Giri Narasimhan. CAP 5510: Introduction to Bioinformatics. ECS 254; Phone: x3748
|
|
|
- Joanna Murphy
- 5 years ago
- Views:
Transcription
1 CAP 5510: Introduction to Bioinformatics Giri Narasimhan ECS 254; Phone: x /15/07 CAP5510 1
2 EM Algorithm Goal: Find θ, Z that maximize Pr (X, Z θ) Initialize: random profile E-step: Using profile, compute a likelihood value z ij for each m-window at position i in input sequence j. M-step: Build a new profile by using every m- window, but weighting each one with value z ij. Stop if converged 2/15/07 CAP MEME [Bailey, Elkan 1994]
3 EM Method: Model Parameters Input: upstream sequences X = {X 1, X 2,, X n }, Motif profile: 4 k matrix θ = (θ rp ), r {A,C,G,T} 1 p k θ rp = Pr(residue r in position p of motif) Background distribution: θ r0 = Pr(residue r in background) 2/15/07 CAP5510 3
4 Z = {Z ij }, where Z ij = EM Method: Hidden Information 1, if motif instance starts at position i of X j 0, otherwise Iterate over probabilistic models that could generate X and Z, trying to converge on this solution, i.e., maximize Pr (X, Z θ). 2/15/07 CAP5510 4
5 Statistical Evaluation Z-score of a motif with a certain frequency: Information Content or Relative Entropy of an alignment or profile: Maximum a Posteriori (MAP) Score: Model Vs Background Score: Obs( w) Exp( w) z( w) = Var( w) IC( M) = MAP( M ) 4 m i= 1 j= 1 = 4 i= 1 j= 1 m mi, j log b m n Pr( w M ) L( w) = = Pr( w Bg ) i, log j m Π j = 1 m m i, j i b b i, j i i i, j 2/15/07 CAP Counts Frequencies
6 Predicting Motifs in Whole Genome MEME: EM algorithm [ Bailey et al., 1994 ] AlignACE: Gibbs Sampling Approach [ Hughes et al., 2000 ] Consensus: Greedy Algorithm Based [ Hertz et al., 1990 ] ANN-Spec: Artificial Neural Network and a Gibbs sampling method [ Workman et al., 2000 ] YMF: Enumerative search [Sinha et al., 2003 ] 2/15/07 CAP5510 6
7 Gibbs Sampling for Motif Detection 2/15/07 CAP5510 7
8 Protein Structures Sequences of amino acid residues 20 different amino acids Primary Secondary Tertiary Quaternary 2/15/07 CAP5510 8
9 Proteins Primary structure is the sequence of amino acid residues of the protein, e.g., Flavodoxin: AKIGLFYGTQTGVTQTIAESIQQEFGGESIVDLNDIANADA Different regions of the sequence form local regular secondary structures, such as Alpha helix, beta strands, etc. AKIGLFYGTQTGVTQTIAESIQQEFGGESIVDLNDIANADA Secondary 2/15/07 CAP5510 9
10 α-helix More on Secondary Structures Main chain with peptide bonds Side chains project outward from helix Stability provided by H-bonds between CO and NH groups of residues 4 locations away. β-strand Stability provided by H-bonds with one or more β-strands, forming β-sheets. Needs a β-turn. 2/15/07 CAP
11 Proteins Tertiary structures are formed by packing secondary structural elements into a globular structure. Myoglobin Lambda Cro 2/15/07 CAP
12 Quaternary Structures in Proteins The final structure may contain more than one chain arranged in a quaternary structure. Quaternary Insulin Hexamer 2/15/07 CAP
13 Amino Acid Types Hydrophobic I,L,M,V,A,F,P Charged Basic Acidic Polar K,H,R E,D S,T,Y,H,C,N,Q,W Small A,S,T Very Small Aromatic A,G F,Y,W 2/15/07 CAP
14 Structure of a single amino acid All 3 figures are cartoons of an amino acid residue. 2/15/07 CAP
15 Chains of amino acids Amino acids vs Amino acid residues 2/15/07 CAP
16 Angles φ and ψ in the polypeptide chain 2/15/07 CAP
17 2/15/07 CAP
18 Amino Acid Structures from Klug & Cummings 2/15/07 CAP
19 Amino Acid Structures from Klug & Cummings 2/15/07 CAP
20 Amino Acid Structures from Klug & Cummings 2/15/07 CAP
21 Amino Acid Structures from Klug & Cummings 2/15/07 CAP
22 2/15/07 CAP
23 2/15/07 CAP
24 Alpha Helix 2/15/07 CAP
25 2/15/07 CAP
26 Beta Strand 2/15/07 CAP
27 Active Sites Active sites in proteins are usually hydrophobic pockets/crevices/troughs that involve sidechain atoms. 2/15/07 CAP
28 Active Sites Left PDB 3RTD (streptavidin) and the first site located by the MOE Site Finder. Middle 3RTD with complexed ligand (biotin). Right Biotin ligand overlaid with calculated alpha spheres of the first site. 2/15/07 CAP
29 Secondary Structure Prediction Software 2/15/07 CAP
30 PDB: Protein Data Bank Database of protein tertiary and quaternary structures and protein complexes. Over 29,000 structures as of Feb 1, Structures determined by NMR Spectroscopy X-ray crystallography Computational prediction methods Sample PDB file: Click here [ ] 2/15/07 CAP
31 Protein Folding Unfolded Molten Globule State Folded Native State Rapid (< 1s) Slow ( s) How to find minimum energy configuration? 2/15/07 CAP
32 Modular Nature of Protein Structures Example: Diphtheria Toxin 2/15/07 CAP
33 Protein Structures Most proteins have a hydrophobic core. Within the core, specific interactions take place between amino acid side chains. Can an amino acid be replaced by some other amino acid? Limited by space and available contacts with nearby amino acids Outside the core, proteins are composed of loops and structural elements in contact with water, solvent, other proteins and other structures. 2/15/07 CAP
34 SPDBV RASMOL CHIME Viewing Protein Structures 2/15/07 CAP
35 Structural Classification of Proteins Over 1000 protein families known Sequence alignment, motif finding, block finding, similarity search SCOP (Structural Classification of Proteins) Based on structural & evolutionary relationships. Contains ~ 40,000 domains Classes (groups of folds), Folds (proteins sharing folds), Families (proteins related by function/evolution), Superfamilies (distantly related proteins) 2/15/07 CAP
36 SCOP Family View 2/15/07 CAP
37 CATH: Protein Structure Classification Semi-automatic classification; ~36K domains 4 levels of classification: Class (C), depends on sec. Str. Content α class, β class, α/β class, α+β class Architecture (A), orientation of sec. Str. Topolgy (T), topological connections & Homologous Superfamily (H), similar str and functions. 2/15/07 CAP
38 DALI/FSSP Database Completely automated; 3724 domains Criteria of compactness & recurrence Each domain is assigned a Domain Classification number DC_l_m_n_p representing fold space attractor region (l), globular folding topology (m), functional family (n) and sequence family (p). 2/15/07 CAP
39 Structural Alignment What is structural alignment of proteins? 3-d superimposition of the atoms as best as possible, i.e., to minimize RMSD (root mean square deviation). Can be done using VAST and SARF Structural similarity is common, even among proteins that do not share sequence similarity or evolutionary relationship. 2/15/07 CAP
40 Other databases & tools MMDB contains groups of structurally related proteins SARF structurally similar proteins using secondary structure elements VAST Structure Neighbors SSAP uses double dynamic programming to structurally align proteins 2/15/07 CAP
41 5 Fold Space classes Attractor 1 can be characterized as alpha/beta, attractor 2 as all-beta, attractor 3 as all-alpha, attractor 5 as alpha-beta meander (1mli), and attractor 4 contains antiparallel beta-barrels e.g. OB-fold (1prtF). 2/15/07 CAP
42 Fold Types & Neighbors Structural neighbours of 1urnA (top left). 1mli (bottom right) has the same topology even though there are shifts in the relative orientation of secondary structure elements. 2/15/07 CAP
43 Sequence Alignment of Fold Neighbors 2/15/07 CAP
44 Frequent Fold Types 2/15/07 CAP
45 Protein Structure Prediction Holy Grail of bioinformatics Protein Structure Initiative to determine a set of protein structures that span protein structure space sufficiently well. WHY? Number of folds in natural proteins is limited. Thus a newly discovered proteins should be within modeling distance of some protein in set. CASP: Critical Assessment of techniques for structure prediction To stimulate work in this difficult field 2/15/07 CAP
46 PSP Methods homology-based modeling methods based on fold recognition Threading methods ab initio methods From first principles With the help of databases 2/15/07 CAP
47 Best method for PSP ROSETTA As proteins fold, a large number of partially folded, low-energy conformations are formed, and that local structures combine to form more global structures with minimum energy. Build a database of known structures (I-sites) of short sequences (3-15 residues). Monte Carlo simulation assembling possible substructures and computing energy 2/15/07 CAP
48 Threading Methods See p471, Mount 2/15/07 CAP
49 2/15/07 CAP
CAP 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
Amino Acid Structures from Klug & Cummings. 10/7/2003 CAP/CGS 5991: Lecture 7 1
Amino Acid Structures from Klug & Cummings 10/7/2003 CAP/CGS 5991: Lecture 7 1 Amino Acid Structures from Klug & Cummings 10/7/2003 CAP/CGS 5991: Lecture 7 2 Amino Acid Structures from Klug & Cummings
Protein Structures. 11/19/2002 Lecture 24 1
Protein Structures 11/19/2002 Lecture 24 1 All 3 figures are cartoons of an amino acid residue. 11/19/2002 Lecture 24 2 Peptide bonds in chains of residues 11/19/2002 Lecture 24 3 Angles φ and ψ in the
1. Protein Data Bank (PDB) 1. Protein Data Bank (PDB)
Protein structure databases; visualization; and classifications 1. Introduction to Protein Data Bank (PDB) 2. Free graphic software for 3D structure visualization 3. Hierarchical classification of protein
Protein Structures. Sequences of amino acid residues 20 different amino acids. Quaternary. Primary. Tertiary. Secondary. 10/8/2002 Lecture 12 1
Protein Structures Sequences of amino acid residues 20 different amino acids Primary Secondary Tertiary Quaternary 10/8/2002 Lecture 12 1 Angles φ and ψ in the polypeptide chain 10/8/2002 Lecture 12 2
Basics 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
CMPS 6630: Introduction to Computational Biology and Bioinformatics. Structure Comparison
CMPS 6630: Introduction to Computational Biology and Bioinformatics Structure Comparison Protein Structure Comparison Motivation Understand sequence and structure variability Understand Domain architecture
Analysis and Prediction of Protein Structure (I)
Analysis and Prediction of Protein Structure (I) Jianlin Cheng, PhD School of Electrical Engineering and Computer Science University of Central Florida 2006 Free for academic use. Copyright @ Jianlin Cheng
Protein 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
Protein Structure: Data Bases and Classification Ingo Ruczinski
Protein Structure: Data Bases and Classification Ingo Ruczinski Department of Biostatistics, Johns Hopkins University Reference Bourne and Weissig Structural Bioinformatics Wiley, 2003 More References
Introduction 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.
CAP 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
CMPS 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
CMPS 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
Neural 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
ALL 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
2MHR. Protein structure classification is important because it organizes the protein structure universe that is independent of sequence similarity.
Protein structure classification is important because it organizes the protein structure universe that is independent of sequence similarity. A global picture of the protein universe will help us to understand
Getting To Know Your Protein
Getting To Know Your Protein Comparative Protein Analysis: Part III. Protein Structure Prediction and Comparison Robert Latek, PhD Sr. Bioinformatics Scientist Whitehead Institute for Biomedical Research
Procheck 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
Biophysics 101: Genomics & Computational Biology. Section 8: Protein Structure S T R U C T U R E P R O C E S S. Outline.
Biophysics 101: Genomics & Computational Biology Section 8: Protein Structure Faisal Reza Nov. 11 th, 2003 B101.pdb from PS5 shown at left with: animated ball and stick model, colored CPK H-bonds on, colored
Protein 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
Design 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
Outline. Levels of Protein Structure. Primary (1 ) Structure. Lecture 6:Protein Architecture II: Secondary Structure or From peptides to proteins
Lecture 6:Protein Architecture II: Secondary Structure or From peptides to proteins Margaret Daugherty Fall 2004 Outline Four levels of structure are used to describe proteins; Alpha helices and beta sheets
Statistical Machine Learning Methods for Bioinformatics IV. Neural Network & Deep Learning Applications in Bioinformatics
Statistical Machine Learning Methods for Bioinformatics IV. Neural Network & Deep Learning Applications in Bioinformatics Jianlin Cheng, PhD Department of Computer Science University of Missouri, Columbia
Bioinformatics. Macromolecular structure
Bioinformatics Macromolecular structure Contents Determination of protein structure Structure databases Secondary structure elements (SSE) Tertiary structure Structure analysis Structure alignment Domain
HMM applications. Applications of HMMs. Gene finding with HMMs. Using the gene finder
HMM applications Applications of HMMs Gene finding Pairwise alignment (pair HMMs) Characterizing protein families (profile HMMs) Predicting membrane proteins, and membrane protein topology Gene finding
Molecular 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
Outline. Levels of Protein Structure. Primary (1 ) Structure. Lecture 6:Protein Architecture II: Secondary Structure or From peptides to proteins
Lecture 6:Protein Architecture II: Secondary Structure or From peptides to proteins Margaret Daugherty Fall 2003 Outline Four levels of structure are used to describe proteins; Alpha helices and beta sheets
Protein 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
Motif Prediction in Amino Acid Interaction Networks
Motif Prediction in Amino Acid Interaction Networks Omar GACI and Stefan BALEV Abstract In this paper we represent a protein as a graph where the vertices are amino acids and the edges are interactions
Introduction to Computational Structural Biology
Introduction to Computational Structural Biology Part I 1. Introduction The disciplinary character of Computational Structural Biology The mathematical background required and the topics covered Bibliography
D Dobbs ISU - BCB 444/544X 1
11/7/05 Protein Structure: Classification, Databases, Visualization Announcements BCB 544 Projects - Important Dates: Nov 2 Wed noon - Project proposals due to David/Drena Nov 4 Fri PM - Approvals/responses
Introduction 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
Protein Structure. W. M. Grogan, Ph.D. OBJECTIVES
Protein Structure W. M. Grogan, Ph.D. OBJECTIVES 1. Describe the structure and characteristic properties of typical proteins. 2. List and describe the four levels of structure found in proteins. 3. Relate
114 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
COMP 598 Advanced Computational Biology Methods & Research. Introduction. Jérôme Waldispühl School of Computer Science McGill University
COMP 598 Advanced Computational Biology Methods & Research Introduction Jérôme Waldispühl School of Computer Science McGill University General informations (1) Office hours: by appointment Office: TR3018
Lecture 8: Protein structure analysis
Lecture 8: Protein structure analysis Torgeir R. Hvidsten Professor Norwegian University of Life Sciences Guest lecturer Umeå Plant Science Centre Computational Life Science Cluster (CLiC) Proteins play
Bioinformatics. Proteins II. - Pattern, Profile, & Structure Database Searching. Robert Latek, Ph.D. Bioinformatics, Biocomputing
Bioinformatics Proteins II. - Pattern, Profile, & Structure Database Searching Robert Latek, Ph.D. Bioinformatics, Biocomputing WIBR Bioinformatics Course, Whitehead Institute, 2002 1 Proteins I.-III.
CS612 - Algorithms in Bioinformatics
Fall 2017 Databases and Protein Structure Representation October 2, 2017 Molecular Biology as Information Science > 12, 000 genomes sequenced, mostly bacterial (2013) > 5x10 6 unique sequences available
Details 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
Protein Structure Basics
Protein Structure Basics Presented by Alison Fraser, Christine Lee, Pradhuman Jhala, Corban Rivera Importance of Proteins Muscle structure depends on protein-protein interactions Transport across membranes
Protein structure alignments
Protein structure alignments Proteins that fold in the same way, i.e. have the same fold are often homologs. Structure evolves slower than sequence Sequence is less conserved than structure If BLAST gives
Supersecondary Structures (structural motifs)
Supersecondary Structures (structural motifs) Various Sources Slide 1 Supersecondary Structures (Motifs) Supersecondary Structures (Motifs): : Combinations of secondary structures in specific geometric
Protein Structure Prediction 11/11/05
11/11/05 Protein Structure Prediction & Modeling Bioinformatics Seminars Nov 11 Fri 12:10 BCB Seminar in E164 Lago Building Supertrees Using Distances Steve Willson, Dept of Mathematics http://www.bcb.iastate.edu/courses/bcb691-f2005.html
Examples of Protein Modeling. Protein Modeling. Primary Structure. Protein Structure Description. Protein Sequence Sources. Importing Sequences to MOE
Examples of Protein Modeling Protein Modeling Visualization Examination of an experimental structure to gain insight about a research question Dynamics To examine the dynamics of protein structures To
Supporting 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*
Template 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
Learning Sequence Motif Models Using Expectation Maximization (EM) and Gibbs Sampling
Learning Sequence Motif Models Using Expectation Maximization (EM) and Gibbs Sampling BMI/CS 776 www.biostat.wisc.edu/bmi776/ Spring 009 Mark Craven craven@biostat.wisc.edu Sequence Motifs what is a sequence
Protein 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
Syllabus 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
Intro Secondary structure Transmembrane proteins Function End. Last time. Domains Hidden Markov Models
Last time Domains Hidden Markov Models Today Secondary structure Transmembrane proteins Structure prediction NAD-specific glutamate dehydrogenase Hard Easy >P24295 DHE2_CLOSY MSKYVDRVIAEVEKKYADEPEFVQTVEEVL
Today. Last time. Secondary structure Transmembrane proteins. Domains Hidden Markov Models. Structure prediction. Secondary structure
Last time Today Domains Hidden Markov Models Structure prediction NAD-specific glutamate dehydrogenase Hard Easy >P24295 DHE2_CLOSY MSKYVDRVIAEVEKKYADEPEFVQTVEEVL SSLGPVVDAHPEYEEVALLERMVIPERVIE FRVPWEDDNGKVHVNTGYRVQFNGAIGPYK
Amino Acid Structures from Klug & Cummings. Bioinformatics (Lec 12)
Amino Acid Structures from Klug & Cummings 2/17/05 1 Amino Acid Structures from Klug & Cummings 2/17/05 2 Amino Acid Structures from Klug & Cummings 2/17/05 3 Amino Acid Structures from Klug & Cummings
Reconstructing Amino Acid Interaction Networks by an Ant Colony Approach
Author manuscript, published in "Journal of Computational Intelligence in Bioinformatics 2, 2 (2009) 131-146" Reconstructing Amino Acid Interaction Networks by an Ant Colony Approach Omar GACI and Stefan
Protein 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:
Bio nformatics. Lecture 23. Saad Mneimneh
Bio nformatics Lecture 23 Protein folding The goal is to determine the three-dimensional structure of a protein based on its amino acid sequence Assumption: amino acid sequence completely and uniquely
Protein 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
SCOP. all-β class. all-α class, 3 different folds. T4 endonuclease V. 4-helical cytokines. Globin-like
SCOP all-β class 4-helical cytokines T4 endonuclease V all-α class, 3 different folds Globin-like TIM-barrel fold α/β class Profilin-like fold α+β class http://scop.mrc-lmb.cam.ac.uk/scop CATH Class, Architecture,
Protein 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
STRUCTURAL BIOINFORMATICS I. Fall 2015
STRUCTURAL BIOINFORMATICS I Fall 2015 Info Course Number - Classification: Biology 5411 Class Schedule: Monday 5:30-7:50 PM, SERC Room 456 (4 th floor) Instructors: Vincenzo Carnevale - SERC, Room 704C;
Protein folding. α-helix. Lecture 21. An α-helix is a simple helix having on average 10 residues (3 turns of the helix)
Computat onal Biology Lecture 21 Protein folding The goal is to determine the three-dimensional structure of a protein based on its amino acid sequence Assumption: amino acid sequence completely and uniquely
Presentation Outline. Prediction of Protein Secondary Structure using Neural Networks at Better than 70% Accuracy
Prediction of Protein Secondary Structure using Neural Networks at Better than 70% Accuracy Burkhard Rost and Chris Sander By Kalyan C. Gopavarapu 1 Presentation Outline Major Terminology Problem Method
SUPPLEMENTARY MATERIALS
SUPPLEMENTARY MATERIALS Enhanced Recognition of Transmembrane Protein Domains with Prediction-based Structural Profiles Baoqiang Cao, Aleksey Porollo, Rafal Adamczak, Mark Jarrell and Jaroslaw Meller Contact:
Structure to Function. Molecular Bioinformatics, X3, 2006
Structure to Function Molecular Bioinformatics, X3, 2006 Structural GeNOMICS Structural Genomics project aims at determination of 3D structures of all proteins: - organize known proteins into families
BCB 444/544 Fall 07 Dobbs 1
BCB 444/544 Lecture 21 Protein Structure Visualization, Classification & Comparison Secondary Structure #21_Oct10 Required Reading (before lecture) Mon Oct 8 - Lecture 20 Protein Secondary Structure Chp
Protein Structure. Hierarchy of Protein Structure. Tertiary structure. independently stable structural unit. includes disulfide bonds
Protein Structure Hierarchy of Protein Structure 2 3 Structural element Primary structure Secondary structure Super-secondary structure Domain Tertiary structure Quaternary structure Description amino
Week 10: Homology Modelling (II) - HHpred
Week 10: Homology Modelling (II) - HHpred Course: Tools for Structural Biology Fabian Glaser BKU - Technion 1 2 Identify and align related structures by sequence methods is not an easy task All comparative
Protein 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
Study of Mining Protein Structural Properties and its Application
Study of Mining Protein Structural Properties and its Application A Dissertation Proposal Presented to the Department of Computer Science and Information Engineering College of Electrical Engineering and
HIV 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
Announcements. Primary (1 ) Structure. Lecture 7 & 8: PROTEIN ARCHITECTURE IV: Tertiary and Quaternary Structure
Announcements TA Office Hours: Brian Eckenroth Monday 3-4 pm Thursday 11 am-12 pm Lecture 7 & 8: PROTEIN ARCHITECTURE IV: Tertiary and Quaternary Structure Margaret Daugherty Fall 2003 Homework II posted
Lecture 10 (10/4/17) Lecture 10 (10/4/17)
Lecture 10 (10/4/17) Reading: Ch4; 125, 138-141, 141-142 Problems: Ch4 (text); 7, 9, 11 Ch4 (study guide); 1, 2 NEXT Reading: Ch4; 125, 132-136 (structure determination) Ch4; 12-130 (Collagen) Problems:
Physiochemical 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)
Protein Structure Prediction Using Multiple Artificial Neural Network Classifier *
Protein Structure Prediction Using Multiple Artificial Neural Network Classifier * Hemashree Bordoloi and Kandarpa Kumar Sarma Abstract. Protein secondary structure prediction is the method of extracting
From Amino Acids to Proteins - in 4 Easy Steps
From Amino Acids to Proteins - in 4 Easy Steps Although protein structure appears to be overwhelmingly complex, you can provide your students with a basic understanding of how proteins fold by focusing
NMR, X-ray Diffraction, Protein Structure, and RasMol
NMR, X-ray Diffraction, Protein Structure, and RasMol Introduction So far we have been mostly concerned with the proteins themselves. The techniques (NMR or X-ray diffraction) used to determine a structure
Copyright 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
Can 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
Heteropolymer. Mostly in regular secondary structure
Heteropolymer - + + - Mostly in regular secondary structure 1 2 3 4 C >N trace how you go around the helix C >N C2 >N6 C1 >N5 What s the pattern? Ci>Ni+? 5 6 move around not quite 120 "#$%&'!()*(+2!3/'!4#5'!1/,#64!#6!,6!
Template 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
Computational 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
Protein 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
Ant Colony Approach to Predict Amino Acid Interaction Networks
Ant Colony Approach to Predict Amino Acid Interaction Networks Omar Gaci, Stefan Balev To cite this version: Omar Gaci, Stefan Balev. Ant Colony Approach to Predict Amino Acid Interaction Networks. IEEE
Alpha-helical Topology and Tertiary Structure Prediction of Globular Proteins Scott R. McAllister Christodoulos A. Floudas Princeton University
Alpha-helical Topology and Tertiary Structure Prediction of Globular Proteins Scott R. McAllister Christodoulos A. Floudas Princeton University Department of Chemical Engineering Program of Applied and
Sequence analysis and comparison
The aim with sequence identification: Sequence analysis and comparison Marjolein Thunnissen Lund September 2012 Is there any known protein sequence that is homologous to mine? Are there any other species
STRUCTURAL BIOINFORMATICS. Barry Grant University of Michigan
STRUCTURAL BIOINFORMATICS Barry Grant University of Michigan www.thegrantlab.org bjgrant@umich.edu Bergen, Norway 28-Sep-2015 Objective: Provide an introduction to the practice of structural bioinformatics,
Prediction 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
A General Model for Amino Acid Interaction Networks
Author manuscript, published in "N/P" A General Model for Amino Acid Interaction Networks Omar GACI and Stefan BALEV hal-43269, version - Nov 29 Abstract In this paper we introduce the notion of protein
Giri 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 2/8/07 CAP5510 1 Pattern Discovery 2/8/07 CAP5510 2 Patterns Nature
Bioinformatics: 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
Pymol Practial Guide
Pymol Practial Guide Pymol is a powerful visualizor very convenient to work with protein molecules. Its interface may seem complex at first, but you will see that with a little practice is simple and powerful
Protein 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
1-D Predictions. Prediction of local features: Secondary structure & surface exposure
1-D Predictions Prediction of local features: Secondary structure & surface exposure 1 Learning Objectives After today s session you should be able to: Explain the meaning and usage of the following local
Bayesian Models and Algorithms for Protein Beta-Sheet Prediction
0 Bayesian Models and Algorithms for Protein Beta-Sheet Prediction Zafer Aydin, Student Member, IEEE, Yucel Altunbasak, Senior Member, IEEE, and Hakan Erdogan, Member, IEEE Abstract Prediction of the three-dimensional
Protein structure analysis. Risto Laakso 10th January 2005
Protein structure analysis Risto Laakso risto.laakso@hut.fi 10th January 2005 1 1 Summary Various methods of protein structure analysis were examined. Two proteins, 1HLB (Sea cucumber hemoglobin) and 1HLM
THE UNIVERSITY OF MANITOBA. PAPER NO: 409 LOCATION: Fr. Kennedy Gold Gym PAGE NO: 1 of 6 DEPARTMENT & COURSE NO: CHEM 4630 TIME: 3 HOURS
PAPER NO: 409 LOCATION: Fr. Kennedy Gold Gym PAGE NO: 1 of 6 DEPARTMENT & COURSE NO: CHEM 4630 TIME: 3 HOURS EXAMINATION: Biochemistry of Proteins EXAMINER: J. O'Neil Section 1: You must answer all of
BCH 4053 Spring 2003 Chapter 6 Lecture Notes
BCH 4053 Spring 2003 Chapter 6 Lecture Notes 1 CHAPTER 6 Proteins: Secondary, Tertiary, and Quaternary Structure 2 Levels of Protein Structure Primary (sequence) Secondary (ordered structure along peptide
Protein Structure Analysis and Verification. Course S Basics for Biosystems of the Cell exercise work. Maija Nevala, BIO, 67485U 16.1.
Protein Structure Analysis and Verification Course S-114.2500 Basics for Biosystems of the Cell exercise work Maija Nevala, BIO, 67485U 16.1.2008 1. Preface When faced with an unknown protein, scientists
THE TANGO ALGORITHM: SECONDARY STRUCTURE PROPENSITIES, STATISTICAL MECHANICS APPROXIMATION
THE TANGO ALGORITHM: SECONDARY STRUCTURE PROPENSITIES, STATISTICAL MECHANICS APPROXIMATION AND CALIBRATION Calculation of turn and beta intrinsic propensities. A statistical analysis of a protein structure
DATE A DAtabase of TIM Barrel Enzymes
DATE A DAtabase of TIM Barrel Enzymes 2 2.1 Introduction.. 2.2 Objective and salient features of the database 2.2.1 Choice of the dataset.. 2.3 Statistical information on the database.. 2.4 Features....