CSE182-L4: Scoring matrices, Dictionary Matching

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CSE182-L4 Scoring matrices, Dictionary Matching
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Protein Sequence Analysis

• What can you do if BLAST does not return a hit?
• Sometimes, homology (evolutionary similarity)
  exists at very low levels of sequence similarity.

• A Accept hits at higher P-value.
• This increases the probability that the sequence
  similarity is a chance event.
• How can we get around this paradox?
• Reformulated Q suppose two sequences B,C have
  the same level of sequence similarity to sequence
  A. If A B are related in function, can we assume
  that A C are? If not, how can we distinguish?

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Protein sequence motifs

• Premise
• The sequence of a protein sequence gives clues
  about its structure and function.
• Not all residues are equally important in
  determining function.
• How can we identify these key residues?

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Prosite

• In some cases the sequence of an unknown protein
  is too distantly related to any protein of known
  structure to detect its resemblance by overall
  sequence alignment. However, relationships can be
  revealed by the occurrence in its sequence of a
  particular cluster of residue types, which is
  variously known as a pattern, motif, signature or
  fingerprint. These motifs arise because specific
  region(s) of a protein which may be important,
  for example, for their binding properties or for
  their enzymatic activity are conserved in both
  structure and sequence. These structural
  requirements impose very tight constraints on the
  evolution of this small but important portion(s)
  of a protein sequence. The use of protein
  sequence patterns or profiles to determine the
  function of proteins is becoming very rapidly one
  of the essential tools of sequence analysis. Many
  authors ( 3,4) have recognized this reality.
  Based on these observations, we decided in 1988,
  to actively pursue the development of a database
  of regular expression-like patterns, which would
  be used to search against sequences of unknown
  function.
• Kay Hofmann ,Philipp Bucher, Laurent Falquet and
  Amos Bairoch
• The PROSITE database, its status in 1999

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Basic idea

• It is a heuristic approach. Start with the
  following
• A collection of sequences with the same function.
• Region/residues known to be significant for
  maintaining structure and function.
• Develop a pattern of conserved residues around
  the residues of interest
• Iterate for appropriate sensitivity and
  specificity

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Zinc Finger domain
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Proteins containing zf domains
How can we find a motif corresponding to a zf
domain
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From alignment to regular expressions
ALRDFATHDDF SMTAEATHDSI
ECDQAATHEAS
ATH-DE

• Search Swissprot with the resulting pattern
• Refine pattern to eliminate false positives
• Iterate

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The sequence analysis perspective

• Zinc Finger motif
• C-x(2,4)-C-x(3)-LIVMFYWC-x(8)-H-x(3,5)-H
• 2 conserved C, and 2 conserved H
• How can we search a database using these motifs?
• The motif is described using a regular
  expression. What is a regular expression?
• How can we search for a match to a regular
  expression? Not allowed to use Perl -)
• The regular expression motif is weak. How can
  we make it stronger

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Profiles

• Start with an alignment of strings of length m,
  over an alphabet A,
• Build an A X m matrix F(fki)
• Each entry fki represents the frequency of symbol
  k in position i

0.71
0.71
0.28
0.14
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Scoring Profiles
Scoring Matrix
i
k
fki
s
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Psi-BLAST idea

• Multiple alignments are important for capturing
  remote homology.
• Profile based scores are a natural way to handle
  this.
• Q What if the query is a single sequence.
• A Iterate
• Find homologs using Blast on query
• Discard very similar homologs
• Align, make a profile, search with profile.

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Psi-BLAST speed

• Two time consuming steps.
• Multiple alignment of homologs
• Searching with Profiles.
• Does the keyword search idea work?

• Pigeonhole principle again
• If profile of length m must score gt T
• Then, a sub-profile of length l must score gt
  lT/m
• Generate all l-mers that score at least lT/M
• Search using an automaton

• Multiple alignment
• Use ungapped multiple alignments only

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CSE182-L6

• Regular Expression Matching
• Protein structure basics

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Zinc Finger domain
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The sequence analysis perspective

• Zinc Finger motif
• C-x(2,4)-C-x(3)-LIVMFYWC-x(8)-H-x(3,5)-H
• 2 conserved C, and 2 conserved H
• How can we search a database using these motifs?
• The motif is described using a regular
  expression. What is a regular expression?

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Regular Expressions

• Concise representation of a set of strings over
  alphabet ?.
• Described by a string over
• R is a r.e. if and only if

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Regular Expression

• Q Let ?A,C,E
• Is (AC)EEC a regular expression?
• (AC)?
• AC..E?

• Q When is a string s in a regular expression?
• R (AC)EEC
• Is CEEC in R?
• AEC?
• ACEE?

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Regular Expression Automata

• Every R.E can be expressed by an automaton (a
  directed graph) with the following properties
• The automaton has a start and end node
• Each edge is labeled with a symbol from ?, or ?

• Suppose R is described by automaton A
• S ? R if and only if there is a path from start
  to end in A, labeled with s.

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Examples Regular Expression Automata

• (AC)EEC

C
A
E
E
start
end
C
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Constructing automata from R.E
?

• R ?
• R ?, ? ? ?
• R R1 R2
• R R1 R2
• R R1

?
?
?
?
?
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Regular Expression Matching

• Given a database D, and a regular expression R,
  is a substring of D in R?

• Is there a string Dl..c that is accepted by the
  automaton of R?

• Simpler Q Is D1..c accepted by the automaton
  of R?

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Alg. For matching R.E.

• If D1..c is accepted by the automaton RA
• There is a path labeled D1Dc that goes from
  START to END in RA

?
D1
D2
Dc
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Alg. For matching R.E.

• If D1..c is accepted by the automaton RA
• There is a path labeled D1Dc that goes from
  START to END in RA
• There is a path labeled D1..Dc-1 from START
  to node u, and a path labeled Dc from u to the
  END

u
D1 .. Dc-1
Dc
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D.P. to match regular expression
u
?
v

• Define
• Au,? Automaton node reached from u after
  reading ?
• Eps(u) set of all nodes reachable from node u
  using epsilon transitions.
• Nc subset of nodes reachable from START node
  after reading D1..c
• Q when is v ? Nc

?
u
Eps(u)
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D.P. to match regular expression

• Q when is v ? Nc?
• A If for some u ? Nc-1, w Au,Dc,
• v ? w Eps(w)

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Algorithm
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The final step

• We have answered the question
• Is D1..c accepted by R?
• Yes, if END ? Nc
• We need to answer
• Is Dl..c (for some l, and some c) accepted by R

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A structural view of proteins
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CS view of a protein

• gtspP00974BPT1_BOVIN Pancreatic trypsin
  inhibitor precursor (Basic protease inhibitor)
  (BPI) (BPTI) (Aprotinin) - Bos taurus (Bovine).
• MKMSRLCLSVALLVLLGTLAASTPGCDTSNQAKAQRPDFCLEPPYTGPCK
  ARIIRYFYNAKAGLCQTFVYGGCRAKRNNFKSAEDCMRTCGGAIGPWENL

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Protein structure basics
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Side chains determine amino-acid type

• The residues may have different properties.
• Aspartic acid (D), and Glutamic Acid (E) are
  acidic residues

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Bond angles form structural constraints
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Various constraints determine 3d structure

• Constraints
• Structural constraints due to physiochemical
  properties
• Constraints due to bond angles
• H-bond formation
• Surprisingly, a few conformations are seen over
  and over again.

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Alpha-helix

• 3.6 residues per turn
• H-bonds between 1st and 4th residue stabilize the
  structure.
• First discovered by Linus Pauling

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Beta-sheet

• Each strand by itself has 2 residues per turn,
  and is not stable.
• Adjacent strands hydrogen-bond to form stable
  beta-sheets, parallel or anti-parallel.
• Beta sheets have long range interactions that
  stabilize the structure, while alpha-helices have
  local interactions.

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Domains

• The basic structures (helix, strand, loop)
  combine to form complex 3D structures.
• Certain combinations are popular. Many sequences,
  but only a few folds

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3D structure

• Predicting tertiary structure is an important
  problem in Bioinformatics.
• Premise Clues to structure can be found in the
  sequence.
• While de novo tertiary structure prediction is
  hard, there are many intermediate, and tractable
  goals.

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Protein Domains

• An important realization (in the last decade) is
  that proteins have a modular architecture of
  domains/folds.
• Example The zinc finger domain is a DNA-binding
  domain.
• What is a domain?
• Part of a sequence that can fold independently,
  and is present in other sequences as well

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Proteins containing zf domains
How can we find a motif corresponding to a zf
domain
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Domain review

• What is a domain?
• How are domains expressed
• Motifs (Regular expression others)
• Multiple alignments
• Profiles
• Profile HMMs

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Databases of protein domains
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http//pfam.wustl.edu/ Also at Sanger
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PROSITE
http//us.expasy.org/prosite/
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http//hmmer.wustl.edu
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HMMER programs

• Hmmalign
• Align a sequence to an HMM
• Hmmbuild
• Build a model from a multiple alignment
• Hmmemit
• Emits a probabilistic sequence from an HMM
• Hmmpfam
• Search PFAM with a sequence query
• Hmmsearch
• Search a sequence database with an HMM query

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Post-translational modification

• Residues undergo modification, usually by
  addition of a chemical group.
• Key mechanism for signal transduction, and many
  other cellular functions
• Some modifications might require single residues
  (Ex phosphorylation). Others might require a
  pattern

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Protein targeting
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Protein targeting

• In 1970, Gunter Blobel showed that proteins have
  an N-terminal signal sequence which directs
  proteins to the membrane.
• Proteins have to be transported to other
  organelles nucleus, mitochondria,
• Can we computationally identify the signal
  which distinguishes the cellular compartment?

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• For transmembrane proteins, can we predict the
  transmembrane, outer, and inner regions?

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Multiple alignment tools
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Tools for secondary structure prediction

• Each residue must be
• given a state
• Helix, Loop, Strand
• HMMs/Neural
• networks are used to
• predict

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Next topic Gene finding