Bioinformatics: Applications

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Bioinformatics Applications

• ZOO 4903
• Fall 2006, MW 1030-1145
• Sutton Hall, Room 312
• Jonathan Wren
• Proteomics II Motifs Domains

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Lecture overview

• What weve talked about so far
• High-throughput detection of protein abundance
  and identification
• Overview
• Proteins have constituent components and
  functional structures
• Proteins can be modified post-translationally

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3 Kinds of Proteomics

• Expression or Analytical Proteomics
• 2 dimensional electrophoresis gels
• Mass Spectrometry, Microsequencing
• Functional or Interaction Proteomics
• Protein Domains motifs
• Post-translational modifications
• Structural Proteomics
• High throughput X-ray Crystallography/Modeling
• High throughput NMR Spectroscopy/Modeling

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How do proteins evolve?

• Proteins couldnt have began with the functions
  (or size) they have today

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How do proteins evolve?

• Proteins couldnt have began with the functions
  (or size) they have today
• Proteins can be broken down into constituent
  components

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The Protein Parts List
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40-60 proteins of unknown function in the human
genome
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Protein structures

• Primary, secondary, tertiary, quaternary

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Methods of grouping proteins

• Protein motif
• Protein domain
• 3-D structure
• Whole-protein

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Protein Domains

• Group of residues with high contact density,
  number of contacts within domains is higher than
  the number of contacts between domains
  
• A stable unit of protein structure that can fold
  autonomously
• A rigid body linked to other domains by flexible
  linkers
• A portion of the protein that can be
  active/stable on its own if you remove it from
  the rest of the protein

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Protein Domains

• The term fold is commonly used in the context
  of a 3D structure
• Together, a group of proteins that share a
  particular domain is known as a family
• Domains are often further qualified with respect
  to function
• Zinc finger bind DNA
• Intracellular domain soluble cytoplasmic
  protein
• Extracellular domain found on the outside of
  the cell membrane

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Protein Domains

• Domains can be 25 to 500 residues long most are
  less than 200 residues
• The average protein contains 2 or 3 domains
• The total number of different types of domains
  2000
• The same or similar domains are found in
  different proteins.Nature is a tinkerer and
  not an inventor (Jacob, 1977).
• Usually, each domain plays a specific role in the
  function of the protein
• Generally, two sequences with over 30 identity
  are likely to have the same fold.

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Linkers

• Domain linkers link the protein domains together
  and have been found to contain an amino acid
  signature that is distinct from the structurally
  compact domains
• Average linker size 8-9 amino acids
• Linkers are flexible and more susceptible to
  protease attack

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Divisibility of proteins by domain helps 3D
Structure Determination

• X-ray crystallography
• grow crystal
• collect diffract. data
• calculate e- density
• trace chain
• NMR spectroscopy
• label protein
• collect NMR spectra
• assign spectra NOEs
• calculate structure using distance geom.

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3D Structure
Proteins share the same fold suggesting homology
Beta B1 Crystallin
Gamma Crystallin C
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Protein Domains
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Motifs are built from Multiple Alignmennts
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Motif detection via MSA
Structure
alignment

tree
Functional site
Lichtarge et al, JMB 1996 Lichtarge et al, JMB
1997 Lichtarge et al, PNAS 1996 Sowa et al, NSB
2001
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Motifs are Functionally Relevant
Trp1 domain of Hop
Dihydropteroate Synthase
Galectin CRD
Cluster Type
Ligand binding site
ET clusters
Structural Epitope Yellow ligand, Blue
Residues within 5Å of the ligand ET Clusters
Yellow ligand, Red Largest Cluster,
Other colors trace residues
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Domains/Motifs

• These domains have conserved sequences
• Often much more similar than their respective
  proteins
• Exon splicing theory (Gilbert)
• Exons correspond to folding domains which in
  turn serve as functional units
• Unrelated proteins may share a single similar
  exon (i.e. ATPase or DNA binding function)

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Sequence Motif regular expressions

• The simplest method of defining short amino acid
  sequence motifs
• Example the nuclear receptor motif
• C-x(2)-C-x-DE-x(5)-HN-FY-x(4)-C-x(2)-C-x(2)
  -F-F-x-R
• DE either D or E
• x(5) five undefined positions
• FYW any non-aromatic amino acid

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Motif Patterns (Regular Expressions)

• Signature Patterns for Functional Motifs

ProClass Motif Alignments
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(No Transcript)
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Simple domains

• Common structural domains
• Membrane spanning
• Signal peptide
• Coiled coil
• Helix-turn-helix

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DNA Binding domainZinc-Finger
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Methyl-binding domains
MBD methyl CpG binding domain
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Multidomain proteins
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Protein domains
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Protein domain abundance is skewed
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Remember Proteins Interact
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Protein Interaction Domains
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Proteins Assemble
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Proteins localize
http//www.cs.ualberta.ca/bioinfo/PA/Sub/
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Web servers that predict secondary structures

• Predict Protein server
• http//www.predictprotein.org/
• TMpred (transmembrane prediction)
• http//www.ch.embnet.org/software/TMPRED_form.html
  
• COILS (coiled coil prediction)
• http//www.ch.embnet.org/software/COILS_form.html
• SignalP (signal peptides)
• http//www.cbs.dtu.dk/services/SignalP/

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Protein Domain Databases

• Known protein domains have been collected in
  databases
• Best database is PROSITE
• The Dictionary of Protein Sites and Patterns
• Maintained by Amos Bairoch, at the Univ. of
  Geneva, Switzerland
• Contains a comprehensive list of documented
  protein domains constructed by expert molecular
  biologists
• Alignments and patterns built by hand!
• http//www.expasy.org/prosite/

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PROSITE is based on Patterns

• Each domain is defined by a simple pattern
• Patterns can have alternate amino acids in each
  position and defined spaces, but no gaps
• Pattern searching is by exact matching, so any
  new variant will not be found (can allow
  mismatches, but this weakens the algorithm)

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PIR Pattern Search

• From Text/Sequence search result or pattern
  search interface
• One Query Sequence Against PROSITE Pattern
  Database
• One Query Pattern (PROSITE or User-Defined)
  Against Sequence DB

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Pattern detection in sequence
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Sequence search using pattern
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PRINTS database

• Most protein families are characterized not by
  one, but by several conserved motifs
• Fingerprints are groups of conserved motifs
  excised from sequence alignments
• Taken together, they provide diagnostic family
  signatures. They are are the basis of the PRINTS
  database, and are stored in the form of aligned
  motifs
• Input on protein families is done manually
• True members match all elements of the
  fingerprint in order, subfamily members may match
  part of fingerprint

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BLOCKS

• The BLOCKS database uses an extension of the
  motif approach
• block an alignment of the motif sequences from
  a family of proteins
• BLOCKS are used to produce the BLOSUM matrices
• e.g. BLOSUM62 is derived from those blocks that
  are at least 62 identical

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Pfam

• Pfam is a collection of alignments of protein
  domain sequences
• Some families generated using HMMs, some created
  by hand
• HMM Hidden Markov Model, a statistical method
  increasingly used in gene and protein modelling
• HMMs are rigorous algorithms which allow for
  varying gap scores

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Integrating Pattern databases

• InterPro - Integrated Documentation Resource of
  Protein Families, Domains and Functional Sites.
• InterPro is a database of protein families,
  domains and functional sites in which
  identifiable features found in known proteins can
  be applied to unknown protein sequences.
• The aim is to provide a one-stop-shop for protein
  family diagnostics

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InterPro

• Member Databases
• Prosite (regular expressions and profiles)
• Pfam, SMART, TIGRFAMs, PIRSF, PANTHER, Gene3D and
  SUPERFAMILY (hidden Markov Models - HMMs)
• PRINTS (groups of aligned, un-weighted motifs)
• ProDom (uses cluster analysis to group sequences)
• Release 13.0 contains 13,147 entries and covers
  77.6 of UniProtKB - 2530773 of 3260640 proteins
• Types of entries Family, Domain, Repeat, PTM,
  Binding Site, Active Site
• http//www.ebi.ac.uk/interpro/

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Discovery of new Motifs

• All of the tools discussed so far rely on a
  database of existing domains/motifs
• How to discover new motifs
• Start with a set of related proteins
• Make a multiple alignment
• Build a pattern or profile
• You will need access to a fairly powerful UNIX
  computer to search databases with custom built
  profiles or HMMs.

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Patterns in Unaligned Sequences

• Sometimes sequences may share just a small common
  region
• common signal peptide
• new transcription factors
• MEME San Diego Supercomputing Facility
• http//meme.sdsc.edu/meme/meme-intro.html

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Post-translational Modifications
Post-translational modification is the chemical
modification of a protein after its translation.
Translation is the process of synthesizing the
peptide chain of amino acids specified by the
nucleotide sequence on the mRNA.
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The Central Dogma

• Transcription
• Translation

It is not necessary that the final product of
translation should be the final product of
protein synthesis.
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Types of Post-translational modifications

• Several types of PTMs characterized. Some of
  them
• Proteolytic cleavage
• Glycosylation (N)
• Methylation (D - E - K)
• Phosphorylation (S - T - Y)
• Sulfation (Y)
• Acetylation (D - E - K)
• Disulfide bond formation (C)
• Carboxylation, Hydroxylation, Prenylation,
  Formylation, etc. 300 PTMs total

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Phosphorylation
Phosphorylation is the addition of a phosphate
(PO4) group to a protein or a small molecule

• Phosphorylation and dephosphorylation
  responsible for activating or deactivation many
  enzymes and receptors
• Phosphorylation catalyzed by various specific
  protein kinases, dephosphorylation by
  phosphatases
• Can occur on Serine, Threonine, Tyrosine
• gt30 of all proteins are phosphorylated during
  their functional life cycle

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Phosphorylation Sites
pY
pT
pS
PO4
PO4
CH3
PO4
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Glycosylation
Glycosylation is the addition of saccharide to a
protein or a lipid molecule

• N-Linked Glycosylation
• Amide nitrogen of Asparagine
• O-Linked Glycosylation
• - Hydroxy oxygen of Serine and Threonine

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PTMs have significant biological functions

• Extend the range of possible functions that can
  be exhibited by a protein by introducing new
  chemical groups.
• Alter the hydrophobicity of a protein (synthesis
  of membrane proteins).
• Activating or inactivating an enzyme.
• Energy metabolism
• Oxidative phosphorylation in respiration
• Photophosphorylation in protein synthesis
• Signal transduction
• Protein degradation
• Blood coagulation
• Immune system

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PTMs affect protein behavior
Mann Nat Biotech 2003
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Post-translational modifications
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PTMs can be characterized or predicted

• Experimental methods
• Crystallography
• Mass Spectrometry
• PTM Prediction tools
• Auto-motif server
• Sulfinator
• NetPhos server
• Predphospho server
• eMOTIF
• PROSITE

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PTM detection

• Pattern prediction (PROSITE)
• Short or weak signal
• Frequent hit producer
• Best method is experimental
• MS/MS detection
• Most methods use  rules  joining pattern
  detection and knowledge to predict sites.

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ExPASY protein tools
http//www.expasy.org/tools/

• ChloroP - Prediction of chloroplast transit
  peptides
• LipoP - Prediction of lipoproteins and signal
  peptides in Gram negative bacteria
• MITOPROT - Prediction of mitochondrial targeting
  sequences
• SignalP - Prediction of signal peptide cleavage
  sites
• NetAcet - Prediction of N-acetyltransferase A
  (NatA) substrates
• NetOGlyc - Prediction of O-GalNAc (mucin type)
  glycosylation sites in mammalian proteins
• NetNGlyc - Prediction of N-glycosylation sites in
  human proteins
• YinOYang - O-beta-GlcNAc attachment sites in
  eukaryotic protein sequences
• big-PI Predictor - GPI Modification Site
  Prediction
• DGPI - Prediction of GPI-anchor and cleavage
  sites (Mirror site)
• Myristoylator - Prediction of N-terminal
  myristoylation by neural networks
• NetPhos - Prediction of Ser, Thr and Tyr
  phosphorylation sites in eukaryotic proteins
• NMT - Prediction of N-terminal N-myristoylation
• PrePS - Prenylation Prediction Suite
• Sulfinator - Prediction of tyrosine sulfation
  sites
• SUMOplot - Prediction of SUMO protein attachment
  sites
• TermiNator - Prediction of N-terminal
  modification

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PTM Databases

• General PTM Databases
• RESID
• Unimod
• Delta Mass
• PTM Databases for Specific Proteins
• Histone sequence database
• Human Protein Reference Database
• Plasma Proteome Database
• Databases for Specific PTMs
• Phospho.ELM Phosphorylation
• GlycoSuiteDB, SweetDB Glycosylation

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Limitations of current PTM databases

• PTMs mostly annotated in a static fashion. e.g.,
  an amino acid is denoted as either modified or
  unmodified. In reality, some amino acids are
  modified under one condition, and return to their
  initial state when the condition changes.
• The status of a specific amino acid site with
  respect to a modification is highly associated
  with biological functionality of the protein. But
  this association is often not annotated in the
  database.
• Phosphorylation vs. signal transduction
• Glycosylation vs. cell-cell interaction
• Different PTMs on the same protein may be
  associated with each other. These associations
  are not annotated in the current databases either.

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Summary

• Many different protein signature databases exist
  (from small patterns to alignments to complex
  HMMs)
• The quality of a database/server is best tested
  with a sequence you know very well
• Positive controls - submit sequences for which
  you know the right answer
• Negative controls - random or shuffled sequences
• Many proteins function only after they have been
  further chemically modified

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For next time

• Homework 6 due
• Read Mount, Chapter 10