The Domain Structure of Proteins: Prediction and Organization.

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The Domain Structure of Proteins Prediction and
Organization.

• Golan Yona
• Dept. of Computer Science
• Cornell University
• (joint work with Niranjan Nagarajan)

Golan Yona, Cornell University
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PDB 1a8y 367aa long MKIIRIETSRIAVPLTKPFKTALRTVYTA
ESVIVRITYDSGAVGWGEAPPTLVITGDSM
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The domain structure of a protein

• A domain is considered the fundamental unit of
  protein structure, folding, function, evolution
  and design.
• Compact
• Stable
• Folds independently?
• Has a specific function

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A protein is a combination of domains
Protein1 Protein2 Protein3
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Any signals that might indicate domain boundaries?

• A very weak signal if any in the sequence
• Usually domain delineation is done based on
  structure
• Best methods available manual!
• But structural information is sparse..

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Definitions and assumptions

• Domain continuous sequence that corresponds to
  an elemental building block of protein folds.
• A subsequence that is likely to be stable as an
  independent folding unit.
• Was formed as an independent unit, and later was
  combined with others more complex functions.
• There are traces of the autonomous units..

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First step..

• Gather data database search
• Histogram of matches is informative but noisy
• Mutations, insertions, deletions, conflicting
  evidence

sequence
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Previous methods

• Methods based on the use of similarity searches
  and knowledge of sequence termini to delineate
  domain boundaries using heuristics/rules (MKDOM,
  Domainer, DIVCLUS, DOMO).
• Methods that rely on expert knowledge of protein
  families to construct models like HMMs to
  identify other members of the family (Pfam,
  TigrFam, SMART).
• Methods that try to infer domain boundaries by
  using sequence information to predict tertiary
  structure first (SnapDragon. Rigdens covariance
  analysis)
• Methods that use multiple alignments to predict
  domain boundaries (PASS, Domination).
• Others..(e.g. CSA and DGS guess based on size)

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How do you evaluate the different methods?

• No universal measures
• A variety of qualitative and quantitative
  evaluation criteria, external resources and
  manual analysis are used to verify domain
  boundaries

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Method outline

• Source/test data SCOP
• Processed data - alignments
• Learning system
• Domain-information-content scores
• NN
• Probabilistic model
• Evaluation
• A Multi-Expert System for the Automatic
  Detection of Protein Domains from Sequence
  Information Niranjan Nagaragan and Golan Yona,
  in the proceedings of RECOMB2003

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Overview
Intron Boundaries
DNA DATA
Seed Sequence
blast search
Sequence Participation
Multiple Alignment
Secondary Structure
Entropy
Neural Network
Correlation
Contact Profile
Physio-Chemical Properties
Final Predictions
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The source/test data set

• PDB structures with their partitions into domains
  as defined in SCOP
• 1ctf domain1 1-76 domain2 77-123
• Remove sequences shorter than 40 aa and almost
  identical entries

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Alignments

• Search each query against a database of 1
  million non-redundant sequences
• Remove fragments first
• Two phase alignment procedure
• First phase blast
• Second phase multiple iteration psi-blast
• Select one representative from each group of
  similar proteins
• Remove proteins that are less than 90 covered
  (missing information)
• Number of domains ranging from 1-7
• Final set 605 multi-domain proteins and 576
  single domain proteins (1/4)

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The domain-information-content of an alignment
column

• Measures that (are believed) to reflect
  structural properties of proteins
• A total of 20 measures
• Conservation measures
• Consistency and correlation measures
• Measures of structural flexibility
• Residue type based measures
• Predicted secondary structure information
• Intron-exon data

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Conservation measures

• Entropy some positions are more conserved than
  others
• Class entropy some positions have preference
  towards a class of amino-acids (similar
  physio-chemical properties)
• Evolutionary pressure (span) sum of pairwise
  similarities
• Motivation consider the mutual similarity of
  amino acids

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Consistency and correlation measures

• All domain appearances should maintain its
  integrity
• Consistency difference in sequence counts
• Asymmetric correlation consistency of individual
  sequences.
• Symmetric correlation reinforcement by missing
  sequences
• Measures are averaged over a window

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Consistency and correlation measures cont.

• Sequence termination strong but elusive
• Fragments
• Premature halt in alignment
• Loosely aligned
• Product of left and right termination scores
  given c sequences that terminate at a position,
  with evalues e1,e2,e3,ec

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Measures of structural flexibility

• Indel entropy variability indicates structural
  flexibility (likely to occur near domain
  boundaries)
• Correlated mutations indicative of contacts
  Contact profiles

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Contact profile
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Residue type based measures

• hydrophobic vs. hydrophilic
• cystines and prolines
• Classes of amino acids

Predicted secondary structures

• Helices and strands are rigid
• Loops are more abundant near domain boundaries

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Intron-exon data

• Exon boundaries are expected to coincide with
  domain boundaries

1
2
Protein1 Protein2 Protein3
1
2
1
3
3
2
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Score refinement and normalization

• Smoothing using a window w (optimized)
• Unification to a single scale zscore over all
  positions

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Maximizing the information content of scores

• Opt for the most distinct distributions of domain
  positions vs. boundary positions
• Affected by the parameters (w smoothing factor)
  and x (boundary window size)
• Use the Jensen-Shannon divergence measure

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Examples
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• Even measures with identical distributions may be
  informative in a mutli-variate model
• To simplify model only the top 12 are selected

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The learning system

• A neural network is trained to model effectively
  the complex decision boundary surface
• Predicts correctly 94 of domain positions and
  88 of the transitions in the test set
• Also tried mapping from multiple positions (local
  input neighborhood) to single/multiple output

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Overview
Intron Boundaries
DNA DATA
Seed Sequence
blast search
Sequence Participation
Multiple Alignment
Secondary Structure
Entropy
Neural Network
Correlation
Contact Profile
Physio-Chemical Properties
Final Predictions
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Hypothesis evaluation

• Simple model refine predictions
• Significant fraction of the positions in a window
  centered at x should be predicted as transitions
• Order transitions by their quality (depth of the
  minima) and reject all transitions that are
  within 30 residues from already predicted
  transitions

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The domain generator model

• Multiple hypotheses find the best one
• Assume a model random generator that moves
  repeatedly between a domain state and a linker
  state and emits one domain or transition at a
  time according to different source probability
  distributions.
• Total probability is the product

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Formally..

• S D1 D2 Dn
• We are given a sequence S (multiple alignment) of
  length L and a possible partition into n domains
  DD1,D2,..Dn of lengths l1,l2,..,ln (NN output)
• Find the partition that will maximize the
  posterior probability P(D/S)
• Maximize the product of the likelihood and the
  prior

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Calculating the prior P(D)

• For an arbitrary protein of length L what is the
  probability to observe D
• Approximate using a simplified model given the
  length of the protein, the generator selects the
  number of domains first and then selects the
  length of one domain at a time, considering the
  domains that were already generated.

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The prior probabilities

• Approximate P0(li/L) by P0(li) normalized to the
  relevant range.
• P0(li/L) is derived based on experimental data

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The prior probabilities (cont.)

• Calculate Prob(n/L) Prob(n,L)/P(L)
• 1
• 2

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The likelihood

• Use probabilities of observed scores considering
  the two different sources
• The model D partitions the sequence S into n
  domains and n-1 transitions D1,T1,D2,T2,,Tn-1,Dn
  that correspond to the subsequences
  s1,t1,s2,t2,..,tn-1,sn
• Assume domains are independent of each other
  (additional test can be used)

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likelihood

• Each term P(si/Di) and P(tj/Tj) is a product over
  the probabilities of the individual positions,
  each one is estimated by the joint probability
  distribution of the 12 features
• How to estimate this probability? (independence
  assumption does not hold)

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Likelihood of individual position

• Given k random variables X1,X2,..,Xk their joint
  prob. Distribution
• Use first order dependencies
• For each pair, calculate the distance between the
  joint prob. Distribution and the product of the
  marginal distributions

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• Sort all pairs based on their dependency, and
  pick the most dependent one (denoted by Y1, Y2)
  and start the expansion
• Select the next one based on the strongest
  dependency with variables that are already in the
  expansion

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• Denote by ZPILLAR(Y) the random variable that Y
  is most dependent on
• Of all possible dependencies involving Y3 pick
  P(Y3/Z) and add it to the expansion
• Proceed until you exhaust all variables
• Maximize support, minimize error
• The expansion is different for domain and
  transition regions

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Finally..

• Enumerate all possible hypotheses, calculate the
  posterior probability for each one, and output
  the one that maximizes the prob.

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Summary of results

• Distance accuracy average distance of the
  predicted transitions from their associated SCOP
  transition points.
• Distance sensitivity average distance of SCOP
  transitions from their associated predicted
  transition points.
• Selectivity percentage of correct predictions
  (within 10 residues from SCOP transitions)
• Coverage percentage of correctly identified SCOP
  transitions (within 10 residues from predicted
  transitions)

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Examples

• PDB ID 2gep
• Domain Definition 8-72, 73-272, 273-352,
  353-497
• Predicted Domains 1-75, 76-270, 271-352,
  353-497
• PFam Definition 1-67, 273-345, 356-425

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Examples

• PDB ID 1b6s chain D
• Domain Definition 1-78, 79-276, 277-355
• Predicted Domains 1-73, 74-271, 272-355
• PFam Definition 30-167

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Examples

• PDB ID 1acc
• Domain Definition 14-735
• Predicted Domains 1-158, 159-583, 584-735
• PFam Definition 103-544

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Conclusions

• A method for predicting the domain structure of a
  protein from sequence information alone
• Protein/DNA data, multiple features, optimization
  based on information theory principles, learning
  system and final prediction using the
  domain-generator model (with confidence values).
• Exhaustive hypothesis evaluation
• Fully automatic and fast
• Perform very well even compared to the best
  manual and semi-manual methods out there (also on
  CATH data)
• Dare to say can be used to verify domain
  assignments based on structural data
• Improvements other learning systems, more
  features

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Acknowledgments

• Niranjan Nagarajan
• SCOP
• CATH
• PSI-BLAST
• Pfam
• InterPro
• NSF