©CMBI 2003

Step 3: ToolsDatabase Searching

•Database Searching•Sequence Alignment•Scoring Matrices•Significance of an alignment

•BLAST, algorithm•BLAST, parameters•BLAST, output•Alignment significance in BLAST

©CMBI 2003

Database Searching

Identify similarities between

novel query sequenceswhose structures and functions are unknown and uncharacterized

and

sequences in (public) databaseswhose structures and functions have been elucidated.

N.B. The similarity might span the entire query sequence or just part of it!

©CMBI 2003

Database searching (2)

– The query sequence is compared/aligned with every sequence in the database.

– High-scoring database sequences are assumed to be evolutionary related to the query sequence.

– If sequences are related by divergence from a common ancestor, there are said to be homologous.

J.Leunissen©CMBI 2003

Sequence Alignment

The purpose of a sequence alignment is to line up all residues in the sequence that were derived from the same residue position in the ancestral gene or protein in any number of sequences

gap = insertion or deletion

©CMBI 2003

Scoring Matrix/Substitution Matrix

– To score quality of an alignment

– Contains scores for pairs of residues (amino acids or nucleic acids) in a sequence alignment

– For protein/protein comparisons: a 20 x 20 matrix of similarity scores where identical amino acids and those of similar character (e.g. Ile, Leu) give higher scores compared to those of different character (e.g. Ile, Asp).

– Symmetric

©CMBI 2003

Substitution Matrices

Not all amino acids are equal– Some are more easily substituted than others– Some mutations occur more often– Some substitions are kept more often

Mutations tend to favor some substitutions– Some amino acids have similar codons– They are more likely to be changed from DNA mutation

Selection tends to favor some substitutions– Some amino acids have similar properties/structure– They are more likely to be kept

©CMBI 2003

PAM250 Matrix

©CMBI 2003

Scoring example

Score of an alignment is the sum of the scores of all pairs of residues in the alignment

sequence 1: TCCPSIVARSNsequence 2: SCCPSISARNT

1 12 12 6 2 5 -1 2 6 1 0 => alignment score = 46

©CMBI 2003

Dayhoff Matrix (1)

– Derived from how often different amino acids replace other amino acids in evolution.

– Created from a dataset of closely similar protein sequences (less than 15% amino acid difference). These could be unambiguously aligned.

– A mutation probability matrix whas derived where the entries reflect the probabilities of a mutational event.

– This matrix is called PAM 1. An evolutionary distance of 1 PAM (point accepted mutation) means there has been 1 point mutation per 100 residues

©CMBI 2003

Dayhoff Matrix (2)

Log odds matrix: logs of elements of PAM matrix.

Score of mutation A B

observed ab mutation rate

mutation rate expected from amino acid frequencies

When using a log odds matrix, the total score of the alignment is given by the sum of the scores for each aligned pair of residues.

= log

©CMBI 2003

Dayhoff Matrix (3)

PAM 1 may be used to generate matrices for greater evolutionary distances by multiplying it repeatedly by itself.

PAM250: – 2,5 mutations per residue– equivalent to 20% matches remaining between two

sequences, i.e. 80% of the amino acid positions are observed to have changed.

– is default in many analysis packages.

©CMBI 2003

BLOSUM Matrix

Limit of Dayhoff matrix:Matrices based on the Dayhoff model of evolutionary rates are of limited value because their substitution rates are derived from alignments of sequences that are at least 85% identical

An alternative approach has been developed by Henikoff and Henikoff using local multiple alignments of more distantly related sequences

©CMBI 2003

BLOSUM Matrix (2)

The BLOSUM matrices (BLOcks SUbstitution Matrix) are based on the BLOCKS database.

The BLOCKS database utilizes the concept of blocks (ungapped amino acid pattern), which act as signatures of a family of proteins.

Substitution frequencies for all pairs of amino acids were then calculated and this used to calculate a log odds BLOSUM matrix.

Different matrices are obtained by varying the identity threshold. For example, the BLOSUM80 matrix was derived using blocks of 80% identity.

Which Matrix to use?

Close relationships (Low PAM, high Blosum)Distant relationships (High PAM, low Blosum)

Reasonable defaults: PAM250, BLOSUM62

J.Kissinger

©CMBI 2003

Significance of alignment (1)

When is an alignment statistically significant?

In other words:

How much different is the alignment score found from scores obtained by aligning random sequences to the query sequence?

Or:

What is the probability that an alignment with this score could have arisen by chance?

©CMBI 2003

Significance of alignment (2)

Database size= 20 x 106 letters

peptide #hits

A 1 x 106

AP 50000IAP 2500LIAP 125WLIAP 6KWLIAP 0,3KWLIAPY 0,015

©CMBI 2003

BLAST – Basic Local Alignment Search Tool

•Find the highest scoring locally optimal alignments between a query sequence and a database.

•Very fast algorithm

•Can be used to search extremely large databases(uses a pre-indexed database which contributes to its great speed)

•Sufficiently sensitive and selective for most purposes

•Robust – the default parameters can usually be used

©CMBI 2003

BLAST Algorithm, Step 1• For a given word length w (usually 3 for proteins) and a given score matrix:

Create a list of all words (w-mers) that can can score >T when compared to w-mers from the query.

P D G 13

P Q A 12 P Q N 12etc.

Below Threshold (T=13)

Query Sequence L N K C K T P Q G Q R L V N QP Q G 18P E G 15 P R G 14P K G 14 P N G 13

Neighborhood Words

Word

P M G 13

©CMBI 2003

BLAST Algorithm, Step 2

• Each neighborhood word gives all positions in the database where it is found (hit list).

P D G 13

P Q G 18P E G 15 P R G 14P K G 14 P N G 13

P M G 13 PMG Database

©CMBI 2003

BLAST Algorithm, Step 3

• The program tries to extend matching segments (seeds) out in both directions by adding pairs of residues. Residues will be added until the incremental score drops below a threshold.

©CMBI 2003

Basic BLAST Algorithms

BLASTN - compares a nucleotide query to a nucleotide database

BLASTP - compares a protein query to a protein 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.

©CMBI 2003

PSI-BLAST

Position-Specific Iterated BLAST

– Distant relationships are often best detected by motif or profile searches rather than pairwise comparisons

– PSI-BLAST first performs a gapped BLAST database search.

– The PSI-BLAST program uses the information from any significant alignments returned to construct a position-specific score matrix, which replaces the query sequence for the next round of database searching.

– PSI-BLAST may be iterated until no new significant alignments are found.

©CMBI 2003

BLAST Input

Steps in running BLAST:

•Entering your query sequence (cut-and-paste)•Select the database(s) you want to search•Choose output parameters•Choose alignment parameters (e.g. scoring matrix, filters,….)

Example query=MAFIWLLSCYALLGTTFGCGVNAIHPVLTGLSKIVNGEEAVPGTWPWQVTLQDRSGFHFC GGSLISEDWVVTAAHCGVRTSEILIAGEFDQGSDEDNIQVLRIAKVFKQPKYSILTVNND ITLLKLASPARYSQTISAVCLPSVDDDAGSLCATTGWGRTKYNANKSPDKLERAALPLLT NAECKRSWGRRLTDVMICGAASGVSSCMGDSGGPLVCQKDGAYTLVAIVSWASDTCSASS GGVYAKVTKIIPWVQKILSSN

©CMBI 2003

BLAST Output (1)

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BLAST Output (2)

A high score, or preferably, clusters of high scores, indicates a likely relationship

A low probability indicates that a match is unlikely to ave arisen by chance

©CMBI 2003

BLAST Output (3)

Low scores with high probabilities suggest that matches have arisen by chance

©CMBI 2003

Alignment Significance in BLAST

P-value (probability) – relates the score returned for an alignment to the likelihood of its

having arisen by chance; in general, the closer the value approaches to zero, the greater the confidence that the match is real.

E-value (expect value)– the number of alignments with a given score that would be

expected to occur at random in the database that has been searched (e.g. if E=10, 10 matches with scores this high are expected to be found by chance).

– A match will only be reported if its E value falls below the threshold set.

– Lower E thresholds are more stringent, and report fewer matches.

©CMBI 2003

BLAST Output (4)

©CMBI 2003

BLAST Output (5)

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BLAST Output (6)