Protein Structure Prediction
|
|
- Stella Beasley
- 5 years ago
- Views:
Transcription
1 Protein Structure Prediction Michael Feig MMTSB/CTBP 2006 Summer Workshop
2 From Sequence to Structure SEALGDTIVKNA
3 Ab initio Structure Prediction Protocol Amino Acid Sequence Conformational Sampling to generate native-like structures Scoring & Clustering to identify most native-like structures 3D Structure
4 Folding with All-Atom Models AAQAAAAQAAAAQAA CHARMM force field Implicit solvent replica exchange simulations 8 replicas, 10 ns/replica
5 Folding with Low-Resolution Model EQQNAFYEILHLPNLNEEQRNGFIQSLKDDPSQSANLLAEAKKLNDAQA SICHO model, MONSSTER simulated annealing run
6 SICHO Lattice Model Monte Carlo simulations: Thr Leu > Attempt move > Compute ΔE Phe > Accept with probability p: Asp %1 "E # 0 p =& 'exp($"e / k BT ) "E > 0 Simulated annealing! MMTSB/CTBP Summer Workshop Constant Temperature Replica Exchange Sampling Kolinski & Skolnick: Proteins 32, 475 (1998) Michael Feig, 2006.
7 Excluded volume SICHO Energy Function Knowledge-Based Terms Side chain burial propensity follows Kyte-Doolittle scale Centrosymmetric bias r g = 2.2 N 0.38 res Ala Arg Asp Ile
8 SICHO Energy Function Statistical Terms Potential of mean force (PMF): p " #E ij i kbt = e p j energy "E = #kt ln(p) extended GLU-GLU r(i,i+4) in Å helix
9 Conformational Sampling with SICHO All-Atom Energy in kcal/mol Protein A RMSD from native in Å MMTSB/CTBP Summer Workshop Michael Feig, 2006.
10 Ab initio Structure Prediction Protocol Amino Acid Sequence Secondary Structure Prediction PSIPRED et al. Efficient Sampling e.g. MONSSTER/SICHO All-Atom Reconstruction Scoring & Clustering e.g. MMGB/SA, DFIRE 3D Structure
11 Secondary Structure Prediction GDPIVKNAKLDSRLANKEALRLL?
12 Secondary Structure Propensities α-helix β-sheet turn Chou & Fasman (1974)
13 Secondary Structure Prediction Methods SABLE PSIPRED PSSP SAM-T99-sec PHD C+F 77.6% 76.2% 75.1% 76.1% 72.3% 50-60% C+F: Chou & Fasman GOR: Garnier, Osguthorpe, Robson
14 Secondary structure prediction Per-Residue Accuracy
15 Realistic ab initio Structure Prediction CHARMM22/GBMV C! RMSD in Å (62 residues)
16 Sampling, Scoring, Clustering CHARMM22/GBMV C! RMSD in Å (62 residues)
17 Ab initio Predictions DNase fragmentation factor NMR structure 1KOY Best-scoring prediction 7.4 Å RMSD
18 Scoring Functions Knowledge-based/statistical derived from known protein structures limited by training data usually fast e.g. DFIRE, RAPDF, prosaii Force field based model physical energy landscape more robust and transferable often expensive (require minimization) e.g. MMPB(GB)/SA, UNRES
19 -3500 Scoring Function Comparison MMGB/SA vs. DFIRE MMGB/SA score DFIRE score C! RMSD in Å radius of gyration C! RMSD in Å C! RMSD in Å
20 Sampling with Restraints Secondary structure bias Secondary structure prediction NMR shift data Distance restraints Experimental restraints (disulfides, NMR, EPR) Side chain contacts from analogous structures Shape restraints cryoem data, small-angle X-ray scattering
21 but the solution may lie elsewhere.
22 Sequence Homology Human thioredoxin (1AUC) SDKIIHLTDDSFDTDVLKADGAILVDFWAEWCGPCKMIAPILDEIADEYQGKLTV A : :....:..::: : :::::::: :.....:.... MVKQIESKTAFQEALDAAGDKLVVVDFSATWCGPCKMIKPFFHSLSEKYSNVIFL - KLNIDQNPGTAPKYGIRGIPTLLLFKNGEVAATKVGALSKGQLKEFLDAN---LA...:..:....::..::.:. :::.: : :: :.:. :. EVDVDDCQDVASECEVKCMPTFQFFKKGQ----KVGEFS-GANKEKLEATINELV E. Coli thioredoxin (1THO)
23 Comparative Modeling Assumption: Proteins with similar sequence have similar structure Human thioredoxin (1AUC) E. Coli thioredoxin (1THO)
24 Structural Templates from Homology Challenges: Correct alignment Loop modeling Side chain rebuilding PGTAPKYGIRGIPTLLLFKNGEVAATKVGALSKGQLKEFLDAN---LA.:....::..::.:. :::.: : :: :.:. :. QDVASECEVKCMPTFQFFKKGQ----KVGEFS-GANKEKLEATINELV
25 Accuracy of Predictions by Homology 100 % predictions % sequence identity <2Å RMSD <5Å RMSD
26 Prediction through Fold Recognition Assumption: Proteins with similar secondary structure share fold 1N91 1JRM
27 Templates through Fold Recognition Challenges: Wrong templates Alignment uncertain Fragment modeling Refinement needed
28 Ab initio Sampling in Template-based Structure Prediction Template provides known protein structure Ab initio sampling of unknown fragments in the context of template
29 Template Restraints Near Flexible Part Restraint potential: U = f " k (r # r 0 ) 2
30 Loop Sampling Methods # Residues Sampling All-Atom Reconstruction Exhaustive Search Torsional Space MC/MD Multi-Scale Fragment-based Program MMTSB Tool Set Modeller (Sali) MMTSB Tool Set Rosetta (Baker)
31 Structural Genomics Efforts
32 Structure Refinement? predicted native (NMR)
Protein Structure Prediction
Protein Structure Prediction Michael Feig MMTSB/CTBP 2009 Summer Workshop From Sequence to Structure SEALGDTIVKNA Folding with All-Atom Models AAQAAAAQAAAAQAA All-atom MD in general not succesful for real
More informationComputer simulations of protein folding with a small number of distance restraints
Vol. 49 No. 3/2002 683 692 QUARTERLY Computer simulations of protein folding with a small number of distance restraints Andrzej Sikorski 1, Andrzej Kolinski 1,2 and Jeffrey Skolnick 2 1 Department of Chemistry,
More informationTemplate Free Protein Structure Modeling Jianlin Cheng, PhD
Template Free Protein Structure Modeling Jianlin Cheng, PhD Associate Professor Computer Science Department Informatics Institute University of Missouri, Columbia 2013 Protein Energy Landscape & Free Sampling
More informationProgramme Last week s quiz results + Summary Fold recognition Break Exercise: Modelling remote homologues
Programme 8.00-8.20 Last week s quiz results + Summary 8.20-9.00 Fold recognition 9.00-9.15 Break 9.15-11.20 Exercise: Modelling remote homologues 11.20-11.40 Summary & discussion 11.40-12.00 Quiz 1 Feedback
More informationProtein Structures: Experiments and Modeling. Patrice Koehl
Protein Structures: Experiments and Modeling Patrice Koehl Structural Bioinformatics: Proteins Proteins: Sources of Structure Information Proteins: Homology Modeling Proteins: Ab initio prediction Proteins:
More informationMMTSB Toolset. Michael Feig MMTSB/CTBP 2006 Summer Workshop. MMTSB/CTBP Summer Workshop Michael Feig, 2006.
MMTSB Toolset Michael Feig MMTSB/CTBP 2006 Summer Workshop MMTSB Tool Set Overview application programs Perl libraries Perl utilities MMTSB Tool Set Functionality Structure manipulation and analysis Classical
More informationPrediction and refinement of NMR structures from sparse experimental data
Prediction and refinement of NMR structures from sparse experimental data Jeff Skolnick Director Center for the Study of Systems Biology School of Biology Georgia Institute of Technology Overview of talk
More informationProtein Structure Prediction, Engineering & Design CHEM 430
Protein Structure Prediction, Engineering & Design CHEM 430 Eero Saarinen The free energy surface of a protein Protein Structure Prediction & Design Full Protein Structure from Sequence - High Alignment
More informationIntroduction to Comparative Protein Modeling. Chapter 4 Part I
Introduction to Comparative Protein Modeling Chapter 4 Part I 1 Information on Proteins Each modeling study depends on the quality of the known experimental data. Basis of the model Search in the literature
More informationTemplate Free Protein Structure Modeling Jianlin Cheng, PhD
Template Free Protein Structure Modeling Jianlin Cheng, PhD Professor Department of EECS Informatics Institute University of Missouri, Columbia 2018 Protein Energy Landscape & Free Sampling http://pubs.acs.org/subscribe/archive/mdd/v03/i09/html/willis.html
More informationProtein Structure Prediction and Display
Protein Structure Prediction and Display Goal Take primary structure (sequence) and, using rules derived from known structures, predict the secondary structure that is most likely to be adopted by each
More informationDesign of a Novel Globular Protein Fold with Atomic-Level Accuracy
Design of a Novel Globular Protein Fold with Atomic-Level Accuracy Brian Kuhlman, Gautam Dantas, Gregory C. Ireton, Gabriele Varani, Barry L. Stoddard, David Baker Presented by Kate Stafford 4 May 05 Protein
More informationPhysiochemical Properties of Residues
Physiochemical Properties of Residues Various Sources C N Cα R Slide 1 Conformational Propensities Conformational Propensity is the frequency in which a residue adopts a given conformation (in a polypeptide)
More informationProtein Structure Prediction
Page 1 Protein Structure Prediction Russ B. Altman BMI 214 CS 274 Protein Folding is different from structure prediction --Folding is concerned with the process of taking the 3D shape, usually based on
More information114 Grundlagen der Bioinformatik, SS 09, D. Huson, July 6, 2009
114 Grundlagen der Bioinformatik, SS 09, D. Huson, July 6, 2009 9 Protein tertiary structure Sources for this chapter, which are all recommended reading: D.W. Mount. Bioinformatics: Sequences and Genome
More informationProtein Structure Determination
Protein Structure Determination Given a protein sequence, determine its 3D structure 1 MIKLGIVMDP IANINIKKDS SFAMLLEAQR RGYELHYMEM GDLYLINGEA 51 RAHTRTLNVK QNYEEWFSFV GEQDLPLADL DVILMRKDPP FDTEFIYATY 101
More informationBioinformatics: Secondary Structure Prediction
Bioinformatics: Secondary Structure Prediction Prof. David Jones d.jones@cs.ucl.ac.uk LMLSTQNPALLKRNIIYWNNVALLWEAGSD The greatest unsolved problem in molecular biology:the Protein Folding Problem? Entries
More informationAbInitioProteinStructurePredictionviaaCombinationof Threading,LatticeFolding,Clustering,andStructure Refinement
PROTEINS: Structure, Function, and Genetics Suppl 5:149 156 (2001) DOI 10.1002/prot.1172 AbInitioProteinStructurePredictionviaaCombinationof Threading,LatticeFolding,Clustering,andStructure Refinement
More informationContact map guided ab initio structure prediction
Contact map guided ab initio structure prediction S M Golam Mortuza Postdoctoral Research Fellow I-TASSER Workshop 2017 North Carolina A&T State University, Greensboro, NC Outline Ab initio structure prediction:
More informationPROTEIN SECONDARY STRUCTURE PREDICTION: AN APPLICATION OF CHOU-FASMAN ALGORITHM IN A HYPOTHETICAL PROTEIN OF SARS VIRUS
Int. J. LifeSc. Bt & Pharm. Res. 2012 Kaladhar, 2012 Research Paper ISSN 2250-3137 www.ijlbpr.com Vol.1, Issue. 1, January 2012 2012 IJLBPR. All Rights Reserved PROTEIN SECONDARY STRUCTURE PREDICTION:
More informationProtein structure. Protein structure. Amino acid residue. Cell communication channel. Bioinformatics Methods
Cell communication channel Bioinformatics Methods Iosif Vaisman Email: ivaisman@gmu.edu SEQUENCE STRUCTURE DNA Sequence Protein Sequence Protein Structure Protein structure ATGAAATTTGGAAACTTCCTTCTCACTTATCAGCCACCT...
More informationBIOINF 4120 Bioinformatics 2 - Structures and Systems - Oliver Kohlbacher Summer Protein Structure Prediction I
BIOINF 4120 Bioinformatics 2 - Structures and Systems - Oliver Kohlbacher Summer 2013 9. Protein Structure Prediction I Structure Prediction Overview Overview of problem variants Secondary structure prediction
More informationPresenter: She Zhang
Presenter: She Zhang Introduction Dr. David Baker Introduction Why design proteins de novo? It is not clear how non-covalent interactions favor one specific native structure over many other non-native
More informationChemical Shift Restraints Tools and Methods. Andrea Cavalli
Chemical Shift Restraints Tools and Methods Andrea Cavalli Overview Methods Overview Methods Details Overview Methods Details Results/Discussion Overview Methods Methods Cheshire base solid-state Methods
More informationHomology Modeling (Comparative Structure Modeling) GBCB 5874: Problem Solving in GBCB
Homology Modeling (Comparative Structure Modeling) Aims of Structural Genomics High-throughput 3D structure determination and analysis To determine or predict the 3D structures of all the proteins encoded
More informationProtein structure prediction. CS/CME/BioE/Biophys/BMI 279 Oct. 10 and 12, 2017 Ron Dror
Protein structure prediction CS/CME/BioE/Biophys/BMI 279 Oct. 10 and 12, 2017 Ron Dror 1 Outline Why predict protein structure? Can we use (pure) physics-based methods? Knowledge-based methods Two major
More informationProtein modeling and structure prediction with a reduced representation
Vol. 51 No. 2/2004 349 371 QUARTERLY Review Protein modeling and structure prediction with a reduced representation Andrzej Kolinski Faculty of Chemistry, Warsaw University, Warszawa, Poland Received:
More informationCMPS 3110: Bioinformatics. Tertiary Structure Prediction
CMPS 3110: Bioinformatics Tertiary Structure Prediction Tertiary Structure Prediction Why Should Tertiary Structure Prediction Be Possible? Molecules obey the laws of physics! Conformation space is finite
More informationCMPS 6630: Introduction to Computational Biology and Bioinformatics. Tertiary Structure Prediction
CMPS 6630: Introduction to Computational Biology and Bioinformatics Tertiary Structure Prediction Tertiary Structure Prediction Why Should Tertiary Structure Prediction Be Possible? Molecules obey the
More informationTOUCHSTONE II: A New Approach to Ab Initio Protein Structure Prediction
Biophysical Journal Volume 85 August 2003 1145 1164 1145 TOUCHSTONE II: A New Approach to Ab Initio Protein Structure Prediction Yang Zhang,* Andrzej Kolinski,* y and Jeffrey Skolnick* *Center of Excellence
More informationAb-initio protein structure prediction
Ab-initio protein structure prediction Jaroslaw Pillardy Computational Biology Service Unit Cornell Theory Center, Cornell University Ithaca, NY USA Methods for predicting protein structure 1. Homology
More information3D Structure. Prediction & Assessment Pt. 2. David Wishart 3-41 Athabasca Hall
3D Structure Prediction & Assessment Pt. 2 David Wishart 3-41 Athabasca Hall david.wishart@ualberta.ca Objectives Become familiar with methods and algorithms for secondary Structure Prediction Become familiar
More informationSupporting Online Material for
www.sciencemag.org/cgi/content/full/309/5742/1868/dc1 Supporting Online Material for Toward High-Resolution de Novo Structure Prediction for Small Proteins Philip Bradley, Kira M. S. Misura, David Baker*
More informationA Method for the Improvement of Threading-Based Protein Models
PROTEINS: Structure, Function, and Genetics 37:592 610 (1999) A Method for the Improvement of Threading-Based Protein Models Andrzej Kolinski, 1,2 * Piotr Rotkiewicz, 1,2 Bartosz Ilkowski, 1,2 and Jeffrey
More informationBioinformatics: Secondary Structure Prediction
Bioinformatics: Secondary Structure Prediction Prof. David Jones d.t.jones@ucl.ac.uk Possibly the greatest unsolved problem in molecular biology: The Protein Folding Problem MWMPPRPEEVARK LRRLGFVERMAKG
More informationProtein Structure Determination from Pseudocontact Shifts Using ROSETTA
Supporting Information Protein Structure Determination from Pseudocontact Shifts Using ROSETTA Christophe Schmitz, Robert Vernon, Gottfried Otting, David Baker and Thomas Huber Table S0. Biological Magnetic
More informationALL LECTURES IN SB Introduction
1. Introduction 2. Molecular Architecture I 3. Molecular Architecture II 4. Molecular Simulation I 5. Molecular Simulation II 6. Bioinformatics I 7. Bioinformatics II 8. Prediction I 9. Prediction II ALL
More informationSteps in protein modelling. Structure prediction, fold recognition and homology modelling. Basic principles of protein structure
Structure prediction, fold recognition and homology modelling Marjolein Thunnissen Lund September 2012 Steps in protein modelling 3-D structure known Comparative Modelling Sequence of interest Similarity
More informationProtein Secondary Structure Prediction
part of Bioinformatik von RNA- und Proteinstrukturen Computational EvoDevo University Leipzig Leipzig, SS 2011 the goal is the prediction of the secondary structure conformation which is local each amino
More informationBioinformatics III Structural Bioinformatics and Genome Analysis Part Protein Secondary Structure Prediction. Sepp Hochreiter
Bioinformatics III Structural Bioinformatics and Genome Analysis Part Protein Secondary Structure Prediction Institute of Bioinformatics Johannes Kepler University, Linz, Austria Chapter 4 Protein Secondary
More informationSecondary Structure. Bioch/BIMS 503 Lecture 2. Structure and Function of Proteins. Further Reading. Φ, Ψ angles alone determine protein structure
Bioch/BIMS 503 Lecture 2 Structure and Function of Proteins August 28, 2008 Robert Nakamoto rkn3c@virginia.edu 2-0279 Secondary Structure Φ Ψ angles determine protein structure Φ Ψ angles are restricted
More informationProtein Structure Prediction
Protein Structure Prediction Notes from Chapter 5 of Computational Biology an Application-Oriented View by A.P. Gultyaev 1 Protein Structure Prediction Primary Structure Secondary Structure Prediction
More informationMonte Carlo simulation of proteins through a random walk in energy space
JOURNAL OF CHEMICAL PHYSICS VOLUME 116, NUMBER 16 22 APRIL 2002 Monte Carlo simulation of proteins through a random walk in energy space Nitin Rathore and Juan J. de Pablo a) Department of Chemical Engineering,
More informationComputational Protein Design
11 Computational Protein Design This chapter introduces the automated protein design and experimental validation of a novel designed sequence, as described in Dahiyat and Mayo [1]. 11.1 Introduction Given
More informationHomology Modeling I. Growth of the Protein Data Bank PDB. Basel, September 30, EMBnet course: Introduction to Protein Structure Bioinformatics
Swiss Institute of Bioinformatics EMBnet course: Introduction to Protein Structure Bioinformatics Homology Modeling I Basel, September 30, 2004 Torsten Schwede Biozentrum - Universität Basel Swiss Institute
More informationCopyright Mark Brandt, Ph.D A third method, cryogenic electron microscopy has seen increasing use over the past few years.
Structure Determination and Sequence Analysis The vast majority of the experimentally determined three-dimensional protein structures have been solved by one of two methods: X-ray diffraction and Nuclear
More informationCourse Notes: Topics in Computational. Structural Biology.
Course Notes: Topics in Computational Structural Biology. Bruce R. Donald June, 2010 Copyright c 2012 Contents 11 Computational Protein Design 1 11.1 Introduction.........................................
More informationGiri Narasimhan. CAP 5510: Introduction to Bioinformatics. ECS 254; Phone: x3748
CAP 5510: Introduction to Bioinformatics Giri Narasimhan ECS 254; Phone: x3748 giri@cis.fiu.edu www.cis.fiu.edu/~giri/teach/bioinfs07.html 2/15/07 CAP5510 1 EM Algorithm Goal: Find θ, Z that maximize Pr
More informationproteins Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field
proteins STRUCTURE O FUNCTION O BIOINFORMATICS Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field Dong Xu1 and Yang Zhang1,2* 1 Department
More informationProtein Secondary Structure Prediction using Feed-Forward Neural Network
COPYRIGHT 2010 JCIT, ISSN 2078-5828 (PRINT), ISSN 2218-5224 (ONLINE), VOLUME 01, ISSUE 01, MANUSCRIPT CODE: 100713 Protein Secondary Structure Prediction using Feed-Forward Neural Network M. A. Mottalib,
More informationTemplate-Based Modeling of Protein Structure
Template-Based Modeling of Protein Structure David Constant Biochemistry 218 December 11, 2011 Introduction. Much can be learned about the biology of a protein from its structure. Simply put, structure
More informationCAP 5510: Introduction to Bioinformatics CGS 5166: Bioinformatics Tools. Giri Narasimhan
CAP 5510: Introduction to Bioinformatics CGS 5166: Bioinformatics Tools Giri Narasimhan ECS 254; Phone: x3748 giri@cis.fiu.edu www.cis.fiu.edu/~giri/teach/bioinff18.html Proteins and Protein Structure
More informationProtein Structure Prediction II Lecturer: Serafim Batzoglou Scribe: Samy Hamdouche
Protein Structure Prediction II Lecturer: Serafim Batzoglou Scribe: Samy Hamdouche The molecular structure of a protein can be broken down hierarchically. The primary structure of a protein is simply its
More informationStructural Alignment of Proteins
Goal Align protein structures Structural Alignment of Proteins 1 2 3 4 5 6 7 8 9 10 11 12 13 14 PHE ASP ILE CYS ARG LEU PRO GLY SER ALA GLU ALA VAL CYS PHE ASN VAL CYS ARG THR PRO --- --- --- GLU ALA ILE
More informationNeural Networks for Protein Structure Prediction Brown, JMB CS 466 Saurabh Sinha
Neural Networks for Protein Structure Prediction Brown, JMB 1999 CS 466 Saurabh Sinha Outline Goal is to predict secondary structure of a protein from its sequence Artificial Neural Network used for this
More informationMolecular Modeling lecture 2
Molecular Modeling 2018 -- lecture 2 Topics 1. Secondary structure 3. Sequence similarity and homology 2. Secondary structure prediction 4. Where do protein structures come from? X-ray crystallography
More informationTASSER: An Automated Method for the Prediction of Protein Tertiary Structures in CASP6
PROTEINS: Structure, Function, and Bioinformatics Suppl 7:91 98 (2005) TASSER: An Automated Method for the Prediction of Protein Tertiary Structures in CASP6 Yang Zhang, Adrian K. Arakaki, and Jeffrey
More informationCentral Dogma. modifications genome transcriptome proteome
entral Dogma DA ma protein post-translational modifications genome transcriptome proteome 83 ierarchy of Protein Structure 20 Amino Acids There are 20 n possible sequences for a protein of n residues!
More informationSyllabus of BIOINF 528 (2017 Fall, Bioinformatics Program)
Syllabus of BIOINF 528 (2017 Fall, Bioinformatics Program) Course Name: Structural Bioinformatics Course Description: Instructor: This course introduces fundamental concepts and methods for structural
More informationProtein Secondary Structure Prediction
Protein Secondary Structure Prediction Doug Brutlag & Scott C. Schmidler Overview Goals and problem definition Existing approaches Classic methods Recent successful approaches Evaluating prediction algorithms
More informationProtein structure prediction. CS/CME/BioE/Biophys/BMI 279 Oct. 10 and 12, 2017 Ron Dror
Protein structure prediction CS/CME/BioE/Biophys/BMI 279 Oct. 10 and 12, 2017 Ron Dror 1 Outline Why predict protein structure? Can we use (pure) physics-based methods? Knowledge-based methods Two major
More informationProtein Dynamics. The space-filling structures of myoglobin and hemoglobin show that there are no pathways for O 2 to reach the heme iron.
Protein Dynamics The space-filling structures of myoglobin and hemoglobin show that there are no pathways for O 2 to reach the heme iron. Below is myoglobin hydrated with 350 water molecules. Only a small
More informationProtein Modeling. Generating, Evaluating and Refining Protein Homology Models
Protein Modeling Generating, Evaluating and Refining Protein Homology Models Troy Wymore and Kristen Messinger Biomedical Initiatives Group Pittsburgh Supercomputing Center Homology Modeling of Proteins
More informationPacking of Secondary Structures
7.88 Lecture Notes - 4 7.24/7.88J/5.48J The Protein Folding and Human Disease Professor Gossard Retrieving, Viewing Protein Structures from the Protein Data Base Helix helix packing Packing of Secondary
More informationComputational Molecular Biology. Protein Structure and Homology Modeling
Computational Molecular Biology Protein Structure and Homology Modeling Prof. Alejandro Giorge1 Dr. Francesco Musiani Sequence, function and structure relationships v Life is the ability to metabolize
More informationCAP 5510 Lecture 3 Protein Structures
CAP 5510 Lecture 3 Protein Structures Su-Shing Chen Bioinformatics CISE 8/19/2005 Su-Shing Chen, CISE 1 Protein Conformation 8/19/2005 Su-Shing Chen, CISE 2 Protein Conformational Structures Hydrophobicity
More informationImproved Protein Secondary Structure Prediction
Improved Protein Secondary Structure Prediction Secondary Structure Prediction! Given a protein sequence a 1 a 2 a N, secondary structure prediction aims at defining the state of each amino acid ai as
More informationApril, The energy functions include:
REDUX A collection of Python scripts for torsion angle Monte Carlo protein molecular simulations and analysis The program is based on unified residue peptide model and is designed for more efficient exploration
More informationProtein Structure Analysis with Sequential Monte Carlo Method. Jinfeng Zhang Computational Biology Lab Department of Statistics Harvard University
Protein Structure Analysis with Sequential Monte Carlo Method Jinfeng Zhang Computational Biology Lab Department of Statistics Harvard University Introduction Structure Function & Interaction Protein structure
More informationWhat makes a good graphene-binding peptide? Adsorption of amino acids and peptides at aqueous graphene interfaces: Electronic Supplementary
Electronic Supplementary Material (ESI) for Journal of Materials Chemistry B. This journal is The Royal Society of Chemistry 21 What makes a good graphene-binding peptide? Adsorption of amino acids and
More informationMolecular Modeling. Prediction of Protein 3D Structure from Sequence. Vimalkumar Velayudhan. May 21, 2007
Molecular Modeling Prediction of Protein 3D Structure from Sequence Vimalkumar Velayudhan Jain Institute of Vocational and Advanced Studies May 21, 2007 Vimalkumar Velayudhan Molecular Modeling 1/23 Outline
More informationDetails of Protein Structure
Details of Protein Structure Function, evolution & experimental methods Thomas Blicher, Center for Biological Sequence Analysis Anne Mølgaard, Kemisk Institut, Københavns Universitet Learning Objectives
More informationHIV protease inhibitor. Certain level of function can be found without structure. But a structure is a key to understand the detailed mechanism.
Proteins are linear polypeptide chains (one or more) Building blocks: 20 types of amino acids. Range from a few 10s-1000s They fold into varying three-dimensional shapes structure medicine Certain level
More informationConformational Sampling in Template-Free Protein Loop Structure Modeling: An Overview
# of Loops, http://dx.doi.org/10.5936/csbj.201302003 CSBJ Conformational Sampling in Template-Free Protein Loop Structure Modeling: An Overview Yaohang Li a,* Abstract: Accurately modeling protein loops
More informationSupplementary figure 1. Comparison of unbound ogm-csf and ogm-csf as captured in the GIF:GM-CSF complex. Alignment of two copies of unbound ovine
Supplementary figure 1. Comparison of unbound and as captured in the GIF:GM-CSF complex. Alignment of two copies of unbound ovine GM-CSF (slate) with bound GM-CSF in the GIF:GM-CSF complex (GIF: green,
More informationCOMBINED MULTIPLE SEQUENCE REDUCED PROTEIN MODEL APPROACH TO PREDICT THE TERTIARY STRUCTURE OF SMALL PROTEINS
COMBINED MULTIPLE SEQUENCE REDUCED PROTEIN MODEL APPROACH TO PREDICT THE TERTIARY STRUCTURE OF SMALL PROTEINS ANGEL R. ORTIZ 1, ANDRZEJ KOLINSKI 1,2, JEFFREY SKOLNICK 1 1 Department of Molecular Biology,
More informationBasics of protein structure
Today: 1. Projects a. Requirements: i. Critical review of one paper ii. At least one computational result b. Noon, Dec. 3 rd written report and oral presentation are due; submit via email to bphys101@fas.harvard.edu
More informationTools for Cryo-EM Map Fitting. Paul Emsley MRC Laboratory of Molecular Biology
Tools for Cryo-EM Map Fitting Paul Emsley MRC Laboratory of Molecular Biology April 2017 Cryo-EM model-building typically need to move more atoms that one does for crystallography the maps are lower resolution
More informationTable 1. Crystallographic data collection, phasing and refinement statistics. Native Hg soaked Mn soaked 1 Mn soaked 2
Table 1. Crystallographic data collection, phasing and refinement statistics Native Hg soaked Mn soaked 1 Mn soaked 2 Data collection Space group P2 1 2 1 2 1 P2 1 2 1 2 1 P2 1 2 1 2 1 P2 1 2 1 2 1 Cell
More informationCan protein model accuracy be. identified? NO! CBS, BioCentrum, Morten Nielsen, DTU
Can protein model accuracy be identified? Morten Nielsen, CBS, BioCentrum, DTU NO! Identification of Protein-model accuracy Why is it important? What is accuracy RMSD, fraction correct, Protein model correctness/quality
More informationMolecular simulation and structure prediction using CHARMM and the MMTSB Tool Set Free Energy Methods
Molecular simulation and structure prediction using CHARMM and the MMTSB Tool Set Free Energy Methods Charles L. Brooks III MMTSB/CTBP 2006 Summer Workshop CHARMM Simulations The flow of data and information
More information3DRobot: automated generation of diverse and well-packed protein structure decoys
Bioinformatics, 32(3), 2016, 378 387 doi: 10.1093/bioinformatics/btv601 Advance Access Publication Date: 14 October 2015 Original Paper Structural bioinformatics 3DRobot: automated generation of diverse
More informationIntroduction to" Protein Structure
Introduction to" Protein Structure Function, evolution & experimental methods Thomas Blicher, Center for Biological Sequence Analysis Learning Objectives Outline the basic levels of protein structure.
More informationCan a continuum solvent model reproduce the free energy landscape of a β-hairpin folding in water?
Can a continuum solvent model reproduce the free energy landscape of a β-hairpin folding in water? Ruhong Zhou 1 and Bruce J. Berne 2 1 IBM Thomas J. Watson Research Center; and 2 Department of Chemistry,
More informationApplication of Statistical Potentials to Protein Structure Refinement from Low Resolution Ab Initio Models
Hui Lu* Jeffrey Skolnick Laboratory of Computational Genomics, Donald Danforth Plant Science Center, 975 N Warson St., St. Louis, MO 63132 Received 5 June 2002; accepted 12 August 2003 Application of Statistical
More informationFigure 1. Molecules geometries of 5021 and Each neutral group in CHARMM topology was grouped in dash circle.
Project I Chemistry 8021, Spring 2005/2/23 This document was turned in by a student as a homework paper. 1. Methods First, the cartesian coordinates of 5021 and 8021 molecules (Fig. 1) are generated, in
More informationOrientational degeneracy in the presence of one alignment tensor.
Orientational degeneracy in the presence of one alignment tensor. Rotation about the x, y and z axes can be performed in the aligned mode of the program to examine the four degenerate orientations of two
More information7.91 Amy Keating. Solving structures using X-ray crystallography & NMR spectroscopy
7.91 Amy Keating Solving structures using X-ray crystallography & NMR spectroscopy How are X-ray crystal structures determined? 1. Grow crystals - structure determination by X-ray crystallography relies
More informationFlexPepDock In a nutshell
FlexPepDock In a nutshell All Tutorial files are located in http://bit.ly/mxtakv FlexPepdock refinement Step 1 Step 3 - Refinement Step 4 - Selection of models Measure of fit FlexPepdock Ab-initio Step
More informationSection Week 3. Junaid Malek, M.D.
Section Week 3 Junaid Malek, M.D. Biological Polymers DA 4 monomers (building blocks), limited structure (double-helix) RA 4 monomers, greater flexibility, multiple structures Proteins 20 Amino Acids,
More informationMajor Types of Association of Proteins with Cell Membranes. From Alberts et al
Major Types of Association of Proteins with Cell Membranes From Alberts et al Proteins Are Polymers of Amino Acids Peptide Bond Formation Amino Acid central carbon atom to which are attached amino group
More information) P = 1 if exp # " s. + 0 otherwise
Supplementary Material Monte Carlo algorithm procedures. The Monte Carlo conformational search algorithm has been successfully applied by programs dedicated to finding new folds (Jones 2001; Rohl, Strauss,
More informationHelix-coil and beta sheet-coil transitions in a simplified, yet realistic protein model
Macromol. Theory Simul. 2000, 9, 523 533 523 Full Paper: A reduced model of polypeptide chains and protein stochastic dynamics is employed in Monte Carlo studies of the coil-globule transition. The model
More informationSupplementary Information Intrinsic Localized Modes in Proteins
Supplementary Information Intrinsic Localized Modes in Proteins Adrien Nicolaï 1,, Patrice Delarue and Patrick Senet, 1 Department of Physics, Applied Physics and Astronomy, Rensselaer Polytechnic Institute,
More informationChapter 9. Loop Simulations. Maxim Totrov. Abstract. 1. Introduction
Chapter 9 Loop Simulations Maxim Totrov Abstract Loop modeling is crucial for high-quality homology model construction outside conserved secondary structure elements. Dozens of loop modeling protocols
More informationMulti-Scale Hierarchical Structure Prediction of Helical Transmembrane Proteins
Multi-Scale Hierarchical Structure Prediction of Helical Transmembrane Proteins Zhong Chen Dept. of Biochemistry and Molecular Biology University of Georgia, Athens, GA 30602 Email: zc@csbl.bmb.uga.edu
More informationBuilding 3D models of proteins
Building 3D models of proteins Why make a structural model for your protein? The structure can provide clues to the function through structural similarity with other proteins With a structure it is easier
More informationProcheck output. Bond angles (Procheck) Structure verification and validation Bond lengths (Procheck) Introduction to Bioinformatics.
Structure verification and validation Bond lengths (Procheck) Introduction to Bioinformatics Iosif Vaisman Email: ivaisman@gmu.edu ----------------------------------------------------------------- Bond
More informationEBBA: Efficient Branch and Bound Algorithm for Protein Decoy Generation
EBBA: Efficient Branch and Bound Algorithm for Protein Decoy Generation Martin Paluszewski og Pawel Winter Technical Report no. 08-08 ISSN: 0107-8283 Dept. of Computer Science University of Copenhagen
More informationEnergy Minimization of Protein Tertiary Structure by Parallel Simulated Annealing using Genetic Crossover
Minimization of Protein Tertiary Structure by Parallel Simulated Annealing using Genetic Crossover Tomoyuki Hiroyasu, Mitsunori Miki, Shinya Ogura, Keiko Aoi, Takeshi Yoshida, Yuko Okamoto Jack Dongarra
More information