Transcript Bioinformatics

Bioinformatics
Richard Tseng and Ishawar Hosamani
Outline
• Homology modeling (Ishwar)
• Structural analysis
– Structure prediction
– Structure comparisons
• Cluster analysis
– Partitioning method
– Density-based method
• Phylogenetic analaysis
Structural Analysis
• Overview
– Structure prediction
– Structural alignment
– Similarity
• Tools for protein structure prediction
– Protein
• Secondary structure prediction: SSEA
http://protein.cribi.unipd.it/ssea/
• Tertiary structure prediction:
– Wurst: http://www.zbh.uni-hamburg.de/wurst/
– LOOPP: http://cbsuapps.tc.cornell.edu/loopp.aspx
• WURST( Torda et al. (2004) Wurst: A protein threading server with a
structural scoring function, sequence profiles and optimized substitution
matrices Nucleic Acids Res., 32, W532-W535)
• Rationale
– Alignment: Sequence to structure alignments are
done with a Smith-Waterman style alignment and
the Gotoh algorithm
– Score function: fragment-based sequence to
structure compatibility score and a pure
sequence-sequence component substitution score
– Library: Dali PDB90 (24599 srtuctures)
• Tools for structure comparison
– Pair structures comparison:
• TopMatch
• Matras: (http://biunit.naist.jp/matras/)
– Multiple structures comparison:
• 3D-surfer
• Matras: (http://biunit.naist.jp/matras/)
• TopMatch (Sippl & Wiederstein (2008) A note on difficult structure
alignment problems. Bioinformatics 24, 426-427)
– Rationale:
• Structure alignment:
http://www.cgl.ucsf.edu/home/meng/grpmt/structalign.html
• Similarity measurement
S a ,b  La  Lb  Da ,b  2
– Input format
• PDB, SCOP and CATH code
• PDB structure directly
– Exercise: http://topmatch.services.came.sbg.ac.at/
• 3D-surfer (David La et al. 3D-SURFER: software for high throughput
protein surface comparison and analysis. Bioinformatics , in press. (2009))
– Rationale
1. Define a surface function
2. Transform the surface function into a 3D Zernike
description function
– Input format
•
•
Z nlm r , ,    Rnl r Yl m  ,  
PDB and CATH code
PDB structure directly
– Exercise: http://dragon.bio.purdue.edu/3d-surfer/
Cluster analysis
• Goal:
– Grouping the data into classes or clusters, so that
objects within a cluster have high similarity in
comparison to one another but are very dissimilar
to objects in other clusters.
• Methods
– Partitioning method: k-means
– Density-based method: Ordering Points to
Identify the Clustering Structure (OPTICS)
• k-means
– Rationale: Partition n observations into k clusters
in which each observation belongs to the cluster
with the nearest mean
k
E    p  mi
– Exercise
2
i 1 pCi
http://cgm.cs.ntust.edu.tw/etrex/kMeansClustering/kMeansClustering2
.html
• OPTICS
– Rationle: Partition
observations based
on the density of
similar objects
– Exercise
http://www.dbs.informatik.unimuenchen.de/Forschung/KDD/Clustering/OPTICS/Demo/
• Example: Folding of Trp-cage peptide
Phylogenetic analysis
• Overviews
– Comparisons of more than two sequences
– Analysis of gene families, including functional
predictions
– Estimation of evolutionary relationships among
organisms
• Theoretical tree
– Parsimony method
– Distance matrix method
– Maximum likelihood and Bayesian method
– Invariants method
• Software
– Collections of tools
http://evolution.genetics.washington.edu/phylip/software.html
– A web server version for tree construction and display
• PHYLIP, http://bioweb2.pasteur.fr/phylogeny/intro-en.html
• Interactive tree of life, http://itol.embl.de/
– Mostly common used stand alone software
• PHYLIP, tool for evaluating similarity of nucleotide and amino
acid sequences.
http://evolution.gs.washington.edu/phylip.html
• TreeView, tool for visualization and manipulation of family
tree.
http://taxonomy.zoology.gla.ac.uk/rod/treeview.html
• Matlab - bioinformatics tool box
• Example: Alignment phylogenetic tree of
Tubulin family
– Searching homologous sequences of Tubulin (PDB
code: 1JFF) from RCSB protein databank
• Blast for pair sequence alignment
• Clustalw for comparative sequence alignment
– Evaluating protein distance matrix
• using “Protdist” of PHYILIP (Particularly, Point Accepted
Mutation (PAM) matrix is used)
– Clustering proteins using “Neighbor” of PHYILIP
(Neightboring-Joint method is considered)
• Example: n-distance phylogenetic tree
– Evaluating n-distance matrix
• n-distance method
– Clustering proteins using “Neighbor” of PHYILIP
(Neightboring-Joint method is considered)
• 16S and 18S Ribosomal RNA sequenecs of 35 organisms
Summary
• Homology modeling
• Tools for structure prediction and comparisons
• Tools for phylogenetic tree construction
Thanks for your attention!!
•Protein distance matrix
1Z5V_A
3CB2_A
1JFF_B
1FFX_B
1TUB_B
1Z2B_B
1Z5V_A
0
0.000010
1.349411
1.349411
1.303115
1.345634
3CB2_A
0.000010
0
1.350506
1.350506
1.303115
1.346730
1JFF_B
1.349411
1.350506
0
0.000010
0.000010
0.010729
1FFX_B
1.349411
1.350506
0.000010
0
0.000010
0.010729
1TUB_B
1.303115
1.303115
0.000010
0.000010
0
0.006725
1Z2B_B
1.345634
1.346730
0.010729
0.010729
0.006725
0
•Tubulin family tree
• n-distance method
– Frequency count of “n-letter words” p  f / N
MREIVHIQAGQCGNQIGAKFWEVISDEHGIDPTGSYHGDSDLQLERINVYYNE
– n-dsiatnce matrix
D n , '   p  p '

– Advantage:
1. Identify fully conservative words located at nearly the
same sites
2. Effecient