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Olaseni Sode
Olaseni Sode Work Address: 5735 S. Ellis Ave. [email protected] Searle 231 +1 (314) 856-2373 The University of Chicago Chicago, IL 60615 Home Address: 1119 E Hyde Park Blvd [email protected] Apt. 3E +1 (314) 856-2373 Chicago, IL 60615 http://www.olaseniosode.com Education University of Illinois at Urbana-Champaign Urbana, IL Ph.D. in Chemistry September 2012 – Supervisor: Professor So Hirata – Thesis title: A Theoretical Study of Molecular Crystals – Transferred from the University of Florida in 2010 with Prof. Hirata Morehouse College Atlanta, GA B.S. & B.A. in Chemistry and French December 2006 – Studied at the Université de Nantes, Nantes, France (2004-2005) Research Experience The University of Chicago Chicago, IL Postdoctoral Research – research advisor: Dr. Gregory A. Voth 2012–present – Studied proposed proton transport minimum free-energy pathways in [FeFe]-hydrogenase via molecular dynamics simulations and the multistate empirical valence bond technique. – Collaborated to develop the multiscale string method to accelerate refinement of chemical reaction pathways of adenosine triphosphate (ATP) hydrolysis in globular and filamentous actin proteins. University of California, San Diego La Jolla, CA Visiting Scholar – research advisor: Dr. Francesco Paesani 2014, 2015 – Developed and implemented a reactive molecular dynamics scheme for condensed phase systems of the hydrated hydronium ions. University of Illinois at Urbana-Champaign Urbana, IL Doctoral Research – research advisor: Dr. So Hirata 2010-2012 – Characterized the vibrational structure of extended systems including infrared and Raman-active vibrations, as well as phonon dispersions and densities of states in the first Brillouin zone. – Developed zero-point vibrational energy analysis scheme for molecular clusters using a fragmented, local basis scheme. -
Pysimm: a Python Package for Simulation of Molecular Systems
SoftwareX 6 (2016) 7–12 Contents lists available at ScienceDirect SoftwareX journal homepage: www.elsevier.com/locate/softx pysimm: A python package for simulation of molecular systems Michael E. Fortunato a, Coray M. Colina a,b,∗ a Department of Chemistry, University of Florida, Gainesville, FL 32611, United States b Department of Materials Science and Engineering and Nuclear Engineering, University of Florida, Gainesville, FL 32611, United States article info a b s t r a c t Article history: In this work, we present pysimm, a python package designed to facilitate structure generation, simulation, Received 15 August 2016 and modification of molecular systems. pysimm provides a collection of simulation tools and smooth Received in revised form integration with highly optimized third party software. Abstraction layers enable a standardized 1 December 2016 methodology to assign various force field models to molecular systems and perform simple simulations. Accepted 5 December 2016 These features have allowed pysimm to aid the rapid development of new applications specifically in the area of amorphous polymer simulations. Keywords: ' 2016 The Authors. Published by Elsevier B.V. Amorphous polymers Molecular simulation This is an open access article under the CC BY license Python (http://creativecommons.org/licenses/by/4.0/). Code metadata Current code version v0.1 Permanent link to code/repository used for this code version https://github.com/ElsevierSoftwareX/SOFTX-D-16-00070 Legal Code License MIT Code versioning system used git Software code languages, tools, and services used python2.7 Compilation requirements, operating environments & Linux dependencies If available Link to developer documentation/manual http://pysimm.org/documentation/ Support email for questions [email protected] 1. -
An Ab Initio Materials Simulation Code
CP2K: An ab initio materials simulation code Lianheng Tong Physics on Boat Tutorial , Helsinki, Finland 2015-06-08 Faculty of Natural & Mathematical Sciences Department of Physics Brief Overview • Overview of CP2K - General information about the package - QUICKSTEP: DFT engine • Practical example of using CP2K to generate simulated STM images - Terminal states in AGNR segments with 1H and 2H termination groups What is CP2K? Swiss army knife of molecular simulation www.cp2k.org • Geometry and cell optimisation Energy and • Molecular dynamics (NVE, Force Engine NVT, NPT, Langevin) • STM Images • Sampling energy surfaces (metadynamics) • DFT (LDA, GGA, vdW, • Finding transition states Hybrid) (Nudged Elastic Band) • Quantum Chemistry (MP2, • Path integral molecular RPA) dynamics • Semi-Empirical (DFTB) • Monte Carlo • Classical Force Fields (FIST) • And many more… • Combinations (QM/MM) Development • Freely available, open source, GNU Public License • www.cp2k.org • FORTRAN 95, > 1,000,000 lines of code, very active development (daily commits) • Currently being developed and maintained by community of developers: • Switzerland: Paul Scherrer Institute Switzerland (PSI), Swiss Federal Institute of Technology in Zurich (ETHZ), Universität Zürich (UZH) • USA: IBM Research, Lawrence Livermore National Laboratory (LLNL), Pacific Northwest National Laboratory (PNL) • UK: Edinburgh Parallel Computing Centre (EPCC), King’s College London (KCL), University College London (UCL) • Germany: Ruhr-University Bochum • Others: We welcome contributions from -
Real-Time Pymol Visualization for Rosetta and Pyrosetta
Real-Time PyMOL Visualization for Rosetta and PyRosetta Evan H. Baugh1, Sergey Lyskov1, Brian D. Weitzner1, Jeffrey J. Gray1,2* 1 Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America, 2 Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, Maryland, United States of America Abstract Computational structure prediction and design of proteins and protein-protein complexes have long been inaccessible to those not directly involved in the field. A key missing component has been the ability to visualize the progress of calculations to better understand them. Rosetta is one simulation suite that would benefit from a robust real-time visualization solution. Several tools exist for the sole purpose of visualizing biomolecules; one of the most popular tools, PyMOL (Schro¨dinger), is a powerful, highly extensible, user friendly, and attractive package. Integrating Rosetta and PyMOL directly has many technical and logistical obstacles inhibiting usage. To circumvent these issues, we developed a novel solution based on transmitting biomolecular structure and energy information via UDP sockets. Rosetta and PyMOL run as separate processes, thereby avoiding many technical obstacles while visualizing information on-demand in real-time. When Rosetta detects changes in the structure of a protein, new coordinates are sent over a UDP network socket to a PyMOL instance running a UDP socket listener. PyMOL then interprets and displays the molecule. This implementation also allows remote execution of Rosetta. When combined with PyRosetta, this visualization solution provides an interactive environment for protein structure prediction and design. Citation: Baugh EH, Lyskov S, Weitzner BD, Gray JJ (2011) Real-Time PyMOL Visualization for Rosetta and PyRosetta. -
Automated Construction of Quantum–Classical Hybrid Models Arxiv:2102.09355V1 [Physics.Chem-Ph] 18 Feb 2021
Automated construction of quantum{classical hybrid models Christoph Brunken and Markus Reiher∗ ETH Z¨urich, Laboratorium f¨urPhysikalische Chemie, Vladimir-Prelog-Weg 2, 8093 Z¨urich, Switzerland February 18, 2021 Abstract We present a protocol for the fully automated construction of quantum mechanical-(QM){ classical hybrid models by extending our previously reported approach on self-parametri- zing system-focused atomistic models (SFAM) [J. Chem. Theory Comput. 2020, 16 (3), 1646{1665]. In this QM/SFAM approach, the size and composition of the QM region is evaluated in an automated manner based on first principles so that the hybrid model describes the atomic forces in the center of the QM region accurately. This entails the au- tomated construction and evaluation of differently sized QM regions with a bearable com- putational overhead that needs to be paid for automated validation procedures. Applying SFAM for the classical part of the model eliminates any dependence on pre-existing pa- rameters due to its system-focused quantum mechanically derived parametrization. Hence, QM/SFAM is capable of delivering a high fidelity and complete automation. Furthermore, since SFAM parameters are generated for the whole system, our ansatz allows for a con- venient re-definition of the QM region during a molecular exploration. For this purpose, a local re-parametrization scheme is introduced, which efficiently generates additional clas- sical parameters on the fly when new covalent bonds are formed (or broken) and moved to the classical region. arXiv:2102.09355v1 [physics.chem-ph] 18 Feb 2021 ∗Corresponding author; e-mail: [email protected] 1 1 Introduction In contrast to most protocols of computational quantum chemistry that consider isolated molecules, chemical processes can take place in a vast variety of complex environments. -
A Study on Cheminformatics and Its Applications on Modern Drug Discovery
Available online at www.sciencedirect.com Procedia Engineering 38 ( 2012 ) 1264 – 1275 Internatio na l Conference on Modeling Optimisatio n and Computing (ICMOC 2012) A Study on Cheminformatics and its Applications on Modern Drug Discovery B.Firdaus Begama and Dr. J.Satheesh Kumarb aResearch Scholar, Bharathiar University, Coimbatore, India, [email protected] bAssistant Professor, Bharathiar University, Coimbatore, India, [email protected] Abstract Discovering drugs to a disease is still a challenging task for medical researchers due to the complex structures of biomolecules which are responsible for disease such as AIDS, Cancer, Autism, Alzimear etc. Design and development of new efficient anti-drugs for the disease without any side effects are becoming mandatory in the recent history of human life cycle due to changes in various factors which includes food habit, environmental and migration in human life style. Cheminformaticds deals with discovering drugs based in modern drug discovery techniques which in turn rectifies complex issues in traditional drug discovery system. Cheminformatics tools, helps medical chemist for better understanding of complex structures of chemical compounds. Cheminformatics is a new emerging interdisciplinary field which primarily aims to discover Novel Chemical Entities [NCE] which ultimately results in design of new molecule [chemical data]. It also plays an important role for collecting, storing and analysing the chemical data. This paper focuses on cheminformatics and its applications on drug discovery and modern drug discovery techniques which helps chemist and medical researchers for finding solution to the complex disease. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Noorul Islam Centre for Higher Education. -
An Explicit-Solvent Conformation Search Method Using Open Software
An explicit-solvent conformation search method using open software Kari Gaalswyk and Christopher N. Rowley Department of Chemistry, Memorial University of Newfoundland, St. John’s, Newfoundland and Labrador, Canada ABSTRACT Computer modeling is a popular tool to identify the most-probable conformers of a molecule. Although the solvent can have a large effect on the stability of a conformation, many popular conformational search methods are only capable of describing molecules in the gas phase or with an implicit solvent model. We have developed a work-flow for performing a conformation search on explicitly-solvated molecules using open source software. This method uses replica exchange molecular dynamics (REMD) to sample the conformational states of the molecule efficiently. Cluster analysis is used to identify the most probable conformations from the simulated trajectory. This work-flow was tested on drug molecules a-amanitin and cabergoline to illustrate its capabilities and effectiveness. The preferred conformations of these molecules in gas phase, implicit solvent, and explicit solvent are significantly different. Subjects Biophysics, Pharmacology, Computational Science Keywords Conformation search, Explicit solvent, Cluster analysis, Replica exchange molecular dynamics INTRODUCTION Many molecules can exist in multiple conformational isomers. Conformational isomers have the same chemical bonds, but differ in their 3D geometry because they hold different torsional angles (Crippen & Havel, 1988). The conformation of a molecule can affect Submitted 5 April 2016 chemical reactivity, molecular binding, and biological activity (Struthers, Rivier & Accepted 6May2016 Published 31 May 2016 Hagler, 1985; Copeland, 2011). Conformations differ in stability because they experience different steric, electrostatic, and solute-solvent interactions. The probability, p,ofa Corresponding author Christopher N. -
Python Tools in Computational Chemistry (And Biology)
Python Tools in Computational Chemistry (and Biology) Andrew Dalke Dalke Scientific, AB Göteborg, Sweden EuroSciPy, 26-27 July, 2008 “Why does ‘import numpy’ take 0.4 seconds? Does it need to import 228 libraries?” - My first Numpy-discussion post (paraphrased) Your use case isn't so typical and so suffers on the import time end of the balance. - Response from Robert Kern (Others did complain. Import time down to 0.28s.) 52,000 structures PDB doubles every 2½ years HEADER PHOTORECEPTOR 23-MAY-90 1BRD 1BRD 2 COMPND BACTERIORHODOPSIN 1BRD 3 SOURCE (HALOBACTERIUM $HALOBIUM) 1BRD 4 EXPDTA ELECTRON DIFFRACTION 1BRD 5 AUTHOR R.HENDERSON,J.M.BALDWIN,T.A.CESKA,F.ZEMLIN,E.BECKMANN, 1BRD 6 AUTHOR 2 K.H.DOWNING 1BRD 7 REVDAT 3 15-JAN-93 1BRDB 1 SEQRES 1BRDB 1 REVDAT 2 15-JUL-91 1BRDA 1 REMARK 1BRDA 1 .. ATOM 54 N PRO 8 20.397 -15.569 -13.739 1.00 20.00 1BRD 136 ATOM 55 CA PRO 8 21.592 -15.444 -12.900 1.00 20.00 1BRD 137 ATOM 56 C PRO 8 21.359 -15.206 -11.424 1.00 20.00 1BRD 138 ATOM 57 O PRO 8 21.904 -15.930 -10.563 1.00 20.00 1BRD 139 ATOM 58 CB PRO 8 22.367 -14.319 -13.591 1.00 20.00 1BRD 140 ATOM 59 CG PRO 8 22.089 -14.564 -15.053 1.00 20.00 1BRD 141 ATOM 60 CD PRO 8 20.647 -15.054 -15.103 1.00 20.00 1BRD 142 ATOM 61 N GLU 9 20.562 -14.211 -11.095 1.00 20.00 1BRD 143 ATOM 62 CA GLU 9 20.192 -13.808 -9.737 1.00 20.00 1BRD 144 ATOM 63 C GLU 9 19.567 -14.935 -8.932 1.00 20.00 1BRD 145 ATOM 64 O GLU 9 19.815 -15.104 -7.724 1.00 20.00 1BRD 146 ATOM 65 CB GLU 9 19.248 -12.591 -9.820 1.00 99.00 1 1BRD 147 ATOM 66 CG GLU 9 19.902 -11.351 -10.387 1.00 99.00 1 1BRD 148 ATOM 67 CD GLU 9 19.243 -10.169 -10.980 1.00 99.00 1 1BRD 149 ATOM 68 OE1 GLU 9 18.323 -10.191 -11.782 1.00 99.00 1 1BRD 150 ATOM 69 OE2 GLU 9 19.760 -9.089 -10.597 1.00 99.00 1 1BRD 151 ATOM 70 N TRP 10 18.764 -15.737 -9.597 1.00 20.00 1BRD 152 ATOM 71 CA TRP 10 18.034 -16.884 -9.090 1.00 20.00 1BRD 153 ATOM 72 C TRP 10 18.843 -17.908 -8.318 1.00 20.00 1BRD 154 ATOM 73 O TRP 10 18.376 -18.310 -7.230 1.00 20.00 1BRD 155 . -
Computational Chemistry for Chemistry Educators Shawn C
Volume 1, Issue 1 Journal Of Computational Science Education Computational Chemistry for Chemistry Educators Shawn C. Sendlinger Clyde R. Metz North Carolina Central University College of Charleston Department of Chemistry Department of Chemistry and Biochemistry 1801 Fayetteville Street, 66 George Street, Durham, NC 27707 Charleston, SC 29424 919-530-6297 843-953-8097 [email protected] [email protected] ABSTRACT 1. INTRODUCTION In this paper we describe an ongoing project where the goal is to The majority of today’s students are technologically savvy and are develop competence and confidence among chemistry faculty so often more comfortable using computers than the faculty who they are able to utilize computational chemistry as an effective teach them. In order to harness the student’s interest in teaching tool. Advances in hardware and software have made technology and begin to use it as an educational tool, most faculty research-grade tools readily available to the academic community. members require some level of instruction and training. Because Training is required so that faculty can take full advantage of this chemistry research increasingly utilizes computation as an technology, begin to transform the educational landscape, and important tool, our approach to chemistry education should reflect attract more students to the study of science. this. The ability of computer technology to visualize and manipulate objects on an atomic scale can be a powerful tool to increase both student interest in chemistry as well as their level of Categories and Subject Descriptors understanding. Computational Chemistry for Chemistry Educators (CCCE) is a project that seeks to provide faculty the J.2 [Physical Sciences and Engineering]: Chemistry necessary knowledge, experience, and technology access so that they can begin to design and incorporate computational approaches in the courses they teach. -
Reactive Molecular Dynamics Study of the Thermal Decomposition of Phenolic Resins
Article Reactive Molecular Dynamics Study of the Thermal Decomposition of Phenolic Resins Marcus Purse 1, Grace Edmund 1, Stephen Hall 1, Brendan Howlin 1,* , Ian Hamerton 2 and Stephen Till 3 1 Department of Chemistry, Faculty of Engineering and Physical Sciences, University of Surrey, Guilford, Surrey GU2 7XH, UK; [email protected] (M.P.); [email protected] (G.E.); [email protected] (S.H.) 2 Bristol Composites Institute (ACCIS), Department of Aerospace Engineering, School of Civil, Aerospace, and Mechanical Engineering, University of Bristol, Bristol BS8 1TR, UK; [email protected] 3 Defence Science and Technology Laboratory, Porton Down, Salisbury SP4 0JQ, UK; [email protected] * Correspondence: [email protected]; Tel.: +44-1483-686-248 Received: 6 March 2019; Accepted: 23 March 2019; Published: 28 March 2019 Abstract: The thermal decomposition of polyphenolic resins was studied by reactive molecular dynamics (RMD) simulation at elevated temperatures. Atomistic models of the polyphenolic resins to be used in the RMD were constructed using an automatic method which calls routines from the software package Materials Studio. In order to validate the models, simulated densities and heat capacities were compared with experimental values. The most suitable combination of force field and thermostat for this system was the Forcite force field with the Nosé–Hoover thermostat, which gave values of heat capacity closest to those of the experimental values. Simulated densities approached a final density of 1.05–1.08 g/cm3 which compared favorably with the experimental values of 1.16–1.21 g/cm3 for phenol-formaldehyde resins. -
How to Modify LAMMPS: from the Prospective of a Particle Method Researcher
chemengineering Article How to Modify LAMMPS: From the Prospective of a Particle Method Researcher Andrea Albano 1,* , Eve le Guillou 2, Antoine Danzé 2, Irene Moulitsas 2 , Iwan H. Sahputra 1,3, Amin Rahmat 1, Carlos Alberto Duque-Daza 1,4, Xiaocheng Shang 5 , Khai Ching Ng 6, Mostapha Ariane 7 and Alessio Alexiadis 1,* 1 School of Chemical Engineering, University of Birmingham, Birmingham B15 2TT, UK; [email protected] (I.H.S.); [email protected] (A.R.); [email protected] (C.A.D.-D.) 2 Centre for Computational Engineering Sciences, Cranfield University, Bedford MK43 0AL, UK; Eve.M.Le-Guillou@cranfield.ac.uk (E.l.G.); A.Danze@cranfield.ac.uk (A.D.); i.moulitsas@cranfield.ac.uk (I.M.) 3 Industrial Engineering Department, Petra Christian University, Surabaya 60236, Indonesia 4 Department of Mechanical and Mechatronic Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia 5 School of Mathematics, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK; [email protected] 6 Department of Mechanical, Materials and Manufacturing Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih 43500, Malaysia; [email protected] 7 Department of Materials and Engineering, Sayens-University of Burgundy, 21000 Dijon, France; [email protected] * Correspondence: [email protected] or [email protected] (A.A.); [email protected] (A.A.) Citation: Albano, A.; le Guillou, E.; Danzé, A.; Moulitsas, I.; Sahputra, Abstract: LAMMPS is a powerful simulator originally developed for molecular dynamics that, today, I.H.; Rahmat, A.; Duque-Daza, C.A.; also accounts for other particle-based algorithms such as DEM, SPH, or Peridynamics. -
Preparing and Analyzing Large Molecular Simulations With
Preparing and Analyzing Large Molecular Simulations with VMD John Stone, Senior Research Programmer NIH Center for Macromolecular Modeling and Bioinformatics, University of Illinois at Urbana-Champaign VMD Tutorials Home Page • http://www.ks.uiuc.edu/Training/Tutorials/ – Main VMD tutorial – VMD images and movies tutorial – QwikMD simulation preparation and analysis plugin – Structure check – VMD quantum chemistry visualization tutorial – Visualization and analysis of CPMD data with VMD – Parameterizing small molecules using ffTK Overview • Introduction • Data model • Visualization • Scripting and analytical features • Trajectory analysis and visualization • High fidelity ray tracing • Plugins and user-extensibility • Large system analysis and visualization VMD – “Visual Molecular Dynamics” • 100,000 active users worldwide • Visualization and analysis of: – Molecular dynamics simulations – Lattice cell simulations – Quantum chemistry calculations – Cryo-EM densities, volumetric data • User extensible scripting and plugins • http://www.ks.uiuc.edu/Research/vmd/ Cell-Scale Modeling MD Simulation VMD – “Visual Molecular Dynamics” • Unique capabilities: • Visualization and analysis of: – Trajectories are fundamental to VMD – Molecular dynamics simulations – Support for very large systems, – “Particle” systems and whole cells now reaching billions of particles – Cryo-EM densities, volumetric data – Extensive GPU acceleration – Quantum chemistry calculations – Parallel analysis/visualization with MPI – Sequence information MD Simulations Cell-Scale