Sequence and Structure Alignment Z. Luthey-Schulten, UIUC Pittsburgh, 2006 VMD 1.8.5

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Sequence and Structure Alignment Z. Luthey-Schulten, UIUC Pittsburgh, 2006 VMD 1.8.5

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?

Sequence-Sequence Alignment Smith-Watermann Needleman-Wunsch Sequence-Structure Alignment Threading Hidden Markov, Clustal Structure-Structure Alignment STAMP - Barton and Russell CE - Bourne et al. Seq. 1: a 1 a 2 a 3 - - a 4 a 5 a n Seq. 2: c 1 - c 2 c 3 c 4 c 5 - c m Sequence Database Searches Blast and Psi-Blast

Sequence-Sequence Alignment Smith-Watermann Needleman-Wunsch Sequence-Structure Alignment Threading Hidden Markov, Clustal Structure-Structure Alignment STAMP - Barton and Russell CE - Bourne et al. Profile 1: A 1 A 2 A 3 - - A 4 A 5 A n Profile 2: C 1 - C 2 C 3 C 4 C 5 - C m Sequence Database Searches SCOP, Astral PDB Blast and Psi-Blast NCBI Swiss Prot

cut

paste Choice of substitution matrix and gap penalty

Final Result: Sequence Alignment - Approximate

Tutorial for the material of this lecture available

Sequence Alignment & Dynamic Programming substitution index i number of possible alignments Seq. 1: a 1 a 2 a 3 - - a 4 a 5 a n Seq. 2: c 1 - c 2 c 3 c 4 c 5 - c m gap index j Smith-Waterman alignment algorithm objective function gap penalty substitution matrix traceback defined through choice of maximum

Blosum 30 Substitution Matrix AA not resolved

Amino Acid Three Letter and One Letter Code

Sequence Alignment & Dynamic Programming Seq. 1: a 1 a 2 a 3 - - a 4 a 5 a n Seq. 2: c 1 - c 2 c 3 c 4 c 5 - c m number of possible alignments: Needleman-Wunsch alignment algorithm S : substitution matrix 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 Score Matrix H: Traceback gap penalty W = - 6

Needleman-Wunsch Global Alignment Similarity Values Initialization of Gap Penalties http://www.dkfz-heidelberg.de/tbi/bioinfo/practicalsection/aliapplet/index.html

Filling out the Score Matrix H http://www.dkfz-heidelberg.de/tbi/bioinfo/practicalsection/aliapplet/index.html

Traceback and Alignment The Alignment Traceback (blue) from optimal score http://www.dkfz-heidelberg.de/tbi/bioinfo/practicalsection/aliapplet/index.html

Protein Structure Prediction 1-D protein sequence SISSIRVKSKRIQLG Homology Modeling/ FR E = E + E match gap Target Sequence SISSRVKSKRIQLGLNQAELAQKV------GTTQ QFANEFKVRRIKLGYTQ----TNVGEALAAVHGS Known structure(s) 3-D protein structure

Target sequence Sequence-Structure Alignment Alignment between target(s) and scaffold(s) Scaffold structure A 1 A 2 A 3 A 4 A 5 1. Energy Based Threading* H + E = Econtact + E profile + EH bonds n ( p) E profile = γ i i, i E contact = i, j 2 k = 1 ( A, SS SA ) γ ( ct) k i gap ( A, A ) U( r r ) i j 2. Sequence Structure Profile Alignments k ij Clustal, Hidden Markov (HMMER, PSSM) with position dependent gap penalties *R. Goldstein, Z. Luthey-Schulten, P. Wolynes (1992, PNAS), K. Koretke et.al. (1996, Proteins)

CM/Fold Recognition Results from CASP5 Lessons Learned The prediction is never better than the scaffold. Threading Energy Function and Profiles need improvement.

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? Use evolution and eliminate redundancy with QR factorization

Structural Domains

Profile - Multiple Structural Alignments Representative Profile of AARS Family Catalytic Domain

STAMP - Multiple Structural Alignments 1. Initial Alignment Inputs Multiple Sequence alignment Ridged Body Scan 2. Refine Initial Alignment & Produce Multiple Structural Alignment 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. R. Russell, G. Barton (1992) Proteins 14: 309.

Multiple Structural Alignments STAMP cont d 2. 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.

Initial Pairwise Superposition - Single Linkage Cluster N = 4 proteins, N(N-1)/2 pairs Table of RMS Dendogram: Initial step starts by fitting B to D (0.5 A), then A to C (1.0A), then fitting AC to BD using average coordinates for AC and BD pairs

Variation in Secondary Structure STAMP Output Eargle, O Donoghue, Luthey-Schulten, UIUC 2004 R. Russell, G. Barton (1992) Proteins 14: 309

Stamp Output/Clustal Format From multiple structure alignment compute position probabilities for amino acids and gaps!!!!

HMM / Clustal Models of Transmembrane Proteins N neutralized loop 1 2 3 4 5 6 7 Metalated Active State: Before Odorant Binding loop penetration N O 1 2 3 4 5 6 7 C C Bacteriorhodpsin/Rhodopsins Olfactory Receptor/Bovine Rhodopsin J. Wang, Z. Luthey-Schulten, K. Suslick (2003) PNAS 100(6):3035-9

Stamp Profile - 3 Structures

Clustal Profile-Profile Alignment Profile 1 Structures Profile 2 Sequence

PSSM-based approach I. Construction of Profile II Database search 1 2 3 4 5 Sequence 1 - B B - C Sequence 2 C C - - C Sequence 3 C B C C B Multiple Sequence Alignment Position Specific Amino Acid Probabilities j 1 2 3 4 5 Align every sequence in the database to the profile using Dynamic Programming algorithm. Sequence represented by S(S 1,S 2,,S nres ) Progressive alignment score = H(i,j) P(C j ) 1 0.33 0.5 1 0.67 P(B j ) 0 0.67 0.5 0 0.33 Position specific score for aligning i th residue of S to j th position of profile for j=1,2,...,m aln and i=1,2,...,n res Traceback gives the optimal alignment of the sequence S to profile. Altschul, et. al., Nucleic Acids Research, 25:3389-3402. Also useful: http://apps.bioneq.qc.ca/twiki/bin/view/knowledgebase/pssm

Refine Structure Prediction with Modeller 6.2 Sethi and Luthey-Schulten, UIUC 2003 Modeller 6.2 A. Sali, et al.

Web Resources for Computational Biology Sequences and Structure Databases: Proteins and Nucleic Acids SwissProt (www.expasy.org/sprot/) NCBI (www.ncbi.nlm.nih.gov/) CRW (www.rna.icmb.utexas.edu/) PDB (www.rcsb.org/pdb) SCOP/Astral (scop.mrc-lmb.cam.ac.uk/scop/) Genomic Context DOE IMG (http://img.jgi.doe.gov/) NMPDR (www.nmprdr.org) Biological Ontologies: Pathways and Functions GO, COG, KEGG (http://www.genome.jp/) 3D Protein Structure Prediction Servers: (See CASP6/CASP7) Robetta (http://robetta.bakerlab.org) Tasser (http://cssb.biology.gatech.edu/skolnick/webservice/tasserlite/index.html SwissModel (http://swissmodel.expasy.org/swiss-model.html

Programs for Computational Biology - continued - Multiple Sequence Alignment Programs; CLUSTAl, TCOFFEE,.. Database Search Programs: BLAST, PSIBLAST, HMMER RNA 2nd Structure Prediction: MFOLD, Vienna, Multiple Sequence/Structure Alignment, DB Search, Analysis,. MultiSeq in VMD (NIH Resource UIUC) Discovery Studio ($Accelyrs) Chimera (NIH Resource UCSF),. Molecular Dynamics Simulations for Proteins, Lipids, RNA NAMD2 (NIH Resource UIUC) CHARMM ($ Accelyrs/Harvard) AMBER ($Accelyrs/Scripps) SYBIL, MOE ($),.

Non-redundant Representative Sets Too much information 129 Structures Multidimensional QR factorization of alignment matrix, A. Economy of information 16 representatives QR computes a set of maximal linearly independent structures. P. O Donoghue and Z. Luthey-Schulten (2003) MMBR 67:550-571. P. O Donoghue and Z. Luthey-Schulten (2005) J. Mol. Biol., 346, 875-894.

On to Evolution of Protein Structure

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