Chemical Shift Restraints Tools and Methods. Andrea Cavalli
|
|
|
- Stella Holland
- 5 years ago
- Views:
Transcription
1 Chemical Shift Restraints Tools and Methods Andrea Cavalli
2 Overview
3 Methods Overview
4 Methods Details Overview
5 Methods Details Results/Discussion Overview
6 Methods
7 Methods Cheshire base solid-state
8 Methods Cheshire base solid-state CamShift new predictor Monte Carlo/Molecular Dynamics
9 Methods Cheshire base solid-state CamShift new predictor Monte Carlo/Molecular Dynamics CamDock protein-protein docking
10 About CHESHIRE: CHEmical SHifts REstraints
11 About CHESHIRE: CHEmical SHifts REstraints 3D structure determination from NMR chemical shifts.
12 About CHESHIRE: CHEmical SHifts REstraints 3D structure determination from NMR chemical shifts. Chemical shifts are easy to measure
13 About CHESHIRE: CHEmical SHifts REstraints 3D structure determination from NMR chemical shifts. Chemical shifts are easy to measure Can be measured with great accuracy
14 About CHESHIRE: CHEmical SHifts REstraints 3D structure determination from NMR chemical shifts. Chemical shifts are easy to measure Can be measured with great accuracy Contain a lot of structural informations (CSI, TALOS,...)
15 About CHESHIRE: CHEmical SHifts REstraints 3D structure determination from NMR chemical shifts. Chemical shifts are easy to measure Can be measured with great accuracy Contain a lot of structural informations (CSI, TALOS,...) In some cases they are the only available data
16 About CHESHIRE: CHEmical SHifts REstraints 3D structure determination from NMR chemical shifts. Chemical shifts are easy to measure Can be measured with great accuracy Contain a lot of structural informations (CSI, TALOS,...) In some cases they are the only available data but...
17 NOE-NMR vs CHESHIRE
18 NOE-NMR vs CHESHIRE NOEs have a direct structural interpretation as distances
19 NOE-NMR vs CHESHIRE NOEs have a direct structural interpretation as distances Chemical shifts values are indirectly related to geometry (SHIFTX, CamShift)
20 NOE-NMR vs CHESHIRE NOEs have a direct structural interpretation as distances Chemical shifts values are indirectly related to geometry (SHIFTX, CamShift) NOEs have long-range information
21 NOE-NMR vs CHESHIRE NOEs have a direct structural interpretation as distances Chemical shifts values are indirectly related to geometry (SHIFTX, CamShift) NOEs have long-range information Chemical shifts are local
22 NOE-NMR vs CHESHIRE NOEs have a direct structural interpretation as distances Chemical shifts values are indirectly related to geometry (SHIFTX, CamShift) NOEs have long-range information Chemical shifts are local NOEs are redundant
23 NOE-NMR vs CHESHIRE NOEs have a direct structural interpretation as distances Chemical shifts values are indirectly related to geometry (SHIFTX, CamShift) NOEs have long-range information Chemical shifts are local NOEs are redundant There is only one chemical shift per atom
24 NOE-NMR vs CHESHIRE NOEs have a direct structural interpretation as distances Chemical shifts values are indirectly related to geometry (SHIFTX, CamShift) NOEs have long-range information Chemical shifts are local NOEs are redundant There is only one chemical shift per atom Clear quality control (number of assigned NOEs, NOEs violation)
25 NOE-NMR vs CHESHIRE NOEs have a direct structural interpretation as distances Chemical shifts values are indirectly related to geometry (SHIFTX, CamShift) NOEs have long-range information Chemical shifts are local NOEs are redundant There is only one chemical shift per atom Clear quality control (number of assigned NOEs, NOEs violation) Weak Q-factor
26 Idea
27 Idea Force field -920 Free Energy Cα-RMSD
28 Idea Force field Chemical shifts Free Energy Chemical Shift Cα-RMSD Cα-RMSD
29 Idea Force field Combined score Chemical shifts Free Energy Chemical Shift Chemical Shift Cα-RMSD Cα-RMSD Cα-RMSD
30 Idea Force field Combined score Chemical shifts Free Energy Chemical Shift Chemical Shift Cα-RMSD Cα-RMSD Cα-RMSD Structures have to be very close to the native one in order to feel chemical shifts score.
31 CHESHIRE Determination or prediction? Experiment Theory
32 CHESHIRE Determination or prediction? X-ray NMR Experiment Theory
33 CHESHIRE Determination or prediction? X-ray NMR ab initio Experiment Theory
34 CHESHIRE Determination or prediction? X-ray NMR Homology modeling > 50 % Homology modeling < 50 % ab initio Experiment Theory
35 CHESHIRE Determination or prediction? X-ray NMR Homology modeling > 50 % CHESHIRE Homology modeling < 50 % ab initio Experiment Theory
36 CHESHIRE Determination or prediction? Jigsaw puzzle
37 Steps Chemical shifts Prediction of local geometry Database SCOP domains Fragment selection SHIFX Fragment assembly Energy function Refinement
38 Local structure 1 Chemical shifts Prediction of local geometry Database Secondary structure prediction P 3 (S 1,S 2,S 3 AA 1,AA 2,AA 3 ), P cs (S Hα,N,Cα,Cβ,AA) E = N N i=1logp 3 (i) K cs logp cs (i) i=1 Secondary structure propensity P(S A)= N S N
39 Local structure 2 Chemical shifts Prediction of local geometry Database Torsion angle prediction S(Φ i,ψ i A,CS)=Sym(B,A)+Sym( CS A, CS B )+Sym(S A,S B ) Three best scoring cluster centers are taken as prediction.
40 Fragment selection Chemical shifts Fragment selection Database E = Fragments of length 3 and 9 aa N N i=1e cs (A i, CS A,B i, CS B )+K tor E tar (Φ i,ψ i,b) i=1 Performance Protein 3Pred TOPOS Ubiquitin FF domain Calbindin HPR
41 Fold Fragment assembly Energy function
42 Fold Fragment assembly Energy function
43 Refinement 1 Chemical shifts Refinement Energy function Energy function E re f = E ff /log(1 C cs ) where C cs = K χ (1 C χ ), C χ correlation of CS type χ χ {Hα,N,Cα,Cβ}
44 Refinement 2
45 Refinement 2 Structure with large Rg are discarded
46 Refinement 2 Structure with large Rg are discarded Side-chains are added
47 Refinement 2 Structure with large Rg are discarded Side-chains are added Initial ranking
48 Refinement 2 Takes one structure at random from the best-list. New structure generated by simulated annealing. Structure with large Rg are discarded Side-chains are added Initial ranking Keeps a list of the 100 best structures
49 Results
50 Results
51 The largest
52 The largest 2GW6, 123 aa 1.72 Å backbone RMSD
53 The smallest
54 The smallest 1PV0, 46 aa 1.37 Å backbone RMSD
55 Solid-State NMR of protein G
56 Solid-State NMR of protein G
57 Solid-State NMR of protein G Structure RMSD N (5.5 A ) Q (RDC) 1P7F GB GB JU K0P
58 Failures
59 Failures 0 S = *NA, R= Score Number of Amino Acids
60 Failures 1ZGG S = *NA, R= Refined Structures Refined Native Structure Expected Score Score -400 Score Score Number of Amino Acids C!-RMSD C!-RMSD
61 Failures 1ZGG S = *NA, R= Refined Structures Refined Native Structure Expected Score Score -400 Score Score Number of Amino Acids C!-RMSD C!-RMSD Why? Usually because the assembly stage does not generate low RMSD models.
62 CamShift
63 CamShift Chemical shifts are predicted using distances to neighboring atoms R N C C H H O R N C C H H O
64 CamShift Chemical shifts are predicted using distances to neighboring atoms Accurate as ShiftX or Sparta and orders of magnitude faster R N C C H H O R N C C H H O
65 CamShift Chemical shifts are predicted using distances to neighboring atoms Accurate as ShiftX or Sparta and orders of magnitude faster CamShift with physical force field and ReX molecular dynamics N R C C H H R N C C H H O O
66 CamShift Chemical shifts are predicted using distances to neighboring atoms Accurate as ShiftX or Sparta and orders of magnitude faster CamShift with physical force field and ReX molecular dynamics ~ 1 A from unfolded for small proteins (1uzc, 1ubq,..) N R C C H H R N C C H H O O
67 CamShift-MD 2jvw: 61 residues Lowest Energy Structure 1.41Å RMSD 2jva: 108 residues Lowest Energy Structure 1.98 Å RMSD
68 CamShift Full No Long range Sparta HN HA N CA CB CO
69 Conclusions
70 Conclusions Protein structure determination with chemical shifts is possible...
71 Conclusions Protein structure determination with chemical shifts is possible... but difficult... very difficult...
72 Conclusions Protein structure determination with chemical shifts is possible... but difficult... very difficult... CHESHIRE works (at the moment) for proteins up to ~100 aa.
73 Conclusions Protein structure determination with chemical shifts is possible... but difficult... very difficult... CHESHIRE works (at the moment) for proteins up to ~100 aa. results are stable ~ Å Cα RMSD.
74 Conclusions Protein structure determination with chemical shifts is possible... but difficult... very difficult... CHESHIRE works (at the moment) for proteins up to ~100 aa. results are stable ~ Å Cα RMSD. self-consistent criterion to (maybe) detect failures of the method.
75 Conclusions Protein structure determination with chemical shifts is possible... but difficult... very difficult... CHESHIRE works (at the moment) for proteins up to ~100 aa. results are stable ~ Å Cα RMSD. self-consistent criterion to (maybe) detect failures of the method. can be used for complexes and with solid-state CS.
76 Conclusions Protein structure determination with chemical shifts is possible... but difficult... very difficult... CHESHIRE works (at the moment) for proteins up to ~100 aa. results are stable ~ Å Cα RMSD. self-consistent criterion to (maybe) detect failures of the method. can be used for complexes and with solid-state CS.
77 Acknowledgments Michele Vendruscolo Chris Dobson Xavier Salvatella Kai Kohlhof Paul Robustelli Danny Hsu Rinaldo Wander Montalvao
Determination of the structure and dynamics of proteins using NMR chemical shifts (CS) and CS enhanced protein data bank (CS-PDB)
Determination of the structure and dynamics of proteins using NMR chemical shifts (CS) and CS enhanced protein data bank (CS-PDB) Biao Fu The Vendruscolo group, Uni. Of Cambridge Intensive Training Course,
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
Protein Structure Prediction
Protein Structure Prediction Michael Feig MMTSB/CTBP 2006 Summer Workshop From Sequence to Structure SEALGDTIVKNA Ab initio Structure Prediction Protocol Amino Acid Sequence Conformational Sampling to
Principles of NMR Protein Spectroscopy. 2) Assignment of chemical shifts in a protein ( 1 H, 13 C, 15 N) 3) Three dimensional structure determination
1) Protein preparation (>50 aa) 2) Assignment of chemical shifts in a protein ( 1 H, 13 C, 15 N) 3) Three dimensional structure determination Protein Expression overexpression in E. coli - BL21(DE3) 1
Magnetic Resonance Lectures for Chem 341 James Aramini, PhD. CABM 014A
Magnetic Resonance Lectures for Chem 341 James Aramini, PhD. CABM 014A jma@cabm.rutgers.edu " J.A. 12/11/13 Dec. 4 Dec. 9 Dec. 11" " Outline" " 1. Introduction / Spectroscopy Overview 2. NMR Spectroscopy
Useful background reading
Overview of lecture * General comment on peptide bond * Discussion of backbone dihedral angles * Discussion of Ramachandran plots * Description of helix types. * Description of structures * NMR patterns
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
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*
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
Tools 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
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
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
Presenter: 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
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
Homology 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
7.91 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
Contact 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:
Protein 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
Modeling for 3D structure prediction
Modeling for 3D structure prediction What is a predicted structure? A structure that is constructed using as the sole source of information data obtained from computer based data-mining. However, mixing
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
Ab-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
Programme 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
Structure determination through NMR
Structure determination through NMR Protein Sample NMR data acquisition Sequential resonance assignment Collection of conformational constraints 3D structure calculations Structure refinement and Analysis
PROTEIN'STRUCTURE'DETERMINATION'
PROTEIN'STRUCTURE'DETERMINATION' USING'NMR'RESTRAINTS' BCMB/CHEM'8190' Programs for NMR Based Structure Determination CNS - Brünger, A. T.; Adams, P. D.; Clore, G. M.; DeLano, W. L.; Gros, P.; Grosse-Kunstleve,
NMR Assay of Purity and Folding
NMR Assay of Purity and Folding Don t Need Resonance Assignments or Labeling 1D requires only 10-50 µm protein concentration 2D Provides A More Detailed Assay 15 N- 1 H HSQC 1 H COSY 13 C HSQC also! Analyze
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
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
Theory and Applications of Residual Dipolar Couplings in Biomolecular NMR
Theory and Applications of Residual Dipolar Couplings in Biomolecular NMR Residual Dipolar Couplings (RDC s) Relatively new technique ~ 1996 Nico Tjandra, Ad Bax- NIH, Jim Prestegard, UGA Combination of
MD Simulation in Pose Refinement and Scoring Using AMBER Workflows
MD Simulation in Pose Refinement and Scoring Using AMBER Workflows Yuan Hu (On behalf of Merck D3R Team) D3R Grand Challenge 2 Webinar Department of Chemistry, Modeling & Informatics Merck Research Laboratories,
Protein Structure Determination Using NMR Restraints BCMB/CHEM 8190
Protein Structure Determination Using NMR Restraints BCMB/CHEM 8190 Programs for NMR Based Structure Determination CNS - Brünger, A. T.; Adams, P. D.; Clore, G. M.; DeLano, W. L.; Gros, P.; Grosse-Kunstleve,
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
Protein 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
Goals. Structural Analysis of the EGR Family of Transcription Factors: Templates for Predicting Protein DNA Interactions
Structural Analysis of the EGR Family of Transcription Factors: Templates for Predicting Protein DNA Interactions Jamie Duke 1,2 and Carlos Camacho 3 1 Bioengineering and Bioinformatics Summer Institute,
Orientational 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
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
Protein Structure Refinement Using 13 C α Chemical. Shift Tensors. Benjamin J. Wylie, Charles D. Schwieters, Eric Oldfield and Chad M.
Protein Structure Refinement Using 13 C α Chemical Shift Tensors Benjamin J. Wylie, Charles D. Schwieters, Eric Oldfield and Chad M. Rienstra * Department of Chemistry, University of Illinois at Urbana-Champaign,
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 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
Deuteration: Structural Studies of Larger Proteins
Deuteration: Structural Studies of Larger Proteins Problems with larger proteins Impact of deuteration on relaxation rates Approaches to structure determination Practical aspects of producing deuterated
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
Human and Server CAPRI Protein Docking Prediction Using LZerD with Combined Scoring Functions. Daisuke Kihara
Human and Server CAPRI Protein Docking Prediction Using LZerD with Combined Scoring Functions Daisuke Kihara Department of Biological Sciences Department of Computer Science Purdue University, Indiana,
Molecular 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
De novo protein structure determination using sparse NMR data
Journal of Biomolecular NMR, 18: 311 318, 2000. KLUWER/ESCOM 2000 Kluwer Academic Publishers. Printed in the Netherlands. 311 De novo protein structure determination using sparse NMR data Peter M. Bowers
1) NMR is a method of chemical analysis. (Who uses NMR in this way?) 2) NMR is used as a method for medical imaging. (called MRI )
Uses of NMR: 1) NMR is a method of chemical analysis. (Who uses NMR in this way?) 2) NMR is used as a method for medical imaging. (called MRI ) 3) NMR is used as a method for determining of protein, DNA,
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
Origin of Chemical Shifts BCMB/CHEM 8190
Origin of Chemical Shifts BCMB/CHEM 8190 Empirical Properties of Chemical Shift υ i (Hz) = γb 0 (1-σ i ) /2π The Larmor frequencies of nuclei depend on the electronic structure of the molecule and the
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
HSQC spectra for three proteins
HSQC spectra for three proteins SH3 domain from Abp1p Kinase domain from EphB2 apo Calmodulin What do the spectra tell you about the three proteins? HSQC spectra for three proteins Small protein Big protein
Carlo Camilloni, Andrea Cavalli,, and Michele Vendruscolo INTRODUCTION
pubs.acs.org/jpcb Assessment of the Use of NMR Chemical Shifts as Replica-Averaged Structural Restraints in Molecular Dynamics Simulations to Characterize the Dynamics of Proteins Carlo Camilloni, Andrea
Residual Dipolar Couplings BCMB/CHEM 8190
Residual Dipolar Couplings BCMB/CHEM 8190 Recent Reviews Prestegard, A-Hashimi & Tolman, Quart. Reviews Biophys. 33, 371-424 (2000). Bax, Kontaxis & Tjandra, Methods in Enzymology, 339, 127-174 (2001)
Protein Structure Determination Using NMR Restraints BCMB/CHEM 8190
Protein Structure Determination Using NMR Restraints BCMB/CHEM 8190 Programs for NMR Based Structure Determination CNS - Brunger, A. T.; Adams, P. D.; Clore, G. M.; DeLano, W. L.; Gros, P.; Grosse-Kunstleve,
Predicting Continuous Local Structure and the Effect of Its Substitution for Secondary Structure in Fragment-Free Protein Structure Prediction
Predicting Continuous Local Structure and the Effect of Its Substitution for Secondary Structure in Fragment-Free Protein Structure Prediction Author Faraggi, Eshel, Yang, Yuedong, Zhang, Shesheng, Zhou,
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
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 2/15/07 CAP5510 1 EM Algorithm Goal: Find θ, Z that maximize Pr
Guided Prediction with Sparse NMR Data
Guided Prediction with Sparse NMR Data Gaetano T. Montelione, Natalia Dennisova, G.V.T. Swapna, and Janet Y. Huang, Rutgers University Antonio Rosato CERM, University of Florance Homay Valafar Univ of
HADDOCK: High Ambiguity
Determination of Protein-Protein complexes HADDOCK: High Ambiguity Driven DOCKing A protein-protein docking approach based on biochemical and/or biophysical data In PDB: >15000 protein structures but
Course 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.........................................
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
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
CS23D: a web server for rapid protein structure generation using NMR chemical shifts and sequence data
W496 W502 Nucleic Acids Research, 2008, Vol. 36, Web Server issue Published online 30 May 2008 doi:10.1093/nar/gkn305 CS23D: a web server for rapid protein structure generation using NMR chemical shifts
Template-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
arxiv: v1 [physics.chem-ph] 23 Sep 2014
![arxiv: v1 [physics.chem-ph] 23 Sep 2014](/thumbs/90/102028178.jpg "arxiv: v1 [physics.chem-ph] 23 Sep 2014")
arxiv:1409.6772v1 [physics.chem-ph] 23 Sep 2014 Abstract The protein chemical shifts holds a large amount of information about the 3-dimensional structure of the protein. A number of chemical shift predictors
Polypeptide Folding Using Monte Carlo Sampling, Concerted Rotation, and Continuum Solvation
Polypeptide Folding Using Monte Carlo Sampling, Concerted Rotation, and Continuum Solvation Jakob P. Ulmschneider and William L. Jorgensen J.A.C.S. 2004, 126, 1849-1857 Presented by Laura L. Thomas and
NMR in Structural Biology
NMR in Structural Biology Exercise session 2 1. a. List 3 NMR observables that report on structure. b. Also indicate whether the information they give is short/medium or long-range, or perhaps all three?
Protein Folding Prof. Eugene Shakhnovich
Protein Folding Eugene Shakhnovich Department of Chemistry and Chemical Biology Harvard University 1 Proteins are folded on various scales As of now we know hundreds of thousands of sequences (Swissprot)
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
SHIFTX2: significantly improved protein chemical shift prediction
J Biomol NMR (2011) 50:43 57 DOI 10.1007/s10858-011-9478-4 ARTICLE SHIFTX2: significantly improved protein chemical shift prediction Beomsoo Han Yifeng Liu Simon W. Ginzinger David S. Wishart Received:
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.
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
Table S1. Primers used for the constructions of recombinant GAL1 and λ5 mutants. GAL1-E74A ccgagcagcgggcggctgtctttcc ggaaagacagccgcccgctgctcgg
SUPPLEMENTAL DATA Table S1. Primers used for the constructions of recombinant GAL1 and λ5 mutants Sense primer (5 to 3 ) Anti-sense primer (5 to 3 ) GAL1 mutants GAL1-E74A ccgagcagcgggcggctgtctttcc ggaaagacagccgcccgctgctcgg
Experimental Techniques in Protein Structure Determination
Experimental Techniques in Protein Structure Determination Homayoun Valafar Department of Computer Science and Engineering, USC Two Main Experimental Methods X-Ray crystallography Nuclear Magnetic Resonance
PROTEIN-PROTEIN DOCKING REFINEMENT USING RESTRAINT MOLECULAR DYNAMICS SIMULATIONS
TASKQUARTERLYvol.20,No4,2016,pp.353 360 PROTEIN-PROTEIN DOCKING REFINEMENT USING RESTRAINT MOLECULAR DYNAMICS SIMULATIONS MARTIN ZACHARIAS Physics Department T38, Technical University of Munich James-Franck-Str.
NMR in Medicine and Biology
NMR in Medicine and Biology http://en.wikipedia.org/wiki/nmr_spectroscopy MRI- Magnetic Resonance Imaging (water) In-vivo spectroscopy (metabolites) Solid-state t NMR (large structures) t Solution NMR
Computational 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
Supporting Information
Supporting Information Micelle-Triggered b-hairpin to a-helix Transition in a 14-Residue Peptide from a Choline-Binding Repeat of the Pneumococcal Autolysin LytA HØctor Zamora-Carreras, [a] Beatriz Maestro,
Homology Modeling. Roberto Lins EPFL - summer semester 2005
Homology Modeling Roberto Lins EPFL - summer semester 2005 Disclaimer: course material is mainly taken from: P.E. Bourne & H Weissig, Structural Bioinformatics; C.A. Orengo, D.T. Jones & J.M. Thornton,
The PROSECCO server for chemical shift predictions in ordered and disordered proteins
J Biomol NMR (2017) 69:147 156 DOI 10.1007/s10858-017-0145-2 ARTICLE The PROSECCO server for chemical shift predictions in ordered and disordered proteins Máximo Sanz Hernández 1 Alfonso De Simone 1 Received:
Building 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
SUPPLEMENTARY ONLINE DATA
SUPPLEMENTARY ONLINE DATA Secreted Isoform of Human Lynx1 (SLURP-2): Spatial Structure and Pharmacology of Interaction with Different Types of Acetylcholine Receptors E.N. Lyukmanova 1,2,*, M.A. Shulepko
Interpreting and evaluating biological NMR in the literature. Worksheet 1
Interpreting and evaluating biological NMR in the literature Worksheet 1 1D NMR spectra Application of RF pulses of specified lengths and frequencies can make certain nuclei detectable We can selectively
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
Docking. GBCB 5874: Problem Solving in GBCB
Docking Benzamidine Docking to Trypsin Relationship to Drug Design Ligand-based design QSAR Pharmacophore modeling Can be done without 3-D structure of protein Receptor/Structure-based design Molecular
Characterization of the free-energy landscapes of proteins by NMR-guided metadynamics
Characterization of the free-energy landscapes of proteins by NMR-guided metadynamics B Results and Discussion BIOPHYSICS ND COMPUTTIONL BIOLOGY SI Text Inset B χ χ Inset SI Text Inset C Folding of GB3
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
Supplementary Figures:
Supplementary Figures: Supplementary Figure 1: The two strings converge to two qualitatively different pathways. A) Models of active (red) and inactive (blue) states used as end points for the string calculations
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
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
Timescales of Protein Dynamics
Timescales of Protein Dynamics From Henzler-Wildman and Kern, Nature 2007 Summary of 1D Experiment time domain data Fourier Transform (FT) frequency domain data or Transverse Relaxation Ensemble of Nuclear
Millisecond Time-scale Protein Dynamics by Relaxation Dispersion NMR. Dmitry M. Korzhnev
Millisecond Time-scale Protein Dynamics by Relaxation Dispersion NMR Dmitry M. Korzhnev Department of Molecular, Microbial and Structural Biology University of Connecticut Health Center 263 Farmington
NMR parameters intensity chemical shift coupling constants 1D 1 H spectra of nucleic acids and proteins
Lecture #2 M230 NMR parameters intensity chemical shift coupling constants Juli Feigon 1D 1 H spectra of nucleic acids and proteins NMR Parameters A. Intensity (area) 1D NMR spectrum: integrated intensity
Can 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,
Finding Similar Protein Structures Efficiently and Effectively
Finding Similar Protein Structures Efficiently and Effectively by Xuefeng Cui A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Doctor of Philosophy
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
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
Geometry-based Computation of Symmetric Homo-oligomeric Protein Complexes
Geometry-based Computation of Symmetric Homo-oligomeric Protein Complexes Christopher Miles Brian Olson Amarda Shehu, Abstract The need to engineer novel therapeutics and functional materials is driving
Timescales of Protein Dynamics
Timescales of Protein Dynamics From Henzler-Wildman and Kern, Nature 2007 Dynamics from NMR Show spies Amide Nitrogen Spies Report On Conformational Dynamics Amide Hydrogen Transverse Relaxation Ensemble
Protein Folding by Robotics
Protein Folding by Robotics 1 TBI Winterseminar 2006 February 21, 2006 Protein Folding by Robotics 1 TBI Winterseminar 2006 February 21, 2006 Protein Folding by Robotics Probabilistic Roadmap Planning
A.D.J. van Dijk "Modelling of biomolecular complexes by data-driven docking"
Chapter 3. Various strategies of using Residual Dipolar Couplings in NMRdriven protein docking: application to Lys48-linked di-ubiquitin and validation against 15 N-relaxation data. Aalt D.J. van Dijk,
NMR Structures in the Cloud Validation & Improvement
NMR Structures in the Cloud Validation & Improvement Centre for Molecular & Biomolecular Informatics Jurgen F. Doreleijers, Geerten W. Vuister, Gert Vriend Outline of the talk Introduction CING: a structure