Appendix8 Shape Grammars in Chapter 9 There Is a Reference to Possible Applications of Shape Grammars. One Such Application Conc
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Formal Languages and Automata Theory Géza Horváth, Benedek Nagy Szerzői Jog © 2014 Géza Horváth, Benedek Nagy, University of Debrecen
Formal Languages and Automata Theory Géza Horváth, Benedek Nagy Created by XMLmind XSL-FO Converter. Formal Languages and Automata Theory Géza Horváth, Benedek Nagy Szerzői jog © 2014 Géza Horváth, Benedek Nagy, University of Debrecen Created by XMLmind XSL-FO Converter. Tartalom Formal Languages and Automata Theory .......................................................................................... vi Introduction ...................................................................................................................................... vii 1. Elements of Formal Languages ...................................................................................................... 1 1. 1.1. Basic Terms .................................................................................................................... 1 2. 1.2. Formal Systems .............................................................................................................. 2 3. 1.3. Generative Grammars .................................................................................................... 5 4. 1.4. Chomsky Hierarchy ....................................................................................................... 6 2. Regular Languages and Finite Automata ...................................................................................... 12 1. 2.1. Regular Grammars ....................................................................................................... 12 2. 2.2. Regular Expressions .................................................................................................... -
The Architecture of the Protein Domain Universe
The architecture of the protein domain universe Nikolay V. Dokholyan Department of Biochemistry and Biophysics, The University of North Carolina at Chapel Hill, School of Medicine, Chapel Hill, NC 27599 ABSTRACT Understanding the design of the universe of protein structures may provide insights into protein evolution. We study the architecture of the protein domain universe, which has been found to poses peculiar scale-free properties (Dokholyan et al., Proc. Natl. Acad. Sci. USA 99: 14132-14136 (2002)). We examine the origin of these scale-free properties of the graph of protein domain structures (PDUG) and determine that that the PDUG is not modular, i.e. it does not consist of modules with uniform properties. Instead, we find the PDUG to be self-similar at all scales. We further characterize the PDUG architecture by studying the properties of the hub nodes that are responsible for the scale-free connectivity of the PDUG. We introduce a measure of the betweenness centrality of protein domains in the PDUG and find a power-law distribution of the betweenness centrality values. The scale-free distribution of hubs in the protein universe suggests that a set of specific statistical mechanics models, such as the self-organized criticality model, can potentially identify the principal driving forces of molecular evolution. We also find a gatekeeper protein domain, removal of which partitions the largest cluster into two large sub- clusters. We suggest that the loss of such gatekeeper protein domains in the course of evolution is responsible for the creation of new fold families. INTRODUCTION The principles of molecular evolution remain elusive despite fundamental breakthroughs on the theoretical front 1-5 and a growing amount of genomic and proteomic data, over 23,000 solved protein structures 6 and protein functional annotations 7-9. -
Cmse 520 Biomolecular Structure, Function And
CMSE 520 BIOMOLECULAR STRUCTURE, FUNCTION AND DYNAMICS (Computational Structural Biology) OUTLINE Review: Molecular biology Proteins: structure, conformation and function(5 lectures) Generalized coordinates, Phi, psi angles, DNA/RNA: structure and function (3 lectures) Structural and functional databases (PDB, SCOP, CATH, Functional domain database, gene ontology) Use scripting languages (e.g. python) to cross refernce between these databases: starting from sequence to find the function Relationship between sequence, structure and function Molecular Modeling, homology modeling Conservation, CONSURF Relationship between function and dynamics Confromational changes in proteins (structural changes due to ligation, hinge motions, allosteric changes in proteins and consecutive function change) Molecular Dynamics Monte Carlo Protein-protein interaction: recognition, structural matching, docking PPI databases: DIP, BIND, MINT, etc... References: CURRENT PROTOCOLS IN BIOINFORMATICS (e-book) (http://www.mrw.interscience.wiley.com/cp/cpbi/articles/bi0101/frame.html) Andreas D. Baxevanis, Daniel B. Davison, Roderic D.M. Page, Gregory A. Petsko, Lincoln D. Stein, and Gary D. Stormo (eds.) 2003 John Wiley & Sons, Inc. INTRODUCTION TO PROTEIN STRUCTURE Branden C & Tooze, 2nd ed. 1999, Garland Publishing COMPUTER SIMULATION OF BIOMOLECULAR SYSTEMS Van Gusteren, Weiner, Wilkinson Internet sources Ref: Department of Energy Rapid growth in experimental technologies Human Genome Projects Two major goals 1. DNA mapping 2. DNA sequencing Rapid growth in experimental technologies z Microrarray technologies – serial gene expression patterns and mutations z Time-resolved optical, rapid mixing techniques - folding & function mechanisms (Æ ns) z Techniques for probing single molecule mechanics (AFM, STM) (Æ pN) Æ more accurate models/data for computer-aided studies Weiss, S. (1999). Fluorescence spectroscopy of single molecules. -
INF2080 Context-Sensitive Langugaes
INF2080 Context-Sensitive Langugaes Daniel Lupp Universitetet i Oslo 15th February 2017 Department of University of Informatics Oslo INF2080 Lecture :: 15th February 1 / 20 Context-Free Grammar Definition (Context-Free Grammar) A context-free grammar is a 4-tuple (V ; Σ; R; S) where 1 V is a finite set of variables 2 Σ is a finite set disjoint from V of terminals 3 R is a finite set of rules, each consisting of a variable and of a string of variables and terminals 4 and S is the start variable Rules are of the form A ! B1B2B3 ::: Bm, where A 2 V and each Bi 2 V [ Σ. INF2080 Lecture :: 15th February 2 / 20 consider the following toy programming language, where you can “declare” and “assign” a variable a value. S ! declare v; S j assign v : x; S this is context-free... but what if we only want to allow assignment after declaration and an infinite amount of variable names? ! context-sensitive! Why Context-Sensitive? Many building blocks of programming languages are context-free, but not all! INF2080 Lecture :: 15th February 3 / 20 this is context-free... but what if we only want to allow assignment after declaration and an infinite amount of variable names? ! context-sensitive! Why Context-Sensitive? Many building blocks of programming languages are context-free, but not all! consider the following toy programming language, where you can “declare” and “assign” a variable a value. S ! declare v; S j assign v : x; S INF2080 Lecture :: 15th February 3 / 20 but what if we only want to allow assignment after declaration and an infinite amount of variable names? ! context-sensitive! Why Context-Sensitive? Many building blocks of programming languages are context-free, but not all! consider the following toy programming language, where you can “declare” and “assign” a variable a value. -
Automata and Grammars
Automata and Grammars Prof. Dr. Friedrich Otto Fachbereich Elektrotechnik/Informatik, Universität Kassel 34109 Kassel, Germany E-mail: [email protected] SS 2018 Prof. Dr. F. Otto (Universität Kassel) Automata and Grammars 1 / 294 Lectures and Seminary SS 2018 Lectures: Thursday 9:00 - 10:30, Room S 11 Start: Thursday, February 22, 2018, 9:00. Seminary: Thursday 10:40 - 12:10, Room S 11 Start: Thursday, February 22, 2018, 10:40. Prof. Dr. F. Otto (Universität Kassel) Automata and Grammars 2 / 294 Exercises: From Thursday to Thursday next week! To pass seminary: At least two successful presentations in class (10 %) and passing of midtem exam on April 5 (10:40 - 11:40) (20 %) Final Exam: (dates have been fixed!) Written exam (2 hours) on May 31 (9:00 - 11:30) in S 11 Oral exam (30 minutes per student) on June 14 (9:00 - 12:30) in S 10 Written exam (2. try) on June 21 (9:00 - 11:30) in S 11 Oral exam (2. try) on June 28 (9:00 - 12:30) in S 11 The written exam accounts for 50 % of the final grade, while the oral exam accounts for 20 % of the final grade. Moodle: Course ”Automata and Grammars” NTIN071. Prof. Dr. F. Otto (Universität Kassel) Automata and Grammars 3 / 294 Literature: P.J. Denning, J.E. Dennis, J.E. Qualitz; Machines, Languages, and Computation. Prentice-Hall, Englewood Cliffs, N.J., 1978. M. Harrison; Introduction to Formal Language Theory. Addison-Wesley, Reading, M.A., 1978. J.E. Hopcroft, R. Motwani, J.D. Ullman; Introduction to Automata Theory, Languages, and Computation. -
Action of Multiple Rice -Glucosidases on Abscisic Acid Glucose Ester
International Journal of Molecular Sciences Article Action of Multiple Rice β-Glucosidases on Abscisic Acid Glucose Ester Manatchanok Kongdin 1, Bancha Mahong 2, Sang-Kyu Lee 2, Su-Hyeon Shim 2, Jong-Seong Jeon 2,* and James R. Ketudat Cairns 1,* 1 School of Chemistry, Institute of Science, Center for Biomolecular Structure, Function and Application, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand; [email protected] 2 Graduate School of Biotechnology and Crop Biotech Institute, Kyung Hee University, Yongin 17104, Korea; [email protected] (B.M.); [email protected] (S.-K.L.); [email protected] (S.-H.S.) * Correspondence: [email protected] (J.-S.J.); [email protected] (J.R.K.C.); Tel.: +82-31-2012025 (J.-S.J.); +66-44-224304 (J.R.K.C.); Fax: +66-44-224185 (J.R.K.C.) Abstract: Conjugation of phytohormones with glucose is a means of modulating their activities, which can be rapidly reversed by the action of β-glucosidases. Evaluation of previously characterized recombinant rice β-glucosidases found that nearly all could hydrolyze abscisic acid glucose ester (ABA-GE). Os4BGlu12 and Os4BGlu13, which are known to act on other phytohormones, had the highest activity. We expressed Os4BGlu12, Os4BGlu13 and other members of a highly similar rice chromosome 4 gene cluster (Os4BGlu9, Os4BGlu10 and Os4BGlu11) in transgenic Arabidopsis. Extracts of transgenic lines expressing each of the five genes had higher β-glucosidase activities on ABA-GE and gibberellin A4 glucose ester (GA4-GE). The β-glucosidase expression lines exhibited longer root and shoot lengths than control plants in response to salt and drought stress. -
Evolution of Biomolecular Structure Class II Trna-Synthetases and Trna
University of Illinois at Urbana-Champaign Luthey-Schulten Group Theoretical and Computational Biophysics Group Evolution of Biomolecular Structure Class II tRNA-Synthetases and tRNA MultiSeq Developers: Prof. Zan Luthey-Schulten Elijah Roberts Patrick O’Donoghue John Eargle Anurag Sethi Dan Wright Brijeet Dhaliwal March 2006. A current version of this tutorial is available at http://www.ks.uiuc.edu/Training/Tutorials/ CONTENTS 2 Contents 1 Introduction 4 1.1 The MultiSeq Bioinformatic Analysis Environment . 4 1.2 Aminoacyl-tRNA Synthetases: Role in translation . 4 1.3 Getting Started . 7 1.3.1 Requirements . 7 1.3.2 Copying the tutorial files . 7 1.3.3 Configuring MultiSeq . 8 1.3.4 Configuring BLAST for MultiSeq . 10 1.4 The Aspartyl-tRNA Synthetase/tRNA Complex . 12 1.4.1 Loading the structure into MultiSeq . 12 1.4.2 Selecting and highlighting residues . 13 1.4.3 Domain organization of the synthetase . 14 1.4.4 Nearest neighbor contacts . 14 2 Evolutionary Analysis of AARS Structures 17 2.1 Loading Molecules . 17 2.2 Multiple Structure Alignments . 18 2.3 Structural Conservation Measure: Qres . 19 2.4 Structure Based Phylogenetic Analysis . 21 2.4.1 Limitations of sequence data . 21 2.4.2 Structural metrics look further back in time . 23 3 Complete Evolutionary Profile of AspRS 26 3.1 BLASTing Sequence Databases . 26 3.1.1 Importing the archaeal sequences . 26 3.1.2 Now the other domains of life . 27 3.2 Organizing Your Data . 28 3.3 Finding a Structural Domain in a Sequence . 29 3.4 Aligning to a Structural Profile using ClustalW . -
The Effect of Dicer Knockout on RNA Interference Using Various Dicer Substrate Interfering RNA Structures
bioRxiv preprint doi: https://doi.org/10.1101/2020.04.19.049817; this version posted April 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. The effect of Dicer knockout on RNA interference using various Dicer substrate interfering RNA structures Min-Sun Song1 and John J Rossi1, 2 * 1Department of Molecular and Cellular Biology, Beckman Research Institute of City of Hope, City of Hope, Duarte, CA 91010, USA 2Irell and Manella Graduate School of Biological Sciences, Beckman Research Institute of City of Hope, City of Hope, Duarte, CA 91010, USA *Corresponding author: John J. Rossi, Email address: [email protected]. Running title: Dicer regulation of DsiRNAs Keywords: Dicer, tetra-loop, Dicer-substrate siRNA(DsiRNA), RNA interference(RNAi), RNA biogenesis, Dicer knockout 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.04.19.049817; this version posted April 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Abstract Dicer-substrate siRNA (DsiRNA) was a useful tool for sequence-specific gene silencing. DsiRNA was proposed to have increased efficacy via RNAi gene silencing, but the molecular mechanism underlying the increased efficacy is not precise. We designed the tetra-looped DsiRNA as the tetra-looped RNAs have been reported more stable structure and increased binding efficiency with RNA and protein. -
Languages Generated by Context-Free and Type AB → BA Rules
Magyar Kutatók 8. Nemzetközi Szimpóziuma 8th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics Languages Generated by Context-Free and Type AB → BA Rules Benedek Nagy Faculty of Informatics, University of Debrecen, H-4010 PO Box 12, Hungary, Research Group on Mathematical Linguistics, Rovira i Virgili University, Tarragona, Spain [email protected] Abstract: Derivations using branch-interchanging and language family obtained by context-free and interchange (AB → BA) rules are analysed. This language family is between the context-free and context-sensitive families helping to fill the gap between them. Closure properties are analysed. Only semi-linear languages can be generated in this way. Keywords: formal languages, Chomsky hierarchy, derivation trees, interchange (permutation) rule, semi-linear languages, mildly context-sensitivity 1 Introduction The Chomsky type grammars and the generated language families are one of the most basic and most important fields of theoretical computer science. The field is fairly old, the basic concepts and results are from the middle of the last century (see, for instance, [2, 6, 7]). The context-free grammars (and languages) are widely used due to their generating power and simple way of derivation. The derivation trees represent the context-free derivations. There is a big gap between the efficiency of context-free and context-sensitive grammars. There are very ‘simple’ non-context-free languages, for instance {an^2 |n ∈ N}, {anbncn|n ∈ N}, etc. It was known in the early 70’s that every context-sensitive language can be generated by rules only of the following types AB → AC, AB → BA, A → BC, A → B and A → a (where A,B,C are nonterminals and a is a terminal symbol). -
Protein Structure Prediction and Design in a Biologically-Realistic Implicit Membrane
bioRxiv preprint doi: https://doi.org/10.1101/630715; this version posted May 8, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Protein structure prediction and design in a biologically-realistic implicit membrane Rebecca F. Alforda, Patrick J. Flemingb,c, Karen G. Flemingb,c, and Jeffrey J. Graya,c aDepartment of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218; bT.C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, MD 21218; cProgram in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218 This manuscript was compiled on May 8, 2019 ABSTRACT. Protein design is a powerful tool for elucidating mecha- MOS (16), and the protein-lipid interactions are scored with nisms of function and engineering new therapeutics and nanotech- a molecular mechanics energy function. All-atom models are nologies. While soluble protein design has advanced, membrane attractive because they can feature hundreds of lipid types to- protein design remains challenging due to difficulties in modeling ward approximating the composition of biological membranes the lipid bilayer. In this work, we developed an implicit approach (17). With current technology, detailed all-atom models can be that captures the anisotropic structure, shape of water-filled pores, used to explore membrane dynamics for hundreds of nanosec- and nanoscale dimensions of membranes with different lipid compo- onds (18): the time scale required to achieve equilibrated sitions. The model improves performance in computational bench- properties on a bilayer with approximately 250 lipids (19). marks against experimental targets including prediction of protein Coarse-grained representations such as MARTINI (20), ELBA orientations in the bilayer, ∆∆G calculations, native structure dis- (21), and SIRAH (22) reduce computation time by mapping crimination, and native sequence recovery. -
Topic 5: the Folding of Biopolymers – RNA and Protein Overview
Topic 5: the folding of biopolymers – RNA and Protein Overview: The main functional biomolecules in cells are polymers – DNA, RNA and proteins For RNA and Proteins, the specific sequence of the polymer dictates its final structure Can we predict the final structure of RNA or protein given just the sequence information? Can we design any biomolecular structure that we want? The world of RNA RNA is a linear polymer built from 4 possible monomers: A, G, U, C These monomers can form complimentary interactions to form base-pairings: A with U and C with G Most often RNA is found as a single-stranded polymer that via base-pairing with complementary regions within its own sequence, is able to fold in on itself RNA has a wide variety of functions that we will now explore RNA function in cell mRNA = messenger RNA RNA’s main function in the cell is to act as a messenger molecule in the process of making a protein DNA (gene) mRNA Protein tRNA = transfer RNA these are used in translation to recognize the 64 codons. There is one tRNA for each codon, each representing one of the 20 amino acids RNA function in cell rRNA = ribosomal RNA these are RNAs that get incorporated into the ribosome to give it part of its function. microRNA or siRNA these are relatively recent discovered form of RNA that is used to regulate gene expression – so called RNA interference (won Nobel prize) destroys target mRNA many developmental genes are regulated by miRNAs in your genome RNA function in cell Riboswitches – many mRNA molecules can detect metabolites by binding them and changing the structure of the mRNA = regulation RNA function in cell Ribozymes – like enzymes (catalytic proteins) except made from RNA. -
Biomolecular Modeling and Simulation: a Prospering Multidisciplinary Field
Annual Review of Biophysics Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field Tamar Schlick,1,2,3 Stephanie Portillo-Ledesma,1 Christopher G. Myers,1 Lauren Beljak,4 Justin Chen,4 Sami Dakhel,4 Daniel Darling,4 Sayak Ghosh,4 Joseph Hall,4 Mikaeel Jan,4 Emily Liang,4 Sera Saju,4 Mackenzie Vohr,4 Chris Wu,4 Yifan Xu,4 and Eva Xue4 1Department of Chemistry, New York University, New York, New York 10003, USA; email: [email protected] 2Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA 3New York University–East China Normal University Center for Computational Chemistry, New York University Shanghai, Shanghai 200122, China 4College of Arts and Science, New York University, New York, New York 10003, USA Annu. Rev. Biophys. 2021. 50:267–301 Keywords First published as a Review in Advance on biomolecular modeling, biomolecular simulation, biomolecular dynamics, February 19, 2021 structure prediction, protein folding, DNA folding, RNA folding, The Annual Review of Biophysics is online at multiscale modeling, citizen science projects, machine learning, artificial biophys.annualreviews.org intelligence https://doi.org/10.1146/annurev-biophys-091720- 102019 Abstract Copyright © 2021 by Annual Reviews. We reassess progress in the field of biomolecular modeling and simulation, Annu. Rev. Biophys. 2021.50:267-301. Downloaded from www.annualreviews.org All rights reserved following up on our perspective published in 2011. By reviewing metrics Access provided by New York University - Bobst Library on 05/12/21. For personal use only. for the field’s productivity and providing examples of success, we under- score the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated.