Download 3 -2 -1 -2 -1 1 2 K

BLAST
et BLAST avancé
J.S. Bernardes/H. Richard
Matériel :
Bioinformatics and Functional Genomics
Jonathan Pevsner, Wiley-Blackwell ed.
Outline
Introduction
Definition of Orthology and Paralogy
A myoglobin example
BLAST search steps
Step 1: Specifying Sequence of interest
Step 2: Selecting BLAST Program
Step 3: Selecting a Database
Step 4: Selecting Search Parameters and Formatting Parameters
BLAST algorithm uses local alignment search strategy
BLAST algorithm parts: list, scan, extend
BLAST algorithm: local alignment search statistics and E value
Making sense of raw scores with bit scores
BLAST algorithm: Relation Between E and p values
BLAST search strategies
General concepts; principles of BLAST searching
How to evaluate the significance of results
How to handle too many or few results
BLAST searching with multidomain protein: HIV-1 Pol
Using BLAST for gene discovery: Find-a-Gene
Finding distantly related proteins: PSI-BLAST
Learning objectives
• Definition of homology, similarity, conservation
• Difference between orthologues and paralogues
• perform BLAST searches at the NCBI website;
• understand how to vary optional BLAST search
parameters;
• explain the three phases of a BLAST search
(compile, scan/extend, trace‐back); • define the
mathematical relationship between expect values
and scores; and
• outline strategies for BLAST searching.
Definitions: identity, similarity, conservation
Homology
Similarity attributed to descent from a common ancestor.
Identity
The extent to which two (nucleotide or amino acid) sequences
are invariant.
Similarity
The extent to which nucleotide or protein sequences are
related. It is based upon identity plus conservation.
Conservation
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Page 70
Changes at a specific position of an amino acid or (less
commonly, DNA) sequence that preserve the physicochemical properties of the original residue.
Globin homologs
myoglobin
hemoglobin
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Fig. 3.1
Page 71
beta globin
beta globin and myoglobin (aligned)
Definitions: two types of homology
Orthologs
Homologous sequences in different species
that arose from a common ancestral gene
during speciation; may or may not be responsible
for a similar function.
Paralogs
Homologous sequences within a single species
that arose by gene duplication.
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Fig. 2-3
Page 22
Myoglobin proteins: examples of orthologs
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Fig. 3.2
Page 72
You can view the sequences at http://bioinfbook.org
Paralogs: members of a gene (protein) family within a
species. This tree shows human globin paralogs.
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Fig. 3.3
Page 73
Orthologs and paralogs are often viewed in a single tree
Source: NCBI
Find BLAST from the home page of NCBI
and select protein BLAST…
Choose align two
or more
sequences…
Enter the two sequences (as
accession numbers or in the
fasta format) and click
BLAST.
Optionally select “Algorithm
parameters” and note the
matrix option.
sequence
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Fig. 3-4
Page 74
Year
Pairwise alignment of human beta globin (the
“query”) and myoglobin (the “subject”)
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Fig. 3-5
Page 75
We’ll examine the highlighted green region of the
alignment in more detail.
How raw scores are calculated: an example
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Fig. 3-5
Page 75
For a set of aligned residues we assign scores based on
matches, mismatches, gap open penalties, and gap extension
penalties. These scores add up to the total raw score.
Why use BLAST?
BLAST searching is fundamental to understanding
the relatedness of any favorite query sequence
to other known proteins or DNA sequences.
Applications include
• identifying orthologs and paralogs
• discovering new genes or proteins
• discovering variants of genes or proteins
• investigating expressed sequence tags (ESTs)
• exploring protein structure and function
BLASTP search at NCBI: overview of web-based search
query: FASTA format
or accession
database
Entrez query
algorithm
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Fig. 4-1
Page 123
parameters
Outline
Introduction
Definition of Orthology and Paralogy
A myoglobin example
BLAST search steps
Step 1: Specifying Sequence of interest
Step 2: Selecting BLAST Program
Step 3: Selecting a Database
Step 4: Selecting Search Parameters and Formatting Parameters
BLAST algorithm uses local alignment search strategy
BLAST algorithm parts: list, scan, extend
BLAST algorithm: local alignment search statistics and E value
Making sense of raw scores with bit scores
BLAST algorithm: Relation Between E and p values
BLAST search strategies
General concepts; principles of BLAST searching
How to evaluate the significance of results
How to handle too many or few results
BLAST searching with multidomain protein: HIV-1 Pol
Using BLAST for gene discovery: Find-a-Gene
Finding distantly related proteins: PSI-BLAST
Step 1: Choose your sequence
Sequence can be input in FASTA format or as accession
number
BLAST step 2: choose program
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Fig. 4-2
Page 124
Step 2. Choose the BLAST program
Program
Input
Database
1
blastn
DNA
DNA
1
blastp
protein
protein
6
blastx
DNA
protein
6
tblastn
protein
DNA
36
tblastx
DNA
DNA
Step 2 (choosing the BLAST program):
DNA can be translated into six reading frames
DNA
3 forward,
3 reverse
frames
protein
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Fig. 4-3
Page 125
This image is from the NCBI Nucleotide entry for HBB
Step 3: choose a database to search (protein databases)
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Table 4-1
Page 126
Step 3: choose a database to search (nucleotide)
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Table 4-2
Page 127
Step 4: optional parameters
You can...
• choose the organism to search
• turn filtering on/off
• change the substitution matrix
• change the expect (e) value
• change the word size
• change the output format
Example: BLASTP human insulin (NP_000198) against a
C. elegans RefSeq database. Varying some parameters
(filtering, compositional adjustments) can greatly affect
the alignment itself.
Step 4a: choose optional BLASTP search parameters
max sequences
short queries
expect threshold
word size
max matches
scoring matrix
gap costs
compositional adjustment
filter
mask
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Fig. 4-4
Page 128
Step 4a: compositional adjustment influences score,
expect value search results
expect = 0.05
Default: conditional
compositional score
matrix adjustment
expect = 0.09
no adjustment
expect = 1e-04
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Fig. 4-5
Page 129
composition-based
statistics
Step 4b: formatting options
The top of the BLAST output summarizes the query,
database, and BLAST algorithm.
Click to access a summary of the search parameters or
a taxonomic report.
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Fig. 4-6
Page 132
Step 4b: formatting options (you can view search parameters)
Expect value
BLOSUM62 matrix
Threshold value T
Size of database
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Fig. 4-7
Page 133
Step 4b: formatting options
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Fig. 4-8
Page 134
Graphic summary of the results shows the alignment
scores (coded by color) and the length of the alignment
(given by the length of the horizontal bars)
BLASTP output includes list of matches; links to the NCBI
protein entry; bit score and E value; and download options
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Fig. 4-9
Page 134
BLAST output can be formatted to display multiple alignment
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Fig. 4-10
Page 135
For BLASTN, CDS output displays amino acids above
DNA sequence of query and subject
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Fig. 4-11
Page 136
Outline
Introduction
Definition of Orthology and Paralogy
A myoglobin example
BLAST search steps
Step 1: Specifying Sequence of interest
Step 2: Selecting BLAST Program
Step 3: Selecting a Database
Step 4: Selecting Search Parameters and Formatting Parameters
BLAST algorithm uses local alignment search strategy
BLAST algorithm parts: list, scan, extend
BLAST algorithm: local alignment search statistics and E value
Making sense of raw scores with bit scores
BLAST algorithm: Relation Between E and p values
BLAST search strategies
General concepts; principles of BLAST searching
How to evaluate the significance of results
How to handle too many or few results
BLAST searching with multidomain protein: HIV-1 Pol
Using BLAST for gene discovery: Find-a-Gene
Perspective
How a BLAST search works
“The central idea of the BLAST
algorithm is to confine attention
to segment pairs that contain a
word pair of length w with a score
of at least T.”
Altschul et al. (1990)
How the original BLAST algorithm works:
three phases
Phase 1: compile a list of word pairs (w=3)
above threshold T
Example: for a human RBP query
…FSGTWYA… (query word is in green)
A list of words (w=3) is:
FSG SGT GTW TWY WYA
YSG TGT ATW SWY WFA
FTG SVT GSW TWF WYS
...
Phase 1: compile a list of words (w=3)
neighborhood
word hits
> threshold
(T=11)
GTW
GSW
ATW
NTW
GTY
GNW
GAW
neighborhood
word hits
< below threshold
6,5,11
6,1,11
0,5,11
0,5,11
6,5,2
22
18
16
16
13
10
9
Fig. 4.11
page 116
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Fig. 4-12
Page 139
Phase 2: scan the database for matches and extend
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Fig. 4-12
Page 139
Phase 3: Traceback to generate gapped alignment
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Fig. 4-12
Page 139
How a BLAST search works: threshold
You can locally install BLAST and modify the threshold
parameter.
The default value for BLASTP is 11.
To change it, enter “-f 16” or “-f 5” in the
advanced options of BLAST+.
Effect of changing the threshold T: Lower T yields
more database hits (black line) and extensions (red)
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Fig. 4-13
Page 140
For BLASTN, the word size is typically 7, 11, or 15
(EXACT match). Changing word size is like changing
threshold of proteins. w=15 gives fewer matches and is
faster than w=11 or w=7.
For megaBLAST (see below), the word size is 28 and
can be adjusted to 64. What will this do? MegaBLAST is
VERY fast for finding closely related DNA sequences!
How to interpret a BLAST search: expect value
It is important to assess the statistical significance
of search results.
For global alignments, the statistics are poorly understood.
For local alignments (including BLAST search results),
the statistics are well understood. The scores follow
an extreme value distribution (EVD) rather than a
normal distribution.
Normal distribution
0.40
0.35
probability
0.30
0.25
0.20
normal
distribution
0.15
0.10
0.05
0
-5
-4
-3
-2
-1
0
x
1
2
3
4
Normal distribution (solid line) compared to
extreme value distribution (dashed line): note
EVD skewing to the right
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Fig. 4-14
Page 141
How to interpret a BLAST search: expect value
The expect value E is the number of alignments
with scores greater than or equal to score S
that are expected to occur by chance in a
database search.
An E value is related to a probability value p.
The key equation describing an E value is:
E = Kmn e-lS
E = Kmn e-lS
This equation is derived from a description
of the extreme value distribution
S = the score
E = the expect value = the number of highscoring segment pairs (HSPs) expected
to occur with a score of at least S
m, n = the length of two sequences
l, K = Karlin Altschul statistics
Some properties of the equation E = Kmn e-lS
• The value of E decreases exponentially with increasing S
(higher S values correspond to better alignments).Very
high scores correspond to very low E values.
•The E value for aligning a pair of random sequences must
be negative! Otherwise, long random alignments would
acquire great scores
• Parameter K describes the search space (database).
• For E=1, one match with a similar score is expected to
occur by chance. For a very much larger or smaller
database, you would expect E to vary accordingly
From raw scores to bit scores
• There are two kinds of scores: raw scores (calculated from
a substitution matrix) and bit scores (normalized scores)
• Bit scores are comparable between different searches
because they are normalized to account for the use of
different scoring matrices and different database sizes
S’ = bit score = (lS - lnK) / ln2
The E value corresponding to a given bit score is:
E = mn 2 -S’
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Page 143
Bit scores allow you to compare results between different
database searches, even using different scoring matrices.
How to interpret BLAST: E values and p values
The expect value E is the number of alignments
with scores greater than or equal to score S
that are expected to occur by chance in a
database search. A p value is a different way of
representing the significance of an alignment.
p = 1 - e-E
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Page 143
How to interpret BLAST: E values and p values
E values of about 1
to 10 are far easier
to interpret than
corresponding p
values.
Very small E
values are very
similar to p values.
__E
10
5
2
1
0.1
0.05
0.001
0.0001
____p
0.99995460
0.99326205
0.86466472
0.63212056
0.09516258 (about 0.1)
0.04877058 (about 0.05)
0.00099950 (about 0.001)
0.00010000
E values are comparable to p values, and are designed to
be more convenient to interpret.
Outline
Introduction
Definition of Orthology and Paralogy
A myoglobin example
BLAST search steps
Step 1: Specifying Sequence of interest
Step 2: Selecting BLAST Program
Step 3: Selecting a Database
Step 4: Selecting Search Parameters and Formatting Parameters
BLAST algorithm uses local alignment search strategy
BLAST algorithm parts: list, scan, extend
BLAST algorithm: local alignment search statistics and E value
Making sense of raw scores with bit scores
BLAST algorithm: Relation Between E and p values
BLAST search strategies
General concepts; principles of BLAST searching
How to evaluate the significance of results
How to handle too many or few results
BLAST searching with multidomain protein: HIV-1 Pol
Using BLAST for gene discovery: Find-a-Gene
Finding distantly related proteins: PSI-BLAST
Overview of BLAST
search strategies
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Fig. 4-15
Page 145
BLASTP search: human RBP4 query,
human RefSeq database
Results include matches (such as CG8) with
high E values and limited identity to the query
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Fig. 4-16
Page 147
“Recipricol” BLASTP search with CG8 as query
includes RBP4 and other lipocalins
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Fig. 4-17
Page 149
This confirms that the finding of CG8 using RBP4 as
a query was a true positive
Pairwise alignment of CG8 with non-homologous
proteins
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Fig. 4-17
Page 149
•
•
•
•
Query and subject are very different lengths
E values are not significant
Matches lack GXW motif
Subjects are not annotated as lipocalins
BLAST searching a multidomain protein: HIV-1 pol
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Fig. 4-18
Page 151
BLAST searching a multidomain protein: HIV-1 pol
The BLAST output includes a graphic of the
various domains in HIV-1 pol
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Fig. 4-19
Page 153
BLAST searching a multidomain protein: HIV-1 pol
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Fig. 4-19
Page 153
This output shows identical residues as a dot (.). Note
that the column positions that contain an arginine (R) can
sometimes also contain a lysine (K) or glutamine (Q) in a
position-specific pattern. This is a preview of the concept
of position-specific scoring matrices (Chapter 5).
Taxonomy report for a BLAST searching HIV-1 pol
Most of the matches
are to viruses, but
there are also
matches to rabbit,
fungal, pig, and insect
sequences.
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Fig. 4-20
Page 154
BLASTP searching HIV-1 pol against bacterial proteins
bacterial matches to HIV-1
retropepsin, reverse
transcriptase domains
bacterial matches to
HIV-1 ribonuclease H
domain
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Fig. 4-21
Page 155
bacterial matches to
HIV-1 integrase core
domain
BLAST searching HIV-1 pol against human sequences
Question: are there human
homologs of HIV-1 pol protein?
Query: HIV-1 Pol
Program: BLASTP
Database: human nr (nonredundant)
Matches: many human proteins share
significant identity.
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Fig. 4-22
Page 156
Question: are there human RNA
transcripts corresponding to HIV-1 pol?
Query: HIV-1 Pol
Program: TBLASTN
Database: human ESTs
Matches: many human genes are actively
transcribed to generate transcripts
homologous to HIV-1 pol.
Outline
Introduction
BLAST search steps
Step 1: Specifying sequence of interest
Step 2: Selecting BLAST program
Step 3: Selecting a database
Step 4: Selecting search parameters and formatting
parameters
Stand-alone BLAST
BLAST algorithm uses local alignment search strategy
BLAST algorithm parts: list, scan, extend
BLAST algorithm: local alignment search statistics and E
value
Making sense of raw scores with bit scores
BLAST algorithm: relation between E and p values
BLAST search strategies
General concepts; principles of BLAST searching
How to evaluate the significance of results
How to handle too many or too few results
BLAST searching with multidomain protein: HIV-1 Pol
“Find-a-gene project” to practice BLAST
Start with the sequence
of a known protein
TBLASTN
Inspect the
output
BLASTX nr
or
BLASTP nr
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Fig. 4-23
Page 157
“Find-a-gene project” example: novel globin
Query: NP_000509
Program: TBLASTN
Database: EST
(nematodes)
Match: novel globin
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Fig. 4-24
Page 158
Confirmation
Query: nematode EST
Program: BLASTX
Best match: a globin, but
not a previously
annotated globin
“Find-a-gene project”
• The find-a-gene project is meant to be a very focused,
specific project to help you understand how to use various
BLAST tools (e.g. TBLASTN, BLASTX, BLASTP) and
various databases.
• You can start with (almost) any protein, from the organism
of your choice, and discover a “novel” gene in another
organism that is homologous but has never been annotated
before as related to your query. Therefore you are
discovering a new gene.
• You can take your new gene/protein, name it, then search it
against databases to confirm it has not been described
before.
• You can further perform multiple sequence alignment
(Chapter 6), phylogeny (Chapter 7), and predict its protein
structure (Chapter 13) and its function (Chapter 14).
Outline
Introduction
BLAST search steps
Step 1: Specifying sequence of interest
Step 2: Selecting BLAST program
Step 3: Selecting a database
Step 4: Selecting search parameters and formatting
parameters
Stand-alone BLAST
BLAST algorithm uses local alignment search strategy
BLAST algorithm parts: list, scan, extend
BLAST algorithm: local alignment search statistics and E
value
Making sense of raw scores with bit scores
BLAST algorithm: relation between E and p values
BLAST search strategies
General concepts; principles of BLAST searching
How to evaluate the significance of results
How to handle too many or too few results
BLAST searching with multidomain protein: HIV-1 Pol
Three problems standard BLAST cannot solve
[1] Use human beta globin as a query against human RefSeq
proteins, and BLASTP does not “find” human myoglobin.
This is because the two proteins are too distantly related.
PSI-BLAST at NCBI as well as hidden Markov models easily
solve this problem.
[2] How can we search using 10,000 base pairs as a query,
or even millions of base pairs? Many BLAST-like tools for
genomic DNA are available such as PatternHunter,
Megablast, BLAT, and LASTZ.
[3] How can we align tens of millions of short reads to a
reference genome?
Position specific iterated BLAST:
PSI-BLAST
The purpose of PSI-BLAST is to look deeper into the
database for matches to your query protein
sequence by employing a scoring matrix that is
customized to your query.
PSI-BLAST is performed in five steps
[1] Select a query and search it against a protein database
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Page 172
PSI-BLAST is performed in five steps
[1] Select a query and search it against a protein database
[2] PSI-BLAST constructs a multiple sequence alignment
then creates a “profile” or specialized position-specific
scoring matrix (PSSM)
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Page 172
Inspect the BLASTP output to identify empirical “rules” regarding
amino acids tolerated at each position
R,I,K
C
D,E,T
K,R,T
N,L,Y,G
1 M
2 K
3 W
4 V
5 W
6 A
7 L
8 L
9 L
10 L
11 A
12 A
13 W
14 A
15 A
16 A
...
37 S
38 G
39 T
40 W
41 Y
42 A
A
-1
-1
-3
0
-3
5
-2
-1
-1
-2
5
5
-2
3
2
4
R N D C Q E G H
-2 -2 -3 -2 -1 -2 -3 -2
1 0 1 -4 2 4 -2 0
-3 -4 -5 -3 -2 -3 -3 20
-3
-3 -3 -4 -1 -3 -3 -4 -4
-3 -4 -5 -3 -2 -3 -3 -3
-2 -2 -2 -1 -1 -1 0 -2
-2 -4 -4 -1 -2 -3 -4 -3
-3 -3 -4 -1 -3 -3 -4 -3
-3 -4 -4 -1 -2 -3 -4 -3
-2 -4 -4 -1 -2 -3 -4 -3
-2 -2 -2 -1 -1 -1 0 -2
-2 -2 -2 -1 -1 -1 0 -2
all the amino acids
-3 -4 -4 -2 -2 -3 -4 -3
from
-2
-1 position
-2 -1 -1 1
-2to 4the
-2
-1 0 -1 -2 2 0 2 -1
end of your PSI-BLAST
-2 -1 -2 -1 -1 -1 3 -2
2
0
0
-3
-2
4
-1
-3
-1
-3
-2
-2
query protein
0
-1
0
-4
-2
-2
-1
-2
-1
-5
-3
-2
-1
-3
-1
-3
-3
-1
0
-2
-1
-2
-2
-1
I L K M F
1 2 -2 6 0
-3 -3 3 -2 -4
-3 -2 -3
-2 1
amino
acids
3 1 -3 1 -1
-3 -2 -3 -2 1
-2 -2 -1 -1 -3
2 4 -3 2 0
2 2 -3 1 3
2 4 -3 2 0
2 4 -3 2 0
-2 -2 -1 -1 -3
-2 -2 -1 -1 -3
1 4 -3 2 1
-2 -2 -1 -2 -3
-3 -3 0 -2 -3
-2 -2 -1 -1 -3
0 0 -1 -2 -3
-2 6 -2 -4 -4
-1 -2 -2 -1 -1
-3 -3 -3 -3 -2
-2 -3 2 -2 -1
-1 0 -2 -2 -2
0
-2
-1
-3
-2
-1
-2
-3
-1
-2
-1
-1
-3
-4
-2
1
3
-3
P
-3
-1
-4
-3
-4
-1
-3
-3
-3
-3
-1
-1
-3
-1
-1
-1
S
-2
0
-3
-2
-3
1
-3
-2
-3
-3
1
1
-3
1
3
1
T
-1
-1
-3
0
-3
0
-1
-1
-1
-1
0
0
-2
-1
0
0
W
-2
-3
12
-3
12
-3
-2
-2
-2
-2
-3
-3
7
-3
-3
-3
Y
-1
-2
2
-1
2
-2
-1
0
-1
-1
-2
-2
0
-3
-2
-2
V
1
-3
-3
4
-3
0
1
3
2
1
0
0
0
-1
-2
-1
-1 4 1 -3
-2 0 -2 -3
-1 1 5 -3
-4 -3 -3 12
-3 -2 -2 2
-1 1 0 -3
-2
-3
-2
2
7
-2
-2
-4
0
-3
-1
0
1 M
2 K
3 W
4 V
5 W
6 A
7 L
8 L
9 L
10 L
11 A
12 A
13 W
14 A
15 A
16 A
...
37 S
38 G
39 T
40 W
41 Y
42 A
A
-1
-1
-3
0
-3
5
-2
-1
-1
-2
5
5
-2
3
2
4
R
-2
1
-3
-3
-3
-2
-2
-3
-3
-2
-2
-2
-3
-2
-1
-2
N
-2
0
-4
-3
-4
-2
-4
-3
-4
-4
-2
-2
-4
-1
0
-1
D
-3
1
-5
-4
-5
-2
-4
-4
-4
-4
-2
-2
-4
-2
-1
-2
C
-2
-4
-3
-1
-3
-1
-1
-1
-1
-1
-1
-1
-2
-1
-2
-1
Q
-1
2
-2
-3
-2
-1
-2
-3
-2
-2
-1
-1
-2
-1
2
-1
E
-2
4
-3
-3
-3
-1
-3
-3
-3
-3
-1
-1
-3
-2
0
-1
G
-3
-2
-3
-4
-3
0
-4
-4
-4
-4
0
0
-4
4
2
3
H
-2
0
-3
-4
-3
-2
-3
-3
-3
-3
-2
-2
-3
-2
-1
-2
I
1
-3
-3
3
-3
-2
2
2
2
2
-2
-2
1
-2
-3
-2
L
2
-3
-2
1
-2
-2
4
2
4
4
-2
-2
4
-2
-3
-2
K
-2
3
-3
-3
-3
-1
-3
-3
-3
-3
-1
-1
-3
-1
0
-1
M
6
-2
-2
1
-2
-1
2
1
2
2
-1
-1
2
-2
-2
-1
F
0
-4
1
-1
1
-3
0
3
0
0
-3
-3
1
-3
-3
-3
P
-3
-1
-4
-3
-4
-1
-3
-3
-3
-3
-1
-1
-3
-1
-1
-1
S
-2
0
-3
-2
-3
1
-3
-2
-3
-3
1
1
-3
1
3
1
T
-1
-1
-3
0
-3
0
-1
-1
-1
-1
0
0
-2
-1
0
0
W
-2
-3
12
-3
12
-3
-2
-2
-2
-2
-3
-3
7
-3
-3
-3
Y
-1
-2
2
-1
2
-2
-1
0
-1
-1
-2
-2
0
-3
-2
-2
V
1
-3
-3
4
-3
0
1
3
2
1
0
0
0
-1
-2
-1
2
0
0
-3
-2
4
-1
-3
-1
-3
-2
-2
0
-1
0
-4
-2
-2
-1
-2
-1
-5
-3
-2
-1
-3
-1
-3
-3
-1
0
-2
-1
-2
-2
-1
0 0 -1 -2 -3
-2 6 -2 -4 -4
-1 -2 -2 -1 -1
-3 -3 -3 -3 -2
-2 -3 2 -2 -1
-1 0 -2 -2 -2
0
-2
-1
-3
-2
-1
-2
-3
-1
-2
-1
-1
-3
-4
-2
1
3
-3
-1 4 1 -3
-2 0 -2 -3
-1 1 5 -3
-4 -3 -3 12
-3 -2 -2 2
-1 1 0 -3
-2
-3
-2
2
7
-2
-2
-4
0
-3
-1
0
1 M
2 K
3 W
4 V
5 W
6 A
7 L
8 L
9 L
10 L
11 A
12 A
13 W
14 A
15 A
16 A
...
37 S
38 G
39 T
40 W
41 Y
42 A
A
-1
-1
-3
0
-3
5
-2
-1
-1
-2
5
5
-2
3
2
4
R
-2
1
-3
-3
-3
-2
-2
-3
-3
-2
-2
-2
-3
-2
-1
-2
N
-2
0
-4
-3
-4
-2
-4
-3
-4
-4
-2
-2
-4
-1
0
-1
D
-3
1
-5
-4
-5
-2
-4
-4
-4
-4
-2
-2
-4
-2
-1
-2
C Q E G H I L K M
-2 -1 -2 -3 -2 1 2 -2 6
-4 2 4 -2 0 -3 -3 3 -2
-3 -2 -3 -3 -3 -3 -2 -3 -2
-1 -3 -3 -4 -4 3 1 -3 1
-3 -2 -3 -3 -3 -3 -2 -3 -2
-1 -1 -1 0 -2 -2 -2 -1 -1
-1 -2 -3 -4 -3 2 4 -3 2
-1 -3 -3 -4 -3 2 2 -3 1
-1
-2 that
-3 -4a -3
2 amino
4 -3 2
note
given
-1 -2 -3 -4 -3 2 4 -3 2
acid
-1
-1 (such
-1 0 as
-2alanine)
-2 -2 -1in-1
-1
-1 query
-1 0 -2
-2 -2 can
-1 -1
your
protein
-2 -2 -3 -4 -3 1 4 -3 2
receive
-1
-1 -2 different
4 -2 -2 scores
-2 -1 -2
-2
0 2 -1alanine—
-3 -3 0 -2
for 2matching
-1 -1 -1 3 -2 -2 -2 -1 -1
F
0
-4
1
-1
1
-3
0
3
0
0
-3
-3
1
-3
-3
-3
P
-3
-1
-4
-3
-4
-1
-3
-3
-3
-3
-1
-1
-3
-1
-1
-1
W
-2
-3
12
-3
12
-3
-2
-2
-2
-2
-3
-3
7
-3
-3
-3
Y
-1
-2
2
-1
2
-2
-1
0
-1
-1
-2
-2
0
-3
-2
-2
V
1
-3
-3
4
-3
0
1
3
2
1
0
0
0
-1
-2
-1
2
0
0
-3
-2
4
-1
-3
-1
-3
-2
-2
0
-1
0
-4
-2
-2
-1
-2
-1
-5
-3
-2
-2
-3
-1
-2
-1
-1
-3
-4
-2
1
3
-3
-1 4 1 -3
-2 0 -2 -3
-1 1 5 -3
-4 -3 -3 12
-3 -2 -2 2
-1 1 0 -3
-2
-3
-2
2
7
-2
-2
-4
0
-3
-1
0
depending on the
-1
0 0 in
0 the
-1 -2
-3 0
position
protein
-3
-1
-3
-3
-1
-2
-1
-2
-2
-1
-2 6 -2 -4 -4
-1 -2 -2 -1 -1
-3 -3 -3 -3 -2
-2 -3 2 -2 -1
-1 0 -2 -2 -2
-2
-1
-3
-2
-1
S
-2
0
-3
-2
-3
1
-3
-2
-3
-3
1
1
-3
1
3
1
T
-1
-1
-3
0
-3
0
-1
-1
-1
-1
0
0
-2
-1
0
0
1 M
2 K
3 W
4 V
5 W
6 A
7 L
8 L
9 L
10 L
11 A
12 A
13 W
14 A
15 A
16 A
...
37 S
38 G
39 T
40 W
41 Y
42 A
A
-1
-1
-3
0
-3
5
-2
-1
-1
-2
5
5
-2
3
2
4
R
-2
1
-3
-3
-3
-2
-2
-3
-3
-2
-2
-2
-3
-2
-1
-2
N
-2
0
-4
-3
-4
-2
-4
-3
-4
-4
-2
-2
-4
-1
0
-1
D
-3
1
-5
-4
-5
-2
-4
-4
-4
-4
-2
-2
-4
-2
-1
-2
2
0
0
-3
-2
4
-1
-3
-1
-3
-2
-2
0
-1
0
-4
-2
-2
-1
-2
-1
-5
-3
-2
C Q E G H I L K M
-2 -1 -2 -3 -2 1 2 -2 6
-4 2 4 -2 0 -3 -3 3 -2
-3 -2 -3 -3 -3 -3 -2 -3 -2
-1 -3 -3 -4 -4 3 1 -3 1
-3 -2 -3 -3 -3 -3 -2 -3 -2
-1 -1 -1 0 -2 -2 -2 -1 -1
-1 -2 -3 -4 -3 2 4 -3 2
-1 -3 -3 -4 -3 2 2 -3 1
note
a given
-1 -2 that
-3 -4
-3 2 amino
4 -3 2
-1 -2(such
-3 -4as-3tryptophan)
2 4 -3 2
acid
-1 -1 -1 0 -2 -2 -2 -1 -1
in
-1 your
-1 -1query
0 -2 protein
-2 -2 -1can
-1
-2 -2 -3different
-4 -3 1scores
4 -3 2
receive
-1 -1 -2 4 -2 -2 -2 -1 -2
for
-2 matching
2 0 2 -1 -3 -3 0 -2
-1 -1 -1 3 -2 -2 -2 -1 -1
tryptophan—depending
on
in -3
the 0
-1 the
0 0position
0 -1 -2
-3 -2 -2 6 -2 -4 -4 -2
protein
-1
-3
-3
-1
-1
-2
-2
-1
-1 -2 -2 -1 -1
-3 -3 -3 -3 -2
-2 -3 2 -2 -1
-1 0 -2 -2 -2
-1
-3
-2
-1
-2
-3
-1
-2
-1
-1
F
0
-4
1
-1
1
-3
0
3
0
0
-3
-3
1
-3
-3
-3
P
-3
-1
-4
-3
-4
-1
-3
-3
-3
-3
-1
-1
-3
-1
-1
-1
S
-2
0
-3
-2
-3
1
-3
-2
-3
-3
1
1
-3
1
3
1
T
-1
-1
-3
0
-3
0
-1
-1
-1
-1
0
0
-2
-1
0
0
W
-2
-3
12
-3
12
-3
-2
-2
-2
-2
-3
-3
7
-3
-3
-3
Y
-1
-2
2
-1
2
-2
-1
0
-1
-1
-2
-2
0
-3
-2
-2
V
1
-3
-3
4
-3
0
1
3
2
1
0
0
0
-1
-2
-1
-3
-4
-2
1
3
-3
-1 4 1 -3
-2 0 -2 -3
-1 1 5 -3
-4 -3 -3 12
-3 -2 -2 2
-1 1 0 -3
-2
-3
-2
2
7
-2
-2
-4
0
-3
-1
0
PSI-BLAST is performed in five steps
[1] Select a query and search it against a protein database
[2] PSI-BLAST constructs a multiple sequence alignment
then creates a “profile” or specialized position-specific
scoring matrix (PSSM)
[3] The PSSM is used as a query against the database
B&FG 3e
Page 172
PSI-BLAST is performed in five steps
[1] Select a query and search it against a protein database
[2] PSI-BLAST constructs a multiple sequence alignment
then creates a “profile” or specialized position-specific
scoring matrix (PSSM)
[3] The PSSM is used as a query against the database
[4] PSI-BLAST estimates statistical significance (E values)
B&FG 3e
Page 172
PSI-BLAST is performed in five steps
[1] Select a query and search it against a protein database
[2] PSI-BLAST constructs a multiple sequence alignment
then creates a “profile” or specialized position-specific
scoring matrix (PSSM)
[3] The PSSM is used as a query against the database
[4] PSI-BLAST estimates statistical significance (E values)
[5] Repeat steps [3] and [4] iteratively, typically 5 times.
At each new search, a new profile is used as the query.
B&FG 3e
Page 172
Position-specific scoring matrix (PSSM)
B&FG 3e
Fig. 5.3
Page 173
PSI-BLAST: dramatic increase in number of hits
B&FG 3e
Table 5.2
Page 174
Given this query, a standard BLASTP search would produce about
9 hits with low expect values. This PSI-BLAST search produces
>200 hits after 3 or 4 iterations.
Note that PSI-BLAST E values
can improve dramatically!
After 1st iteration:
Expect = 4e-04
Alignment length = 87 amino acids
After 2nd iteration:
Expect = 1e-36
Alignment length = 110 amino acids
After 3rd iteration:
Expect = 2e-33
Alignment length = 146 amino acids
B&FG 3e
Fig. 5.4
Page 175
The universe of lipocalins (each dot is a protein)
retinol-binding
protein
apolipoprotein D
odorant-binding
protein
Scoring matrices let you focus on the big (or small) picture
retinol-binding
protein
your RBP query
Scoring matrices let you focus on the big (or small) picture
PAM250
PAM30
retinol-binding
retinol-binding
protein
protein
Blosum80
Blosum45
PSI-BLAST generates scoring matrices
more sensitive than PAM or BLOSUM
retinol-binding
protein
PSI‐BLAST algorithm increases the sensitivity of a database search by
detecting homologous matches with relatively low sequence identity
B&FG 3e
Fig. 5.5
Page 176
PSI-BLAST: the problem of corruption
In PSI-BLAST once a match is incorporated into a PSSM it will
never be removed, even if it is wrong (i.e. even if it is a false
positive that is not truly homologous to the query). Not only
will it stay, it may lead to the inclusion of many other related
false positive hits.
There are three main approaches to removing false positives:
B&FG 3e
Page 177
(1) Filter biased amino acid regions. (This is an option in
BLAST.)
(2) Lower the expect value threshold to make the search more
stringent.
(3) Visually inspect the output from each PSI-BLAST iteration
and remove suspicious matches (by unchecking the
corresponding boxes).