Bioinformatics and Machine Learning: the Prediction of Protein Structures on a Genomic Scale Pierre Baldi Dept. Information and Computer Science Institute for Genomics and Bioinformatics University of California, Irvine

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Bioinformatics and Machine Learning the
Prediction of Protein Structures on a Genomic
ScalePierre BaldiDept. Information and
Computer ScienceInstitute for Genomics and
BioinformaticsUniversity of California, Irvine
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• tggaagggctaattcactcccaacgaagacaagatatccttgatctgtgg
  atctaccacacacaaggctacttccctgattagcagaactacacaccagg
  gccagggatcagatatccactgacctttggatggtgctacaagctagtac
  cagttgagccagagaagttagaagaagccaacaaaggagagaacaccagc
  ttgttacaccctgtgagcctgcatggaatggatgacccggagagagaagt
  gttagagtggaggtttgacagccgcctagcatttcatcacatggcccgag
  agctgcatccggagtacttcaagaactgctgacatcgagcttgctacaag
  ggactttccgctggggactttccagggaggcgtggcctgggcgggactgg
  ggagtggcgagccctcagatcctgcatataagcagctgctttttgcctgt
  actgggtctctctggttagaccagatctgagcctgggagctctctggcta
  actagggaacccactgcttaagcctcaataaagcttgccttgagtgcttc
  aagtagtgtgtgcccgtctgttgtgtgactctggtaactagagatccctc
  agacccttttagtcagtgtggaaaatctctagcagtggcgcccgaacagg
  gacctgaaagcgaaagggaaaccagaggagctctctcgacgcaggactcg
  gcttgctgaagcgcgcacggcaagaggcgaggggcggcgactggtgagta
  cgccaaaaattttgactagcggaggctagaaggagagagatgggtgcgag
  agcgtcagtattaagcgggggagaattagatcgatgggaaaaaattcggt
  taaggccagggggaaagaaaaaatataaattaaaacatatagtatgggca
  agcagggagctagaacgattcgcagttaatcctggcctgttagaaacatc
  agaaggctgtagacaaatactgggacagctacaaccatcccttcagacag
  gatcagaagaacttagatcattatataatacagtagcaaccctctattgt
  gtgcatcaaaggatagagataaaagacaccaaggaagctttagacaagat
  agaggaagagcaaaacaaaagtaagaaaaaagcacagcaagcagcagctg
  acacaggacacagcaatcaggtcagccaaaattaccctatagtgcagaac
  atccaggggcaaatggtacatcaggccatatcacctagaactttaaatgc
  atgggtaaaagtagtagaagagaaggctttcagcccagaagtgataccca
  tgttttcagcattatcagaaggagccaccccacaagatttaaacaccatg
  ctaaacacagtggggggacatcaagcagccatgcaaatgttaaaagagac
  catcaatgaggaagctgcagaatgggatagagtgcatccagtgcatgcag
  ggcctattgcaccaggccagatgagagaaccaaggggaagtgacatagca
  ggaactactagtacccttcaggaacaaataggatggatgacaaataatcc
  acctatcccagtaggagaaatttataaaagatggataatcctgggattaa
  ataaaatagtaagaatgtatagccctaccagcattctggacataagacaa
  ggaccaaaggaaccctttagagactatgtagaccggttctataaaactct
  aagagccgagcaagcttcacaggaggtaaaaaattggatgacagaaacct
  tgttggtccaaaatgcgaacccagattgtaagactattttaaaagcattg
  ggaccagcggctacactagaagaaatgatgacagcatgtcagggagtagg
  aggacccggccataaggcaagagttttggctgaagcaatgagccaagtaa
  caaattcagctaccataatgatgcagagaggcaattttaggaaccaaaga
  aagattgttaagtgtttcaattgtggcaaagaagggcacacagccagaaa
  ttgcagggcccctaggaaaaagggctgttggaaatgtggaaaggaaggac
  accaaatgaaagattgtactgagagacaggctaattttttagggaagatc
  tggccttcctacaagggaaggccagggaattttcttcagagcagaccaga
  gccaacagccccaccagaagagagcttcaggtctggggtagagacaacaa
  ctccccctcagaagcaggagccgatagacaaggaactgtatcctttaact
  tccctcaggtcactctttggcaacgacccctcgtcacaataaagataggg
  gggcaactaaaggaagctctattagatacaggagcagatgatacagtatt
  agaagaaatgagtttgccaggaagatggaaaccaaaaatgatagggggaa
  ttggaggttttatcaaagtaagacagtatgatcagatactcatagaaatc
  tgtggacataaagctataggtacagtattagtaggacctacacctgtcaa
  cataattggaagaaatctgttgactcagattggttgcactttaaattttc
  ccattagccctattgagactgtaccagtaaaattaaagccaggaatggat
  ggcccaaaagttaaacaatggccattgacagaagaaaaaataaaagcatt
  agtagaaatttgtacagagatggaaaaggaagggaaaatttcaaaaattg
  ggcctgaaaatccatacaatactccagtatttgccataaagaaaaaagac
  agtactaaatggagaaaattagtagatttcagagaacttaataagagaac
  tcaagacttctgggaagttcaattaggaataccacatcccgcagggttaa
  aaaagaaaaaatcagtaacagtactggatgtgggtgatgcatatttttca
  gttcccttagatgaagacttcaggaagtatactgcatttaccatacctag
  tataaacaatgagacaccagggattagatatcagtacaatgtgcttccac
  agggatggaaaggatcaccagcaatattccaaagtagcatgacaaaaatc
  ttagagccttttagaaaacaaaatccagacatagttatctatcaatacat
  ggatgatttgtatgtaggatctgacttagaaatagggcagcatagaacaa
  aaatagaggagctgagacaacatctgttgaggtggggacttaccacacca
  gacaaaaaacatcagaaagaacctccattcctttggatgggttatgaact
  ccatcctgataaatggacagtacagcctatagtgctgccagaaaaagaca
  gctggactgtcaatgacatacagaagttagtggggaaattgaattgggca
  agtcagatttacccagggattaaagtaaggcaattatgtaaactccttag
  aggaaccaaagcactaacagaagtaataccactaacagaagaagcagagc
  tagaactggcagaaaacagagagattctaaaagaaccagtacatggagtg
  tattatgacccatcaaaagacttaatagcagaaatacagaagcaggggca
  aggccaatggacatatcaaatttatcaagagccatttaaaaatctgaaaa
  caggaaaatatgcaagaatgaggggtgcccacactaatgatgtaaaacaa
  ttaacagaggcagtgcaaaaaataaccacagaaagcatagtaatatgggg
  aaagactcctaaatttaaactgcccatacaaaaggaaacatgggaaacat
  ggtggacagagtattggcaagccacctggattcctgagtgggagtttgtt
  aatacccctcccttagtgaaattatggtaccagttagagaaagaacccat
  agtaggagcagaaaccttctatgtagatggggcagctaacagggagacta
  aattaggaaaagcaggatatgttactaatagaggaagacaaaaagttgtc
  accctaactgacacaacaaatcagaagactgagttacaagcaatttatct
  agctttgcaggattcgggattagaagtaaacatagtaacagactcacaat
  atgcattaggaatcattcaagcacaaccagatcaaagtgaatcagagtta
  gtcaatcaaataatagagcagttaataaaaaaggaaaaggtctatctggc
  atgggtaccagcacacaaaggaattggaggaaatgaacaagtagataaat
  tagtcagtgctggaatcaggaaagtactatttttagatggaatagataag
  gcccaagatgaacatgagaaatatcacagtaattggagagcaatggctag
  tgattttaacctgccacctgtagtagcaaaagaaatagtagccagctgtg
  ataaatgtcagctaaaaggagaagccatgcatggacaagtagactgtagt
  ccaggaatatggcaactagattgtacacatttagaaggaaaagttatcct
  ggtagcagttcatgtagccagtggatatatagaagcagaagttattccag
  cagaaacagggcaggaaacagcatattttcttttaaaattagcaggaaga
  tggccagtaaaaacaatacatactgacaatggcagcaatttcaccggtgc
  tacggttagggccgcctgttggtgggcgggaatcaagcaggaatttggaa
  ttccctacaatccccaaagtcaaggagtagtagaatctatgaataaagaa
  ttaaagaaaattataggacaggtaagagatcaggctgaacatcttaagac
  agcagtacaaatggcagtattcatccacaattttaaaagaaaagggggga
  ttggggggtacagtgcaggggaaagaatagtagacataatagcaacagac
  atacaaactaaagaattacaaaaacaaattacaaaaattcaaaattttcg
  ggtttattacagggacagcagaaatccactttggaaaggaccagcaaagc
  tcctctggaaaggtgaaggggcagtagtaatacaagataatagtgacata
  aaagtagtgccaagaagaaaagcaaagatcattagggattatggaaaaca
  gatggcaggtgatgattgtgtggcaagtagacaggatgaggattagaaca
  tggaaaagtttagtaaaacaccatatgtatgtttcagggaaagctagggg
  atggttttatagacatcactatgaaagccctcatccaagaataagttcag
  aagtacacatcccactaggggatgctagattggtaataacaacatattgg
  ggtctgcatacaggagaaagagactggcatttgggtcagggagtctccat
  agaatggaggaaaaagagatatagcacacaagtagaccctgaactagcag
  accaactaattcatctgtattactttgactgtttttcagactctgctata
  agaaaggccttattaggacacatagttagccctaggtgtgaatatcaagc
  aggacataacaaggtaggatctctacaatacttggcactagcagcattaa
  taacaccaaaaaagataaagccacctttgcctagtgttacgaaactgaca
  gaggatagatggaacaagccccagaagaccaagggccacagagggagcca
  cacaatgaatggacactagagcttttagaggagcttaagaatgaagctgt
  tagacattttcctaggatttggctccatggcttagggcaacatatctatg
  aaacttatggggatacttgggcaggagtggaagccataataagaattctg
  caacaactgctgtttatccattttcagaattgggtgtcgacatagcagaa
  taggcgttactcgacagaggagagcaagaaatggagccagtagatcctag
  actagagccctggaagcatccaggaagtcagcctaaaactgcttgtacca
  attgctattgtaaaaagtgttgctttcattgccaagtttgtttcataaca
  aaagccttaggcatctcctatggcaggaagaagcggagacagcgacgaag
  agctcatcagaacagtcagactcatcaagcttctctatcaaagcagtaag
  tagtacatgtaacgcaacctataccaatagtagcaatagtagcattagta
  gtagcaataataatagcaatagttgtgtggtccatagtaatcatagaata
  taggaaaatattaagacaaagaaaaatagacaggttaattgatagactaa
  tagaaagagcagaagacagtggcaatgagagtgaaggagaaatatcagca
  cttgtggagatgggggtggagatggggcaccatgctccttgggatgttga
  tgatctgtagtgctacagaaaaattgtgggtcacagtctattatggggta
  cctgtgtggaaggaagcaaccaccactctattttgtgcatcagatgctaa
  agcatatgatacagaggtacataatgtttgggccacacatgcctgtgtac
  ccacagaccccaacccacaagaagtagtattggtaaatgtgacagaaaat
  tttaacatgtggaaaaatgacatggtagaacagatgcatgaggatataat
  cagtttatgggatcaaagcctaaagccatgtgtaaaattaaccccactct
  gtgttagtttaaagtgcactgatttgaagaatgatactaataccaatagt
  agtagcgggagaatgataatggagaaaggagagataaaaaactgctcttt
  caatatcagcacaagcataagaggtaaggtgcagaaagaatatgcatttt
  tttataaacttgatataataccaatagataatgatactaccagctataag
  ttgacaagttgtaacacctcagtcattacacaggcctgtccaaaggtatc
  ctttgagccaattcccatacattattgtgccccggctggttttgcgattc
  taaaatgtaataataagacgttcaatggaacaggaccatgtacaaatgtc
  agcacagtacaatgtacacatggaattaggccagtagtatcaactcaact
  gctgttaaatggcagtctagcagaagaagaggtagtaattagatctgtca
  atttcacggacaatgctaaaaccataatagtacagctgaacacatctgta
  gaaattaattgtacaagacccaacaacaatacaagaaaaagaatccgtat
  ccagagaggaccagggagagcatttgttacaataggaaaaataggaaata
  tgagacaagcacattgtaacattagtagagcaaaatggaataacacttta
  aaacagatagctagcaaattaagagaacaatttggaaataataaaacaat
  aatctttaagcaatcctcaggaggggacccagaaattgtaacgcacagtt
  ttaattgtggaggggaatttttctactgtaattcaacacaactgtttaat
  agtacttggtttaatagtacttggagtactgaagggtcaaataacactga
  aggaagtgacacaatcaccctcccatgcagaataaaacaaattataaaca
  tgtggcagaaagtaggaaaagcaatgtatgcccctcccatcagtggacaa
  attagatgttcatcaaatattacagggctgctattaacaagagatggtgg
  taatagcaacaatgagtccgagatcttcagacctggaggaggagatatga
  gggacaattggagaagtgaattatataaatataaagtagtaaaaattgaa
  ccattaggagtagcacccaccaaggcaaagagaagagtggtgcagagaga
  aaaaagagcagtgggaataggagctttgttccttgggttcttgggagcag
  caggaagcactatgggcgcagcctcaatgacgctgacggtacaggccaga
  caattattgtctggtatagtgcagcagcagaacaatttgctgagggctat
  tgaggcgcaacagcatctgttgcaactcacagtctggggcatcaagcagc
  tccaggcaagaatcctggctgtggaaagatacctaaaggatcaacagctc
  ctggggatttggggttgctctggaaaactcatttgcaccactgctgtgcc
  ttggaatgctagttggagtaataaatctctggaacagatttggaatcaca
  cgacctggatggagtgggacagagaaattaacaattacacaagcttaata
  cactccttaattgaagaatcgcaaaaccagcaagaaaagaatgaacaaga
  attattggaattagataaatgggcaagtttgtggaattggtttaacataa
  caaattggctgtggtatataaaattattcataatgatagtaggaggcttg
  gtaggtttaagaatagtttttgctgtactttctatagtgaatagagttag
  gcagggatattcaccattatcgtttcagacccacctcccaaccccgaggg
  gacccgacaggcccgaaggaatagaagaagaaggtggagagagagacaga
  gacagatccattcgattagtgaacggatccttggcacttatctgggacga
  tctgcggagcctgtgcctcttcagctaccaccgcttgagagacttactct
  tgattgtaacgaggattgtggaacttctgggacgcagggggtgggaagcc
  ctcaaatattggtggaatctcctacagtattggagtcaggaactaaagaa
  tagtgctgttagcttgctcaatgccacagccatagcagtagctgagggga
  cagatagggttatagaagtagtacaaggagcttgtagagctattcgccac
  atacctagaagaataagacagggcttggaaaggattttgctataagatgg
  gtggcaagtggtcaaaaagtagtgtgattggatggcctactgtaagggaa
  agaatgagacgagctgagccagcagcagatagggtgggagcagcatctcg
  agacctggaaaaacatggagcaatcacaagtagcaatacagcagctacca
  atgctgcttgtgcctggctagaagcacaagaggaggaggaggtgggtttt
  ccagtcacacctcaggtacctttaagaccaatgacttacaaggcagctgt
  agatcttagccactttttaaaagaaaaggggggactggaagggctaattc
  actcccaaagaagacaagatatccttgatctgtggatctaccacacacaa
  ggctacttccctgattagcagaactacacaccagggccaggggtcagata
  tccactgacctttggatggtgctacaagctagtaccagttgagccagata
  agatagaagaggccaataaaggagagaacaccagcttgttacaccctgtg
  agcctgcatgggatggatgacccggagagagaagtgttagagtggaggtt
  tgacagccgcctagcatttcatcacgtggcccgagagctgcatccggagt
  acttcaagaactgctgacatcgagcttgctacaagggactttccgctggg
  gactttccagggaggcgtggcctgggcgggactggggagtggcgagccct
  cagatcctgcatataagcagctgctttttgcctgtactgggtctctctgg
  ttagaccagatctgagcctgggagctctctggctaactagggaacccact
  gcttaagcctcaataaagcttgccttgagtgcttcaagtagtgtgtgccc
  gtctgttgtgtgactctggtaactagagatccctcagacccttttagtca
  gtgtggaaaatctctagca

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SCALES
organisms genome genes
virus 10-100,000 10
bacteria 5 Mb 3,000
single cell 15Mb 6,000
simple animal 100Mb 15,000
man 3,000Mb 30-40,000

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Examples of Computational Problems

• Physical and Genetic Maps
• Genome assembly
• Pairwise and Multiple Alignments
• Motif Detection/Discrimination/Classification
• Data Base Searches and Mining
• Phylogenetic Tree Reconstruction
• Gene Finding and Gene Parsing
• Protein Secondary Structure Prediction
• Protein Tertiary Structure Prediction
• Protein Function Prediction
• Comparative genomics
• DNA microarray analysis
• Gene regulation/regulatory networks

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Machine Learning

• Extract information from the data automatically
  (inference) via a process of model fitting
  (learning from examples).
• Model Selection Neural Networks, Hidden Markov
  Models, Stochastic Grammars, Bayesian Networks,
  Graphical Models, Kernel Methods
• Model Fitting Gradient Methods, Monte Carlo
  Methods,
• Machine learning approaches are most useful in
  areas where there is a lot of data but little
  theory.

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Three Key Factors for Expansion

• Data Mining/Machine Learning Expansion is fueled
  by
• Progress in sensors, data storage, and data
  management.
• Computing power.
• Theoretical framework.

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Utility of Structural Information
(Baker and Sali, 2001)
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CAVEAT
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REMARKS

• Structure/Folding
• Backbone/Full Atom
• Homology Modeling
• Protein Threading
• Ab Initio (Physical Potentials, Statistical
  Mechanics/Lattice Models)
• Lego Approach
• Statistical/Machine Learning (Training Sets, SS
  prediction)
• Mixtures ab-initio with statistical potentials,
  machine learning with profiles, etc.

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ß-sheet
ß-ladders
ß-strands
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PROTEIN STRUCTURE PREDICTION
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Intestinal Fatty Acid Binding Protein (beta
barrel)
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Helices

• 1GRJ (Grea Transcript Cleavage Factor From
  Escherichia Coli)

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Antiparallel ß-sheets

• 1MSC (Bacteriophage Ms2 Unassembled Coat Protein
  Dimer)

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Parallel ß-sheets

• 1FUE (Flavodoxin)

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Contact map
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Secondary structure prediction
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SPARSE ENCODING

• PROTEIN SEQUENCE MAPVC

 
 
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GRAPHICAL MODELS

• Bayesian statistics and modeling leads to very
  high-dimensional distributions P(D,H,M) which are
  typically intractable.
• Need for factorization into independent clusters
  of variables that reflect the local (Markovian)
  dependencies of the world and the data.
• Hence the general theory of graphical models.
• Directed models reflect temporal and causality
  relationships NNs, HMMs, Bayesian networks, etc.
• Directed models are used for instance in expert
  systems.
• Undirected models reflect correlations Random
  Markov Fields, Boltzmann machines, etc.)
• Undirected models are used for instance in image
  modeling problems.
• Directed/Undirected and other models are
  possible.

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GLOBAL FACTORIZATIONFOR BAYESIAN NETWORKS

• X1, ,Xn random variables associated with the
  vertices of a DAG Directed Acyclic Graph
• The local conditional distributions P(XiXj j
  parent of i) are the parameters of the model.
  They can be represented by look-up tables
  (costly) or other more compact parameterizations
  (Sigmoidal Belief Networks, XOR, etc).
• The global distribution is the product of the
  local characteristicsP(X1,,Xn) ?i P(XiXj
  j parent of i)

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DATA PREPARATION

•  
• Starting point PDB data base.
•        Remove sequences not determined by X ray
  diffraction.
•        Remove sequences where DSSP crashes.
•        Remove proteins with physical chain
  breaks (neighboring AA having
  distances exceeding 4 Angstroms)
•        Remove sequences with resolution worst
  than 2.5 Angstroms.
•        Remove chains with less than 30 AA.
•        Remove redundancy (Hobohms algorithm,
  Smith-Waterman, PAM 120, etc.)
• Build multiple alignments (BLAST,
  PSI-BLAST, etc.)

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SECONDARY STRUCTURE PROGRAMS

• DSSP (Kabsch and Sander, 1983) works by
  assigning potential backbone hydrogen bonds
  (based on the 3D coordinates of the backbone
  atoms) and subsequently by identifying repetitive
  bonding patterns.
•   STRIDE (Frishman and Argos, 1995) in addition
  to hydrogen bonds, it uses also dihedral angles.
•   DEFINE (Richards and Kundrot, 1988) uses
  difference distance matrices for evaluating the
  match of interatomic distances in the protein to
  those from idealized SS.

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SECONDARY STRUCTURE ASSIGNMENTS

• DSSP classes 
• H alpha helix
• E sheet
• G 3-10 helix
• S kind of turn
• T beta turn
• B beta bridge
• I pi-helix (very rare)
• C the rest
• CASP (harder) assignment 
• a H and G
• ß E and B
• ? the rest
• Alternative assignment 
• a H
• ß B
• ? the rest

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ENSEMBLES
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FUNDAMENTAL LIMITATIONS

• 100 CORRECT RECOGNITION IS PROBABLY IMPOSSIBLE
  FOR SEVERAL REASONS
• SOME PROTEINS DO NOT FOLD SPONTANEOUSLY OR MAY
  NEED CHAPERONES
• QUATERNARY STRUCTURE BETA-STRAND PARTNERS MAY BE
  ON A DIFFERENT CHAIN
• STRUCTURE MAY DEPEND ON OTHER VARIABLES
  ENVIRONMENT, PH
• DYNAMICAL ASPECTS
• FUZZINESS OF DEFINITIONS AND ERRORS IN DATABASES

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BB-RNNs
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2D RNNs
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2D INPUTS

• AA at positions i and j
• Profiles at positions i and j
• Correlated profiles at positions i and j
• Secondary Structure, Accessibility, etc.

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PERFORMANCE ()
6Å 8Å 10Å 12Å
non-contacts 99.9 99.8 99.2 98.9
contacts 71.2 65.3 52.2 46.6
all 98.5 97.1 93.2 88.5
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COARSE MAPS
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COARSE ANGLE PREDICTION
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1b0ya (6Å coarse)
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STRUCTURAL PROTEOMICSSUITE

• SSpro secondary structure
• SSpro8 secondary structure
• ACCpro accessibility
• CONpro contact number
• DI-pro disulphide bridges
• BETA-pro beta partners
• CMAP-pro contact map
• CCMAP-pro coarse contact map
• CON23D-pro contact map to 3D
• 3D-pro 3D structure

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• SISQQTVWNQMATVRTPLNFDSSKQSFCQFSVDLLGGGISVDKTGDWITL
  VQNSPISNLL
• CCCECCCCCCEEEECCCCCCCCCCCCEEEEEEECCCCEEEECCCCCCEEE
  EECCHHHHHH
• CCCEEEEECEEEEECCCCCCCTCCCCEEEEEEEETCSEEEECTTTTEEEE
  EECCHHHHHH
• -----------------------------------
  ----
• --------------------------
  
• -------------------------------
  
• --------------------------
  
• eeeeee---e--e-e-eee-ee-eee---------e-e--eeeeee----
  ----------
• RVAAWKKGCLMVKVVMSGNAAVKRSDWASLVQVFLTNSNSTEHFDACRWT
  KSEPHSWELI
• HHHHHHCCCEEEEEEEEEECCEEECCCCCEEEEEEEECCCCCCCCCEEEE
  EECCCCCCCC
• HHHHHHTTCEEEEEEEEEEEEEEECCCCCEEEEEEEECCCTTCCCEEEEE
  EECCTCCEEE
• -----------------------
  ----------
• --------------------
  ----
• -----------------
  ----
• ------------------
  ----
• -----ee---e-------e-e-ee-e-e-e-----e--eeee--e-----
  --e-e-ee-e

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Advantage of Machine Learning

• Machine learning systems take time to train
  (weeks).
• Once trained however they can predict structures
  almost faster than proteins can fold.
• Predict or search protein structures on a genomic
  or bioengineering scale .

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INSIGHTS INTO NEURAL SYSTEMS

• Architectures with 6 layers
• Propagation of output results from the center to
  the periphery
• Role of lateral computations

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DAG-RNNs APPROACH

• Two steps
• 1. Build relevant DAG to connect inputs, outputs,
  and hidden variables
• 2. Use a deterministic (neural network)
  parameterization together with appropriate
  stationarity assumptions/weight sharing
• Process structured data of variable size,
  topology, and dimensions efficiently
• Sequences, trees, d-lattices, graphs, etc
• Other applications

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Structural Databases

• PPDB Poxvirus Proteomic Database
• ICBS Inter Chain Beta Sheet Database

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Poxvirus Family

• Great medical relevance
• Reasonable size of the genomes
• Potential threat as bioterrorism weapons
• Need for poxvirus vaccines and drugs

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PSPDB

• Poxvirus Structural Proteomic DataBase
• Vaccinia/Variola
• 260 genes or so per family member

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Smallpox Background

• Smallpox virus variola major and variola minor
  viruses
• Smallpox has caused more deaths in human history
  than any other known pathogen
• Case-fatality rate of 30
• Higher fatality rate for virgin soil
  populations
• Americas and Hawaii
• Biological Weapon
• Used by British soldiers in French and Indian
  Wars and American Revolution
• Smallpox vaccine using vaccinia virus
• Widely used beginning in 19th century
• Smallpox eradicated in 1980
• Other poxviruses can evolve into deadly human
  pathogens
• Monkeypox
• Potential manipulation by humans
• World population is becoming virgin soil

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Screenshot Details B13R
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ß-sheet
ß-ladders
ß-strands
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Interchain ß-sheet
Interchain ß-ladders
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1DFN Defensin
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Why a database of ICBS interactions?

• Role at multiple structural levels
• Dimerization, oligomerization
• Protein-protein interaction
• Protein and peptide aggregation
• Protein-nucleic acid interaction
• Multiple functions / pathways
• Involved in diseases
• Artificial ß-sheet mimicks exist (Nowick)
• There existed no systematic method to identify
  and characterize them within (or between) known
  structures.

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Source PDB structures
1a72
Protein Data Bank
PDB Asymmetric Crystallographic Unit
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Sources PDB and PQS structures
1a72
Protein Data Bank
Protein Quaternary Structure server

• Copy

• Translate and rotate copies

• Assess (crystal packing vs. biological molecule)

PDB Asymmetric unit
PQS 1 or more likely biological macromolecule
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Strength of ICBS interactions and ICBS index
Image provided by James Nowick
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Ranking ICBS interactions the ICBS index
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Update and Query of the ICBS Database
PUBLIC DATABASES
PDB
PQS
UPDATE OF THE ICBS DATABASE
Get PDB PQS structures
Determine secondary structure (DSSP)
Find inter-chain beta-ladders
-Count H-bonds in ICBS -Count heavy atom contacts
-Compute ICBS index -Extract miscellaneous data
Update the database
ICBS USER INTERFACE
ICBS database / Web server
Query form
Results pages
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Prevalence of ICBS interactions(August 11, 2002)

• 4,869 ICBS entries for 22,513 structures scanned
• 2,536 PDB entries (14,6) with ß-sheet
  interactions (ladders of length gt 1), over 17,313
  entries with peptide or protein chains
• identical chains 59.9 different 40.1
• antiparallel interchain ladders 68.3 parallel
  8.5 mixed 23.2

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Conclusion

• The database is used to
• Gain insight on ICBS interactions
• Find potential drug targets
• Guide the development of artificial molecules
• Extensions
• Contents
• Protein-protein interactions (between structures)
• Annotation
• Sequence analysis
• Curated, non-redundant set
• Statistical characterization (interface
  context)
• Interchain vs. intrachain comparison
• Prediction
• Issues specificity, binding strength

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PROTEIN DOCKING

• Problem of protein flexibility

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• Inclusion of B-factor (a measure of positional
  uncertainty and flexibility) into energy
  calculations
• B-factor incorporation into structure
  Prediction/Assessment
• Reverse drug design search an active site
  database using a small molecule as a query
• Automated active site detection
• New comparison algorithm
• ADME/Toxicity Prediction?

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ACKNOWLEDGMENTS

• UCI
• Gianluca Pollastri, Steve Hampson
• Arlo Randall, Pierre-Francois Baisnee, S. Josh
  Swamidass, Jianlin Chen, Yimeng Dou, Yann
  Pecout, Alessandro Vullo, Lin Wu
• DTU Soren Brunak
• Columbia Burkhard Rost
• U of Florence Paolo Frasconi
• U of Bologna Rita Casadio, Piero Fariselli
• www.igb.uci.edu/tools.htm
• www.ics.uci.edu/pfbaldi