Identification of Protein Domains

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Identification of Protein Domains

• Eden Dror
• Menachem Schechter

Computational Biology Seminar 2004
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Overview

• Introduction to protein domains.
• Classification of homologs.
• Representing a domain.
• PSSM
• HMM
• Internet resources
• Pfam
• SMART
• PROSITE
• InterPro
• Research example.

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Protein domains

• A discrete portion of a protein assumed to fold
  independently, and possessing its own function.
• Mobile domain (module) a domain that can be
  found associated with different domain
  combinations in different proteins.

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Protein domains

• The assumption The domain is the fundamental
  unit of protein structure and function.
• Protein family all proteins containing a
  specific domain.

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What can we learn from them?

• Common ancestors homology information of a set
  of proteins.
• Homology can induce properties of a protein like
  functionality localization.
• Therefore, domains can be used to classify a new
  protein to a family, inferring functionality.

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Classification of homologs

• Homology is not a sufficiently well-defined term
  to describe the evolutionary relationships
  between genes.
• Homologous genes can be derived by two major
  ways
• Gene duplication (in the same species).
• Speciation (splitting of one species into two).

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Classification of homologs
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Classification of homologs

• Orthologs Two genes from two different species
  that derive from a single gene in the last common
  ancestor of the species.
• Paralogs Two genes that derive from a single
  gene that was duplicated within a genome.

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Classification of homologs
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Classification of homologs

• Inparalogs - paralogs that evolved by gene
  duplication after the speciation event.
• Outparalogs - paralogs that evolved by gene
  duplication before the speciation event.

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Classification of homologs
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What can we learn from them?

• Ortholog proteins are evolutionary, and typically
  functional counterparts in different species.
• Paralog proteins are important for detecting
  lineage-specific adaptations.
• Both of them can reveal information on a specific
  species or a set of species.

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Protein domains summary

• By identifying domains we can
• infer functionality localization of a protein.
• Learn on a specific species.
• Learn on a set of species as a group.

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Domain representation

• Different methods to represent (model) domains
• Patterns (regular expressions).
• PSSM (Position specific score matrix).
• HMM (Hidden Markov model).

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PSSM

• Position specific score matrix
• Score matrix representing the score for having
  each amino acid in a given position in a specific
  sequence.
• Based on the independent probabilities P(ai) of
  observing amino acid a in position i.

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PSSM Example
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PSSM Identifying a domain

• Given a sequence and a PSSM
• Run over all positions.
• Score each sub-sequence according to the matrix.

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HMM Hidden Markov Model

• Markov model a way of describing a process that
  goes through a series of states.
• Each state has a probability of transitioning to
  the other states.
• xi is a random variable of state.

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HMM Markov Model

• Example
• States are Î 0,1

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HMM Markov Model

• Transition matrix

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HMM Markov Model

• State transition example
• States are the nucleotides A, T, G, C.

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HMM Hidden Markov Model

• Hidden Markov model
• Each state x emits an output y, at a specific
  probability.
• We only know the output (observations).
• Thus, the states are hidden.

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HMM Hidden Markov Model

• Example states are Î 0,1, output Î 0,1

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HMM Hidden Markov Model

• Emission matrix

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HMM What can we do with it?

• Given (A, B)
• Probability of given states and outputs

• Probability of a given output sequence

• Most likely sequence of states that generated a
  given output sequence

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HMM What can we do with it?

• Learning
• Given state and output sequences calculate the
  most probable (A, B).
• Easy when the states are known.
• Otherwise use a training algorithm.

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HMM Profile HMM

• Use HMM to represent sequence families.
• A particular type of HMM suited to modeling
  multiple alignments.
• (Assume we have a multiple alignment).

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HMM Trivial profile HMM

• We begin with ungapped regions.
• Each position corresponds to a state.
• Transitions are of probability 1.

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HMM Trivial profile HMM

• Let ei(a) be the independent probability of
  observing amino acid a in position i.
• The probability of a new sequence x, according to
  the model

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HMM Trivial profile HMM

• We can score the sequence x
• Where q indicates the probability under a random
  model.

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HMM Trivial profile HMM

• Consider the values
• They behave like elements in a score matrix.
• The trivial profile HMM is equivalent to a PSSM.

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HMM profile HMM

• Lets untrivialize by allowing for gaps
  insertions and deletions.
• Start off with the PSSM HMM.

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HMM profile HMM

• Handling insertions
• Introduce new states Ij match insertions after
  position j.
• These states have random emission probabilities.

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HMM profile HMM

• The score of a gap of length k

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HMM profile HMM

• Handling deletions
• Introduce silent states Dj.
• These states do not emit.

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HMM profile HMM

• The complete profile HMM

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Internet resources

• Databases of protein families.
• Family information and identification.
• Considerations
• Type of representation (pattern, PSSM, HMM).
• Choice of seed multiple alignment proteins.
• Quality control.
• Database features (links, annotations, views).
• Database Specificity (organism, functions).

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Pfam Home
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Pfam

• Protein families database of alignments and HMMs
• Uses profile-HMMs to represent families.
• For each family in Pfam you can
• Look at multiple alignments
• View protein domain architectures
• Examine species distribution
• Follow links to other databases
• View known protein structures

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Pfam Databases

• 2 databases
• Pfam-A curated multiple alignments.
• Grows slowly.
• Quality controlled by experts.
• Pfam-B automatic clustering (ProDom derived).
• Complements Pfam-A.
• New sequences instantly incorporated.
• Unchecked false positives, etc.

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Pfam Features

• Search by Sequence, keyword, domain, taxonomy.
• Browsing by family or genome.
• Evolutionary tree

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Pfam Construction

• Source of seed alignments
• Pfam-B families.
• Published articles.
• 'domain hunting' studies.
• occasionally using entries from other databases
  (e.g. MEROPS for peptidases).

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Pfam Domain information
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Pfam Domain organization
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Pfam Multiple alignment
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Pfam HMM logo
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Pfam Species distribution
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Pfam Genome comparison
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PROSITE

• Database of protein families.
• Matching according to simple patterns or PSSM
  profiles.
• Browsing all proteins of a specific family.
• Latest release knows 1696 protein families.

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PROSITE Features

• Comprehensive domain documentation.
• All profile matches checked by experts.
• Specificity/sensitivity
• Specificity true-pos/all-pos
• Sensitivity true-pos/(true-pos false-neg)

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PROSITE Example

• Specificity of Zinc finger C2H2 type domain

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SMART
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SMART

• Simple Modular Architecture Research Tool
• Identification and annotation of genetically
  mobile domains and the analysis of domain
  architectures.
• SMART consists of a library of HMMs.
• Knows 665 HMMs to date.

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SMART Features

• finding proteins containing specific domains
  i.e. of the same family
• Function prediction
• Sub-cellular localization
• Binding partners
• Architecture
• Alternative splicing information
• Orthology information

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SMART Domain selection example

• Tyrosine kinase (TyrKc) AND Transmembrane region
  (TRANS)

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InterPro

• InterPro combines 9 other databases such as
  SMART, Pfam, Prodom and more.
• Queries can use many different methods (as the
  other databases use different methods).
• However, thresholds are predefined and cannot be
  changed for those methods.

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InterPro

• Provides more results, but can sometimes be
  redundant.
• Coverage statistics
• 93 of Swiss-Prot v42.5 128540 out of 138922
  proteins
• 81 of TrEMBL v25.5 819966 out of 1013263
  proteins

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InterPro Features

• Searching by Protein/DNA sequences
• Finding domains homologs
• List of InterPro entries of type
• Family
• Domain
• Repeat
• PTM- Post Transcriptional modifications
• Binding Site
• Active Site
• Keyword

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InterPro Example

• Kringle domain

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Research Example Introduction

• Goal The systematic identification of novel
  protein domain families.
• Using computational methods.

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Research Example Method
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Research Example Results

• 28 New Domains identified
• 15 domains in diverse contexts, in different
  species.
• 3 domains species specific.
• 7 domains with weak similarity to previously
  described domains.
• 3 extension domains.

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Predictions of Function

• On the basis of reports in literature and/or
  occurrence with other identified domains,
  functional features can be predicted for our
  novel domain families.
• Examples
• Chromatin binding
• Protein Interaction
• Predicted sub-cellular localization

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Predictions of FunctionChromatin-Binding example

• The novel domain CSZ is contained in protein
  SPT6, which regulates transcription via chromatin
  structure modification.
• SPT6 has a histone-binding capability,
  experimentally confirmed.
• Other domains (S1, SH2) in SPT6 are unlikely to
  bind histones or chromatin.
• Conclusion CSZ has a predicted histone binding
  function.

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Predictions of FunctionLocalization example

• Some of the novel domains are only found within
  proteins from the initial set of nuclear domains.
• This predicts that these domains have a nuclear
  function.
• The other domains are likely to have roles in
  both nucleus and cytoplasm.

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Conclusion

• Domains are the functional units of proteins.
• Identifying a domain within a new protein may
  teach us much about it.
• There are several types of models to represent
  domains.
• These models can also be used to identify the
  domain they represent.
• Many Internet databases available to catalogue
  and identify families.
• Protocol to identify new domains using old ones.

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Resources

• Pfamhttp//www.sanger.ac.uk/Software/Pfam/
• SMART http//smart.embl-heidelberg.de/
• PROSITEhttp//www.expasy.org/prosite/
• InterProhttp//www.ebi.ac.uk/interpro/

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The End