Introduction to Bioinformatics 20120

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Introduction to Bioinformatics20120

• Gianluca Pollastri
• office CS A1.07
• email gianluca.pollastri_at_ucd.ie

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Credits

• Richard Lathrop and Pierre Baldis Bioinformatics
  courses at University of California _at_ Irvine.

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Course overview

• Context DNA, RNA, proteins
• Resources GenBank, PDB, etc.
• Algorithms for sequence comparison.
• Phylogenetics.
• Structural bioinformatics protein structure
  prediction.

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Lecture notes

• http//gruyere.ucd.ie/2007_courses/20120/
• confidential..

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Recommended/useful readings

• No book is actually required
• Introduction to Bioinformatics
• Lesk
• Introduction to Computational Molecular Biology
• Setubal, Meidanis
• Bioinformatics the Machine Learning approach
• Baldi, Brunak

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• CS 20120, Introduction to Bioinformatics
• Assignment 1, 29 January 2007
• 10 of the overall mark
• To hand in by midnight of February 12
• 1. identify your favourite pet
• 2. get the protein sequence for one of its genes
  on
• a. http//www.ncbi.nlm.nih.gov/entrez/
• 3. BLAST your sequence against UniProt at
• a. http//www.ebi.ac.uk/blast2/index.html?UniProt
• 4. If you get less than 6 results from 6
  different organisms, go back to 2 and choose
  another protein
• 5. Select 6 sequences returned by BLAST, from 6
  different organisms (ticking the appropriate
  boxes and downloading them in fasta format will
  give you the right input format for the next
  step)
• 6. Run clustalW on them using the page (be
  patient, might take time)
• a. http//www.ebi.ac.uk/clustalw/index.html
• 7. Draw a phylogenetic tree for your guide tree
  (.dnd) using an online viewer, e.g.
• a. http//bioweb.pasteur.fr/seqanal/interfaces/dra
  wtree.html
• 8. email me (gianluca.pollastri_at_ucd.ie)
• a. your protein sequence UniProt record

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DNA summary

• A string of 4 letters (vs the 20 of proteins).
• Very very long, packed several times over, first
  as a double helix, then into more complicated
  shapes.
• The double helix allows DNA to make copies of
  itself.

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How this works summary

• DNA-gtRNA-gtprotein, through transcription and
  translation
• The above is true of genes in DNA. Lots of DNA
  does not make proteins, hence does something else
  (or nothing)
• Telling where genes are is simple in prokaryotes,
  more complicated in eukaryotes where introns and
  exons kick in.
• Not all genes are active in every cell at any
  given time.

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Evolution

• Genotype vs Phenotype
• Genotype changes
• Random mutations
• Recombination
• Gene duplication
• Gene flow
• Phylogenetics reverse engineering evolution.
• Molecular phylogenetics the above, based on DNA
  and protein sequences.

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Public resources

• A gigantic amount of information is now
  available.
• Most of it is publicly available.
• Much of it is raw, i.e. unannotated or only
  mildly annotated.

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Nucleic acid (DNA/RNA) sequence databases

• One main database arising from a partnership
  between GenBANK at the NCBI (National Center for
  Biotechnology Information USA), the EMBL data
  library at the EBI (European Bioinformatics
  Institute UK) and the DNA Data Bank at the NIG
  (National Institute of Genetics Japan).
• Daily exchanges between the 3 partners to keep
  the databases synchronised.
• DNA and RNA sequences curated, archived,
  distributed.
• Sequences from genome projects, scientific
  articles, patent applications. Most scientific
  journals require DNA and RNA sequences related to
  each publication to be publicly available.
• Sequences deposited early and going through a
  review cycle unannotated.. preliminary..
  unreviewed.. standard.
• Format human and computer readable.

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• LOCUS NM_205854 462 bp
  mRNA linear PRI 12-JAN-2007
• DEFINITION Homo sapiens surfactant associated
  protein G (SFTPG), mRNA.
• ACCESSION NM_205854 XM_371808
• VERSION NM_205854.2 GI122056695
• KEYWORDS .
• SOURCE Homo sapiens (human)
• ORGANISM Homo sapiens
• Eukaryota Metazoa Chordata
  Craniata Vertebrata Euteleostomi
• Mammalia Eutheria Euarchontoglires
  Primates Haplorrhini
• Catarrhini Hominidae Homo.
• REFERENCE 1 (bases 1 to 462)
• AUTHORS Zhang,Z. and Henzel,W.J.
• TITLE Signal peptide prediction based on
  analysis of experimentally
• verified cleavage sites
• JOURNAL Protein Sci. 13 (10), 2819-2824
  (2004)
• PUBMED 15340161
• REFERENCE 2 (bases 1 to 462)
• AUTHORS Clark,H.F., Gurney,A.L., Abaya,E.,
  Baker,K., Baldwin,D., Brush,J.,
• Chen,J., Chow,B., Chui,C.,
  Crowley,C., Currell,B., Deuel,B.,

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• FEATURES Location/Qualifiers
• source 1..462
• /organism"Homo sapiens"
• /mol_type"mRNA"
• /db_xref"taxon9606"
• /chromosome"6"
• /map"6p21.33"
• gene 1..462
• /gene"SFTPG"
• /note"surfactant associated
  protein G synonyms UNQ541,
• GSGL541"
• /db_xref"GeneID389376"
• /db_xref"HGNC18386"
• /db_xref"HPRD18265"
• CDS 80..316
• /gene"SFTPG"
• /codon_start1
• /product"surfactant
  associated protein G precursor"
• /protein_id"NP_995326.1"

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Protein sequence databases

• UniProt, resulting from the merger of the PIR
  (Protein Information Resource) at the National
  Biomedical Research Foundation at the Georgetown
  University Medical Center (DC, USA), SWISS-PROT
  at the Swiss Institute of Bioinformatics in
  Geneva and the TrEMBL at the EBI.
• SWISS-PROT is human-annotated, TrEMBL is EMBL
  translated (PIR is smaller).
• TrEMBL more than 90 of UniProt, but somewhat
  hypothetical proteins inferred from genes in
  DNA, but many genes might not actually make
  proteins or be genes at all.
• Smaller, specialistic databases associated to
  UniProt ENZYME DB, PROSITE, etc.

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Databases of structures

• The main database of protein structures is the
  PDB (Protein Data Bank).
• The PDB started in 1971 at Brookhaven National
  Labs (NY, USA) and is now a distributed
  organisation (Research Collaboratory for
  Structural Bioinformatics, www.rcsb.org) of US
  partners (Rutgers, NJ San Diego Supercomputer
  Centre, Ca NIST, Md).
• The PDB includes protein structures (and a few
  DNA and other structures) determined by X-ray
  crystallography and Nuclear Magnetic Resonance.
• It used to include a few structures predicted
  computationally, but these have recently been
  dropped.

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• HEADER ANTIBACTERIAL PROTEIN
  17-DEC-82 1ACX 1ACX 3
• COMPND ACTINOXANTHIN
  1ACX 4
• SOURCE (ACTINOMYCES GLOBISPORUS, NUMBER 1131)
  1ACXD 1
• AUTHOR V.Z.PLETNEV,A.P.KUZIN
  1ACX 6
• REVDAT 7 31-JAN-94 1ACXF 1 REMARK
  1ACXF 1
• REVDAT 6 15-OCT-90 1ACXE 1 REMARK
  1ACXE 1
• REVDAT 5 16-APR-87 1ACXD 1 SOURCE
  REMARK 1ACXD 2
• REVDAT 4 25-APR-86 1ACXC 1 REMARK
  1ACXC 1
• REVDAT 3 28-FEB-84 1ACXB 1 REMARK
  1ACXB 1
• REVDAT 2 30-SEP-83 1ACXA 1 REVDAT
  1ACXA 1
• REVDAT 1 09-MAR-83 1ACX 0
  1ACXA 2
• REMARK 1
  1ACX 7
• REMARK 1 REFERENCE 1
  1ACX 8
• REMARK 1 AUTH V.Z.PLETNEV,A.P.KUZIN,L.V.MALIN
  INA 1ACX 9
• REMARK 1 TITL ACTINOXANTHIN STRUCTURE AT THE
  ATOMIC LEVEL 1ACXB 2
• REMARK 1 TITL 2 (RUSSIAN)
  1ACX 11
• REMARK 1 REF BIOORG.KHIM.
  V. 8 1637 1982 1ACXB 3
• REMARK 1 REFN ASTM BIKHD7 UR ISSN 0132-3423
  364 1ACXF 2
• REMARK 1 REFERENCE 2
  1ACXB 4

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• ATOM 1 N ALA 1 9.484 -7.014
  7.366 1.00 1.00 1ACX 115
• ATOM 2 CA ALA 1 8.411 -6.863
  6.372 1.00 1.00 1ACX 116
• ATOM 3 C ALA 1 7.066 -6.704
  7.086 1.00 1.00 1ACX 117
• ATOM 4 O ALA 1 6.627 -5.580
  7.375 1.00 1.00 1ACX 118
• ATOM 5 CB ALA 1 8.300 -8.147
  5.531 1.00 1.00 1ACX 119
• ATOM 6 N PRO 2 6.433 -7.831
  7.359 1.00 1.00 1ACX 120
• ATOM 7 CA PRO 2 5.128 -7.820
  8.039 1.00 1.00 1ACX 121
• ATOM 8 C PRO 2 5.252 -7.094
  9.380 1.00 1.00 1ACX 122
• ATOM 9 O PRO 2 6.283 -7.190
  10.062 1.00 1.00 1ACX 123
• ATOM 10 CB PRO 2 4.772 -9.269
  8.170 1.00 1.00 1ACX 124
• ATOM 11 CG PRO 2 6.091 -10.022
  8.162 1.00 1.00 1ACX 125
• ATOM 12 CD PRO 2 7.000 -9.172
  7.275 1.00 1.00 1ACX 126
• ATOM 13 N ALA 3 4.202 -6.375
  9.735 1.00 1.00 1ACX 127
• ATOM 14 CA ALA 3 4.192 -5.622
  10.994 1.00 1.00 1ACX 128
• ATOM 15 C ALA 3 2.765 -5.553
  11.542 1.00 1.00 1ACX 129
• ATOM 16 O ALA 3 1.796 -5.402
  10.781 1.00 1.00 1ACX 130
• ATOM 17 CB ALA 3 4.623 -4.168
  10.734 1.00 1.00 1ACX 131
• ATOM 18 N PHE 4 2.655 -5.665
  12.854 1.00 1.00 1ACX 132
• ATOM 19 CA PHE 4 1.340 -5.621
  13.509 1.00 1.00 1ACX 133

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X-ray crystallography

• Crystallography is very time- and
  resource-consuming.
• For the reason above, we know the structure of
  only about 40,000 proteins (while we know the
  sequence of nearly 4,000,000 of them).
• Crystallography might produce high resolution
  results or not, depending on a number of factors.
  The quality of a structure is expressed by a
  number of indexes, including
• Resolution, in Angstrom (1Å1-10m). Less than
  2.5Å is OK, more is not ideal. (For examples
  sake, the distance between the main Carbon atoms
  of two consecutive amino acids is about 4Å)
• B-factors local measure of quality/uncertainty

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Nuclear Magnetic Resonance (NMR)

• Significantly faster than crystallography, but
• cannot deal with long proteins (although it is
  getting better at that)
• rather than a single structure it produces a
  bundle of potential structures, more or less
  similar
• it doesnt provide an explicit measure of quality
• implicitly, the level of similarity between
  different structures in the bundle provide a
  quality index (structures very similar implies
  good reliability, and vice versa)

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Public tools summary

• DNA/RNA databases GenBANK/EMBL - 3-way
  consortium
• Protein sequences UniProt - SWISS-PROT, TrEMBL,
  etc.
• Protein structures Protein Data Bank (PDB)
• A massive amount of portals, servers, boutique
  databases, etc., some good, some a bit less.
• To sort out the above, a lot of servers
  benchmarking other servers (e.g. EVA, LiveBench,
  etc.)