Protein structure determination cb1 3dexp · 63 © Burkhard Rost 38 Notation: protein structure 1D,...

/63© Burkhard Rost

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title: Computational Biology 1 - Protein structure: Protein structure determinationshort title: cb1_3dexp

lecture: Protein Prediction 1 - Protein structure Computational Biology 1 - TUM Summer 2015

/63© Burkhard Rost

Videos: YouTube / www.rostlab.org THANKS : Tim Karl + Carlo Di Domenico Special lectures:

• TBA No lecture:

• 05/12 Student assembly (SVV) • 05/14 Ascension day • 05/26 Whitsun holiday • 06/04 Corpus Christi LAST lecture: Jul 7 Examen: Jul 9

• Makeup: Oct 13, 2015 - morning/noon

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CONTACT: Inga Weise [email protected]

Tim Karl

Tatyana Goldberg

Edda Kloppmann

Andrea Schaffer-

hans

Jonas Reeb

Carlo Di [email protected]

http://www.rostlab.org

mailto:[email protected]

mailto:[email protected]

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Recap: 3D prediction by

comparative modeling

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Zones

Day

light

Zon

e

Twili

ght Z

one

Mid

nigh

t Zon

eprofile - profile

sequence - profilesequence - sequence

sequ

ence

sim

ilar

->

stru

ctur

e sim

ilar

B Rost (1997) Fold Des 2:S19-24B Rost (1999) Protein Eng 12:85-94

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human - fly - bacteria

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 & H Park (unpublished) / 4IW3: JS Scotti (unpublished)3lbn: G Buhrman et al.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 - human lime: 3lbn Rash - human orange: 2y8e Rab6 - fly purple: 2y8e hydroxylase P putida

Andrea Schaffer-

hans

Slide from:

PIDE: pairwise identical residues

19%

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MODELLERlots of whistles and bells, downloadable, very accurate

SWISS-MODELautomated, increasingly comprehensive and flexible

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Comparative modeling methods

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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 possible

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60s - Washington Post 70s - New York Times 90s - Washington Post

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Protein structure prediction problem* solved!

*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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Answer the question in groups

??

???

© Wikipedia

How to assess whether prediction method works or not?

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Critical Assessment of Structure Prediction April-May (Organizers): collect experimental structures (since 2004 from structural genomics) June-August: Prediction seasondeadline: predictions in before experimental structures are published September-November: Assessors divine December: Meeting to discuss results

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CASP: how it works

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CASPOnly homology modeling good No general prediction of 3D from sequence, yet Important improvements in many fields!

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Protein Structure Prediction

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Servers, META-servers, META-META, …

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TBM • overall good

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CASP 9 results

A Kryshtafovych et al. 2011 Proteins, 79 Suppl 10:196-207

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Problems with CASP

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100 proteins Method A uses 53 and reaches

Performance=x Method B uses 35 and reaches

Performance=x+y Is B better?

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CASP examples

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100 proteins enough?

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+y% enough?

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Method A - 53 cases Method B - 35 cases

OK?

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Comparisons based on apples and oranges Analysis of irrelevant types of test cases Inappropriate ranking

• Conclusions based on insignificant differences Different categories evaluated differently

too few targets

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Problems of CASP

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What is better: 29 different proteins

11 identical ones

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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

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Pairwise comparison matrix

© Burkhard Rost (Columbia New York)

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Comparative modeling/homology modeling most accurate way to predict structure as good and as complete as the templateè depends on quality and similarity of template mostly driven by accuracy of alignmentè driven by alignment quality loop modeling: still not fully there, yet side chain modeling: unclear how well we do

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Conclusion: Comparative modeling

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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

Number Percentage

PDB 84,413 1Xray 75,068 89NMR 8,723 10

EM ElectronMicroscopy 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

© 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)

Number Percentage

PDB 84,413 1

Xray 75,068 89

NMR 8,723 10

EM ElectronMicroscopy 428 1

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Protein structure by NMR* spectroscopy

© Wikipedia

Number Percentage

PDB 84,413 1

Xray 75,068 89

NMR 8,723 10

EM ElectronMic

428 1

* NMR: Nuclear Magnetic Resonance

NMR tube

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Protein structure by NMR* spectroscopy

© 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

EM ElectronMic

428 1

* NMR: Nuclear Magnetic Resonance

NYSBC - City College New

York City

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Protein structure by cryo-EM

© Wikipedia

Number Percentage

PDB 84,413 1

Xray 75,068 89

NMR 8,723 10

EM ElectronMic

428 1

* EM: Cryo-Electron Microscopy

4 Ångstrøm 8Å 16Å 32Å GroEL - J Wang & DC Bosvert (2004) 1j4z Images: Vossman 2007 - Wikipedia

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Protein structure by cryo-EMNumber Percentage

PDB 84,413 1

Xray 75,068 89

NMR 8,723 10

EM ElectronMic

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

© 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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1951 e.g. :L Pauling and RB Corey (1951) Configurations of Polypeptide Chains with Favored Orientations Around Single Bonds: Two New Pleated Sheets PNAS 37: 729-40 1953 e.g.: L Pauling and RB Corey (1953) Two Rippled-sheet Configurations of Polypeptide Chains, and a Note About the Pleated Sheets PNAS 39: 253-6

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Linus Pauling: introduce concept

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/

http://www.nobelprize.org/nobel_prizes/chemistry/laureates/1954/

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Hydrogen-bond formation

© Wikipediahttp://www.ausetute.com.au/proteins.html

strandhelix

http://www.ausetute.com.au/proteins.html

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First protein structures

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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Different evaluation criteria applied:

Assignment coverage: DEFINE Geometry (fitting ideal sec str segments)FM Richards & CE Kundrot (1988) Proteins 3:71-84

Enthalpic energy: DSSP W Kabsch & C Sander (1983) Biopolymers 22:2577-637

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Secondary structure assignment

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Answer the question in groups

??

???

© Wikipedia

How to assign secondary structure from 3D structure?

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puts ideal secondary structure into 3D file ideal matches are extended to allow for irregularities or curvature ideal segments are assigned according to “distance masks”

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DEFINE - concept to assign secondary structure

FM Richards & CE Kundrot (1988) Proteins 3:71-84

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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

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Pauling’s H-bond pattern used in DSSP

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H -> helix (i,i+4) helix H G -> 310 helix (i,i+3) helix H T -> turn of helix other L E -> extended/strand strand E B -> beta-bulge strand E S -> bend (no H-bond) other L “ “ -> loop other L

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DSSP assigns 7 “states”

7 DSSP states

3 state map

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Expert assignment: STRIDE D Frishman & P Argos (1995) Proteins 23:566-79

Predictability: NNass

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Mostly methods agree

CAF Andersen & B Rost (2003) Methods Biochem Anal 44:341-63 Fig. 17.7

CAF Andersen & B Rost (2003) Methods Biochem Anal 44:341-63 Fig. 17.6

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01: 04/14 Tue: no lecture 02: 04/16 Thu: no lecture 03: 04/21 Tue: Organization of lecture: intro into cells & biology 04: 04/23 Thu: Intro I - acids/structure 05: 04/28 Tue: Intro 2 - domains/3D comparisons 06: 04/30 Thu: Alignment 1 07: 05/05 Tue: Alignment 2 08: 05/07 Thu: Comparative modeling 1 09: 05/12 Tue: SKIP: student assembly (SVV) 10: 05/14 Thu: SKIP: Ascension Day 11: 05/19 Tue: Comparative modeling 2/Experimental structure determination /Sec str assignment 12: 05/21 Thu: 3D -> 1D: Secondary structure assignment 13: 05/26 Tue: SKIP: Whitsun holiday (05/23-26) 14: 05/28 Thu: 1D: Secondary structure prediction 1 15: 06/02 Tue: 1D: Secondary structure prediction 1 16: 06/04 Thu: SKIP: Corpus Christi 17: 06/09 Tue: 1D: Secondary structure prediction 2 18: 06/11 Thu: 1D: Transmembrane structure prediction 1 19: 06/16 Tue: 1D: Transmembrane structure prediction 2 20: 06/18 Thu: 1D: Solvent accessibility prediction 21: 06/23 Tue: 1D: Disorder prediction 22: 06/25 Thu: 2D prediction 23: 06/30 Tue: 3D prediction 1 24: 07/02 Thu: Nobel prize symposium 25: 07/07 Tue: wrap up 26: 07/09 Thu: examen, no lecture 27: 07/14 Tue: examen, no lecture 28: 07/16 Thu: examen alternative, no lecture

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Lecture plan (CB1 structure)

Protein structure determination cb1 3dexp · 63 © Burkhard Rost 38 Notation: protein structure 1D,...

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