Evolution and scoring rules
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Evolution and Scoring Rules. Example Score = 5 x (# matches) + (-4) x (# mismatches) + + (-7) x (total length of all gaps) Example Score = 5 x (# matches) + (-4) x (# mismatches) + + (-5) x (# gap openings) + (-2) x (total length of all gaps). Scoring Matrices.

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Evolution and scoring rules
Evolution and Scoring Rules

  • Example Score =

    5 x (# matches) + (-4) x (# mismatches) +

    + (-7) x (total length of all gaps)

  • Example Score =

    5 x (# matches) + (-4) x (# mismatches) +

    + (-5) x (# gap openings) + (-2) x (total length of all gaps)

Scoring rules vs scoring matrices
Scoring Rules vs. Scoring Matrices

  • Nucleotide vs. Amino Acid Sequence

  • The choice of a scoring rule can strongly influence the outcome of sequence analysis

  • Scoring matrices implicitly represent a particular theory of evolution

  • Elements of the matrices specify the similarity of one residue to another

Translation - Protein Synthesis:Every 3 nucleotides (codon) are translated into one amino acid





Protein: 20 amino acids




Translation - Protein Synthesis

RNA Protein

5’ -> 3’ : N-term -> C-term

Log Likelihoods used as Scoring Matrices:PAM - % Accepted Mutations:1500 changes in 71 groups w/ > 85% similarityBLOSUM – Blocks Substitution Matrix:2000 “blocks” from 500 families

Likelihood ratio for aligning a single pair of residues
Likelihood Ratio for Aligning a Single Pair of Residues

  • Above: the probability that two residues are aligned by evolutionary descent

  • Below: the probability that they are aligned by chance

  • Pi, Pj are frequencies of residue i and j in all protein sequences (abundance)

Blosum matrices for protein
BLOSUM matrices for Protein

  • S. Henikoff and J. Henikoff (1992). “Amino acid substitution matrices from protein blocks”. PNAS 89: 10915-10919

  • Training Data: ~2000 conserved blocks from BLOCKS database. Ungapped, aligned protein segments. Each block represents a conserved region of a protein family

Constructing blosum matrices of specific similarities
Constructing BLOSUM Matrices of Specific Similarities

  • Sets of sequences have widely varying similarity. Sequences with above a threshold similarity are clustered.

  • If clustering threshold is 62%, final matrix is BLOSUM62

Constructing a blosum matr 1 counting mutations
Constructing a BLOSUM matr. training sequences1. Counting mutations

Constructing a blosum matr 2 tallying mutation frequencies
Constructing a BLOSUM matr. training sequences2. Tallying mutation frequencies

Constructing a blosum matr 3 matrix of mutation probs
Constructing a BLOSUM matr. training sequences3. Matrix of mutation probs.

5 obtaining a blosum matrix
5. Obtaining a BLOSUM matrix training sequences

1 2 3 mutation frequency table
1.2.3.Mutation Frequency Table training sequences

5 obtaining blosum62 matrix
5. Obtaining BLOSUM62 Matrix training sequences

Pam matrices point accepted mutations

PAM Matrices training sequences(Point Accepted Mutations)

Mutations accepted by natural selection

Pam matrices
PAM Matrices training sequences

  • Accepted Point Mutation

  • Atlas of Protein Sequence and Structure,

    Suppl 3, 1978, M.O. Dayhoff.

    ed. National Biomedical Research Foundation, 1

  • Based on evolutionary principles

Pam phylogenetic tree
PAM: Phylogenetic Tree training sequences

Pam accepted point mutation
PAM: Accepted Point Mutation training sequences

Mutability training sequences

Total Mutation Rate training sequences

is the total mutation rate of all amino acids

Normalize total mutation rate
Normalize Total Mutation Rate training sequences

Mutation Probability Matrix Normalized training sequences

Such that the

Total Mutation Rate is 1%

-- PAM1 mutation prob. matr. --PAM2 Mutation Probability Matrix?

-- Mutations that happen in twice the evolution period of that for a PAM1

Pam matrix assumptions
PAM Matrix: Assumptions Probability Matrix?

In two pam1 periods
In two PAM1 periods: Probability Matrix?

  • {AR} = {AA and AR} or

    {AN and NR} or

    {AD and DR} or

    … or

    {AV and VR}

Pam k mutation prob matrix
PAM-k Mutation Prob. Matrix Probability Matrix?

Pam 1 log likelihood matrix
PAM-1 log likelihood matrix Probability Matrix?

Pam k log likelihood matrix
PAM-k log likelihood matrix Probability Matrix?

PAM-250 Probability Matrix?

  • PAM60—60%, PAM80—50%, Probability Matrix?

  • PAM120—40%

  • PAM-250 matrix provides a better scoring alignment than lower-numbered PAM matrices for proteins of 14-27% similarity

Sources of error in pam
Sources of Error in PAM Probability Matrix?

Comparing scoring matrix

PAM Probability Matrix?

Based on extrapolation of a small evol. Period

Track evolutionary origins

Homologous seq.s during evolution


Based on a range of evol. Periods

Conserved blocks

Find conserved domains

Comparing Scoring Matrix

Choice of Scoring Matrix Probability Matrix?

Global alignment with affine gaps

Global Alignment with Probability Matrix? Affine Gaps

Complex Dynamic Programming

Problem w independent gap penalties
Problem w/ Independent Gap Penalties Probability Matrix?

  • The occurrence of x consecutive deletions/insertions is more likely than the occurrence of x isolated mutations

  • We should penalize x long gap less than x

    times of the penalty for one gap

Affine gap penalty
Affine Gap Penalty Probability Matrix?

  • w2 is the penalty for each gap

  • w1 is the _extra_ penalty for the 1st gap

Scoring rule not additive
Scoring Rule not Additive! Probability Matrix?

  • We need to know if the current gap is a new gap or the continuation of an existing gap

  • Use three Dynamic Programming matrices to keep track of the previous step

S1 is the vertical sequence Probability Matrix?

S2 is the horizontal sequence

  • (From Diagonal) a(i,j): current position is a match

  • (From Left) b(i,j): current position is a gap in S1

  • (From Above) c(i,j): current position is a gap in S2

    Filling the next element in each matrix depends on the previous step, which is stored in the three matrices.

Last step a match Probability Matrix?

a gap in S2

a gap in S1

new gap in S2

a continued gap in S2

a gap in S2 following

a gap in S1

Decisions in seq alignment
Decisions in Seq. Alignment Probability Matrix?

  • Local or global alignment?

  • Which program to use

  • Type of scoring matrix

  • Value of gap penalty

A ij 10
A Probability Matrix?ij*10

PAM-k log-likelihood matrix Probability Matrix?