blosum scoring matrices - iitkhome.iitk.ac.in/~rsankar/courses/lec04.pdfdr. r. sankar, bse 633...

Dr. R. Sankar, BSE 633 (2020)

BLOSUM Scoring Matrices

Derived from UNGAPPED alignments

Based on observed amino acid substitutions in a large set of ~2000 conserved AA patterns (Blocks)

These BLOCKS are constructed from a protein sequence database PROSITE

They represent 500 families of related proteins (similar biochemical functions); act as signatures

Different type of sequence analysis; much larger data set than PAM matrices Henikoff & Henikoff (1992)


PROSITE Database of protein families and domains. By analyzing the constant and variable properties of groups of similar sequences, it is possible to derive a signature for a protein family or domain, which distinguishes its members from all other unrelated proteins. A protein signature can be used to assign a newly sequenced protein to a specific family of proteins and thus to formulate hypotheses about its function. PROSITE currently contains patterns and profiles specific for more than a thousand protein families or domains. Each of these signatures comes with documentation providing background information on the structure and function of these proteins.


PROSITE: Database of Protein Families and Domains

•Post-translational modifications

•Compositional biased regions

•Domains

•DNA or RNA associated proteins

•Enzymes

•Electron transport proteins

•Other transport proteins

•Structural proteins

•Receptors

•Cytokines & growth factors

•Hormones and active peptides

•Toxins

•Inhibitors

•Protein secretion and chaperones

•Others


Mark the conserved blocks

Eliminate those sequences that are more than x% identity


Constructing a BLOSUM matr. 1. Counting mutations


2. Tallying mutation frequencies


3. Matrix of mutation probs.


4. Calculate abundance of each residue (Marginal prob)


5. Obtaining a BLOSUM matrix


If we expect to find a and b aligned together in homologous sequences more often than we expect

them to occur by chance (pab>fa fb), then the odds ratio is greater than one and the score is positive.


BLOSUM62 amino acid substitution matrix


PAM & BLOSUM Matrices

PAM60, PAM80, PAM120, PAM250: represent a level of 60%, 80%, 120% and 250% of change expected

250% level of change seems very large; sequences with this level of divergence still have about 20% similarity

BLOSUM60, BLOSUM80: derived from patterns that were 60% and 80% identical respectively

PAM – based on mutational model of evolution

BLOSUM: based on all amino acid changes observed in an aligned region from a related family of proteins

This is regardless of overall similarity between protein sequences


Choice of Scoring Matrix


Other scoring matrices

Simple identity; match or mismatch

Genetic code changes

Based on chemical similarity, molecular volume, polarity, hydrophobicity

Based on structurally aligned 3D structures

Based on 400X400 dipeptide substitution matrix

Transmembrane proteins


Gap Penalty

Gap opening penalty (g)

Gap extension penalty (r)

Gap score wx = g + rx (affine gap penalty)

wx = g + (r-1)x

x = length of the gap

When do you use penalties for end gaps?


Affine Gap Penalties • In nature, a series of k indels often come as a single

event rather than a series of k single nucleotide events:

Normal scoring would give the same score for both alignments

This is more likely.

This is less likely.

ATA__GGC ATGATCGC

ATA_G_GC ATGATCGC


End Gap Penalties No end gap penalty

Gaps will be liberally placed at the ends of alignments by DP

Usually best to include gap penalty for global alignments

Will not affect local alignment

Sequences of same length: Include end gap penalty

Sequences of unknown homology/different lengths:

May be better not to include end gap penalty

One sequence contained in the other: Reasonable to include end gap penalty for the shorter sequence

However, at least once test by including end-gap penalty


High mismatch and gap penalties:

local alignments

longest region of exact matches

Mismatch penalty > 2 * match:

gap penalty becomes a decisive parameter

Mismatch penalty < 2* gap penalty

large number of possible alignments

Different scenarios in alignments


Optimal combinations of scoring matrices and gap penalties

1.Some scoring matrices are superior to others at finding related proteins based on sequence or structure

2.Gap penalties that for a given scoring matrix are adjusted to produce a local alignments are the most suitable

3.Significance of alignment scores should be estimated to identify related sequences

blosum scoring matrices - iitkhome.iitk.ac.in/~rsankar/courses/lec04.pdfdr. r. sankar, bse 633...

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