SequenceFunction Relationships

1
Sequence-Function Relationships

• Stuart M. Brown
• New York University School of Medicine

2
Overview

• DNA Structure and Function
• Regulatory Sites in DNA
• Finding Genes in DNA Sequences
• RNA Structure
• Protein Structure and Function
• Protein Motifs

3
Sequence Analysis on the Web

• Can analyze sequence using a mainframe (GCG), on
  a Mac/PC (MacVector, OMIGA, LaserGene, etc.) or
  with free tools on the Web
• Web tools are often best
• Available to everyone
• Constantly upgraded
• But not always available and subject to random
  change

4
DNA Structure

• Primary the sequence itself
• Secondary double helix
• Tertiary supercoiled, bent, etc.
• Quaternary complexes with proteins
• Histones
• RNA Polymerase
• DNA binding proteins (transcription factors)
• Chromosome structure
• centromeres telomeres

5
Phage CRO repressor bound to DNA Andrew Coulson
Roger Sayles with RasMol, Univ. of Edinburgh
1993
6
DNA Information Content

• Just a 4 letter alphabet (GATC)
• Encodes proteins with 3 letter codons
• Punctuation determines transcription starts and
  stops
• Transcripitonal regulation (promoters, enhancers,
  etc.)
• Determines its own replication

7
Many DNA Regulatory Sequences are Known

• Databases of promoters, enhancers, etc.
• TransFac the Transcription Factor database
• 4342 entries w/ known protein binding and
  transcriptional regulatory functions
• Maintained by Gesellschaft for Biotechnologische
  Forschung mbH (Braunschweig, Germany)
• The Eukaryotic Promoter Database(EPD)
• Bucher Trifonov. (1986) NAR 14 10009-26
• 1314 entries taken directly from scientific
  literature
• Maintained by ISREC (Lausanne, Switzerland) as a
  subset of the EMBL

8
Tools to find TF sites in DNA

• GCG FINDPATTERNS with TFSITES.DAT
• Macintosh (Signal Scan), PC/UNIX (Promoter Scan)
• Dr. Dan S. Prestridge, Univ. of Minnesota

9
TF Binding sites lack information

• Most TF binding sites are determined by just a
  few base pairs (typically 6)
• This is not enough information for proteins to
  locate unique promoters for each gene
• TF's bind cooperatively and combinatorially
• The key is in the location in relation to each
  other and to the transcription units of genes

10
Websites for Promoter finding

• Promoter Scan NIH Bioinformatics (BIMAS)
• http//bimas.dcrt.nih.gov/molbio/proscan/
• Promoter Scan II Univ. of Minnesota Axyx
  Pharmaceuticals
• http//biosci.cbs.umn.edu/software/proscan/promote
  rscan.htm
• Signal Scan NIH Bioinformatics (BIMAS)
• http//bimas.dcrt.nih.gov80/molbio/signal/index.h
  tml
• Transcription Element Search (TESS) Center for
  Bioinformatics, Univ. of Pennsylvania
• http//www.cbil.upenn.edu/tess/
• Search TransFac at GBF with MatInspector,
  PatSearch, and FunSiteP
• http//transfac.gbf-braunschweig.de/TRANSFAC/progr
  ams.html
• TargetFinder Telethon Inst.of Genetics and
  Medicine, Milan, Italy
• http//hercules.tigem.it/TargetFinder.html

11
Finding Genes in Genomic DNA

• Translate (in all 6 reading frames) and look for
  similarity to known protein sequences
• Translate and look for long Open Reading Frames
  (ORFs) between start and stop codons
• Look for known gene markers
• TAATAA box, intron splice sites, etc.
• Statistical methods (codon preference)

12
Gene Finding on the Web

• GRAIL Oak Ridge Natl. Lab, Oak Ridge, TN
• http//compbio.ornl.gov/grailexp
• ORFfinder NCBI
• http//www.ncbi.nlm.nih.gov/gorf/gorf.html
• DNA translation Univ. of Minnesota Med. School
• http//alces.med.umn.edu/webtrans.html
• GenLang
• http//cbil.humgen.upenn.edu/sdong/genlang.html
• BCM GeneFinder Baylor College of Medicine,
  Houston, TX
• http//dot.imgen.bcm.tmc.edu9331/seq-search/gene-
  search.html
• http//dot.imgen.bcm.tmc.edu9331/gene-finder/gf.h
  tml

13
Genomic Sequence

• Once each gene is located on the chromosome, it
  becomes possible to get upstream genomic sequence
• This is where the transcription factor binding
  sites are located
• Search for known TF sites, and discover new ones
  (among co-regulated genes)

14
Intron/Exon structure

• Gene finding programs work well in bacteria
• None of these gene prediction programs do an
  adequate job predicting intron/exon boundaries
• The only reasonable gene models are based on
  alignment of cDNAs to genome sequence
• Perhaps 50 of all human genes still do not have
  a correct coding sequence defined

15
RNA Structure

• Similar to DNA - base pairing
• Smaller molecules, free to take on more complex
  shapes
• tRNA, ribozymes, self-splicing introns

16
tRNA Structures
17
RNA Information Content

• Primary structure (sequence) contains
• Information for 3-D self-assembly
• Genetic code for amino acids in protein
• Translation start and stop signals
• Intron splicing signals
• Controls for RNA stability and transcription level

18
RNA Secondary Structure

• Rules for base pairing and free energy
  minimization are known
• Characteristic tRNA stem-loop structures
• Michael Zuker created the computer program
  FoldRNA
• GCG, UNIX/Mac/PC freeware, in commercial
  products, and on the Web
• Can predict many RNA secondary structures, not
  necessarily the optimal or true structure

19
Protein Sequence Analysis

• Molecular properties (pH, mol. wt. isoelectric
  point, hydrophobicity)
• Secondary Structure
• Super-secondary (signal peptide, coiled-coil,
  trans-membrane, etc.)
• 3-D prediction, Threading
• Domains, motifs, etc.

20
Self-assembly

• Proteins self-assemble in solution
• All of the information necessary to determine the
  complex 3-D structure is in the amino acid
  sequences
• Structure determines function
• lock key model of enzyme function
• Know the sequence, know the function?
• Nearly infinite complexity

21
Structure prediction

• Protein Structure prediction is the Holy Grail
  of bioinformatics
• Since structure function, then structure
  prediction should allow protein design, design of
  inhibitors, etc.
• Huge amounts of genome data - what are the
  functions of all of these proteins?

22
Chemical Properties of Proteins

• Proteins are linear polymers of 20 amino acids
• Chemical properties of the protein are determined
  by its amino acids
• Molecular wt., pH, isoelectric point are simple
  calculations from amino acid composition
• Hydrophobicity is a property of groups of amino
  acids - best examined as a graph

23
Hydrophobicity Plot
P53_HUMAN (P04637) human cellular tumor antigen
p53 Kyte-Doolittle hydrophilicty, window19
24
Web Sites for Simple Protein Analysis

• Protein Hydrophobicity Server Bioinformatics
  Unit, Weizmann Institute of Science , Israel
• http//bioinformatics.weizmann.ac.il/hydroph/
• SAPS - statistical analysis of protein sequences
  composition, charge, hydrophobic and
  transmembrane segments, cysteine spacings,
  repeats and periodicity
• http//www.isrec.isb-sib.ch/software/SAPS_form.htm
  l

25
Secondary Structure

• Protein secondary structure takes one of three
  forms
• Alpha helix
• Beta pleated sheet
• Turn
• 2ndary structure is predicted within a small
  window
• Many different algorithms, not highly accurate
• Better predictions from a multiple alignment

26
GCG Protein Analysis Toolkit

• Isoelectric plots aa charge as a function of pH
• PeptideStructure secondary structure predictions
  
• PlotStructure plots protein secondary structure
• PepPlot plots protein secondary structure and
  hydrophobicity in parallel panels
• Moment makes a contour plot of the helical
  hydrophobic moment
• HelicalWheel plots a peptide sequence as a
  helical wheel to help you recognize
  alpha-helical regions.

27
Structure Prediction on the Web

• Secondary Structural Content Prediction (SSCP)
  EMBL, Heidelberg
• http//www.bork.embl-heidelberg.de/SSCP/sscp_seq.h
  tml
• BCM Search Launcher Protein Secondary Structure
  Prediction Baylor College of Medicine
• http//dot.imgen.bcm.tmc.edu9331/seq-search/struc
  -predict.html
• PREDATOR EMBL, Heidelberg
• http//www.embl-heidelberg.de/cgi/predator_serv.pl
  
• UCLA-DOE Protein Fold Recognition Server
• http//www.doe-mbi.ucla.edu/people/fischer/TEST/ge
  tsequence.html

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Sample Structure Prediction
29
Super-secondary Structure

• Common structural motifs
• Membrane spanning (GCG TransMem)
• Signal peptide (GCG SPScan)
• Coiled coil (GCG CoilScan)
• Helix-turn-helix (GCG HTHScan)

30
Web servers that predict these structures

• Predict Protein server EMBL Heidelberg
• http//www.embl-heidelberg.de/predictprotein/
• SOSUI Tokyo Univ. of Ag. Tech., Japan
• http//www.tuat.ac.jp/mitaku/adv_sosui/submit.htm
  l
• TMpred (transmembrane prediction) ISREC (Swiss
  Institute for Experimental Cancer Research)
• http//www.isrec.isb-sib.ch/software/TMPRED_form.h
  tml
• COILS (coiled coil prediction) ISREC
• http//www.isrec.isb-sib.ch/software/COILS_form.ht
  ml
• SignalP (signal peptides) Tech. Univ. of Denmark
  
• http//www.cbs.dtu.dk/services/SignalP/

31
3-D Structure

• Cannot be accurately predicted from sequence
  alone (known as ab initio)
• Levinthals paradox a 100 aa protein has 3200
  possible backbone configurations - many orders of
  magnitude beyond the capacity of the fastest
  computers
• There are perhaps only a few hundred basic
  structures, but we dont yet have this vocabulary
  or the ability to recognize variants on a theme

32
Threading Protein Structures

• Best bet is to compare with similar sequences
  that have known structures gtgt Threading
• Only works for proteins with gt25 sequence
  similarity to a protein with known structure
• Current state of the art requires many days of
  computing on a dedicated workstation
• Some websites offer quick approximations
• Will improve as more 3-D structures are described
• Another aspect of the Genome Project

33
Predicted Structure
34
Protein Data Base

• There is a database of all known protein
  structures called the PDB.
• These have been determined by X-ray
  crystalography and/or NMR.
• Anyone download and view these structures with a
  PDB viewer program.

35
RasMol

• RasMol is the simplest PDB viewer.
• http//www.umass.edu/microbio/rasmol/
• It can work together with a web browser to let
  you view the structure of any sequence found with
  Entrez that has a known 3-D structure.

36
Websites for 3-D structure prediction

• UCLA-DOE Protein Fold Recognition
• http//www.doe-mbi.ucla.edu/people/fischer/TEST/ge
  tsequence.html
• SwissModel ExPASy, Univ. of Geneva
• http//www.expasy.ch/swissmod/SWISS-MODEL.html
• CPHmodels Technical Univ. of Denmark
• http//www.cbs.dtu.dk/services/CPHmodels/

37
Searching for Patterns in Proteins
38
Protein Domains/Motifs

• Proteins are built out of functional units know
  as domains (or motifs)
• These domains have conserved sequences
• Often much more similar than their respective
  proteins
• Exon splicing theory (W. 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)

39
Protein Motif Databases

• Known protein motifs 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.

40
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)

41
Tools for PROSITE searches

• Free Mac program MacPattern
• ftp//ftp.ebi.ac.uk/pub/software/mac/macpattern.hq
  x
• Free PC program (DOS) PATMAT
• ftp//ncbi.nlm.nih.gov/repository/blocks/patmat.do
  s
• GCG provides the program MOTIFS
• Also in virtually all commercial programs
  MacVector, OMIGA, LaserGene, etc.

42
Websites for PROSITE Searches

• ScanProsite at ExPASy Univ. of Geneva
• http//expasy.hcuge.ch/sprot/scnpsit1.html
• Network Protein Sequence Analysis Institut de
  Biologie et Chimie des Protéines, Lyon, France
• http//pbil.ibcp.fr/NPSA/npsa_prosite.html
• PPSRCH EBI, Cambridge, UK
• http//www2.ebi.ac.uk/ppsearch/

43
Profiles

• Profiles are tables of amino acid frequencies at
  each position in a motif
• They are built from multiple alignments
• PROSITE entries also contain profiles built from
  an alignment of proteins that match the pattern
• Profile searching is more sensitive than pattern
  searching - uses an alignment algorithm, allows
  gaps

44
GCG ProfileSearch

• GCG has a set of profile analysis tools.
• Start with a multiple alignment
• Create a profile with ProfileMake
• ProfileSearch scans a database with your
  profile
• ProfileSegments displays alignments between a
  profile and matching database sequences
• ProfileGap makes pairwise alignments between a
  single sequence and a profile

45
Websites for Profile searching

• PROSITE ProfileScan ExPASy, Geneva
• http//www.isrec.isb-sib.ch/software/PFSCAN_form.h
  tml
• BLOCKS (builds profiles from PROSITE entries and
  adds all matching sequences in SwissProt) Fred
  Hutchinson Cancer Research Center, Seattle,
  Washington, USA
• http//www.blocks.fhcrc.org/blocks_search.html
• PRINTS (profiles built from automatic alignments
  of OWL non-redundant protein databases)
  http//www.biochem.ucl.ac.uk/cgi-bin/fingerPRINTSc
  an/fps/PathForm.cgi

46
More Protein Motif Databases

• PFAM (1344 protein family HMM profiles built by
  hand) Washington Univ., St. Louis
• http//pfam.wustl.edu/hmmsearch.shtml
• ProDom (profiles built from PSI-BLAST automatic
  multiple alignments of the SwissProt database)
  INRA, Toulouse, France
• http//www.toulouse.inra.fr/prodom/doc/blast_form.
  html
• This is my favorite protein database - nicely
  colored results

47
Hidden Markov Models

• Hidden Markov Models (HMMs) are a more
  sophisticated form of profile analysis.
• Rather than build a table of amino acid
  frequencies at each position, they model the
  transition from one amino acid to the next.
• Pfam is built with HMMs.
• GCG version 10.2 (released March 2001) has added
  a bunch of HMM tools (and Pfam).

48
Sample ProDom Output
49
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.

50
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//www.sdsc.edu/MEME/meme/website/meme.html
• - GCG also includes the MEME program

51
Summary

• DNA has genes and other information
• Transcription factors
• RNA has predictable structures
• Proteins have predictable 2ndary structures and
  functional domains, but generally cant predict
  new 3-D structures