Transcript MultipleSeqAlign

BLAST and Psi-BLAST and
MSA
Nov. 1, 2012
Workshop-Use BLAST2 to determine local
sequence similarities.
Homework #6 due Nov 8
Chapter 5, Problem 8
 Chapter 6, Problems 1 and 4.

What are the different BLAST
programs?
blastp
 compares an amino acid query sequence against a protein sequence
database
blastn
 compares a nucleotide query sequence against a nucleotide
sequence database
blastx
 compares a nucleotide query sequence translated in all reading
frames against a protein sequence database
tblastn
 compares a protein query sequence against a nucleotide sequence
database dynamically translated in all reading frames
tblastx
 compares the six-frame translations of a nucleotide query sequence
against the six-frame translations of a nucleotide sequence
database. Please note that tblastx program cannot be used with the
nr database on the BLAST Web page.
What are the different BLAST
programs? (continued)
psi-blast
 Compares a protein sequence to a protein database. Performs the
comparison in an iterative fashion in order to detect homologs that
are evolutionarily distant.
blast2
 Compares two protein or two nucleotide sequences.
The E value
(false positive expectation value)
The Expect value (E) is a parameter that describes the number
of “hits” one can "expect" to see just by chance when
searching a database of a particular size. It decreases
exponentially as the Similarity Score (S) increases (inverse
relationship). The higher the Similarity Score, the lower
the E value. Essentially, the E value describes the random
background noise that exists for matches between two
sequences. The E value is used as a convenient way to
create a “significance” threshold for reporting results.
When the E value is increased from the default value prior
to a sequence search, a larger list with more low-similarity
scoring hits can be reported. An E value of 1 assigned to a
hit can be interpreted as meaning that in a database of the
current size you might expect to see 1 match with a similar
score simply by chance.
E value (Karlin-Altschul statistics)
E = K•m•n•e-λS
Where K is a scaling factor (constant), m is the
length of the query sequence, n is the length of the
database sequence, λ is the decay constant, S is the
similarity score.
If S increases, E decreases exponentially.
If the decay constant increases, E decreases
exponentially
If m•n increases the “search space” increases. Then
there is a greater chance for a random “hit” and E
increases. A larger database will increase E.
However, larger query sequence often results in a
lower E value. Why???
Thought problem
A homolog to a query sequence resides in two
databases. One is the UniProt database and the
other is the PDB database. After performing
BLAST search against the UniProt database you
obtain an E value of 1. After performing the
BLAST search against the PDB database you
obtain an E value of 0.0625. What is the ratio of
the sizes of the two databases?
Using BLAST to get quick answers
to bioinformatics problems
Task
BLAST method
Predict protein Perform blastp on
function (1)
PIR or Swiss-Prot
database
Predict protein Perform tblastn
function (2)
on NR database
Predict protein Perform blastp
structure
against PDB
Trad. Method
Perform wet-lab
experiment
Perform wet-lab
experiment
Structure prediction
software, x-ray
crystal., NMR
Using BLAST to get quick answers
to bioinformatics problems (cont.)
Task
BLAST method
Trad. Method
Locate genes in a Divide genome into 2-5
genome
kb sequences. Perform
blastx against NR protein
datbase
Find distantly
Perform psi-blast
related proteins
Run gene prediction
software. Perform
microarray analysis or
RNAs
No traditional method
Identify DNA
sequence
Screen genomic DNA
library
Perform blastn
Filtering Repetitive Sequences
Over 50% of genomic DNA is repetitive
This is due to:





retrotransposons
ALU region
microsatellites
centromeric sequences, telomeric sequences
5’ Untranslated Region of ESTs
Example of EST with simple low complexity region:
T27311
GGGTGCAGGAATTCGGCACGAGTCTCTCTCTCTCTCTCTCTCTCTCTC
TCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTC
Filtering Repetitive Sequences
and Masking
Options available for user.
PSI-BLAST
PSI-position specific iterative
a position specific scoring matrix (PSSM) is
constructed automatically from multiple HSPs of
initial BLAST search. Normal E value threshold is
used.
The PSSM is created as the new scoring matrix for
a second BLAST search. A low E value threshold
is used (E=.001).
Result-1) obtains distantly related sequences
2) finds the important residues that provide
function or structure.
Workshop
Is the American crocodile (Crocodylus
acutus) more closely related to the sea turtle
(Cheloniidae) or to the chicken (Gallus
gallus)? Choose mitochondrial ribosomal
RNA 12S from each species and compare
using blast2. Record percent nucleotide
identities, percent similarities and lengths of
query/sbjct sequences in your answer.
Multiple Sequence Alignment
Collection of three or more amino acid (or
nucleic acid) sequences partially or
completely aligned.
Aligned residues tend to occupy
corresponding positions in the 3-D structure
of each aligned protein.
General steps to multiple alignment.
Create Alignment
Edit the alignment to ensure that regions of functional
or structural similarity are preserved
USED FOR:
Phylogenetic Structure Find conserved motifs Design of
Analysis
to deduce function
PCR primers
Analysis
Practical use of MSA
Helps to place protein into a group of
related proteins. It will provide insight into
function, structure and evolution.
Identifies sequencing errors
Identifies important regulatory regions in
the promoters of genes.
Clustal W (Thompson et al.,
1994)
CLUSTAL=Cluster alignment
The underlying concept is that groups of
sequences are phylogenetically related. If they
can be aligned, then one can construct a
phylogenetic tree.
Phylogenetic tree-a tree showing the evolutionary
relationships among various biological species or
other entities that are believed to have a common
ancestor.
Flowchart of computation steps in
Clustal W (Thompson et al., 1994)
Pairwise alignment: calculation of distance matrix
Creation of unrooted neighbor-joining tree
Rooted NJ tree (guide tree) and calculation of sequence weights
Progressive alignment following the guide tree
Preliminary pairwise alignments
Compare each pair of sequences.
A
Different
sequences
-
B
.87
-
C
.59 .60
A B
C
Each number represents the number
of exact matches divided by the
sequence length (ignoring gaps).
Thus, the higher the number the more
closely related the two sequences are.
In this matrix, sequence A is 87% identical to sequence B
Step 1-Calculation of Distance
Matrix
Use the Distance Matrix to create a Guide Tree to
determine the “order” of the sequences.
Hbb-Hu
1
-
Hbb-Ho
2
.17
-
Hba-Hu
3
.59
.60
-
Hba-Ho
4
.59
.59
.13
-
Myg-Ph
5
.77
.77
.75
.75
-
Gib-Pe
6
.81
.82
.73
.74
.80
-
Lgb-Lu
7
.87
.86
.86
.88
.93
.90
-
1
2
3
4
5
6
7
D = 1 – (I)
I = # of identical aa’s in pairwise global alignment
D = Difference score
total number of aa’s in shortest sequence
Step 2-Create an unrooted NJ tree
Myg-Ph
Hba-Ho
Hba-Hu
Hbb-Ho
Gib-Pe
Hbb-Hu
Lgb-Lu
Step 3-Create Rooted NJ Tree
Weight
Alignment
Order of alignment:
1 Hba-Hu vs Hba-Ho
2 Hbb-Hu vs Hbb-Ho
3 A vs B
4 Myg-Ph vs C
5 Gib-Pe vs D
6 Lgh-Lu vs E
Table 6.2 Sequence weight calculations
Sequence number
Sequence name
Raw sequence
weight
Normalized
sequence weight.
1
2
Hbb-Hu
Hbb-Ho
0.223
0.226
0.506
0.511
3
Hba-Hu
0.193
0.437
4
Hba-Ho
0.203
0.459
5
Myg-Ph
0.411
0.930
6
Gib-Pe
0.399
0.903
7
Lgb-Lu
0.442
1.000
Step 4-Progressive alignment
Step 4-Progressive alignment
Scoring during
progressive
alignment
M(t,v) = 0; M(t,i) = -1; M(l,v) = 1; M(l,i) = 2
Following the steps in the above figure, calculation of the score for the comparison of A and B at the
outlined position is:
0 * 0.506*0.437 = 0
-1 * 0.506*0.459 = -.232
1 * 0.511 * 0.437 = .223
2 * 0.511 * 0.459 = .469
(0 + (-0.232) + 0.223 + 0.469)/4 = 0.460
Rules for alignment
Short stretches of 5 hydrophilic residues often indicate loop or random
coil regions (not essential for structure) and therefore gap penalties are
reduced reduced for such stretches.
Gap penalties for closely related sequences are lowered compared to
more distantly related sequences (“once a gap always a gap” rule). It
is thought that those gaps occur in regions that do not disrupt the
structure or function.
Alignments of proteins of known structure show that proteins gaps do
not occur more frequently than every eight residues. Therefore
penalties for gaps increase when required at 8 residues or less for
alignment. This gives a lower alignment score in that region.
A gap weight is assigned after each aa according the frequency that
such a gap naturally occurs after that aa in nature
Amino acid weight matrices
As we know, there are many scoring
matrices that one can use depending on the
relatedness of the aligned proteins.
As the alignment proceeds to longer
branches the aa scoring matrices are
changed to those more suitable for distant
evolutionary relationships. The length of
the branch is used to determine which
matrix to use and contributes to the
alignment score.
Example of Sequence Alignment
using Clustal W
Asterisk represents identity
: represents high similarity
. represents low similarity
Multiple Alignment
Considerations
Quality of guide tree. It would be good to have a set of
closely related sequences in the alignment to set the
pattern for more divergent sequences.
If the initial alignments have a problem, the problem is
magnified in subsequent steps.
CLUSTAL W is best when aligning sequences that are
related to each other over their entire lengths
Do not use when there are variable N- and C- terminal
regions
If protein is enriched for G,P,S,N,Q,E,K,R then these
residues should be removed from gap penalty list.
(what types of residues are these?)
Reference: http://www-igbmc.u-strasbg.fr/BioInfo/ClustalW/