HEURISTIC APPROACHES

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# HEURISTIC APPROACHES - PowerPoint PPT Presentation

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Presentation Transcript

### Heuristic approaches & scoring matrices

MSc Medical Biochemistry, Ph.D,.

Introduction
• Two 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.
• Breaking of the sequence into defined word size.
• Finding a match or HSP (High Scoring Pair).
• Alignment of the word and extending the alignment.
Breaking of the sequence into defined word size

Query : AILDTGATGDA

Word size : 4

AILDTGATGDA

AILD

ILDT

LDTG

DTGA

TGAT

GATG

ATGD

TGDA

Finding a High scoring Pair

MQVWGWAILDTVATDAAMLL

AILD

Extending the alignment

MQVWGWAILDTVATDAAMLL

……………..AILDTGATGDA……

Parameters in BLAST result

Percentage of Homology

Scoring of the alignment

No of residues aligned

E-value

FastA algorithm
• The 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 M

1 2 3 4 5 6 7 8 9 10 11 12 13 14

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

Alignment of the sequences

F 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 schemes

Identity scoring matrix

• Residue 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
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 Matrices
• These 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.