Pfam:%20multiple%20sequence%20alignments%20and%20HMM-profiles%20of%20protein%20domains

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Pfam multiple sequence alignments and
HMM-profiles of protein domains

• Xianhui Li
• 03-02-2004

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Outline

• What is Pfam?
• What is a Hidden Markove model (the methodology
  underlying Pfam)?
• How to use Pfam and sample output

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pfam

• Pfam is a database of multiple alignments of
  protein domains or conserved protein regions.
• The alignments represent some evolutionary
  conserved structure which has implications for
  the protein's function.
• Profile hidden Markov models (profile HMMs) built
  from the Pfam alignments can be very useful for
  automatically recognizing that a new protein
  belongs to an existing protein family, even if
  the homology is weak.

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Overview of Pfam Database

• Pfam A contains curated families each with an
  associated profile HMM that can be used for
  alignment and database searching
• Annotation --contains several compulsory fields
• Seed alignment a manually verified multiple
  alignment of a representative set of sequences
• HMM profile turned a multiple sequence
  alignment into a position-specific scoring
  system.
• Full alignment generated automatically from the
  seed HMM-profile by searching Swisssprot for all
  detectable members and aligning them to the HMM
  profile
• Pfam B are clustered automatically, allowing
  Pfam to be comprehensive

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Pfam Sequence Database Coverage
residue
Sequence
Data shown is from Pfam v2.0 as of 1998 with 527
families. Current version is Pfam 12.0 (January
2004) contains alignments and models for 7316
protein families, based on the Swissprot 42.5 and
SP-TrEMBL 25.6 protein sequence databases
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Markov Model

• Simplest example Each state emits (or,
  equivalently, recognizes) a particular element
  with probability 1.

Example sequences 1234 234 14 121214 2123334

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Probabilistic Emission

• If we let the states define a set of emission
  probabilities for elements, we can no longer be
  sure which state we are in given a particular
  element of a sequence BCCD or BCCD ?

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Hidden Markov Models (HMM)

• Emission uncertainty means the sequence doesn't
  identify a unique path. The states are
  hidden
• Probability of a sequence is sum of all paths
  that can produce it

p(bccd) 0.5 0.2 0.1 0.3 0.75 0.6
0.8 0.9 0.5 0.7 0.75 0.6
0.2 0.6 0.8 0.9 0.000972
0.013608 0.01458
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HMMs for homology

• Homology model ancestral residue (match)
  states, insertion states, deletion states.

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Profile HMM
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Searching Pfam

• Web site provide users the ability to search
  query protein sequences against one, all, or a
  few PfamHMM.
• _http//www.sanger.ac.uk/Pfam
• _http//genome.wustl.edu/Pfam
• --http//www.cgr.ki.se/Pfam
• . Software Users can use Pfam HMM-profile to
  search locally using the freely available
  HMMERsoftware package at http//genome.wustle.e
  du/eddy/hmmer.htmlhmmer

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Sample Pfam Query Results
Score Query Start Query End Hmm Start Hmm end Pfam Family Description
97.57 104 153 1 50 DAG_PE bind Phorbol Estser/ diacylglycerol binding domain
92.44 169 216 1 50 DAG_PE-bind Phorbol Estser/ diacylglycerol binding domain
137.88 240 328 1 92 C2 C2 domain
276.16 413 674 1 247 pkinase Eukaryotic protein kinase domain
84.44 675 741 1 69 pkinase_C Protein kinase C terminal domain
70.99 807 857 17 69 pkinase_C Protein kinase C terminal domain
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Acknowledgements

• Some slides adapted from lectures by Larry Hunter
  at University of Colorado Health Sciences Center
• Altmann Lab for critical comments