protein structure determination cb1 3dexp lecture: protein ... · experiments determine protein...
TRANSCRIPT
© Burkhard Rost (TUM Munich) /701
title: Protein structure determinationshort title: cb1_3dexp
lecture: Protein Prediction 1 - Protein structure Computational Biology 1 TUM summer 2014
Monday May 19, 2014
© Burkhard Rost (TUM Munich) /70
Announcements
Videos: YouTube / www.rostlab.orgTHANKS : Tim Karl + Jonas ReebSpecial lectures:• Apr 15 - Andrea Schafferhans
No lecture:• Apr 17/22 Easter• May 01 Thu May day• May 06 Tue Student assembly• May 29 Thu Ascension day• Jun 03 Tue no room• Jun 10 Tue Whitsun holidays• Jun 19 Thu Corpus Christi
LAST lecture: July 1Examen: July 8 • Makeup: Oct 21 - morning
CONTACT: Lothar Richter [email protected]
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TimKarl
LotharRichter
JonasReeb
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Recap: 3D prediction by
comparative modeling
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Zones
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Day
light
Zon
e
Twili
ght Z
one
Mid
nigh
t Zon
eprofile - profile
sequence - profilesequence - sequence
sequ
ence
sim
ilar
->st
ruct
ure s
imila
r
B Rost (1997) Fold Des 2:S19-24B Rost (1999) Protein Eng 12:85-94
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human - fly - bacteria
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3gft: Y Tong et al. & H Park (unpublished) / 4IW3: JS Scotti (unpublished)3lbn: G Buhrman et al. & C Mattos (2010) PNAS 107:4931-6.2y8e: M Walden, HT Jenkins, TA Edwards (2011) Acta Crystallogr F 67:744
green: 3gft K-Ras - humanlime: 3lbn Rash - humanorange: 2y8e Rab6 - flypurple: 2y8e hydroxylase P putida
AndreaSchaffer-
hans
Slide from:
PIDE: pairwise identical residues
19%
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Comparative modeling methods
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MODELLERlots of whistles and bells, downloadable, very accurate
SWISS-MODELautomated, increasingly comprehensive and flexible
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Comparative modeling applicable to about 1/3 of all proteins
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Comparative modeling 2
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Goal of structure prediction
Epstein & Anfinsen, 1961: sequence uniquely determines structure
• INPUT: sequence
3D structureand function
• OUTPUT:
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protein folding from first principles should then be
possible10
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Protein structure prediction problem solved!
60s - Washington Post
70s - New York Times
90s - Washington Post
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Problem: predict the 3D structure of a protein from sequence alone
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How would you assess prediction performance?
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??
???
How to get those into the prediction?
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CASP
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Critical Assessment of Structure PredictionApril-May (Organizers): collect experimental structures (since 2004 from structural genomics)June-August: Prediction seasondeadline: predictions in before experimental structures are publishedSeptember-November: Assessors divineDecember: Meeting to discuss results
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CASP
Protein Structure Prediction
© Burkhard Rost (Columbia New York)
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CASP
Protein Structure Prediction
Only homology modeling good
© Burkhard Rost (Columbia New York)
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CASP
Protein Structure Prediction
Only homology modeling goodNo general prediction of 3D from sequence, yet
© Burkhard Rost (Columbia New York)
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CASP
Protein Structure Prediction
Only homology modeling goodNo general prediction of 3D from sequence, yetImportant improvements in many fields!
© Burkhard Rost (Columbia New York)
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Servers, META-servers, META-META, …
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CASP 9 results
TBM • overall good
17A Kryshtafovych et al. 2011 Proteins, 79 Suppl 10:196-207
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Problems with CASP
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Problems of CASP
Comparisons based on apples and orangesAnalysis of irrelevant types of test casesInappropriate rankingConclusions based on insignificant differencesDifferent categories evaluated differentlytoo few targets
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Ranking not stable!
29 different worse than 11 identical
VA Eyrich, IYY Koh, D Przybylski, O Graña, F Pazos, A Valencia and B Rost (2003) Proteins 53 Suppl 6 548-60
© Burkhard Rost (Columbia New York)
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Pairwise comparison matrix
© Burkhard Rost (Columbia New York)
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Conclusion: Comparative modeling
Comparative modeling/homology modeling most accurate way to predict structureas good and as complete as the templateè depends on quality and similarity of templatemostly driven by accuracy of alignmentè driven by alignment qualityloop modeling: still not fully there, yetside chain modeling: unclear how well we do
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3D from experiment 2 co-ordinates
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3D details - 3D cartoon
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Structure by experiment
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Experiments determine protein structure
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Number Percentage
PDB 84,413 1Xray 75,068 89NMR 8,723 10EMElectronMicroscopy 428 1
PDB (Protein Data Bank) Helen Berman (Rutgers Univ, New Brunswick) &
Phil Bourne (UCSD San Diego)
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Protein structure by X-ray crystallography
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Number Percentage
PDB 84,413 1
Xray 75,068 89
NMR 8,723 10
EMElectronMicroscopy
428 1
© Wikipedia
Myoglobin structure * JC Kendrew, G Bodo, HM Dintzis, RG Parrish, H Wyckoff & DC Phillips (1958) Nature 181:662-6* THIS 1mbo: SE Philips JMB 142:531-54(image Wikipedia Aza Toth)(Hemoglobin: Max Perutz 1959)
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Protein structure by NMR* spectroscopy
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© Wikipedia
Number Percentage
PDB 84,413 1
Xray 75,068 89
NMR 8,723 10
EMElectronMicroscopy
428 1
* NMR: Nuclear Magnetic Resonance
NMR tube
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Protein structure by NMR* spectroscopy
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© Wikipedia
900 MHz NMR machine
1ssu: Y Kamikubo et al. & HJ Dyson (2004) Biochemistry 43:6519-34
Number Percentage
PDB 84,413 1
Xray 75,068 89
NMR 8,723 10
EMElectronMicroscopy
428 1
* NMR: Nuclear Magnetic Resonance
NYSBC - City College New
York City
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Protein structure by cryo-EM
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© Wikipedia
Number Percentage
PDB 84,413 1
Xray 75,068 89
NMR 8,723 10
EMElectronMicroscopy
428 1
* EM: Cryo-Electron Microscopy
4 Ångstrøm 8Å 16Å 32ÅGroEL - J Wang & DC Bosvert (2004) 1j4zImages: Vossman 2007 - Wikipedia
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Protein structure by cryo-EM
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Number Percentage
PDB 84,413 1
Xray 75,068 89
NMR 8,723 10
EMElectronMicroscopy
428 1
* EM: Cryo-Electron Microscopy
T Ju, M Baker, W Chiu (2006) Computer-Aided Design 39:352-60
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Structure resolution
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© PDB
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Notation: protein structure 1D, 2D, 3DPQITLWQRPLVTIKIGGQLKEALLDTGADDTVL
PP PQQQYFFQVISSIVRLLSTLWWQEDRKQAKRRRPQPPPPPVVTKFVVLIITTKEKAALIVHYKKFIILVIEENGGGGGTGQQKRRPPLWWVVFKVEESKKVVGLGLLILLLLLVVDDDDDTTTTTGGGGGAAAAADDDDDDDAKESSTTVIIVIVVVIVL
1281757077
120238169200247114740
904
466268
11831
1241
292449726217
102691
140
1109760691481976248590
690
730
415371597395000
5851300
79586900
EEEEE
EEEEEE
EEEEEEE
EE
EEEEE
EEEEEE
EE
kcal/mol0 -1 -2 -3 -4 -5
1 10 20 30 40 50 60 70 80 90
1
10
20
30
40
50
60
70
80
90
1D1D 2D2D 3D3D
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Secondary structure stabilized by
hydrogen bonds
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Linus Pauling: introduce conceptL Pauling and RB Corey (1951) Configurations of Polypeptide Chains with Favored Orientations Around Single Bonds: Two New Pleated Sheets PNAS 37: 729-40L Pauling, RB Corey and HR Branson (1951) The Structure of Proteins: Two Hydrogen-bonded Helical Configurations of the Polypeptide Chain PNAS 37: 205-34L Pauling, RB Corey and HR Branson (1951) Two Hydrogen-Bonded Helical Configurations of the Polypeptide Chain PNAS 37: 205-11L Pauling and RB Corey (1953) Two Rippled-sheet Configurations of Polypeptide Chains, and a Note About the Pleated Sheets PNAS 39: 253-6L Pauling and RB Corey (1953) Two Pleated-sheet Configurations of Polypeptide Chains Involving Both cis and trans Amide Groups PNAS 39: 247-52
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Linus Pauling
Nobel Foundation: The Nobel Prize in Chemistry 1954 was awarded to Linus Pauling "for his research into the nature of the chemical bond and its application to the elucidation of the structure of complex substances”.http://www.nobelprize.org/nobel_prizes/chemistry/laureates/1954/
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Hydrogen-bond formation
36© Wikipedia
http://www.ausetute.com.au/proteins.html
strandhelix
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3D details - 3D cartoon
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3D details - 3D cartoon
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Pauling Nobel Prize 1954 -
first protein structure when?
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First protein structures
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John Kendrew Max Perutz
myoglobin
JC Kendrew et al. & DC Phillips (Mar 1958) Nature 181: 662–6.
hemoglobin
MF Perutz et al. & AC North (1960) Nature 185: 416-22.
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Secondary structure assignment
Different evaluation criteria applied:
Assignment coverage: DEFINEGeometry (fitting ideal sec str segments)FM Richards & CE Kundrot (1988) Proteins 3:71-84
Enthalpic energy: DSSPW Kabsch & C Sander (1983) Biopolymers 22:2577-637
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DSSPDictionary of
Secondary Structure of Proteins
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Secondary Structure Assignment: DSSP
Dictionary of protein Secondary Structure for Proteins
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Wolfgang Kabsch & Chris Sander (1983) Biopolymers 22:2577-637
Wolfgang KabschChris Sander
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DSSP: W Kabsch & C Sander (1983) Biopolymers 22: 2577-2637
DSSP: Coulomb
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L Pauling & RB Corey (1953) PNAS 39:247-252L Pauling, RB Corey & HR Branson (1951) PNAS 37:205-234W Kabsch & C Sander (1983) Biopolymers 22:2577-2637
DSSP
Pauling’s H-bond pattern used in DSSP
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L Pauling & RB Corey (1953) PNAS 39:247-252L Pauling, RB Corey & HR Branson (1951) PNAS 37:205-234W Kabsch & C Sander (1983) Biopolymers 22:2577-2637
DSSP
Pauling’s H-bond pattern used in DSSP
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L Pauling & RB Corey (1953) PNAS 39:247-252L Pauling, RB Corey & HR Branson (1951) PNAS 37:205-234W Kabsch & C Sander (1983) Biopolymers 22:2577-2637
DSSP
Pauling’s H-bond pattern used in DSSP
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DSSP assigns 7 “states”
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H -> helix (i,i+4) helix HG -> 310 helix (i,i+3) helix HT -> turn of helix other LE -> extended/strand strand EB -> beta-bulge strand ES -> bend (no H-bond) other L“ “ -> loop other L
7 DSSP states 3 state map
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Secondary structure assignment
Different evaluation criteria applied:
Assignment coverage: DEFINEGeometry (fitting ideal sec str segments)FM Richards & CE Kundrot (1988) Proteins 3:71-84
Enthalpic energy: DSSPW Kabsch & C Sander (1983) Biopolymers 22:2577-637 Expert assignment: STRIDED Frishman & P Argos (1995) Proteins 23:566-79
Predictability: NNass
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1D: secondarystructureprediction
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Notation: protein structure 1D, 2D, 3DPQITLWQRPLVTIKIGGQLKEALLDTGADDTVL
PP PQQQYFFQVISSIVRLLSTLWWQEDRKQAKRRRPQPPPPPVVTKFVVLIITTKEKAALIVHYKKFIILVIEENGGGGGTGQQKRRPPLWWVVFKVEESKKVVGLGLLILLLLLVVDDDDDTTTTTGGGGGAAAAADDDDDDDAKESSTTVIIVIVVVIVL
1281757077
120238169200247114740
904
466268
11831
1241
292449726217
102691
140
1109760691481976248590
690
730
415371597395000
5851300
79586900
EEEEE
EEEEEE
EEEEEEE
EE
EEEEE
EEEEEE
EE
kcal/mol0 -1 -2 -3 -4 -5
1 10 20 30 40 50 60 70 80 90
1
10
20
30
40
50
60
70
80
90
1D1D 2D2D 3D3D
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Words
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Secondary structure prediction2ndary structure prediction2D prediction
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Close Homology (Sequence Id. > 60% Psi-Blast Eval < 10-20)
Distant Homology (Domain, Motif)
Machine Learning (NN, SVM)
Protein Space:
X=Positive Y=Negative
Protein function classification
© Kaz Wrzeszczynski: Thesis
W
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Coverage of structure space
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Secondary structure prediction
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DSSP secondary assignment has 8 “states”
H = HelixG = 310 helixI = Pi helixE = Extended (strand)B = beta-bridge, single strand residueT = Turn, i.e. one turn of helixS = bent“ “ = loop
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Goal of secondary structure prediction
LEDKSPDHNPTGID
AKGKPMDRNFTGRNHPPKDSS
AAQVKDALTK
LEQWGTLAQLRAIWEQELTDFPEFLTMMARQETWLGWLTI
helix strand
loop
LAVIGVLMKW
FVFLMIEKIYHKLT
DIRVGLTYYIAQ
VNTFVGTFAAVAHAL
56W Kabsch & C Sander (1985) Identical pentapetides with different backbones. Nature 317:207
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??
???
How pentapeptides occur in 2 states?
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Secondary structure prediction methods
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L Pauling, RB Corey and HR Branson (1951) Two Hydrogen-Bonded Helical Configurations of the Polypeptide Chain. PNAS 37:205-211.L Pauling, RB Corey and HR Branson (1951) The Structure of Proteins: Two Hydrogen-bonded Helical Configurations of the Polypeptide Chain. PNAS 37:205-234.AG Szent-Györgyi & C Cohen (1957) Role of proline in polypeptide chain configuration of proteins. Science 126:697.some are more equal than others ...
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Sec str pred methods: single residues
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Pauling, RB Corey and HR Branson (1951) Two Hydrogen-Bonded Helical Configurations of the Polypeptide Chain. PNAS 37:205-211.L Pauling, RB Corey and HR Branson (1951) The Structure of Proteins: Two Hydrogen-bonded Helical Configurations of the Polypeptide Chain. PNAS 37:205-234.AG Szent-Györgyi & C Cohen (1957) Role of proline in polypeptide chain configuration of proteins. Science 126:697.MF Perutz, MG Rossmann, AF Cullis, G Muirhead, G Will and AT North (1960) Structure of haemoglobin: a three-dimensional Fourier synthesis at 5.5 Å resolution, obtained by X-ray analysis. Nature 185:416-422.JC Kendrew, RE Dickerson, BE Strandberg, RJ Hart, DR Davies and DC Phillips (1960) Structure of myoglobin: a three-dimensional Fourier synthesis at 2 Å resolution. Nature 185:422-427.
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Simple prediction: frequency
First step (Szent-Györgyi)Proline breaks a helixHelices span several turns, i.e. >4 residues-> identify helices/non-helices
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Proline bends main chain
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Simple prediction: frequency
First step (Szent-Györgyi)Proline breaks a helixHelices span several turns, i.e. >4 residues-> identify helices/non-helices
from Proline to odds for all ....
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Simple prediction: frequency
from Proline to odds for all
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....,....1....,....2....QEKSPREVTMKKGDILTLLNSTNK E..E EEEEEE
AA D E G I K L M N P Q R S T V
E 1 1 3 1 1 1
L 1 1 1 4 1 1 1 1 2 1
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Secondary structure prediction methods
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single residues (1. generation)• Chou-Fasman, GOR 1957-70/80
Robson B & Pain RH (1971) Analysis of the Code Relating Sequence to Conformation in Proteins: Possible Implications for the Mechanism of Formation of Helical Regions. J. Mol. Biol. 58:237-259.Chou PY & Fasman GD (1974) Prediction of protein conformation. Biochemistry 13:211-215.Garnier J, Osguthorpe DJ and Robson B (1978) Analysis of the accuracy and Implications of simple methods for predicting the secondary structure of globular proteins. J. Mol. Biol. 120:97-120.
Monday May 19, 2014
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how to assess performance?
problem 1: where to get secondary structure from?
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how to assess performance?
problem 2: how to measure?
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Secondary structure prediction accuracy
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• Q3 : three-state per-residue accuracy
number of correctly predicted residues in states helix, strand, otherQ3= ---------------------------------------------------------------------------- number of residues in protein
Schulz GE & Schirmer RH (1979) Prediction of secondary structure from the amino acid sequence. In: (eds). Principles of protein structure. Berlin: Springer-Verlag, pp 108-130.
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Secondary structure prediction methods
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single residues (1. generation)• Chou-Fasman, GOR 1957-70/80
published: 63% accuracy
Robson B & Pain RH (1971) Analysis of the Code Relating Sequence to Conformation in Proteins: Possible Implications for the Mechanism of Formation of Helical Regions. J. Mol. Biol. 58:237-259.Chou PY & Fasman GD (1974) Prediction of protein conformation. Biochemistry 13:211-215.Garnier J, Osguthorpe DJ and Robson B (1978) Analysis of the accuracy and Implications of simple methods for predicting the secondary structure of globular proteins. J. Mol. Biol. 120:97-120.
Monday May 19, 2014
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Secondary Structure Assignment: DSSP
Dictionary of protein Secondary Structure for ProteinsASSESSING secondary structure prediction
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Wolfgang Kabsch & Chris Sander (1983) Biopolymers 22:2577-637
Wolfgang KabschChris Sander
Monday May 19, 2014
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Secondary structure prediction methods
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single residues (1. generation)• Chou-Fasman, GOR 1957-70/80
50-55% accuracy (assessed in 1994)
Robson B & Pain RH (1971) Analysis of the Code Relating Sequence to Conformation in Proteins: Possible Implications for the Mechanism of Formation of Helical Regions. J. Mol. Biol. 58:237-259.Chou PY & Fasman GD (1974) Prediction of protein conformation. Biochemistry 13:211-215.Garnier J, Osguthorpe DJ and Robson B (1978) Analysis of the accuracy and Implications of simple methods for predicting the secondary structure of globular proteins. J. Mol. Biol. 120:97-120.
Monday May 19, 2014
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Lecture plan (CB1: Structure)-generic01: 2014/04/08 Tue: sorry02: 2014/04/10 Thu: welcome: who we are03: 2014/04/15 Tue: Intro I - acids/structure (Andrea Schafferhans)04: 2014/04/17 Thu: SKIP: Easter vacation05: 2014/04/22 Tue: SKIP: Easter vacation06: 2014/04/24 Thu: Intro 2 - domains07: 2014/04/29 Tue: Intro 3 - 3D comparisons08: 2014/05/01 Thu: SKIP: “May day” - (NOT to be confused with “m’aidez”)09: 2014/05/06 Tue: SKIP: student assembly (SVV)10: 2014/05/08 Thu: Alignment 111: 2014/05/13 Tue: Alignment 2 12: 2014/05/15 Thu: Comparative modeling 113: 2014/05/20 Tue: CM2 + Experimental structure determination + Secondary structure prediction 114: 2014/05/22 Thu: Secondary structure prediction 215: 2014/05/27 Tue: 1D: Secondary structure prediction 116: 2014/05/29 Thu: SKIP: holiday (Ascension Day)17: 2014/06/03 Tue: SKIP: no room 18: 2014/06/05 Thu: 1D: Secondary structure prediction 219: 2014/06/10 Tue: SKIP: Whitsun holidays20: 2014/06/12 Thu: 1D: Transmembrane helix prediction21: 2014/06/17 Tue: Nobel prize symposium22: 2014/06/19 Thu: SKIP: Corpus Christi (Fronleichnam)23: 2014/06/24 Tue: 1D: Transmembrane strand prediction, solvent accessibility24: 2014/06/26 Thu: 2D prediction25: 2014/07/01 Tue: 3D prediction/wrap up26: 2014/07/03 Thu: wrap up again27: 2014/07/08 Tue: examen, no lecture28: 2014/07/10 Thu: no lecture
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