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Systems biology - figure out circuits. of regulation, predict outcome ... Very simple to find these patterns - can even use the

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computing patterns in biology

Computing Patterns in Biology

Stuart M. Brown

New York University School of Medicine

why compute biological patterns
Why Compute Biological Patterns?
  • Because we can
        • (computer scientists love to find “interesting” problems)
        • patterns are beautiful
  • Its practical - helps with genecloning experiments, predict functions of new proteins
  • Systems biology - figure out circuits of regulation, predict outcome of changes, design new biological systems
overview
Overview
  • Restriction sites
  • Finding genes in DNA sequences
  • Regulatory sites in DNA
  • Protein signals (transport and processing)
  • Protein functional Motifs
  • Protein families
  • Protein 3-D structure
restriction sites
Restriction Sites
  • Bacteria make restriction enzymes that cut DNA at specific sequences (4-8 base patterns)
  • Very simple to find these patterns - can even use the “Find” function of your web browser or word processor
  • Open any page of text and look for “CAT”
    • you now have a restriction site search program!
nebcutter2
NEBcutter2

http://tools.neb.com/NEBcutter2/

finding genes in genomic dna
Translate (in all 6 reading frames) and look for similarity to known protein sequences

Look for long Open Reading Frames (ORFs) between start and stop codons (start=ATG, stop=TAA, TAG, TGA)

Look for known gene markers

TAATAA box, intron splice sites, etc.

Statistical methods (codon preference)

Finding Genes in Genomic DNA
slide7

GCCACATGTAGATAATTGAAACTGGATCCTCATCCCTCGCCTTGTACAAAAATCAACTCCAGATGGATCTAAGATTTAAATCTAACACCTGAAACCATAAAAATTCTAGGAGATAACACTGGCAAAGCTATTCTAGACATTGGCTTAGGCAAAGAGTTCGTGACCAAGAACCCAAAAGCAAATGCAACAAAAACAAAAATAAATAGGTGGGACCTGATTAAACTGAAAAGCCTCTGCACAGCAAAAGAAATAATCAGCAGAGTAAACAGACAACCCACAGAATGAGAGAAAATATTTGCAAACCATGCATCTGATGACAAAGGACTAATATCCAGAATCTACAAGGAACTCAAACAAATCAGCAAGAAAAAAATAACCCCATCAAAAAGTGGGCAAAGGAATGAATAGACAATTCTCAAAATATACAAATGGCCAATAAACATACGAAAAACTGTTCAACATCACTAATTATCAGGGAAATGCAAATTAAAACCACAATGAGATGCCACCTTACTCCTGCAAGAATGGCCATAATAAAAAAAAATCAAAAAAGAATAAATGTTGGTGTGAATGTGGTGAAAAGAGAACACTTTGACACTGCTGGTGGGAATGGAAACTAGTACAACCACTGTGGAAAACAGTACCGAGATTTCTTAAAGAACTACAAGTAGAACTACCATTTGATCCAGCAATCCCACTACTGGGTATCTACCCAGAGGAAAAGAAGTCATTATTTGAAAAAGACACTTGTACATACATGTTTATAGCAGCACAATTTGCAATTGCAAAGATATGGAACCAGTCTAAATGCCCATCAACCAACAAATGGATAAAGAAAATATGGTATATATACACCATGGAACACTACTCAGCCATAAAAAGGAACAAAATAATGGCAACTCACAGATGGAGTTGGAGACCACTATTCTAAGTGAAATAACTCAGGAATGGAAAACCAAATATTGTATGTTCTCACTTATAAGTGGGAGCTAAGCTATGAGGACAAAAGGCATAAGAATTATACTATGGACTTTGGGGACTCGGGGGAAAGGGTGGGAGGGGGATGAGGGACAAAAGACTACACATTGGGTGCAGTGTACACTGCTGAGGTGATGGGTGCACCAAAATCTCAGAAATTACCACTAAAGAACTTATCCATGTAACTAAAAACCACCTCTACCCAAATAATTTTGAAATAAAAAATAAAAATATTTTAAAAAGAACTCTTTAAAATAAATAATGAAAAGCACCAACAGACTTATGAACAGGCAATAGAAAAAATGAGAAATAGAAAGGAATACAAATAAAAGTACAGAAAAAAAATATGGCAAGTTATTCAACCAAACTGGTAATTTGAAATCCAGATTGAAATAATGCAAAAAAAAGGCAATTTCTGGCACCATGGCAGACCAGGTACCTGGATGATCTGTTGCTGAAAACAACTGAAAATGCTGGTTAAAATATATTAACACATTCTTGAATACAGTCATGGCCAAAGGAAGTCACATGACTAAGCCCACAGTCAAGGAGTGAGAAAGTATTCTCTACCTACCATGAGGCCAGGGCAAGGGTGTGCACTTTTTTTTTTCTTCTGTTCATTGAATACAGTCACTGTGTATTTTACATACTTTCATTTAGTCTTATGACAATCCTATGAAACAAGTACTTTTAAAAAAATTGAGATAACAGTTGCATACCGTGAAATTCATCCATTTAAAGTGAGCAATTCACAGGTGCAGCTAGCTCAGTCAGCAGAGCATAAGACTCTTAAAGTGAACAATTCAGTGCTTTTTAGTATATTCACAGAGTTGTGCAACCATCACCACTATCTAATTGGTCTTAGTCTGTTTGGGCTGCCATAACAAAATACCACAAACTGGATAGCTCATAAACAACAGGCATTTATTGCTCACAGTTCTAGAGGCTGGAAGTGCAAGATTAAGATGCCAGCAGATTCTGTGTCTGCTGAGGGCCTGTTCCTCATAGAAGGTGCCCTCTTGCTGAATTCTCACATGGTGGAAGGGGGAAAACAAGCTTGCATTGCAAAGAGGTGGGCCTCTTTAATCCCAAAGGCCCCACCTCTAAAAGGCCCCACTTCTGAATACCATTACATTGAGAATTAAGTTTCAACATAGGAATTTGGGGGAACACAAATATCCAGACTGTAGCATAATTCCAGAACGGATTCATGCCACATGTAGATAATTGAAACTGGATCCTCATCCCTCGCCTTGTACAAAAATCAACTCCAGATGGATCTAAGATTTAAATCTAACACCTGAAACCATAAAAATTCTAGGAGATAACACTGGCAAAGCTATTCTAGACATTGGCTTAGGCAAAGAGTTCGTGACCAAGAACCCAAAAGCAAATGCAACAAAAACAAAAATAAATAGGTGGGACCTGATTAAACTGAAAAGCCTCTGCACAGCAAAAGAAATAATCAGCAGAGTAAACAGACAACCCACAGAATGAGAGAAAATATTTGCAAACCATGCATCTGATGACAAAGGACTAATATCCAGAATCTACAAGGAACTCAAACAAATCAGCAAGAAAAAAATAACCCCATCAAAAAGTGGGCAAAGGAATGAATAGACAATTCTCAAAATATACAAATGGCCAATAAACATACGAAAAACTGTTCAACATCACTAATTATCAGGGAAATGCAAATTAAAACCACAATGAGATGCCACCTTACTCCTGCAAGAATGGCCATAATAAAAAAAAATCAAAAAAGAATAAATGTTGGTGTGAATGTGGTGAAAAGAGAACACTTTGACACTGCTGGTGGGAATGGAAACTAGTACAACCACTGTGGAAAACAGTACCGAGATTTCTTAAAGAACTACAAGTAGAACTACCATTTGATCCAGCAATCCCACTACTGGGTATCTACCCAGAGGAAAAGAAGTCATTATTTGAAAAAGACACTTGTACATACATGTTTATAGCAGCACAATTTGCAATTGCAAAGATATGGAACCAGTCTAAATGCCCATCAACCAACAAATGGATAAAGAAAATATGGTATATATACACCATGGAACACTACTCAGCCATAAAAAGGAACAAAATAATGGCAACTCACAGATGGAGTTGGAGACCACTATTCTAAGTGAAATAACTCAGGAATGGAAAACCAAATATTGTATGTTCTCACTTATAAGTGGGAGCTAAGCTATGAGGACAAAAGGCATAAGAATTATACTATGGACTTTGGGGACTCGGGGGAAAGGGTGGGAGGGGGATGAGGGACAAAAGACTACACATTGGGTGCAGTGTACACTGCTGAGGTGATGGGTGCACCAAAATCTCAGAAATTACCACTAAAGAACTTATCCATGTAACTAAAAACCACCTCTACCCAAATAATTTTGAAATAAAAAATAAAAATATTTTAAAAAGAACTCTTTAAAATAAATAATGAAAAGCACCAACAGACTTATGAACAGGCAATAGAAAAAATGAGAAATAGAAAGGAATACAAATAAAAGTACAGAAAAAAAATATGGCAAGTTATTCAACCAAACTGGTAATTTGAAATCCAGATTGAAATAATGCAAAAAAAAGGCAATTTCTGGCACCATGGCAGACCAGGTACCTGGATGATCTGTTGCTGAAAACAACTGAAAATGCTGGTTAAAATATATTAACACATTCTTGAATACAGTCATGGCCAAAGGAAGTCACATGACTAAGCCCACAGTCAAGGAGTGAGAAAGTATTCTCTACCTACCATGAGGCCAGGGCAAGGGTGTGCACTTTTTTTTTTCTTCTGTTCATTGAATACAGTCACTGTGTATTTTACATACTTTCATTTAGTCTTATGACAATCCTATGAAACAAGTACTTTTAAAAAAATTGAGATAACAGTTGCATACCGTGAAATTCATCCATTTAAAGTGAGCAATTCACAGGTGCAGCTAGCTCAGTCAGCAGAGCATAAGACTCTTAAAGTGAACAATTCAGTGCTTTTTAGTATATTCACAGAGTTGTGCAACCATCACCACTATCTAATTGGTCTTAGTCTGTTTGGGCTGCCATAACAAAATACCACAAACTGGATAGCTCATAAACAACAGGCATTTATTGCTCACAGTTCTAGAGGCTGGAAGTGCAAGATTAAGATGCCAGCAGATTCTGTGTCTGCTGAGGGCCTGTTCCTCATAGAAGGTGCCCTCTTGCTGAATTCTCACATGGTGGAAGGGGGAAAACAAGCTTGCATTGCAAAGAGGTGGGCCTCTTTAATCCCAAAGGCCCCACCTCTAAAAGGCCCCACTTCTGAATACCATTACATTGAGAATTAAGTTTCAACATAGGAATTTGGGGGAACACAAATATCCAGACTGTAGCATAATTCCAGAACGGATTCAT

intron exon structure
Intron/Exon structure
  • Gene finding programs work well in bacteria
  • None of the 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

(transcription start, intron splice sites)

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.edu:9331/seq-search/gene-search.html

http://dot.imgen.bcm.tmc.edu:9331/gene-finder/gf.html

Gene Finding on the Web
genomic sequence
Genomic Sequence
  • Once each gene is located on the chromosome, it becomes possible to get upstream genomic sequence
  • This is where transcription factor (TF) binding sites are located
    • promoters and enhancers
  • Search for known TF sites, and discover new ones (among co-regulated genes)
slide11

Phage CRO repressor bound to DNA

Andrew Coulson & Roger Sayles with RasMol, Univ. of Edinburgh 1993

many dna regulatory sequences are known
Databases of promoters, enhancers, etc.

TransFacthe 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

Many DNA Regulatory Sequences are Known
tf binding sites lack information
TF Binding sites lack information
  • Most TF binding sites are determined by just a few base pairs (typically 6)
  • Sequence is variable (consensus)
  • 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
  • Can use information from alignment of related genes
tools to find tf sites in dna
GCG: FINDPATTERNS

with database file: TFSITES.DAT

Macintosh (Signal Scan), PC/UNIX (Promoter Scan)

Dr. Dan S. Prestridge, Univ. of Minnesota

Tools to find TF sites in DNA
websites for promoter finding
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/promoterscan.htm

Signal Scan: NIH Bioinformatics (BIMAS)

http://bimas.dcrt.nih.gov:80/molbio/signal/index.html

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/programs.html

TargetFinder: Telethon Inst.of Genetics and Medicine, Milan, Italy

http://hercules.tigem.it/TargetFinder.html

protein sequence analysis
Molecular properties (pH, mol. wt. isoelectric point, hydrophobicity)

Motifs (signal peptide, coiled-coil, trans-membrane, etc.)

Protein Families

Secondary Structure (helix vs. beta-sheet)

3-D prediction, Threading

Protein Sequence Analysis
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

Chemical Properties of Proteins
hydrophobicity plot
Hydrophobicity Plot

P53_HUMAN (P04637) human cellular tumor antigen p53

Kyte-Doolittle hydrophilicty, window=19

web sites for simple protein analysis
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.html

emboss protein analysis toolkit
EMBOSS Protein Analysis Toolkit
  • plotorf:simple open reading frame finder
  • Garnier: predicts 2ndary structure
  • Charge: plot of protein charge
  • Octanol: hydrophobicity plot
  • Pepwindow: hydorpathy plot
  • pepinfo:plotsprotein secondary structure and hydrophobicity in parallel panels
  • tmap: predict transmembrane regions
  • Topo: draws a map of transmembrane protein
  • Pepwheel: shows protein sequence as helical wheel
  • Pepcoil: predicts coiled-coil domains
  • Helixturnhelix: predicts helix-turn-helix domains
simple motifs
Common structural motifs

Membrane spanning

Signal peptide

Coiled coil

Helix-turn-helix

Simple Motifs
super secondary structure
Common structural motifs

Membrane spanning (GCG= TransMem)

Signal peptide (GCG= SPScan)

Coiled coil (GCG= CoilScan)

Helix-turn-helix (GCG = HTHScan)

“Super-secondary” Structure
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.html

TMpred (transmembrane prediction): ISREC (Swiss Institute for Experimental Cancer Research)

http://www.isrec.isb-sib.ch/software/TMPRED_form.html

COILS (coiled coil prediction): ISREC

http://www.isrec.isb-sib.ch/software/COILS_form.html

SignalP (signal peptides): Tech. Univ. of Denmark

http://www.cbs.dtu.dk/services/SignalP/

Web servers that predict these structures
protein domains motifs
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)
protein motif databases
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
    • Alignments and patterns built by hand!
prosite is based on patterns
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)
tools for prosite searches
Tools for PROSITE searches

Free Mac program: MacPattern

  • ftp://ftp.ebi.ac.uk/pub/software/mac/macpattern.hqx

Free PC program (DOS): PATMAT

  • ftp://ncbi.nlm.nih.gov/repository/blocks/patmat.dos

GCG provides the program MOTIFS

  • Also in virtually all commercial programs: MacVector, OMIGA, LaserGene, etc.
websites for prosite searches
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/
profiles
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
emboss profilesearch
EMBOSS ProfileSearch
  • EMBOSS has a set of profile analysis tools.
  • Start with a multiple alignment
    • fuzzpro: protein pattern search
    • preg: regular expression search of a protein sequence
    • prophecy: create a profile
    • profit:scans a database with your profile
    • prophetmakes pairwise alignments between a single sequence and a profile
    • patmatmotifs: scan a query protein with the PROSITE motif database
websites for profile searching
Websites for Profile searching
  • PROSITE ProfileScan: ExPASy, Geneva
    • http://www.isrec.isb-sib.ch/software/PFSCAN_form.html
  • 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/fingerPRINTScan/fps/PathForm.cgi
more protein motif databases
More Protein Motif Databases
  • PFAM(1344 protein familyHMM 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]

hidden markov models
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.
  • EMBOSS HMM tools (HMMER):

ehmmBuild ehmmCalibrate

ehmmSearch ehmmPfam

ehmmAlign ehmmEmit

ehmmFetch ehmmIndex

discovery of new motifs
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.
patterns in unaligned sequences
Patterns in Unaligned Sequences
  • Sometimes sequences may share just a small common region
        • transcription factors
  • MEME:San Diego Supercomputing Facility

http://www.sdsc.edu/MEME/meme/website/meme.html

  • EMBOSSalso includes the MEME program
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

Self-assembly
structure prediction
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?
3 d structure
Cannot be accurately predicted from sequence alone (known as ab initio)

Levinthal’s paradox: a 100 aa protein has 3200possible backbone configurations - many orders of magnitude beyond the capacity of the fastest computers

There are perhaps only a few hundred basic structures, but we don’t yet have this vocabulary or the ability to recognize variants on a theme

3-D Structure
secondary structure
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
structure prediction on the web
Secondary Structural Content Prediction (SSCP): EMBL, Heidelberg

http://www.bork.embl-heidelberg.de/SSCP/sscp_seq.html

BCM Search Launcher: Protein Secondary Structure Prediction: Baylor College of Medicine

http://dot.imgen.bcm.tmc.edu:9331/seq-search/struc-predict.html

PREDATOR: EMBL, Heidelberg

http://www.embl-heidelberg.de/cgi/predator_serv.pl

Structure Prediction on the Web
threading protein structures
Threading Protein Structures

Best bet is to compare with similar sequences that have known structures >> Threading

  • Only works for proteins with >25% sequence similarity to a protein with known structure
  • Some websites offer quick approximations
  • Will improve as more 3-D structures are described
websites for 3 d structure prediction
Websites for 3-D structure prediction

UCLA-DOE Protein Fold Recognition

http://www.doe-mbi.ucla.edu/people/fischer/TEST/getsequence.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/

view known protein structures
View Known Protein Structures
  • GenBank includes a database of protein 3-D structures and a free viewer: Cn3D
  • GenBank database is derived from PDB (Protein Data Base)
    • primary repository of protein structure data
    • determined by X-ray crystallography and/or NMR
    • has its own data format and many free viewers
    • some are very sophisticated - can calculate intermolecular distances
slide52
Cn3D
  • Cn3D is a helper application that allows you to view three dimensional structures from NCBI's Entrez database.
  • Cn3D runs on Windows, MacOS, and Unix/Linux.
  • Cn3D simultaneously displaysstructure, sequence, and alignment, it also allows the user to set display styles for features of interest.

http://www.ncbi.nlm.nih.gov/Structure/CN3D/cn3d.shtml

By being tightly coupled to the genomic and literature databases, Cn3D is the ideal program for viewing structures found in the NCBI databases.

rasmol
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.

summary
Summary
  • Restriction sites are trivial to compute, but very useful
  • Genomic DNA has genes and other information –> transcription factors
  • Proteins have predictable 2ndary structures and functional domains, but generally can’t predict new 3-D structures
  • Can visualize and compare known structures