DOCSLIB.ORG

Protein Secondary Structure Prediction by Fuzzy Min Max Neural Network with Compensatory Neurons

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Protein Secondary Structure Prediction by Fuzzy Min Max Neural Network with Compensatory Neurons Protein Secondary Structure Prediction by Fuzzy Min Max Neural Network with Compensatory Neurons Sudipta Saha Protein Secondary Structure Prediction by Fuzzy Min Max Neural Network with Compensatory Neurons Thesis submitted in partial fulfillment of the requirements for the degree Of Master of Technology In Computer Science & Engineering By Sudipta Saha (Roll No. 06CS6023) Under the supervision of Prof. Jayanta Mukhopadhyay Department of Computer Science & Engineering Indian Institute of Technology, Kharagpur-721302 West Bengal, India May, 2008. Department Of Computer Science & Engineering Indian Institute of Technology Kharagpur-721302, India Certificate This is to certify that the thesis titled “Protein Secondary Structure Prediction by Fuzzy Min-Max Neural Network with Compensatory Neurons”, submitted by Sudipta Saha, to the Department of Computer Science and Engineering, in partial fulfillment for the award of the degree of Master of Technology is a bona fide record of work carried out by him under my supervision and guidance. The thesis has fulfilled all the requirements as per the regulations of the institute and, in my opinion, has reached the standard needed for submission. Prof. Jayanta Mukhopadhyay Dept. of Computer Science and Engineering Indian Institute of Technology Kharagpur -721302, India Dedicated To My parents and wife Acknowledgement I take this opportunity to express my deep sense of gratitude to my guide Dr. Jayanta Mukhopadhyay for his guidance, support and inspiration throughout the duration of the work. I would like to specially acknowledge the help and encouragement I received from Dr. P. K. Biswas of Department of Electronics and Electrical Communication Engineering and Dr. A. K. Majumdar of Department of Computer Science & Engineering, IIT Kharagpur. In addition, I am thankful to all the faculty members, staffs and research scholars of the Department of Computer Science and Engineering and my friends for providing me adequate help whenever required. I am also grateful to my parents for their constant encouragement and financial support in my years of studies. Lastly I would like to thank my wife Sipra Saha, for all the love. Sudipta Saha Dept. of Computer Science and Engineering Indian Institute of Technology Kharagpur -721302, India Date: May 05, 2008 i Abstract Neural Networks are extensively being used now-a-days for predicting the three dimensional structures of the proteins. Different types of neural networks are employed for this work still now. There are several levels of three dimensional structures of the proteins. In our work a special kind of neural network has been employed for predicting the secondary structure of the proteins from the primary structure. This neural network combines the neural network concept with the fuzzy logic. The method of prediction also uses the algorithm described by Chou– Fasman [4] to break the ties between different classes of predictions. The basic algorithm used here for the fuzzy min-max neural network has been taken from [3]. Some small drawbacks of the training algorithm has been identified and removed as a part of our work. The prediction is tried with the improved neural network. Apart from these, some domain knowledge relating to the nature of the protein secondary structures are also used to post–process the prediction output of the basic neural network to get improved prediction accuracy. So far more than 25000 of proteins have been sequenced and the three dimensional structures of these proteins are also determined. In our work it has been tried to extract as much information as possible from the already sequenced proteins. To achieve this, we have employed multiple instances of the neural network and trained them with different set of data. The protein data bank is used as a primary resource for the protein data for both training and testing our prediction system. The overall accuracy (Q3) achieved is around 70%. It is better than existing statistics based prediction systems like Chou-Fasman [1], GOR I [2] and it is comparable to some of the neural network based systems. ii Contents List of Figures v List of Tables vi 1. Introduction ............................................................................... 1 1.1 Motivation of the Work ..................................................................... 1 1.2 Objective of the Work ....................................................................... 2 1.3 Organization of the Thesis ................................................................ 3 2. Protein Structure ....................................................................... 5 2.1 Introduction ..................................................................................... 5 2.2 Molecular Structure of Protein .......................................................... 6 2.3 3D Structures of Protein .................................................................. 7 2.4 Ramachandran Diagram ................................................................ 10 2.5 Different Secondary Structures of Protein ....................................... 12 2.6 Levinthal’s Paradox ........................................................................ 14 2.7 Summary ....................................................................................... 15 3. Literature Survey .................................................................... 16 3.1 Introduction ................................................................................... 16 3.2 Chou-Fasman Method .................................................................... 17 3.3 GOR Method .................................................................................. 19 3.4 PhD Method ................................................................................... 20 3.5 PSI-Pred Method ............................................................................ 21 3.6 JPred Method ................................................................................. 22 3.7 Summary ....................................................................................... 23 iii Contents 4. Neural Network Architecture ................................................... 24 4.1 Introduction ................................................................................... 24 4.2 FMNN ............................................................................................ 25 4.3 FMCN ............................................................................................ 31 4.4 Improvements on FMCN ................................................................. 40 4.5 Summary ....................................................................................... 48 5. Secondary Structure Prediction with Improved FMCN ............. 49 5.1 Introduction ................................................................................... 49 5.2 Application of FMCN ...................................................................... 50 5.3 Accuracy Measurement Techniques ................................................ 59 5.4 Complexity in Using FMCN ............................................................. 62 5.5 Experimental Results ..................................................................... 63 5.6 Summary ....................................................................................... 68 6. Multiple Instantiations of FMCN-units ..................................... 69 6.1 Introduction ................................................................................... 69 6.2 System Architecture ....................................................................... 70 6.3 Experimental Results ..................................................................... 78 6.4 Summary ....................................................................................... 85 7. Conclusion and Future Works .................................................. 86 7.1 Conclusion ..................................................................................... 86 7.2 Future Work .................................................................................. 88 Appendix A ....................................................................................... 90 Appendix B ....................................................................................... 91 Appendix C ....................................................................................... 94 Appendix D .................................................................................... 110 Bibliography ................................................................................... 114 iv List of Figures Fig 2.1 ....................................................................................................... 6 Fig 2.2 ....................................................................................................... 8 Fig 2.3 ....................................................................................................... 9 Fig 2.4 ....................................................................................................... 9 Fig 2.5 ..................................................................................................... 10 Fig 2.6 ..................................................................................................... 11 Fig 2.7 ..................................................................................................... 12 Fig 2.8 ....................................................................................................
Load more

Recommended publications
  • Biointerface Research in Applied Chemistry
    Volume 9, Issue 1, 2019, 3817 - 3824 ISSN 2069-5837 Biointerface Research in Applied Chemistry www.BiointerfaceResearch.com https://doi.org/10.33263/BRIAC91.817824 Original Review Article Open Access Journal Received: 06.01.2019 / Revised: 29.01.2019 / Accepted: 10.02.2019 / Published on-line: 15.02.2019 Protein structure from the essential amino acids to the 3D structure Diaa Atta 1,*, Abd Elaziz Mahmoud 1, Ahmed Fakhry 1 1Spectroscopy Department, National Research Centre, Giza, Egypt. *corresponding author e-mail address:[email protected] ABSTRACT Protein structure is a hot topic, not only to the specialist, but with others like the physicists. So this review is targeting those who are not biologists and have to deal with the protein in their research. In this review we travel with the protein structures from the amino acids and its classifications, and how the polypeptide chain is formed from these building blocks up to the final 3D structure. We introduced the secondary structure species like helices with its different types and how it is formed; also the beta sheet formation and types are explained briefly. Finally the tertiary and quaternary structures are presented. The approaches of molecular modeling as well as other important computational methods present significant contribution to studying proteins. Keywords: Protein structure- Amino acid-Primary structure- Secondary structure- tertiary structure-quaternary structure. 1. INTRODUCTION One from the most important and used class of molecules is In the protein structure there is very strange information that any the proteins class. It is engineered to incorporate any molecule that protein is constructed from only 20 different amino acids (the one can imagine, and convert it from the ion structure into a large standard amino acids) [8].
    [Show full text]
  • MYSTERIES of the EQUILATERAL TRIANGLE, First Published 2010
    MYSTERIES OF THE EQUILATERAL TRIANGLE Brian J. McCartin Applied Mathematics Kettering University HIKARI LT D HIKARI LTD Hikari Ltd is a publisher of international scientific journals and books. www.m-hikari.com Brian J. McCartin, MYSTERIES OF THE EQUILATERAL TRIANGLE, First published 2010. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission of the publisher Hikari Ltd. ISBN 978-954-91999-5-6 Copyright c 2010 by Brian J. McCartin Typeset using LATEX. Mathematics Subject Classification: 00A08, 00A09, 00A69, 01A05, 01A70, 51M04, 97U40 Keywords: equilateral triangle, history of mathematics, mathematical bi- ography, recreational mathematics, mathematics competitions, applied math- ematics Published by Hikari Ltd Dedicated to our beloved Beta Katzenteufel for completing our equilateral triangle. Euclid and the Equilateral Triangle (Elements: Book I, Proposition 1) Preface v PREFACE Welcome to Mysteries of the Equilateral Triangle (MOTET), my collection of equilateral triangular arcana. While at first sight this might seem an id- iosyncratic choice of subject matter for such a detailed and elaborate study, a moment’s reflection reveals the worthiness of its selection. Human beings, “being as they be”, tend to take for granted some of their greatest discoveries (witness the wheel, fire, language, music,...). In Mathe- matics, the once flourishing topic of Triangle Geometry has turned fallow and fallen out of vogue (although Phil Davis offers us hope that it may be resusci- tated by The Computer [70]). A regrettable casualty of this general decline in prominence has been the Equilateral Triangle. Yet, the facts remain that Mathematics resides at the very core of human civilization, Geometry lies at the structural heart of Mathematics and the Equilateral Triangle provides one of the marble pillars of Geometry.
    [Show full text]
  • Tertiary Alphabet for the Observable Protein Structural Universe”
    Supporting Information for Mackenzie et al., “Tertiary Alphabet for the Observable Protein Structural Universe” SI Methods strongly correlate, resulting in shallow angles and large positive average cosines. Fig. S1 shows this general behavior, except that it also exhibits some oscillations RMSD Cutoff Derivation before it converges to zero. These oscillations have to do with a characteristic length of secondary structural elements (SSEs) separated by turns and loops, such Fundamentally, optimal rigid-body superposition is an instance of linear that within 15-30 residues a chain is expected to dramatically turn, often in regression, whose goal is to minimize the sum squared deviation (SSD) between almost the opposite direction. In fact, the overall curve fits very closely to a corresponding coordinates of two sets of atoms. Regression quality is typically product of exponential decay (due to the chain “forgetting” its direction) and a assessed using the mean squared error (MSE), an estimate of the error variance, sinusoid (from SSE-related oscillations), as shown in Fig. S1. Specifically, the computed as SSD �, where � is the number of degrees of freedom. The RMSD equation we used was exp − � � ∙ cos � ∙ 2� � , where � is the separation (in metric used in structural superposition is similar to the square root of MSE, but residues), � is the characteristic persistence length, and � is the period of uses the number of atoms in place of �. This is statistically justified only when oscillations. Optimally fitting parameters were � ≈ 33 and � ≈ 10 residues. atoms are entirely independent of each other, which is clearly not the case in Based on this, we chose to use a correlation length parameter for our RMSD general.
    [Show full text]
  • Functional Evaluation of the Π-Helix in the NAD(P)
    Article Cite This: Biochemistry 2018, 57, 4469−4477 pubs.acs.org/biochemistry Functional Evaluation of the π‑Helix in the NAD(P)H:FMN Reductase of the Alkanesulfonate Monooxygenase System Jonathan M. Musila,† Dianna L. Forbes, and Holly R. Ellis* Department of Chemistry and Biochemistry, Auburn University, Auburn, Alabama 36849, United States *S Supporting Information ABSTRACT: A subgroup of enzymes in the NAD(P)H:FMN reduc- tase family is comprised of flavin reductases from two-component monooxygenase systems. The diverging structural feature in these FMN reductases is a π-helix centrally located at the tetramer interface that is generated by the insertion of an amino acid in a conserved α4 helix. The Tyr insertional residue of SsuE makes specific contacts across the dimer interface that may assist in the altered mechanistic properties of this enzyme. The Y118F SsuE variant maintained the π−π stacking interactions at the tetramer interface and had kinetic parameters similar to those of wild-type SsuE. Substitution of the π-helical residue (Tyr118) to Ala or Ser transformed the enzymes into flavin-bound SsuE variants that could no longer support flavin reductase and desulfonation activities. These variants existed as dimers and could form protein−protein interactions with SsuD even though flavin transfer was not sustained. The ΔY118 SsuE variant was flavin-free as purified and did not undergo the tetramer to dimer oligomeric shift with the addition of flavin. The absence of desulfonation activity can be attributed to the inability of ΔY118 SsuE to promote flavin transfer and undergo the requisite oligomeric changes to support desulfonation.
    [Show full text]
  • Toward a Database of Geometric Interrelationships of Protein Secondary Structure Elements for De Novo Protein Design, Prediction and Analysis
    University of New Orleans ScholarWorks@UNO University of New Orleans Theses and Dissertations Dissertations and Theses 12-17-2010 Toward a Database of Geometric Interrelationships of Protein Secondary Structure Elements for De Novo Protein Design, Prediction and Analysis Augustine Ada Orgah University of New Orleans Follow this and additional works at: https://scholarworks.uno.edu/td Recommended Citation Orgah, Augustine Ada, "Toward a Database of Geometric Interrelationships of Protein Secondary Structure Elements for De Novo Protein Design, Prediction and Analysis" (2010). University of New Orleans Theses and Dissertations. 100. https://scholarworks.uno.edu/td/100 This Thesis-Restricted is protected by copyright and/or related rights. It has been brought to you by ScholarWorks@UNO with permission from the rights-holder(s). You are free to use this Thesis-Restricted in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/or on the work itself. This Thesis-Restricted has been accepted for inclusion in University of New Orleans Theses and Dissertations by an authorized administrator of ScholarWorks@UNO. For more information, please contact [email protected]. Toward a Database of Geometric Interrelationships of Protein Secondary Structure Elements for De Novo Protein Design, Prediction and Analysis A Thesis Submitted to the Graduate Faculty of the University of New Orleans in partial fulfillment of the Requirements for the degree of Master of Science in Computer Science Bioinformatics by Augustine Ada Orgah B.S.
    [Show full text]
  • From Sequence to Structure
    1 From Sequence to Structure The genomics revolution is providing gene sequences in exponentially increasing numbers. Converting this sequence information into functional information for the gene products coded by these sequences is the challenge for post-genomic biology. The first step in this process will often be the interpretation of a protein sequence in terms of the three- dimensional structure into which it folds. This chapter summarizes the basic concepts that underlie the relationship between sequence and structure and provides an overview of the architecture of proteins. 1-0 Overview: Protein Function and Architecture 1-1 Amino Acids 1-2 Genes and Proteins 1-3 The Peptide Bond 1-4 Bonds that Stabilize Folded Proteins 1-5 Importance and Determinants of Secondary Structure 1-6 Properties of the Alpha Helix 1-7 Properties of the Beta Sheet 1-8 Prediction of Secondary Structure 1-9 Folding 1-10 Tertiary Structure 1-11 Membrane Protein Structure 1-12 Protein Stability: Weak Interactions and Flexibility 1-13 Protein Stability: Post-Translational Modifications 1-14 The Protein Domain 1-15 The Universe of Protein Structures 1-16 Protein Motifs 1-17 Alpha Domains and Beta Domains 1-18 Alpha/Beta, Alpha+Beta and Cross-Linked Domains 1-19 Quaternary Structure: General Principles 1-20 Quaternary Structure: Intermolecular Interfaces 1-21 Quaternary Structure: Geometry 1-22 Protein Flexibility 1-0 Overview: Protein Function and Architecture Binding TATA binding protein Myoglobin Specific recognition of other molecules is central to protein function. The molecule that is bound (the ligand) can be as small as the oxygen molecule that coordinates to the heme group of myoglobin, or as large as the specific DNA sequence (called the TATA box) that is bound—and distorted—by the TATA binding protein.
    [Show full text]
  • Protocol Optimization of Molecular Dynamics Simulations of Apolipoproteins
    BUILDING BETTER MODELS: PROTOCOL OPTIMIZATION OF MOLECULAR DYNAMICS SIMULATIONS OF APOLIPOPROTEINS A THESIS Presented to the University Honors Program California State University, Long Beach In Partial Fulfillment of the Requirements for the University Honors Program Certificate Yessica K. Gomez Fall 2016 ABSTRACT BUILDING BETTER MODELS: PROTOCOL OPTIMIZATION OF MOLECULAR DYNAMICS SIMULATIONS OF APOLIPOPROTEINS By Yessica K. Gomez December 2016 Apolipoproteins are biologically-ubiquitous lipid transport proteins that have been implicated in the onset of Alzheimer’s disease and serve as a target for therapies focused on drug delivery to the brain. Their distinct topology and electrostatic makeup have contributed to the lack of computational chemistry studies on them. In this study, we report a many-microsecond data set comprising a variety of molecular dynamics simulations meant to probe the effects of varying force field, long-range electrostatics, and cut-off distances on three different apolipoprotein structures. The results display a clear consensus on the ideal settings to recreate biological conditions, with all three structures exhibiting the best behavior using the AMBER-94 force field with particle mesh Ewald (PME) and mid-range cut-off distances. These results are in line with previous force field studies and will serve as an initiation point for future high-temperature unfolding studies that will provide new details regarding the mechanics of the folding pathway. ACKNOWLEDGEMENTS My initial thanks goes to Dr. Sorin, without whose mentoring and resources this project would not have been possible. Thank you as well to all the members of the Sorin Lab I have worked with over the past three years – particularly Dakota and Xavier – and all others who collaborated with me on this project.
    [Show full text]
  • Exploring the Functional Roles of Π-Helices and Flavin-Induced Oligomeric State Changes in Flavin Reductases of Two-Component Monooxygenase Systems
    Exploring the Functional Roles of π-helices and Flavin-Induced Oligomeric State Changes in Flavin Reductases of Two-Component Monooxygenase Systems by Jonathan Makau Musila A dissertation submitted to the Graduate Faculty of Auburn University in partial fulfillment of the requirements for the Degree of Doctor of Philosophy Auburn, Alabama May 6, 2017 Keywords: Oligomerization, kinetics, desulfonation, π-helix, dis(asso)ciation Copyright 2017 by Jonathan Makau Musila Approved by Holly R. Ellis, Chair, Professor of Chemistry and Biochemistry Douglas C. Goodwin, Associate Professor of Chemistry and Biochemistry Evert C. Duin, Associate Professor of Chemistry and Biochemistry Steven Mansoorabadi, Assistant Professor of Chemistry and Biochemistry Abstract Sulfur-containing biomolecules participate in various chemical and structural functions in enzymes. When sulfate is limiting, bacteria upregulate ssi enzymes to utilize organosulfonates as an alternative source. The alkanesulfonate monooxygenase enzymes mitigate sulfur scarcity through desulfonation of various alkanesulfonates releasing sulfite which is incorporated into sulfur-containing biomolecules. This two-component monooxygenase system utilizes flavin as a substrate with the SsuE enzyme supplying reduced flavin to SsuD. It is unclear what structural properties of flavin reductases of two-component systems dictate catalysis. The SsuE enzyme undergoes a tetramer to dimer oligomeric switch in the presence of FMN. Oligomeric state changes are common in flavin reductases but the roles and regulation of the quaternary structural changes have not been evaluated. Intriguingly, the flavin reductases of two-component systems contain π- helices located at the tetramer interface. π-Helices are generated by a single amino acid insertion in an established α-helix to confer an evolutionary advantage.
    [Show full text]
  • PYTHIA: Deep Learning Approach for Local Protein Conformation Prediction
    International Journal of Molecular Sciences Article PYTHIA: Deep Learning Approach for Local Protein Conformation Prediction Gabriel Cretin 1,2 , Tatiana Galochkina 1,2 , Alexandre G. de Brevern 1,2 and Jean-Christophe Gelly 1,2,* 1 Biologie Intégrée du Globule Rouge, Université de Paris, UMR_S1134, BIGR, INSERM, 75015 Paris, France; [email protected] (G.C.); [email protected] (T.G.); [email protected] (A.G.d.B.) 2 Laboratoire d’Excellence GR-Ex, 75015 Paris, France * Correspondence: [email protected] Abstract: Protein Blocks (PBs) are a widely used structural alphabet describing local protein back- bone conformation in terms of 16 possible conformational states, adopted by five consecutive amino acids. The representation of complex protein 3D structures as 1D PB sequences was previously successfully applied to protein structure alignment and protein structure prediction. In the current study, we present a new model, PYTHIA (predicting any conformation at high accuracy), for the prediction of the protein local conformations in terms of PBs directly from the amino acid sequence. PYTHIA is based on a deep residual inception-inside-inception neural network with convolutional block attention modules, predicting 1 of 16 PB classes from evolutionary information combined to physicochemical properties of individual amino acids. PYTHIA clearly outperforms the LOCUS- TRA reference method for all PB classes and demonstrates great performance for PB prediction on particularly challenging proteins from the CASP14 free modelling category. Keywords: protein structure; protein blocks; prediction; deep learning Citation: Cretin, G.; Galochkina, T.; de Brevern, A.G.; Gelly, J.-C. PYTHIA: Deep Learning Approach for Local 1.
    [Show full text]
  • Structural and Functional Annotation of Uncharacterized Protein NCGM946K2 146 of Mycobacterium Tuberculosis: an In-Silico Approach †
    Proceedings Structural and Functional Annotation of Uncharacterized Protein NCGM946K2_146 of Mycobacterium Tuberculosis: An In-Silico Approach † Abu Saim Mohammad Saikat 1, Rabiul Islam 2,*, Shahriar Mahmud 3, Md. Abu Sayeed Imran 3, Mohammad Shah Alam 4, Mahmudul Hasan Masud 4 and Md. Ekhlas Uddin 5 1 Department of Biochemistry and Molecular Biology, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh; [email protected] 2 Divisional DNA Screening Laboratory, Faridpur Medical College Hospital, Ministry of Women & Children Affairs, Dhaka 7800, Bangladesh 3 Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia 7003, Bangladesh; [email protected] (S.M.); [email protected] (M.A.S.I.) 4 Department of Microbiology, Gono Bishwabidalay, Savar, Dhaka 1344, Bangladesh; [email protected] (M.S.A.); [email protected] (M.H.M.) 5 Department of Biochemistry and Molecular Biology, Gono Bishwabidalay, Savar, Dhaka 1344, Bangladesh; [email protected] * Correspondence: [email protected] † Presented at the 1st International Electronic Conference on Microbiology, 2–30 November 2020; Available online: https://ecm2020.sciforum.net/. Published: 30 December 2020 Abstract: The human pathogen Mycobacterium tuberculosis (MTB) is indeed one of the renowned, important, longtime infectious diseases, tuberculosis (TB). Interestingly, MTB infection has become one of the world’s leading causes of human death. In trehalose synthase, the protein NCGM 946K2 146 found in MTB has an important role. For carbohydrate transport and metabolism, trehalose synthase is required. The protein is not clarified yet, though. In this research, an in silico approach was, therefore, formulated for functional and structural documentation of the uncharacterized protein NCGM946K2_146.Three distinct servers, including Modeller, Phyre2, and Swiss Model, were used to evaluate the predicted tertiary structure.
    [Show full text]
  • Protein Folding Prediction with Genetic Algorithms
    Protein Folding Prediction with Genetic Algorithms A Thesis Submitted to the Faculty of Department of Computer Science and Engineering National Sun Yat-sen University by Yi-Yao Huang In Partial Fulfillment of the Requirements for the Degree of Master of Science July 9, 2004 TABLE OF CONTENTS Page LIST OF FIGURES ................................. 4 LIST OF TABLES .................................. 5 ABSTRACT ..................................... 0 Chapter 1. Introduction .............................. 1 Chapter 2. Preliminaries .............................. 3 2.1 Amino Acids in Proteins . .................... 3 2.2 Levels of Protein Structures . .................... 3 2.3 The Hydrophobic-hydrophilic Model .................... 7 2.4 Genetic Algorithm . ........................... 8 Chapter 3. Protein Structure Prediction Methods ................ 11 3.1 Strategies of Protein Structure Prediction . ............. 11 3.2 Representations of Protein Sequences on the Lattice Model........ 13 3.3 Previous PSP Methods for the HP Model . ............. 15 Chapter 4. A New Method Based on the Lattice Model ............. 17 4.1 Motivation .................................. 17 4.2 Overall Steps of the Algorithm . .................... 17 4.3 The Fitness Function . ........................... 18 4.4 Example . .................................. 22 Chapter 5. Experimental Results ......................... 27 Page Chapter 6. Conclusions ............................... 31 BIBLIOGRAPHY .................................. 33 LIST OF FIGURES Figure Page 2.1 Levels
    [Show full text]
  • Local Protein Structures Bernard Offmann, Manoj Tyagi, Alexandre De Brevern
    Local Protein Structures Bernard Offmann, Manoj Tyagi, Alexandre de Brevern To cite this version: Bernard Offmann, Manoj Tyagi, Alexandre de Brevern. Local Protein Structures. Current Bioinfor- matics, Benthams Science, 2007, 2, pp.165-202. inserm-00175058 HAL Id: inserm-00175058 https://www.hal.inserm.fr/inserm-00175058 Submitted on 11 May 2010 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. HAL authorLocal manuscript Protein Structures (Offmann, Tyagi & de Brevern) Current Bioinformatics 2007;2:165-202 Preprint for Current Bioinformatics 2007 HAL author manuscript inserm-00175058, version 1 Local Protein Structures Offmann B. 1, Tyagi M. 1+ & de Brevern A.G. 2* 1 Laboratoire de Biochimie et Génétique Moléculaire, Université de La Réunion, 15, avenue René Cassin, BP7151, 97715 Saint Denis Messag Cedex 09, La Réunion, France 2 Equipe de Bioinformatique Génomique et Moléculaire (EBGM), INSERM UMR-S 726, Université Paris Diderot, case 7113, 2, place Jussieu, 75251 Paris, France * Corresponding author: mailing address: Dr. de Brevern A.G., Equipe de Bioinformatique Génomique et Moléculaire (EBGM), INSERM UMR-S 726, Université Paris Diderot, case 7113, 2, place Jussieu, 75251 Paris, France E-mail : [email protected] Tel: (33) 1 44 27 77 31 Fax: (33) 1 43 26 38 30 key words: secondary structure, protein folds, structure-sequence relationship, structural alphabet, protein blocks, molecular modeling, ab initio .
    [Show full text]