z. luthey-schulten, uiuc
TRANSCRIPT
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Sequence and Structure AlignmentZ. Luthey-Schulten, UIUC
San Francisco, 2005
VMD 1.83
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Why Look at More Than One Sequence?1. Multiple Sequence Alignment shows patterns of conservation
2. What and how many sequences should be included?
3. Where do I find the sequences and structures for MS alignment?
4. How to generate pairwise and multiple sequence alignments?
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Sequence-Sequence Alignment• Smith-Watermann
• Needleman-Wunsch
• Threading• Hidden Markov, Clustal
Sequence-Structure Alignment
Structure-Structure Alignment• STAMP - Barton and Russell
• CE - Bourne et al.
Seq. 1: a1 a2 a3 - - a4 a5…anSeq. 2: c1 - c2 c3 c4 c5 - …cm
Sequence Database Searches• Blast and Psi-Blast
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Sequence-Sequence Alignment• Smith-Watermann
• Needleman-Wunsch
• Threading• Hidden Markov, Clustal
Sequence-Structure Alignment
Structure-Structure Alignment• STAMP - Barton and Russell
• CE - Bourne et al.
Sequence Database Searches• Blast and Psi-Blast
Profile 1: A1 A2 A3 - - A4 A5…AnProfile 2: C1 - C2 C3 C4 C5 - …Cm
Swiss Prot
SCOP, AstralPDB
NCBI
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cut
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paste
Choice ofsubstitution matrixand gap penalty
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Final Result: Sequence Alignment - Approximate
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Tutorial for thematerial of thislecture available
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Sequence Alignment & Dynamic Programming
Seq. 1: a1 a2 a3 - - a4 a5…anSeq. 2: c1 - c2 c3 c4 c5 - …cm
number of possible alignments
Smith-Waterman alignment algorithm
objective function
substitution matrix
gap penalty
index i
index j
substitution
gap
traceback defined through choice of maximum
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A R N D C Q E G H I L K M F P S T W Y V B Z X 5 -2 -1 -1 -2 0 -1 1 -2 -1 -2 -1 -1 -3 -2 1 0 -3 -2 0 -1 -1 0 A -2 9 0 -1 -3 2 -1 -3 0 -3 -2 3 -1 -2 -3 -1 -2 -2 -1 -2 -1 0 -1 R -1 0 8 2 -2 1 -1 0 1 -2 -3 0 -2 -3 -2 1 0 -4 -2 -3 4 0 -1 N -1 -1 2 9 -2 -1 2 -2 0 -4 -3 0 -3 -4 -2 0 -1 -5 -3 -3 6 1 -1 D -2 -3 -2 -2 16 -4 -2 -3 -4 -4 -2 -3 -3 -2 -5 -1 -1 -6 -4 -2 -2 -3 -2 C 0 2 1 -1 -4 8 2 -2 0 -3 -2 1 -1 -4 -2 1 -1 -1 -1 -3 0 4 -1 Q -1 -1 -1 2 -2 2 7 -3 0 -4 -2 1 -2 -3 0 0 -1 -2 -2 -3 1 5 -1 E 1 -3 0 -2 -3 -2 -3 8 -2 -4 -4 -2 -2 -3 -1 0 -2 -2 -3 -4 -1 -2 -1 G -2 0 1 0 -4 0 0 -2 13 -3 -2 -1 1 -2 -2 -1 -2 -5 2 -4 0 0 -1 H -1 -3 -2 -4 -4 -3 -4 -4 -3 6 2 -3 1 1 -2 -2 -1 -3 0 4 -3 -4 -1 I -2 -2 -3 -3 -2 -2 -2 -4 -2 2 6 -2 3 2 -4 -3 -1 -1 0 2 -3 -2 -1 L -1 3 0 0 -3 1 1 -2 -1 -3 -2 6 -1 -3 -1 0 0 -2 -1 -2 0 1 -1 K -1 -1 -2 -3 -3 -1 -2 -2 1 1 3 -1 7 0 -2 -2 -1 -2 1 1 -3 -2 0 M -3 -2 -3 -4 -2 -4 -3 -3 -2 1 2 -3 0 9 -4 -2 -1 1 4 0 -3 -4 -1 F -2 -3 -2 -2 -5 -2 0 -1 -2 -2 -4 -1 -2 -4 11 -1 0 -4 -3 -3 -2 -1 -2 P 1 -1 1 0 -1 1 0 0 -1 -2 -3 0 -2 -2 -1 5 2 -5 -2 -1 0 0 0 S 0 -2 0 -1 -1 -1 -1 -2 -2 -1 -1 0 -1 -1 0 2 6 -4 -1 1 0 -1 0 T -3 -2 -4 -5 -6 -1 -2 -2 -5 -3 -1 -2 -2 1 -4 -5 -4 19 3 -3 -4 -2 -2 W -2 -1 -2 -3 -4 -1 -2 -3 2 0 0 -1 1 4 -3 -2 -1 3 9 -1 -3 -2 -1 Y 0 -2 -3 -3 -2 -3 -3 -4 -4 4 2 -2 1 0 -3 -1 1 -3 -1 5 -3 -3 -1 V -1 -1 4 6 -2 0 1 -1 0 -3 -3 0 -3 -3 -2 0 0 -4 -3 -3 5 2 -1 B -1 0 0 1 -3 4 5 -2 0 -4 -2 1 -2 -4 -1 0 -1 -2 -2 -3 2 5 -1 Z 0 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 0 -1 -2 0 0 -2 -1 -1 -1 -1 -1 X
Blosum 40 Substitution MatrixAA not resolved
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Amino Acid Three Letter and One Letter Code
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Sequence Alignment & Dynamic Programming
Seq. 1: a1 a2 a3 - - a4 a5…anSeq. 2: c1 - c2 c3 c4 c5 - …cm
number of possible alignments:
Needleman-Wunsch alignment algorithm
S : substitution matrix Score Matrix H: Traceback A R N D C Q E G H I L K M F P S T W Y V B Z X 5 -2 -1 -1 -2 0 -1 1 -2 -1 -2 -1 -1 -3 -2 1 0 -3 -2 0 -1 -1 0 A -2 9 0 -1 -3 2 -1 -3 0 -3 -2 3 -1 -2 -3 -1 -2 -2 -1 -2 -1 0 -1 R -1 0 8 2 -2 1 -1 0 1 -2 -3 0 -2 -3 -2 1 0 -4 -2 -3 4 0 -1 N -1 -1 2 9 -2 -1 2 -2 0 -4 -3 0 -3 -4 -2 0 -1 -5 -3 -3 6 1 -1 D -2 -3 -2 -2 16 -4 -2 -3 -4 -4 -2 -3 -3 -2 -5 -1 -1 -6 -4 -2 -2 -3 -2 C 0 2 1 -1 -4 8 2 -2 0 -3 -2 1 -1 -4 -2 1 -1 -1 -1 -3 0 4 -1 Q -1 -1 -1 2 -2 2 7 -3 0 -4 -2 1 -2 -3 0 0 -1 -2 -2 -3 1 5 -1 E 1 -3 0 -2 -3 -2 -3 8 -2 -4 -4 -2 -2 -3 -1 0 -2 -2 -3 -4 -1 -2 -1 G -2 0 1 0 -4 0 0 -2 13 -3 -2 -1 1 -2 -2 -1 -2 -5 2 -4 0 0 -1 H -1 -3 -2 -4 -4 -3 -4 -4 -3 6 2 -3 1 1 -2 -2 -1 -3 0 4 -3 -4 -1 I -2 -2 -3 -3 -2 -2 -2 -4 -2 2 6 -2 3 2 -4 -3 -1 -1 0 2 -3 -2 -1 L -1 3 0 0 -3 1 1 -2 -1 -3 -2 6 -1 -3 -1 0 0 -2 -1 -2 0 1 -1 K -1 -1 -2 -3 -3 -1 -2 -2 1 1 3 -1 7 0 -2 -2 -1 -2 1 1 -3 -2 0 M -3 -2 -3 -4 -2 -4 -3 -3 -2 1 2 -3 0 9 -4 -2 -1 1 4 0 -3 -4 -1 F -2 -3 -2 -2 -5 -2 0 -1 -2 -2 -4 -1 -2 -4 11 -1 0 -4 -3 -3 -2 -1 -2 P 1 -1 1 0 -1 1 0 0 -1 -2 -3 0 -2 -2 -1 5 2 -5 -2 -1 0 0 0 S 0 -2 0 -1 -1 -1 -1 -2 -2 -1 -1 0 -1 -1 0 2 6 -4 -1 1 0 -1 0 T -3 -2 -4 -5 -6 -1 -2 -2 -5 -3 -1 -2 -2 1 -4 -5 -4 19 3 -3 -4 -2 -2 W -2 -1 -2 -3 -4 -1 -2 -3 2 0 0 -1 1 4 -3 -2 -1 3 9 -1 -3 -2 -1 Y 0 -2 -3 -3 -2 -3 -3 -4 -4 4 2 -2 1 0 -3 -1 1 -3 -1 5 -3 -3 -1 V -1 -1 4 6 -2 0 1 -1 0 -3 -3 0 -3 -3 -2 0 0 -4 -3 -3 5 2 -1 B -1 0 0 1 -3 4 5 -2 0 -4 -2 1 -2 -4 -1 0 -1 -2 -2 -3 2 5 -1 Z 0 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 0 -1 -2 0 0 -2 -1 -1 -1 -1 -1 X
gap penalty W = - 6
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Needleman-Wunsch Global Alignment
Similarity Values Initialization of Gap Penalties
http://www.dkfz-heidelberg.de/tbi/bioinfo/PracticalSection/AliApplet/index.html
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Filling out the Score Matrix H
http://www.dkfz-heidelberg.de/tbi/bioinfo/PracticalSection/AliApplet/index.html
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Traceback and Alignment
The Alignment
Traceback (blue) from optimal score
http://www.dkfz-heidelberg.de/tbi/bioinfo/PracticalSection/AliApplet/index.html
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SISSIRVKSKRIQLG…
1-D protein sequence
3-D protein structure
Protein Structure Prediction
Target Sequence
Known structure(s)
SISSRVKSKRIQLGLNQAELAQKV------GTTQ…QFANEFKVRRIKLGYTQ----TNVGEALAAVHGS…
Homology Modeling/ FR
gapmatch EEE +=
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A1 A3A2 A4 A5 …
“Scaffold”structureTarget sequence Alignment between
target(s) and scaffold(s)
Sequence-Structure Alignment
gapbondsHprofilecontact EEEEH +++= −
1. Energy Based Threading*
( )∑=n
iiii
pprofile SASSAE ,,)(γ
( ) ( )∑∑=
−∗=ji k
ijkjictkcontact rrUAAE
,
2
1
)( ,γ
*R. Goldstein, Z. Luthey-Schulten, P. Wolynes (1992, PNAS), K. Koretke et.al. (1996, Proteins)
2. Sequence – Structure Profile Alignments
Clustal, Hidden Markov (HMMER, PSSM)with position dependent gap penalties
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CM/Fold Recognition Results from CASP5Lessons Learned
The prediction is never better than the scaffold.
Threading Energy Function andProfiles need improvement.
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Structural Profiles
1.Structure more conserved than sequences!!! Similar structures at the Family and Superfamily levels. Add more structural information
2.Which structures and sequences to include? Evolution, QR redunancy
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Structural Domains
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Profile - Multiple StructuralAlignments
Representative Profile of AARS Family Catalytic Domain
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STAMP - Multiple Structural Alignments1. Initial Alignment Inputs• Multiple Sequence alignment• Ridged Body “Scan”
2. Refine Initial Alignment & Produce Multiple Structural Alignment
R. Russell, G. Barton (1992) Proteins 14: 309.
•Dynamic Programming (Smith-Waterman) through P matrix gives optimal set of equivalent residues.•This set is used to re-superpose the two chains. Then iterate until alignment score is unchanged.•This procedure is performed for all pairs.
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Multiple Structural Alignments
STAMP – cont’d2. Refine Initial Alignment & Produce Multiple Structural Alignment
Alignment score:
Multiple Alignment:•Create a dendrogram using the alignment score.•Successively align groups of proteins (from branch tips to root).•When 2 or more sequences are in a group, then average coordinates are used.
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Variation in Secondary StructureSTAMP Output
Eargle, O’Donoghue, Luthey-Schulten, UIUC 2004 R. Russell, G. Barton (1992) Proteins 14: 309
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Stamp Output/Clustal Format
From multiple structure alignment compute position probabilities for amino acids and gaps!!!!
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PSSM-based approach
Altschul, et. al., Nucleic Acids Research, 25:3389-3402. Also useful: http://apps.bioneq.qc.ca/twiki/bin/view/Knowledgebase/PSSM
1 2 3 4 5Sequence 1 - B B - CSequence 2 C C - - CSequence 3 C B C C B
0.3300.50.670P(Bj)
0.6710.50.331P(Cj)
54321j
Position Specific Amino Acid Probabilities
Multiple Sequence Alignment
Position specific score for aligning ith residue of S to jth position of profile
I. Construction of Profile II Database searchAlign every sequence in thedatabase to the profile usingDynamic Programming algorithm.
Sequence represented by S(S1,S2,…,Snres)
Progressive alignment score = H(i,j)
for j=1,2,....,maln and i=1,2,....,Nres
Traceback gives the optimalalignment of the sequence S toprofile.
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HMM / Clustal Models ofTransmembrane Proteins
Bacteriorhodpsin/Rhodopsins
Metalated Active State:Before Odorant Binding
looppenetration
1 2 3 4 5 6 7
neutralized loop
N
C
1 2 3 4 5 6 7
N
C
O
Olfactory Receptor/Bovine RhodopsinJ. Wang, Z. Luthey-Schulten, K. Suslick (2003) PNAS 100(6):3035-9
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Stamp Profile - 3 Structures
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Clustal Profile-Profile Alignment
Profile 1 StructuresProfile 2 Sequence
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Refine Structure Prediction withModeller 6.2
Sethi and Luthey-Schulten, UIUC 2003 Modeller 6.2 A. Sali, et al.
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Comparative Modeling Tools in SwissProt - SwissModel
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Structure Prediction Resources - on linehttp://cgat.ukm.my/spores/Predictory/structure_prediction.html
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Cont’d - Web Resources
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Multidimensional QR factorization
of alignment matrix, A.
Non-redundant Representative Sets of Structures
P. O’Donoghue and Z. Luthey-Schulten (2003) MMBR 67:550-571.
P. O’Donoghue and Z. Luthey-Schulten (2004) J. Mol. Biol., in press.
Too much information129 Structures
Economy of information16 representatives
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0.500.53
00.330.672
0011
Mi IiMi Di+1Mi Mi+1i
0.67
0.5
0.33
1
C(Mi)
0.33
0.5
0.67
0
B(Mi)
1
4
3
2
i
State transition Probabilities (ST)
Position specific amino acidProbabilities
Amino acid probabilities at insert states is equal to thebackground probability of occurrence of the corresponding
amino acid.P(C|I) = 7/11=0.64P(B|I) = 4/11=0.36
Leads to affine gap penalty.P(-|D) = 1.
HMM-based approach 1 2 3 4 5Sequence 1 - B B - CSequence 2 C C - - CSequence 3 C B C C B
State M1 M2 M3 I3 M4
Mi-1 Mi Mi+1… …
Di-1 Di Di+1
Ii-1 Ii
State Path of Sequence 3
M1 M2 M3 I3 M4
c B C C BDurbin, Eddy, Krogh and Mitchison, Biological Sequence Analysis, Cambridge, 1998
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Generation of HMM ProfileGoal: To select right model and assign match states to selected columns in MSA.The model (M) is constructed so that it generates MSA with the highest probability.
The probability of obtaining the MSA from the profile is:
where Ak represents the kth aligned sequence.
The profile is constructed using dynamic programming.
Pj = Highest probability of obtaining MSA till jth column such that j is a match state.Pij = Highest probability of obtaining MSA such that j is a match state and i is the previous match state.where STij represents the probabilities of all state transitions. AIij the probability of all amino acids in insert states between ith and jth columns. AMj represents the probabilities of all amino acids in jth column.
The probability of finding a single aligned sequence A (A1,A2,…,AN) is:
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HMM-based approach
New sequence aligned using dynamic programming.
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Tutorial for thematerial of thislecture available at
http://www.ks.uiuc.edu/Training/Tutorials/
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