Similar Sequence Similar Function

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Similar Sequence Similar Function

• Charles Yan
• Spring 2006

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From Sequence to Function

• Protein sequence determine protein function. Thus
  similar protein sequences have similar functions
• One approach to predict function for a new
  protein is to search for similar proteins
  (homologues) whose functions are known. If the
  similarities are high, it is likely that the new
  protein has the same functions as its homologues

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Homologue Search

• Basic Local Alignment Search Tool (BLAST) finds
  regions of local similarity between sequences.
  The program compares nucleotide or protein
  sequences to sequence databases and calculates
  the statistical significance of matches. BLAST
  can be used to infer functional and evolutionary
  relationships between sequences as well as help
  identify members of gene families

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Dynamic Programming
a1a2a3am b1b2b3bn

• Mi,j MAX
• Mi-1, j-1 Si,j (match/mismatch)
• Mi,j-1 w (gap in sequence 1)
• Mi-1,j w (gap in sequence 2)

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Dynamic Programming

• G A A T T C A G T T A (sequence 1)
• G G A T C G A (sequence 2)

Si,j 1 (match) Si,j 0 (mismatch score) w 0
(gap penalty)
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Dynamic Programming
M1,1 MAXM0,0 1, M1, 0 0, M0,1 0 MAX
1, 0, 0 1
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Dynamic Programming
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Dynamic Programming
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Global and Local Alignment

• A global alignment is an optimal alignment that
  includes all characters from each sequence,
  whereas a local alignment is an optimal alignment
  that includes only the most similar local region
  or regions.

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BLAST

• The BLAST programs (Basic Local Alignment Search
  Tools) are a set of sequence comparison
  algorithms introduced in 1990 that are used to
  search sequence databases for optimal local
  alignments to a query.
• Break the query and database sequences into
  fragments ("words"), and initially seek matches
  between fragments. The initial search is done for
  a word of length "W" that scores at least "T"
  when compared to the query using a given
  substitution matrix.
• Word hits are then extended in either direction
  in an attempt to generate an alignment with a
  score exceeding the threshold of "S". The "T"
  parameter dictates the speed and sensitivity of
  the search.

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BLAST

• Web interface http//www.ncbi.nlm.nih.gov/BLAST/
• Download http//www.ncbi.nlm.nih.gov/BLAST/downloa
  d.shtml

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BLAST
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BLAST
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BLAST
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Substitution Matrix

• A substitution matrix containing values
  proportional to the probability that amino acid i
  mutates into amino acid j for all pairs of amino
  acids

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Substitution Matrix

• The BLOSUM family
• BLOSUM matrices are based on local alignments.
• BLOSUM 62 is a matrix calculated from comparisons
  of sequences with no less than 62 divergence.
• All BLOSUM matrices are based on observed
  alignments they are not extrapolated from
  comparisons of closely related proteins.
• BLOSUM 62 is the default matrix in BLAST 2.0.
  Though it is tailored for comparisons of
  moderately distant proteins, it performs well in
  detecting closer relationships. A search for
  distant relatives may be more sensitive with a
  different matrix.

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Substitution Matrix

• The PAM family
• PAM matrices are based on global alignments of
  closely related proteins.
• The PAM1 is the matrix calculated from
  comparisons of sequences with no more than 1
  divergence.
• Other PAM matrices are extrapolated from PAM1.

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Substitution Matrix

• The relationship between BLOSUM and PAM
  substitution matrices. BLOSUM matrices with
  higher numbers and PAM matrices with low numbers
  are both designed for comparisons of closely
  related sequences. BLOSUM matrices with low
  numbers and PAM matrices with high numbers are
  designed for comparisons of distantly related
  proteins. If distant relatives of the query
  sequence are specifically being sought, the
  matrix can be tailored to that type of search.

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Raw Score S

• The raw score S for an alignment is calculated by
  summing the scores for each aligned position and
  the scores for gaps

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Bit Score S'

• Raw scores have little meaning without detailed
  knowledge of the scoring system used, or more
  simply its statistical parameters K and lambda.
  Unless the scoring system is understood, citing a
  raw score alone is like citing a distance without
  specifying feet, meters, or light years. By
  normalizing a raw score using the formula
  one attains a "bit score" S', which has a
  standard set of units.

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Bit Score S'

• The value S' is derived from the raw alignment
  score S in which the statistical properties of
  the scoring system used have been taken into
  account. Because bit scores have been normalized
  with respect to the scoring system, they can be
  used to compare alignment scores from different
  searches.

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Significance

• The significance of each alignment is computed as
  a P value or an E value
• E value Expectation value. The number of
  different alignents with scores equivalent to or
  better than S that are expected to occur in a
  database search by chance. The lower the E value,
  the more significant the score.
• P value The probability of an alignment
  occurring with the score in question or better.
  The p value is calculated by relating the
  observed alignment score, S, to the expected
  distribution of HSP scores from comparisons of
  random sequences of the same length and
  composition as the query to the database. The
  most highly significant P values will be those
  close to 0. P values and E values are different
  ways of representing the significance of the
  alignment.

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E-value

• In the limit of sufficiently large sequence
  lengths m and n, the statistics of HSP scores are
  characterized by two parameters, K and lambda.
  Most simply, the expected number of HSPs with
  score at least S is given by the formula We
  call this the E-value for the score S.   This
  formula makes eminently intuitive sense. Doubling
  the length of either sequence should double the
  number of HSPs attaining a given score. Also, for
  an HSP to attain the score 2x it must attain the
  score x twice in a row, so one expects E to
  decrease exponentially with score. The parameters
  K and lambda can be thought of simply as natural
  scales for the search space size and the scoring
  system respectively.

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P-value

• The number of random HSPs with score gt S is
  described by a Poisson distribution. This means
  that the probability of finding exactly a HSPs
  with score gtS is given by where E is the
  E-value of S given by equation (1) above.
  Specifically the chance of finding zero HSPs with
  score gtS is e-E, so the probability of finding
  at least one such HSP is This is the P-value
  associated with the score S. For example, if one
  expects to find three HSPs with score gt S, the
  probability of finding at least one is 0.95. The
  BLAST programs report E-value rather than
  P-values because it is easier to understand the
  difference between, for example, E-value of 5 and
  10 than P-values of 0.993 and 0.99995.

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PSI-BAST

• Position specific iterative BLAST (PSI-BLAST)
  refers to a feature of BLAST 2.0 in which a
  profile (or position specific scoring matrix,
  PSSM) is constructed (automatically) from a
  multiple alignment of the highest scoring hits in
  an initial BLAST search. The PSSM is generated by
  calculating position-specific scores for each
  position in the alignment. Highly conserved
  positions receive high scores and weakly
  conserved positions receive scores near zero. The
  profile is used to perform a second (etc.) BLAST
  search and the results of each "iteration" used
  to refine the profile. This iterative searching
  strategy results in increased sensitivity.
• PSI-BLAST uses the blastp program exclusively, so
  there is no need to select the program.

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PSI-BAST
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PSI-BAST

• The threshold value for inclusion in the position
  specific matrix used for PSI-BLAST iterations.
  Hits with E-value less than this threshold will
  be used to constructed the for next round.

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PSI-BAST
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PHI-BLAST

• PHI-BLAST (Pattern-Hit Initiated BLAST) is a
  search program that combines matching of regular
  expressionswith local alignments surrounding the
  match. Given a protein sequence S and a regular
  expression pattern Poccurring in S, PHI-BLAST
  helps answer the question What other protein
  sequences both contain an occurrence of Pand are
  homologous to S in the vicinity of the pattern
  occurrences? PHI-BLAST may be preferable to just
  searching for pattern occurrences because it
  filters out those cases where the pattern
  occurrence is probably random and not indicative
  of homology.

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PHI-BLAST