.-K.); tatu.pantsar@uef.fi -
Molecular Docking Studies of a Cyclic Octapeptide-Cyclosaplin from Sandalwood
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 11 June 2019 doi:10.20944/preprints201906.0091.v1 Peer-reviewed version available at Biomolecules 2019, 9; doi:10.3390/biom9040123 Molecular Docking Studies of a Cyclic Octapeptide-Cyclosaplin from Sandalwood Abheepsa Mishra1, 2,* and Satyahari Dey1 1Plant Biotechnology Laboratory, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur-721302, West Bengal, India; [email protected] (A.M.); [email protected] (S.D.) 2Department of Internal Medicine, The University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA *Correspondence: [email protected]; Tel.:+1(518) 881-9196 Abstract Natural products from plants such as, chemopreventive agents attract huge attention because of their low toxicity and high specificity. The rational drug design in combination with structure based modeling and rapid screening methods offer significant potential for identifying and developing lead anticancer molecules. Thus, the molecular docking method plays an important role in screening a large set of molecules based on their free binding energies and proposes structural hypotheses of how the molecules can inhibit the target. Several peptide based therapeutics have been developed to combat several health disorders including cancers, metabolic disorders, heart-related, and infectious diseases. Despite the discovery of hundreds of such therapeutic peptides however, only few peptide-based drugs have made it to the market. Moreover, until date the activities of cyclic peptides towards molecular targets such as protein kinases, proteases, and apoptosis related proteins have never been explored. In this study we explore the in silico kinase and protease inhibitor potentials of cyclosaplin as well as study the interactions of cyclosaplin with other cancer-related proteins. -
A Simple Method to Measure Sulfonation in Man Using Paracetamol As Probe Drug Natália Marto 1,2*, Judit Morello1,3, Alexandra M
www.nature.com/scientificreports OPEN A simple method to measure sulfonation in man using paracetamol as probe drug Natália Marto 1,2*, Judit Morello1,3, Alexandra M. M. Antunes3, Sofa Azeredo4, Emília C. Monteiro1,5 & Sofa A. Pereira1,5 Sulfotransferase enzymes (SULT) catalyse sulfoconjugation of drugs, as well as endogenous mediators, gut microbiota metabolites and environmental xenobiotics. To address the limited evidence on sulfonation activity from clinical research, we developed a clinical metabolic phenotyping method using paracetamol as a probe substrate. Our aim was to estimate sulfonation capability of phenolic compounds and study its intraindividual variability in man. A total of 36 healthy adult volunteers (12 men, 12 women and 12 women on oral contraceptives) received paracetamol in a 1 g-tablet formulation on three separate occasions. Paracetamol and its metabolites were measured in plasma and spot urine samples using liquid chromatography-high resolution mass spectrometry. A metabolic ratio (Paracetamol Sulfonation Index—PSI) was used to estimate phenol SULT activity. PSI showed low intraindividual variability, with a good correlation between values in plasma and spot urine samples. Urinary PSI was independent of factors not related to SULT activity, such as urine pH or eGFR. Gender and oral contraceptive intake had no impact on PSI. Our SULT phenotyping method is a simple non-invasive procedure requiring urine spot samples, using the safe and convenient drug paracetamol as a probe substrate, and with low intraindividual coefcient of variation. Although it will not give us mechanistic information, it will provide us an empirical measure of an individual’s sulfonator status. To the best of our knowledge, our method provides the frst standardised in vivo empirical measure of an individual’s phenol sulfonation capability and of its intraindividual variability. -
Tanibirumab (CUI C3490677) Add to Cart
5/17/2018 NCI Metathesaurus Contains Exact Match Begins With Name Code Property Relationship Source ALL Advanced Search NCIm Version: 201706 Version 2.8 (using LexEVS 6.5) Home | NCIt Hierarchy | Sources | Help Suggest changes to this concept Tanibirumab (CUI C3490677) Add to Cart Table of Contents Terms & Properties Synonym Details Relationships By Source Terms & Properties Concept Unique Identifier (CUI): C3490677 NCI Thesaurus Code: C102877 (see NCI Thesaurus info) Semantic Type: Immunologic Factor Semantic Type: Amino Acid, Peptide, or Protein Semantic Type: Pharmacologic Substance NCIt Definition: A fully human monoclonal antibody targeting the vascular endothelial growth factor receptor 2 (VEGFR2), with potential antiangiogenic activity. Upon administration, tanibirumab specifically binds to VEGFR2, thereby preventing the binding of its ligand VEGF. This may result in the inhibition of tumor angiogenesis and a decrease in tumor nutrient supply. VEGFR2 is a pro-angiogenic growth factor receptor tyrosine kinase expressed by endothelial cells, while VEGF is overexpressed in many tumors and is correlated to tumor progression. PDQ Definition: A fully human monoclonal antibody targeting the vascular endothelial growth factor receptor 2 (VEGFR2), with potential antiangiogenic activity. Upon administration, tanibirumab specifically binds to VEGFR2, thereby preventing the binding of its ligand VEGF. This may result in the inhibition of tumor angiogenesis and a decrease in tumor nutrient supply. VEGFR2 is a pro-angiogenic growth factor receptor -
PHARMACEUTICAL APPENDIX to the TARIFF SCHEDULE 2 Table 1
Harmonized Tariff Schedule of the United States (2020) Revision 19 Annotated for Statistical Reporting Purposes PHARMACEUTICAL APPENDIX TO THE HARMONIZED TARIFF SCHEDULE Harmonized Tariff Schedule of the United States (2020) Revision 19 Annotated for Statistical Reporting Purposes PHARMACEUTICAL APPENDIX TO THE TARIFF SCHEDULE 2 Table 1. This table enumerates products described by International Non-proprietary Names INN which shall be entered free of duty under general note 13 to the tariff schedule. The Chemical Abstracts Service CAS registry numbers also set forth in this table are included to assist in the identification of the products concerned. For purposes of the tariff schedule, any references to a product enumerated in this table includes such product by whatever name known. -
TE INI (19 ) United States (12 ) Patent Application Publication ( 10) Pub
US 20200187851A1TE INI (19 ) United States (12 ) Patent Application Publication ( 10) Pub . No .: US 2020/0187851 A1 Offenbacher et al. (43 ) Pub . Date : Jun . 18 , 2020 ( 54 ) PERIODONTAL DISEASE STRATIFICATION (52 ) U.S. CI. AND USES THEREOF CPC A61B 5/4552 (2013.01 ) ; G16H 20/10 ( 71) Applicant: The University of North Carolina at ( 2018.01) ; A61B 5/7275 ( 2013.01) ; A61B Chapel Hill , Chapel Hill , NC (US ) 5/7264 ( 2013.01 ) ( 72 ) Inventors: Steven Offenbacher, Chapel Hill , NC (US ) ; Thiago Morelli , Durham , NC ( 57 ) ABSTRACT (US ) ; Kevin Lee Moss, Graham , NC ( US ) ; James Douglas Beck , Chapel Described herein are methods of classifying periodontal Hill , NC (US ) patients and individual teeth . For example , disclosed is a method of diagnosing periodontal disease and / or risk of ( 21) Appl. No .: 16 /713,874 tooth loss in a subject that involves classifying teeth into one of 7 classes of periodontal disease. The method can include ( 22 ) Filed : Dec. 13 , 2019 the step of performing a dental examination on a patient and Related U.S. Application Data determining a periodontal profile class ( PPC ) . The method can further include the step of determining for each tooth a ( 60 ) Provisional application No.62 / 780,675 , filed on Dec. Tooth Profile Class ( TPC ) . The PPC and TPC can be used 17 , 2018 together to generate a composite risk score for an individual, which is referred to herein as the Index of Periodontal Risk Publication Classification ( IPR ) . In some embodiments , each stage of the disclosed (51 ) Int. Cl. PPC system is characterized by unique single nucleotide A61B 5/00 ( 2006.01 ) polymorphisms (SNPs ) associated with unique pathways , G16H 20/10 ( 2006.01 ) identifying unique druggable targets for each stage . -
Maestro 10.2 User Manual
Maestro User Manual Maestro 10.2 User Manual Schrödinger Press Maestro User Manual Copyright © 2015 Schrödinger, LLC. All rights reserved. While care has been taken in the preparation of this publication, Schrödinger assumes no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein. Canvas, CombiGlide, ConfGen, Epik, Glide, Impact, Jaguar, Liaison, LigPrep, Maestro, Phase, Prime, PrimeX, QikProp, QikFit, QikSim, QSite, SiteMap, Strike, and WaterMap are trademarks of Schrödinger, LLC. Schrödinger, BioLuminate, and MacroModel are registered trademarks of Schrödinger, LLC. MCPRO is a trademark of William L. Jorgensen. DESMOND is a trademark of D. E. Shaw Research, LLC. Desmond is used with the permission of D. E. Shaw Research. All rights reserved. This publication may contain the trademarks of other companies. Schrödinger software includes software and libraries provided by third parties. For details of the copyrights, and terms and conditions associated with such included third party software, use your browser to open third_party_legal.html, which is in the docs folder of your Schrödinger software installation. This publication may refer to other third party software not included in or with Schrödinger software ("such other third party software"), and provide links to third party Web sites ("linked sites"). References to such other third party software or linked sites do not constitute an endorsement by Schrödinger, LLC or its affiliates. Use of such other third party software and linked sites may be subject to third party license agreements and fees. Schrödinger, LLC and its affiliates have no responsibility or liability, directly or indirectly, for such other third party software and linked sites, or for damage resulting from the use thereof. -
Kepler Gpus and NVIDIA's Life and Material Science
LIFE AND MATERIAL SCIENCES Mark Berger; [email protected] Founded 1993 Invented GPU 1999 – Computer Graphics Visual Computing, Supercomputing, Cloud & Mobile Computing NVIDIA - Core Technologies and Brands GPU Mobile Cloud ® ® GeForce Tegra GRID Quadro® , Tesla® Accelerated Computing Multi-core plus Many-cores GPU Accelerator CPU Optimized for Many Optimized for Parallel Tasks Serial Tasks 3-10X+ Comp Thruput 7X Memory Bandwidth 5x Energy Efficiency How GPU Acceleration Works Application Code Compute-Intensive Functions Rest of Sequential 5% of Code CPU Code GPU CPU + GPUs : Two Year Heart Beat 32 Volta Stacked DRAM 16 Maxwell Unified Virtual Memory 8 Kepler Dynamic Parallelism 4 Fermi 2 FP64 DP GFLOPS GFLOPS per DP Watt 1 Tesla 0.5 CUDA 2008 2010 2012 2014 Kepler Features Make GPU Coding Easier Hyper-Q Dynamic Parallelism Speedup Legacy MPI Apps Less Back-Forth, Simpler Code FERMI 1 Work Queue CPU Fermi GPU CPU Kepler GPU KEPLER 32 Concurrent Work Queues Developer Momentum Continues to Grow 100M 430M CUDA –Capable GPUs CUDA-Capable GPUs 150K 1.6M CUDA Downloads CUDA Downloads 1 50 Supercomputer Supercomputers 60 640 University Courses University Courses 4,000 37,000 Academic Papers Academic Papers 2008 2013 Explosive Growth of GPU Accelerated Apps # of Apps Top Scientific Apps 200 61% Increase Molecular AMBER LAMMPS CHARMM NAMD Dynamics GROMACS DL_POLY 150 Quantum QMCPACK Gaussian 40% Increase Quantum Espresso NWChem Chemistry GAMESS-US VASP CAM-SE 100 Climate & COSMO NIM GEOS-5 Weather WRF Chroma GTS 50 Physics Denovo ENZO GTC MILC ANSYS Mechanical ANSYS Fluent 0 CAE MSC Nastran OpenFOAM 2010 2011 2012 SIMULIA Abaqus LS-DYNA Accelerated, In Development NVIDIA GPU Life Science Focus Molecular Dynamics: All codes are available AMBER, CHARMM, DESMOND, DL_POLY, GROMACS, LAMMPS, NAMD Great multi-GPU performance GPU codes: ACEMD, HOOMD-Blue Focus: scaling to large numbers of GPUs Quantum Chemistry: key codes ported or optimizing Active GPU acceleration projects: VASP, NWChem, Gaussian, GAMESS, ABINIT, Quantum Espresso, BigDFT, CP2K, GPAW, etc. -
New Computational Approaches for Investigating the Impact of Mutations on the Transglucosylation Activity of Sucrose Phosphorylase Enzyme
Thesis submitted to the Université de la Réunion for award of Doctor of Philosophy in Sciences speciality in bioinformatics New computational approaches for investigating the impact of mutations on the transglucosylation activity of sucrose phosphorylase enzyme Mahesh VELUSAMY Thesis to be presented on 18th December 2018 in front of the jury composed of Mme Isabelle ANDRE, Directeur de Recherche CNRS, INSA de TOULOUSE, Rapporteur M. Manuel DAUCHEZ, Professeur, Université de Reims Champagne Ardennes, Rapporteur M. Richard DANIELLOU, Professeur, Université d'Orléans, Examinateur M. Yves-Henri SANEJOUAND, Directeur de Recherche CNRS, Université de Nantes, Examinateur Mme Irène MAFFUCCI, Maître de Conférences, Université de Technologie de Compiègne, Examinateur Mme Corinne MIRAL, Maître de Conférences HDR, Université de Nantes, Examinateur M. Frédéric CADET, Professeur, Université de La Réunion, Co-directeur de thèse M. Bernard OFFMANN, Professeur, Université de Nantes, Directeur de thèse ெ்்ந்ி ிைு்ூுத் ெ்யாம் ெ்த உதி்ு ையகு் ானகு் ஆ்ற் அிு. -ிு்ு் ுதி் அ்ப் ுுக், எனு த்ைத ேுாி, தா் க்ூி, அ்ண் ு்ு்ுமா், த்ைக பா்பா, ஆ்தா ுு்மா், ீனா, அ்ண் ்ுக், ெபிய்பா, ெபிய்மா ம்ு் உுுைணயா் இு்த அைண்ு ந்ப்கு்ு் எனு மனமா்்த ந்ி. இ்த ஆ்ி்ைக ுுைம அை3த்ு ுுுத்்காரண், எனு ஆ்ி்ைக இய்ுன் ேபராிிய் ெப்னா்் ஆஃ்ேம். ப்ேு துண்கி் நா் மனதாு், ெபாுளாதார அளிு் க்3்ி் இு்தேபாு, என்ு இ்ெனாு த்ைதயாகே இு்ு எ்ைன பா்்ு்ெகா்3ா். ுி்பாக, எனு ூ்றா் ஆ்ு இுிி், அ்்ு எ்ளோ தி்ப்3 க3ைமக் ம்ு் ிர்ிைனக் இு்தாு், அைத்ெபாு்பு்தாு, அ் என்ு ெ்த ெபாுளாதார உதி, ப்கைB்கழக பிு ம்ு் இதர ி்ாக ்ப்த்ப்3 உதிகு்ு எ்ன ைகமா்ு ெகாு்தாு் ஈ3ாகாு.