From PSIBLAST to HMMer

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From PSI-BLAST to HMMer

• Professor Mark Pallen
• Credits Stephanie Minnema
• University of Calgary
• David Wishart
• University of Alberta

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Advanced BLAST Methods

• The NCBI BLAST pages have several advanced BLAST
  methods available
• PSI-BLAST
• PHI-BLAST
• RPS-BLAST
• All are powerful methods based on protein
  similarities

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Position-Specific-Iterated-BLAST

• Intuition
• substitution matrices should be specific to a
  particular site.
• e.g. enalize alanine?glycine more in a helix
• Idea
• Use BLAST with high stringency to get a set of
  closely related sequences.
• Align those sequences to create a new
  substitution matrix for each position.
• Then use that matrix to find additional sequences

• Cycling/iterative method
• Gives increased sensitivity for detecting
  distantly related proteins
• Can give insight into functional relationships
• Very refined statistical methods
• Fast still based on BLAST methods
• Simple to use

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PSI-BLAST Principle

• First, a standard blastp is performed
• The highest scoring hits are used to generate a
  multiple alignment
• A PSSM is generated from the multiple alignment.
  
• Highly conserved residues get high scores
• Less conserved residues get lower scores
• Another similarity search is performed, this time
  using the new PSSM
• Steps 2-4 can be repeated until convergence
• No new sequences appear after iteration

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ExampleAminoacyl tRNA Synthetases

• 20 enzymes for 20 amino acids
• Each is very different
• Big, small, monomers, tetramers
• All bind to their appropriate tRNAs and amino
  acids, with high specificity
• TrpRS and TyrRS share only 13 sequence identity
• BUT, overall structures of TrpTRS and TyrTRS are
  similar
• Structure ? Function relationship

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Same SCOP family based on catalytic domain
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So is there sequence similarity between TyrRS
and TrpRS?

• Given structural similarities, we would expect to
  find sequence similarity
• BUT!
• blastp of E.coli TyrRS against bacterial
  sequences in SwissProt does NOT show similarity
  with TrpRS at e-value cutoff of 10

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No TrpRS!?
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Try Using PSI-BLAST

• PSI-BLAST available from BLAST main page
• Query form just like for blastp
• BUT one extra formatting option must be used
• Format for PSI-BLAST activate the tick box!
• Second e-value cutoff used to determine which
  alignments will be used for PSSM build
  Threshold for inclusion
• First search using TyrRS as query
• Db SwissProt limit Bacteria ORGN
• Threshold for inclusion 0.005

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After A Few Iterations
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TyrRS Similarity to TrpRS!
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Power of PSI-BLAST

• We knew TyrRS and TrpRS were similarly
• Functionally and structurally
• BLASTP gave no indication
• PSI-BLAST was able to detect their weak sequence
  similarity
• Words of caution
• be sure to inspect and think about the results
  included in the PSSM build
• include/exclude sequences on basis of biological
  knowledge you are in the driving seat!
• PSI-BLAST performance varies according to choice
  of matrix, filter, statistics etc just like BLASTP

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Why (not) PSI-BLAST

• If the sequences used to construct the Position
  Specific Scoring Matrices (PSSMs) are all
  homologous, the sensitivity at a given
  specificity improves significantly
• However, if non-homologous sequences are included
  in the PSSMs, they are corrupted. Then they
  pull in more non-homologous sequences, and become
  worse than generic

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Query
Does the query really have a relationship with
the results? One way to check is to run the
search in the opposite direction but often not
reversible even when true homology
Results
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PSI-BLAST caveats

• Increased ability to find distant homologues
• Cost of additional required care to prevent
  non-homologous sequences from being included in
  the PSSM calculation
• When in doubt, leave it out!
• Examine sequences with moderate similarity
  carefully.
• Be particularly cautious about matches to
  sequences with highly biased amino acid content
• Low complexity regions, transmembrane regions and
  coiled-coil regions often display significant
  similarity without homology
• Screen them out of your query sequences!

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PSI-BLASTon the command line

• as with simple BLAST searches, using PSI-BLAST on
  the command line gives the user more power
• opens up additional options, e.g.
• PSI-BLASTing over nucleotide databases
• automating number of iterations
• trying out lots of different settings in parallel
• inputting multiple sequences

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PHI-BLAST

• Pattern Hit Initiated BLAST
• PHI-BLAST principle
• Same method as PSI-BLAST
• Starts first search with query sequence pattern
  for a motif in the query
• PHI-BLAST finds sequences containing the motif
  and having significant sequence similarity in the
  vicinity of the motif occurrence
• Highly specific

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Example TyrRS

• TyrRS contains the aaRS class-I signature
• Want to find sequences containing that motif, and
  regional similarity to TyrRS
• First get the Prosite pattern for the class-I
  signature
• Prosite db of protein families and domains

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http//ca.expasy.org/prosite
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P-x(0,2)-GSTAN-DENQGAPK-x-LIVMFP-HT-LIVMY
AC-G- HNTG-LIVMFYSTAGPC
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Insert Query Sequence
Insert PHI Pattern
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PHI-BLAST Results

• After first search, PHI-BLAST functions same as
  PSI-BLAST
• Result page is the same
• Can iterate in same way
• Try it later if you like

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The Key to PHI- and PSI-BLAST

• Generating the multiple alignments to create
  PSSMs
• Refines scoring in searches
• Annotated collections of multiple alignments
  defining domains exist
• Conserved domain database (CDD)
• Contains 18039 alignments (10013 last year)
• Can search the CDD using CD search
• Uses RPS-BLAST

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RPS-BLAST

• Reverse Position Specific BLAST
• Opposite of PSI-BLAST
• CDD multiple alignments converted to PSSMs
• PSSMs are processed and turned into a searchable
  database
• Queries are searched against PSSMs using
  RPS-BLAST
• Output indicates conserved domains within the
  query sequence

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Example CRADD protein
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Click on picture to see CDD multiple alignment
Click to see alignment with query
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Profile Hidden Markov Models

• statistical models of multiple sequence
  alignments
• capture position-specific information about
• how conserved each column of the alignment is
• which residues are likely
• use position-specific scores for amino acids (or
  nucleotides)
• position specific penalties for opening and
  extending an insertion or deletion.

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Advantages of using HMMs

• HMMs have a formal probabilistic basis
• use probability theory to guide how all the
  scoring parameters should be set
• can do things that more heuristic methods cannot
  do easily
• For example, a profile HMM can be trained from
  unaligned sequences, if a trusted alignment isnt
  yet known
• HMMs have a consistent theory behind gap and
  insertion scores

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Advantages of using HMMs

• In most details, profile HMMs are a slight
  improvement over a carefully constructed profile
• but less skill and manual intervention are
  necessary to use profile HMMs
• HMMs can produce true global alignments, unlike
  BLAST

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Limitations of HMMs

• do not capture any higher-order correlations
• assumes that the identity of a particular
  position is independent of the identity of all
  other positions
• make poor models of RNAs because an HMM cannot
  describe base pairs.
• cf protein threading methods
• which usually include scoring terms for nearby
  amino acids in a three-dimensional protein
  structure.
• slower than and less user-friendly than PSI-BLAST

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Applications of profile HMMs

• Database searching for weak homologies
• Alternative to PSI-BLAST
• Automated annotation of the domain structure of
  proteins

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Applications of profile HMMs

• Useful for organizing sequences into
  evolutionarily related families
• Databases like Pfam constructed by distinguishing
  between
• a stable curated seed alignment of a small
  number of representative sequences
• full alignments of all detectable homologs
• HMMER used to
• make a model of the seed
• search the database for homologs
• automatically produce the full alignment by
  aligning every sequence to the seed consensus

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Constructing a profile HMM

• multiple sequence alignment is made of known
  members of a given protein family
• quality of alignment, number and diversity of the
  sequences crucial for success
• profile HMM of family built from the alignment
• model-building program uses the alignment
  together with its prior knowledge of the general
  nature of proteins
• model-scoring program used to assign a score with
  respect to the model to any sequence of interest
• better the score, the higher the chance that
  query sequence is homologous to protein family in
  the model.
• each sequence in a database scored to find the
  members of the family present in the database.

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Profile HMM programs HMMER

• developed by Sean Eddy
• freely available under GNU General Public License
  
• includes model-building and model-scoring
  programs relevant to homology detection
• contains a program that calibrates a model by
• scoring it against a set of random sequences
• fitting an extreme value distribution to the
  resultant raw scores
• parameters of this distribution then used to
  calculate accurate E-values for sequences of
  interest.

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Programs in the HMMER 2 package

• hmmalign
• Align sequences to existing model
• hmmbuild
• Build a model from multiple sequence alignment.
• hmmcalibrate
• Takes an HMM and empirically determines
  parameters used to make searches more sensitive
  by calculating more accurate E-values
• hmmconvert
• Convert a model file into different formats,
  including a compact HMMER 2 binary format, and
  best effort emulation of GCG profiles.

• hmmemit
• Emit sequences probabilistically from a profile
  HMM.
• hmmfetch
• Get a single model from an HMM database.
• hmmindex
• Index an HMM database.
• hmmpfam
• Search an HMM database for matches to a query
  sequence.
• hmmsearch
• Search a sequence database for matches to an HMM.

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Profile HMM programs SAM

• Developed by the bioinformatics group at the
  University of California, Santa Cruz
• not open source, but free for academic use
• does not include a model-calibration program
• model-scoring program calculates E-values
  directly using a theoretical function that takes
  as its argument the difference between raw scores
  of the query sequence and its reverse
• important component is target99 script, which
  generates a multiple sequence alignment suitable
  for model building

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Clash of the TitansPSI-BLAST v. HMMer v. SAM!

• Nucleic Acids Research, 2002, Vol. 30 No. 19 4321
• SAM consistently produces better models than
  HMMER
• relative performance of the model-scoring
  components varies
• HMMER 1-3 X faster than SAM with large databases
• SAM faster with small ones
• both methods have effective low complexity and
  repeat sequence masking
• accuracy of their E-values was comparable.
• SAM T99 iterative database search procedure
  outperforms PSI-BLAST
• BUT scoring of PSI-BLAST profiles gt 30 X faster
  than scoring of SAM models.

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Summary

• PSI-BLAST
• Input SEQUENCE
• Database SEQUENCES
• Algorithm Constructs a PSSM from an initial pass
  and uses this in the next pass
• Output Distantly related sequences
• sensitive, -specific
• PHI-BLAST
• Input PROFILE SEQUENCE
• Database SEQUENCES
• Algorithm Same as PSI-BLAST except start with a
  profile
• Output Sequences containing the domain and that
  are similar in the domain region
• sensitive, -gt -specific

• RPS-BLAST
• Input SEQUENCE
• Database DOMAINS
• Output Domains found in the sequence
• sensitive, specific
• HMMs
• More sensitive
• But less user-friendly than PSI-BLAST and slower