M.Prasad NaiduMSc Medical Biochemistry, Ph.D,.

IntroductionTwo algorithms are there in these

methods BLAST FASTA

FastA is an algorithm developed by Pearson and Lipman. Its more sensitive than Blast.

Blast is an algorithm developed by Altschul et al., in 1990. It provides tools for high scoring local alignment between two sequences. Now a days, a gapped versions are available.

BLASTP algorithm Blast Algorithm involves the following

steps.1. Breaking of the sequence into defined word size.2. Finding a match or HSP (High Scoring Pair).3. Alignment of the word and extending the alignment.

Breaking of the sequence into defined word size

Query : AILDTGATGDAWord size : 4

AILDTGATGDAAILDAILD

ILDTILDT

LDTGLDTG

DTGADTGA

TGATTGAT

GATGGATG

ATGDATGD

TGDATGDA

Finding a High scoring Pair

MQVWGWAILDTVATDAAMLL

AILD

Extending the alignmentMQVWGWAILDTVATDAAMLL

……………..AILDTGATGDA……

Parameters in BLAST result

Percentage of Homology

Scoring of the alignment

No of residues aligned

E-value

FastA algorithmThe word size in FastA algorithm is

defined as K-tuple.Generally the K-tuple for the algorithm is

either 3 or 4 for nucleotide sequences and 1 or 2 for protein sequences.

FastA algorithm also involves the steps similar to that of the BLAST tool. But the alignment generation procedure is different.

Breaking of the sequence into defined k-tuple

F A M L G F I K Y L P G C M1 2 3 4 5 6 7 8 9 10 11 12 13 14

AA BB CC DD EE FF GG HH II KK LL MM

22 1313 11 55 77 88 44 33

66 1212 1010 1414

NN PP QQ RR SS TT VV WW YY ZZ

1111 99

AA BB CC DD EE FF GG HH II KK LL MM

22 1313 11 55 77 88 44 33

66 1212 1010 1414

NN PP QQ RR SS TT VV WW YY ZZ

1111 99

TT

11GG

22FF

33II

44KK

55YY

66LL

77PP

88GG

99AA

1010CC

1111TT

1212

33 -2-2 33 33 33 -3-3 33 -4-4 -8-8 22

1010 33 33 33

The most occuring number in the algorithm is 3, so the alignment starts after leaving three characters or residues

Alignment of the sequencesF A M L G F I K Y L P G C M

T G F I K Y L P G A C T

Parameters in FASTA result

Percentage of Homology

Scoring of the alignment

No of residues aligned

P-Score

Scoring schemesIdentity scoring matrixResidue to residue scores are represented here in the form of similarity.A 4 X 4 matrix is built for the nucleotides and 20 X 20 matrix for the amino acids.For match score is +1 and mismatch is -1

AA TT GG CC

AA 11 00 00 00

TT 00 11 00 00

GG 00 00 11 00

CC 00 00 00 11

PAM Matrices These were first developed by Margaret Dayhoff and co-workers

in 1978. This model assumes that evolutionary changes follow the markov

model i.e. residual changes occur independent on the previous mutation. One PAM is a unit of evolutionary divergence in which there is 1% amino acid change but it doesn’t imply that 100 PAM results in different aminoacids.

Dayhoff and coworkers have calculated the frequencies of accepted mutations for 1PAM by analyzing closely related families of sequences.

The scores are represented as log odd ratios. The 1PAM can be extended to any no of PAMS. For example,

1PAM table is extended to N X 1PAM. For closely related protein sequences, lower distance PAM is used

and higher PAM is used for variying proteins. PAM 30 is used for closer proteins and PAM 250 for divergent

ones.

BLOSUM MatricesThese matrices are developed by Heinkoff and

Heinkoff in 1991.The matrices have been constructed in a

similar fashion as PAM matrices.The data was derived for local alignment of

distantly related proteins deposited in the BLOCKS database.

BLOSUM 30 is used for comparing highly divergent sequences and BLOSUM 90 is used for closely related proteins.

Commonly used BLOSUM matrix is BLOSUM 62 that is used for proteins with 62% identities.