Mai Muuttunut Pilit Muut Aidi Mini
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Latest Stable Copy and Install It With: Chmod+X Chimera- *.Bin&&./Chimera- *.Bin
Tangram Suite Documentation Release 0.0.8 InsiliChem Mar 26, 2019 General instructions 1 What is Tangram Suite 3 2 How to install Tangram Suite5 3 General usage 7 4 FAQ & Known issues 9 5 List of Tans 11 6 Tangram BondOrder 13 7 Tangram QMSetup 15 8 Tangram DummyAtoms 17 9 Tangram GAUDIView 19 10 Tangram MMSetup 21 11 Tangram NCIPlot 23 12 Tangram NormalModes 25 13 Tangram OrbiTraj 27 14 Tangram PLIP 29 15 Tangram PoPMuSiCGUI 31 16 Tangram PropKaGUI 33 17 Tangram ReVina 35 18 Tangram SubAlign 37 19 Tangram TalaDraw 39 i ii Tangram Suite Documentation, Release 0.0.8 It’s composed of several independent graphical interfaces and commands for UCSF Chimera. General instructions 1 Tangram Suite Documentation, Release 0.0.8 2 General instructions CHAPTER 1 What is Tangram Suite In progress 3 Tangram Suite Documentation, Release 0.0.8 4 Chapter 1. What is Tangram Suite CHAPTER 2 How to install Tangram Suite 2.1 Install the full Tangram Suite (recommended) 1 - If you don’t have UCSF Chimera installed, download the latest stable copy and install it with: chmod+x chimera- *.bin&&./chimera- *.bin 2 - Download the latest installer from the releases page (Linux and MacOS only) and run it with: bash tangram-*.sh 2.2 Using the conda meta-package Instead of using the Bash installer, you can use conda (if you are already using it) to create a new environment with the tangram metapackage, which will handle all the dependencies. While in alpha, both insilichem channels are needed (the main one and also the dev label): conda create-n tangram-c insilichem/label/dev-c insilichem-c omnia-c rdkit-c ,!conda-forge tangram 2.3 Updating extensions Each extension will check if there’s a new release available every time you launch it. -
ORGANIZATION of BIOMOLECULES in a 3-DIMENSIONAL SPACE by Sameer Sajja a Thesis Submitted to the Faculty of the University Of
ORGANIZATION OF BIOMOLECULES IN A 3-DIMENSIONAL SPACE by Sameer Sajja A thesis submitted to the faculty of The University of North Carolina at Charlotte in partial fulfillment of the requirements for the degree of Master of Science in Chemistry Charlotte 2019 Approved by: ________________________________ Dr. Kirill Afonin ________________________________ Dr. Caryn Striplin ________________________________ Dr. Joanna Krueger ________________________________ Dr. Yuri Nesmelov ii ©2019 Sameer Sajja ALL RIGHTS RESERVED iii ABSTRACT SAMEER SAJJA. Organization of Biomolecules in a 3-Dimensional Space (Under the direction of DR. KIRILL A. AFONIN) The human body operates using various stimuli-responsive mechanisms, dictating biological activity via reaction to external cues. Hydrogels are networked hydrophilic polymeric materials that offer the potential to recreate aqueous environments with controlled organization of biomolecules for research purposes and potential therapeutic use. The unique, programmable structure of hydrogels exhibit similar physicochemical properties to those of living tissues while also introducing component-mediated biocompatibility. The design of stimuli-responsive biocompatible hydrogels would further allow for controllable mechanical and functional properties tuned by changes in environmental factors. This study investigates individual biomolecular components needed to potentially engineer biocompatible and stimuli-responsive hydrogels. By utilizing programmable biomolecules such as nucleic acids (DNA and RNA) and proteins -
UCSF Chimera Was Developed by the Computer Graphics Laboratory at the University of California, San Francisco, Under Support of NIH Grant P41-RR01081
matrixcopy apply the transformation matrix of one model to another tcolor color using texture map colors UCSF Chimera matrixget write the current transformation matrices to a ®le texture de®ne texture maps and associated colors QUICK REFERENCE GUIDE June 2007 matrixset read and apply transformation matrices from a ®le thickness move the clipping planes in opposite directions minimize energy-minimize structures turn rotate about the X, Y, or Z axis mmaker (matchmaker) align models in sequence, then in 3D vdw* display van der Waals (VDW) surface modelcolor set color at the model level vdwde®ne* set VDW radii Commands modeldisplay* set display at the model level vdwdensity set VDW surface dot density *reverse function Äcommand available move translate along the X, Y, or Z axis version show copyright information and Chimera version movie capture image frames and assemble them into a movie viewdock start ViewDock and load docking results 2dlabels create arbitrary text labels and place them in 2D namesel name and save the current selection wait suspend command processing until motion has stopped ac enable accelerators (keyboard shortcuts) neon create a shadowed stick/tube image (not on Windows) window adjust the view to contain the speci®ed atoms addaa add an amino acid to a peptide C-terminus objdisplay* display graphical objects windowsize adjust the dimensions of the graphics window addcharge assign partial charges to atoms open* read structures and data, execute command ®les write save a molecule model as a PDB ®le addh add hydrogens pdbrun -
3D-Printing Models for Chemistry
3D-Printing Models for Chemistry: A Step-by-Step Open-Source Guide for Hobbyists, Corporate ProfessionAls, and Educators and Student in K-12 and Higher Education Poster Elisabeth Grace Billman-Benveniste+, Jacob Franz++, Loredana Valenzano-Slough+* +Department of Chemistry, Michigan Technological University, ++Department of Mechanical Engineering, Michigan Technological University *Corresponding Author References 1. “LulzBot TAZ 5.” LulzBot, 14 Aug. 2018, www.lulzbot.com/store/printers/lulzbot-taz-5 2. Gaussian 16, Revision B.01, Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A.; Nakatsuji, H.; Li, X.; Caricato, M.; Marenich, A. V.; Bloino, J.; Janesko, B. G.; Gomperts, R.; Mennucci, B.; Hratchian, H. P.; Ortiz, J. V.; Izmaylov, A. F.; Sonnenberg, J. L.; Williams-Young, D.; Ding, F.; Lipparini, F.; Egidi, F.; Goings, J.; Peng, B.; Petrone, A.; Henderson, T.; Ranasinghe, D.; ZakrzeWski, V. G.; Gao, J.; Rega, N.; Zheng, G.; Liang, W.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; HasegaWa, J.; Ishida, M.; NakaJima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Throssell, K.; Montgomery, J. A., Jr.; Peralta, J. E.; Ogliaro, F.; Bearpark, M. J.; Heyd, J. J.; Brothers, E. N.; Kudin, K. N.; Staroverov, V. N.; Keith, T. A.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, A. P.; Burant, J. C.; Iyengar, S. S.; Tomasi, J.; Cossi, M.; Millam, J. M.; Klene, M.; Adamo, C.; Cammi, R.; Ochterski, J. W.; Martin, R. L.; Morokuma, K.; Farkas, O.; Foresman, J. B.; Fox, D. J. Gaussian, Inc., Wallingford CT, 2016. 3. -
Calculation of Effective Interaction Potentials From
Methodologies of systematic structure-based coarse-graining Alexander Lyubartsev Division of Physical Chemistry Department of Material and Environmental Chemistry Stockholm University E-CAM workshop: State of the art in mesoscale and multiscale modeling Dublin 29-31 May 2017 Outline 1. Multiscale and coarse-grained simulations 2. Systematic structure-based coarse-graining by Inverse Monte Carlo 3. Examples - water and ions - lipid bilayers and lipid assemblies 4. Software - MagiC Example of systematic coarse-graining: DNA in Chromatin: presentation 30 May Computer modeling: from 1st principles to mesoscale Levels of molecular modeling: First-principles Atomistic Meso-scale (Quantum Classical Langevine/ BD, Mechanics) Molecular Dynamics DPD... nuclei electrons atoms coarse-grained biomolecules atoms molecules molecules soft matter 0.1 nm 1.0 nm 10 nm 100 nm 1 000 nm Larger scale more approximations Mesoscale Simulations Length scale: > 10 nm ( nanoscale: 10 - 1000 nm) Atomistic modeling is generally not possible box 10 nm - more than 105 atoms even if doable for 105 - 106 particles - do not forget about time scale! larger size : longer time for equilibration and reliable sampling 105 atoms - time scale should be above 1 ms = 1000 ns Need approximations - coarse-graining Coarse-graining – an example Original size – 2.4Mb Compressed to 24 Kb Levels of coarse- graining Level of coarse-graining can be different. For example, for a DMPC lipid: All-atom model United-atom Coarse-grained Coarse-grained 118 atoms model: 10 sites: 3 sites: 46 united atoms Martini model Cooke model more details - chemical specificity faster computations; larger systems Coarse-graining of solvent: 1) Explicit solvent: 2) Implicit solvent: One or several solvent molecules are No solvent particles but their effect is united in a single site. -
Calculation of Effective Interaction Potentials From
Systematic Coarse-Graining of Molecular Models by the Inverse Monte Carlo: Theory, Practice and Software Alexander Lyubartsev Division of Physical Chemistry Department of Material and Environmental Chemistry Stockholm University Modeling Soft Matter: Linking Multiple Length and Time Scales Kavli Institute of Theoretical Physics, UCSB, Santa Barbara 4-8 June 2012 Soft Matter Simulations length model method time 1Å electron w.f + ab-initio 100 ps 1 nm nuclei BOMD, CPMD 10 nm atomistic classical MD 100 ns coarse-grained Langevine MD, 100 nm µ more DPD, etc 100 s coarse-grained µ 1 m continuous The problem: Larger scale ⇔ more approximations Coarse-graining – an example Original size - 900K Compressed to 24K Coares-graining: reduction degrees of freedom All-atom model Coarse-grained model Large-scale 118 atoms 10 sites simulations We need to: 1) Design Coarse-Grained mapping: specify the important degrees of freedom 2) For “important” degrees of freedom we need interaction potential Question: what is the interaction potential for the coarse-grained model? Formal solution: N-body mean force potential Original (FG = fine grained system) FG ( ) =θ( ) H r 1, r 2,... ,rn R j r1, ...r n n = 118 j = 1,...,10 Usually, centers of mass of selected molecular fragments Partition function : n =∫∏ (−β ( ))= Z dr i exp H FG r 1, ...,r n i=1 n N =∫∏ ∏ δ( −θ ( )) (−β ( ))= dr i dR j R j j r1, ...rn exp H FG r1, ... ,rn = i1 j 1 N =∫∏ (−β ( )) dR j exp H CG R1, ... , RN j =1 1 where β= k B T n N ( )=−1 ∫∏ ∏ δ( −θ ( )) (− ( )) H CG R1, ..., R N β ln dr i R j -
Development and Application of a Computational Platform for Complex Molecular Design Jaime Rodríguez-Guerra Pedregal
ADVERTIMENT. Lʼaccés als continguts dʼaquesta tesi queda condicionat a lʼacceptació de les condicions dʼús establertes per la següent llicència Creative Commons: http://cat.creativecommons.org/?page_id=184 ADVERTENCIA. El acceso a los contenidos de esta tesis queda condicionado a la aceptación de las condiciones de uso establecidas por la siguiente licencia Creative Commons: http://es.creativecommons.org/blog/licencias/ WARNING. The access to the contents of this doctoral thesis it is limited to the acceptance of the use conditions set by the following Creative Commons license: https://creativecommons.org/licenses/?lang=en Development and Application of a Computational Platform for Complex Molecular Design a dissertation submitted by Jaime Rodríguez-Guerra Pedregal & directed by Prof. Dr. Jean-Didier Maréchal in fulfillment of the requirements for the degree of Doctor of Biotechnology Tutor: Prof. Dr. Jordi Joan Cairó Badillo Department of Chemical, Biological and Environmental Engineering Universitat Autònoma de Barcelona July 2018 Development and Application of a Computational Platform for Complex Molecular Design a dissertation submitted by & recommended for acceptance by advisor Jaime Rodríguez-Guerra Pedregal Prof. Dr. Jean-Didier Maréchal Tutor: Prof. Dr. Jordi Joan Cairó Badillo Department of Chemical, Biological and Environmental Engineering Universitat Autònoma de Barcelona July 2018 ©2018 – Jaime Rodríguez-Guerra Pedregal Licensed as Creative Commons BY-NC-ND Attribution-NonCommercial-NoDerivs In the beginning, there was nothing. And God said «Let there be light». And there was light. There was still nothing, but you could see it a lot better. —WoodyAllen. Development and Application of a Computational Platform for Complex Molecular Design by Jaime Rodríguez-Guerra Pedregal Abstract In this dissertation, a series of novel computational modeling tools is reported. -
Hands-On Tutorials of Autodock 4 and Autodock Vina
Hands-on tutorials of AutoDock 4 and AutoDock Vina Pei-Ying Chu (朱珮瑩) Supervisor: Jung-Hsin Lin (林榮信) Research Center for Applied Sciences, Academia Sinica 2018 Frontiers in Computational Drug Design, Academia Sinica, March 16-20, 2018 AutoDock http://autodock.scripps.edu AutoDock is a suite of automated docking tools. It is designed to predict how small molecules, such as substrates or drug candidates, bind to a receptor of known 3D structure. AutoDock 4 is free and is available under the GNU General Public License. 2 AutoDock Vina http://vina.scripps.edu/ Because the scoring functions used by AutoDock 4 and AutoDock Vina are different and inexact, on any given problem, either program may provide a better result. AutoDock Vina is available under the Apache license, allowing commercial and 3 non-commercial use and redistribution. http://autodock.scripps.edu/downloads These programs were installed on VM. 4 http://mgltools.scripps.edu/ AutoDockTools (ADT) is developed to help set up the docking. ADT is included in MGLTools packages. 5 In general, each docking (AutoDock 4 and/or AutoDock Vina) requires: 1. structure of the receptor (protein), in pdbqt format 2. structure of the ligand (small molecule, drug, etc.) in pdbqt format 3. docking and grid parameters (search space) PDBQT format is very similar to PDB format but it includes partial charges ('Q') and AutoDock 4 (AD4) atom types ('T'). • Preparing the ligand involves ensuring that its atoms are assigned the correct AutoDock4 atom types, adding Gasteiger charges if necessary, merging non-polar hydrogens, detecting aromatic carbons if any, and setting up the 'torsion tree'. -
The Effects of Diatom Pore-Size on the Structures and Extensibilities of Single Mucilage Molecules
Edinburgh Research Explorer The effects of diatom pore-size on the structures and extensibilities of single mucilage molecules Citation for published version: Sanka, I, Suyono, E & Alam, P 2017, 'The effects of diatom pore-size on the structures and extensibilities of single mucilage molecules', Carbohydrate Research, vol. 448, pp. 35-42. https://doi.org/10.1016/j.carres.2017.05.014 Digital Object Identifier (DOI): 10.1016/j.carres.2017.05.014 Link: Link to publication record in Edinburgh Research Explorer Document Version: Peer reviewed version Published In: Carbohydrate Research General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 06. Oct. 2021 Carbohydrate Research 448 (2017) 35e42 Contents lists available at ScienceDirect Carbohydrate Research journal homepage: www.elsevier.com/locate/carres The effects of diatom pore-size on the structures and extensibilities of single mucilage molecules * Immanuel Sanka a, b, Eko Agus -
M.Dynamix Studies of Solvation, Solubility and Permeability
5 M.DynaMix Studies of Solvation, Solubility and Permeability Aatto Laaksonen1, Alexander Lyubartsev1 and Francesca Mocci1,2 1Stockholm University, Division of Physical Chemistry, Department of Materials and Environmental Chemistry, Arrhenius Laboratory, Stockholm 2Università di Cagliari, Dipartimento di Scienze Chimiche Cittadella Universitaria di Monserrato, Monserrato, Cagliari 1Sweden 2Italy 1. Introduction During the last four decades Molecular Dynamics (MD) simulations have developed to a powerful discipline and finally very close to the early vision from early 80’s that it would mature and become a computer laboratory to study molecular systems in conditions similar to that valid in experimental studies using instruments giving information about molecular structure, interactions and dynamics in condensed phases and at interfaces between different phases. Today MD simulations are more or less routinely used by many scientists originally educated and trained towards experimental work which later have found simulations (along with Quantum Chemistry and other Computational methods) as a powerful complement to their experimental studies to obtain molecular insight and thereby interpretation of their results. In this chapter we wish to introduce a powerful methodology to obtain detailed and accurate information about solvation and solubility of different categories of solute molecules and ions in water (and other solvents and phases including mixed solvents) and also about permeability and transport of solutes in different non-aqueous phases. Among the most challenging problems today are still the computations of the free energy and many to it related problems. The methodology used in our studies for free energy calculations is our Expanded Ensemble scheme recently implemented in a general MD simulation package called “M.DynaMix”. -
UCSF Chimera[Mdash]A Visualization System for Exploratory Research And
UCSF Chimera—A Visualization System for Exploratory Research and Analysis ERIC F. PETTERSEN, THOMAS D. GODDARD, CONRAD C. HUANG, GREGORY S. COUCH, DANIEL M. GREENBLATT, ELAINE C. MENG, THOMAS E. FERRIN Computer Graphics Laboratory, Department of Pharmaceutical Chemistry, University of California, 600 16th Street, San Francisco, California 94143-2240 Received 24 February 2004; Accepted 6 May 2004 DOI 10.1002/jcc.20084 Published online in Wiley InterScience (www.interscience.wiley.com). Abstract: The design, implementation, and capabilities of an extensible visualization system, UCSF Chimera, are discussed. Chimera is segmented into a core that provides basic services and visualization, and extensions that provide most higher level functionality. This architecture ensures that the extension mechanism satisfies the demands of outside developers who wish to incorporate new features. Two unusual extensions are presented: Multiscale, which adds the ability to visualize large-scale molecular assemblies such as viral coats, and Collaboratory, which allows researchers to share a Chimera session interactively despite being at separate locales. Other extensions include Multalign Viewer, for showing multiple sequence alignments and associated structures; ViewDock, for screening docked ligand orientations; Movie, for replaying molecular dynamics trajectories; and Volume Viewer, for display and analysis of volumetric data. A discussion of the usage of Chimera in real-world situations is given, along with anticipated future directions. Chimera includes full user documentation, is free to academic and nonprofit users, and is available for Microsoft Windows, Linux, Apple Mac OS X, SGI IRIX, and HP Tru64 Unix from http://www.cgl.ucsf.edu/chimera/. © 2004 Wiley Periodicals, Inc. J Comput Chem 25: 1605–1612, 2004 Key words: molecular graphics; extensibility; visualization; multiscale modeling; sequence alignment Introduction had not anticipated. -
Functional Behavior of Molecular Baskets and Structure-Activity Studies on Trapping Organophosphorus Nerve Agents in Water
Functional Behavior of Molecular Baskets and Structure-Activity Studies on Trapping Organophosphorus Nerve Agents in Water DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Yian Ruan Graduate Program in Chemistry The Ohio State University 2014 Dissertation Committee: Jovica D. Badjic, Advisor Christopher M. Hadad Jon R. Parquette Copyright by Yian Ruan 2014 Abstract Molecular recognition is exploited by nature to carry out delicately regulated reactions and form precisely organized structures in living organisms. Enzymes promote reactions by preorganizing substrates and stabilizing transition states in an active site via covalent and non-covalent interactions. Receptor proteins and antibodies can respond selectively to stimuli and trigger subsequent activities. These protein-substrate interactions have been great inspirations for chemists in the design of synthetic receptors as hosts and the study of their molecular recognition properties. Investigation of recognition behaviors can help decipher sub-cellular processes. Moreover, some artificial host-guest complexes have found applications in catalysis, sensing, imaging and drug delivery systems. The Badjic group has developed a family of host molecules called molecular baskets to study the effect of gating on molecular recognition. These baskets possess a cavity formed by a benzene base and phthalimide side walls. Pyridine-based gates close the basket via hydrogen bonds or metal chelation. Tuning the electronic and steric characteristics of gates affects the rate of guests entering and departing the basket. ii With all the knowledge about molecular gating, questions arise as to whether these gates can be applied to other platforms and how the mechanism of gating will be affected.