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Homology Modeling via Protein Threading

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Cannot find new catalytic/binding sites. Brainstorm lack of activity vs activity ... in both the model and the correct structure in an 'alignment dependent' fashion ... – PowerPoint PPT presentation

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Title: Homology Modeling via Protein Threading


1
Homology Modeling via Protein Threading
  • Kristen Huber
  • ECE 697S
  • Topics in Computational Biology
  • April 19, 2006

2
Fundamentals of Protein Threading
  • Protein Modeling
  • Homology Modeling
  • Protein Threading
  • Generalized Overview of a Threading Score
  • Score Methodology based on Multiple Protein
    Structure Alignment

3
Protein Modeling
  • 20,000 entries of proteins in the PDB
  • 1000 - 2000 distinct protein folds in nature
  • Thought to be only several thousand unique folds
    in all
  • Protein Structure Prediction
  • aim of determining the three-dimensional
    structure of proteins from their amino acid
    sequences

4
Types of Structure Prediction
  • De novo protein
  • methods seek to build three-dimensional protein
    models "from scratch"
  • Example Rosetta
  • Comparative protein
  • modeling uses previously solved structures as
    starting points, or templates.
  • Example protein threading

5
Factors that Make Protein Structure Prediction a
Difficult Task
  • The number of possible structures that proteins
    may possess is extremely large, as highlighted by
    the Levinthal paradox
  • The physical basis of protein structural
    stability is not fully understood.
  • The primary sequence may not fully specify the
    tertiary structure.
  • chaperones
  • Direct simulation of protein folding is not
    generally tractable for both practical and
    theoretical reasons.

6
Homology Modeling
  • Homolog a protein related to it by divergent
    evolution from a common ancestor
  • 40 amino-acid identity with its homolog
  • NO large insertions or deletions
  • Produces a predicted structure equivalent to that
    of a medium resolution experimentally solved
    structure
  • 25 of known protein sequences fall in a safe
    area implying they can be modeled reliably

7
Homology Modeling Defined
  • Homology modeling
  • Based on the reasonable assumption that two
    homologous proteins will share very similar
    structures.
  • Given the amino acid sequence of an unknown
    structure and the solved structure of a
    homologous protein, each amino acid in the solved
    structure is mutated computationally, into the
    corresponding amino acid from the unknown
    structure.

8
Homology Modeling Limitations
  • Cannot study conformational changes
  • Cannot find new catalytic/binding sites
  • Brainstorm lack of activity vs activity
  • Chymotrypsionogen, trypsinogen and plasminogen
  • 40 homologous
  • 2 active, 1 no activity, cannot explain why
  • Large Bias towards structure of template
  • Models cannot be docked together

9
Why Homology Modeling?
  • Value in structure based drug design
  • Find common catalytic sites/molecular recognition
    sites
  • Use as a guide to planning and interpreting
    experiments
  • 70-80 chance a protein has a similar fold to
    the target protein due to X-ray crystallography
    or NMR spectroscopy
  • Sometimes its the only option or best guess

10
Protein Threading
  • A target sequence is threaded through the
    backbone structure of a collection of template
    proteins (fold library)
  • Quantitative measure of how well the sequence
    fits the fold
  • Based on assumptions
  • 3-D structures of proteins have characteristics
    that are semi-quantitatively predictable
  • reflect the physical-chemical properties of amino
    acids
  • Limited types of interactions allowed within
    folding

11
Fold Recognition Methods
  • Bowie, Lüthy and Eisenberg (1991)
  • 2 approaches to recognition methods
  • Derive a 1-D profile for each structure in the
    fold library and align the target sequence to
    these profiles
  • Identify amino acids based on core or external
    positions
  • Part of secondary structure
  • Consider the full 3-D structure of the protein
    template
  • Modeled as a set of inter-atomic distances
  • NP-Hard (if include interactions of multiple
    residues)

12
Protein Threading
  • The word threading implies that one drags the
    sequence (ACDEFG...) step by step through each
    location on each template

13
Protein Threading
14
Generalized Threading Score
  • Want to correctly recognize arrangements of
    residues
  • Building a score function
  • potentials of mean force
  • from an optimization calculation.
  • G(rAB) kTln (?AB/ ?AB)
  • G, free energy
  • k and T Boltzmanns constant and temperature
    respectively
  • ? is the observed frequency of AB pairs at
    distance r.
  • ? the frequency of AB pairs at distance r you
    would expect to see by chance.
  • Z-score (ENat - ltEaltgt)/s Ealt
  • Natural energies and mean energies of all the
    wrong structures/ standard deviation

15
Scoring Different Folds
  • Goodness of fit score
  • Based on empirical energy function
  • Modify to take into account pairwise interactions
    and solvation terms
  • High score means good fit
  • Low score means nothing learned

16
Some Threading Programs
  • 3D-pssm (ICNET). Based on sequence profiles,
    solvatation potentials and secondary structure.
  • TOPITS (PredictProtein server) (EMBL). Based on
    coincidence of secondary structure and
    accesibility.
  • UCLA-DOE Structure Prediction Server (UCLA).
    Executes various threading programs and report a
    consensus.
  • 123D Combines substitution matrix, secondary
    structure prediction, and contact capacity
    potentials.
  • SAM/HMM (UCSC). Basen on Markov models of
    alignments of crystalized proteins.
  • FAS (Burnham Institute). Based on profile-profile
    matching algorithms of the query sequence with
    sequences from clustered PDB database.
  • PSIPRED-GenThreader (Brunel)
  • THREADER2 (Warwick). Based on solvatation
    potentials and contacts obtained from crystalized
    proteins.
  • ProFIT CAME (Salzburg)

17
Process of 3D Structure Prediction by Threading
  • Has this protein sequence similarity to other
    with a known structure?
  • Structure related information in the databases
  • Results from threading programs
  • Predicted folding comparison
  • Threading on the structure and mapping of the
    known data
  • A comparison between the threading predicted
    structure and the actual one

18
Protein Threading Based on Multiple Protein
Structure AlignmentTatsuya Akutsu and Kim Lan
SimHuman Genome Center, Institute of Medical
Science, University of Tokyo
  • NP-Hard if include interactions between 2 or more
    AA
  • Determine multiple structural alignments based on
    pair wise structure alignments
  • Center Star Method

19
Center Star Method
  • Let I0 be the maximum number of gap symbols
    placed before the first residue of S0 in any of
    the alignments A(S0 S1) A(S0 SN). Let
    IS0j be the maximum number of gaps placed after
    the last character of S0 in any of the
    alignments, and let Ii be the maximum number of
    gaps placed between character S0i and S0i1,
    where Sji denotes the i-th letter of string Si
  • Create a string S0 by inserting I0 gaps before
    S0, IjSo gaps after S0, and Ij gaps between S0I
    and S0i1.
  • For each Sj (j gt 0), create a pairwise alignment
    A(S0 Sj) between S0 and Sj by inserting gaps
    into Sj so that deletion of the columns
    consisting of gaps from A(S0 Sj) results in the
    same alignment as A(S0 Sj).
  • Simply arrange A(S0 Sj )'s into a single matrix
    A (note that all A(S0 Sj )'s have the same
    length).

20
Simple Threading Algorithm
  • Apply simple score function based on structure
    alignment algorithm
  • Let X x1xN (input amino acid sequence)
  • Ci ( i-th column in A)
  • Test and analyze results and/or apply constraints

21
Protein Threading with Constraints
  • Assume part of the input sequence xixik must
    correspond to part of the structure alignment
    cjcjk
  • Apply constraints

22
Prediction Power
  • Entered in CASP3 competition
  • 17 predictions made
  • 3 targets evaluated as similar to correct folds
  • Only team to create a nearly correct model for
    structure T0043
  • Best in competition
  • 8 evaluated as similar to correct

23
Next time.
  • In depth detail of
  • Multiple structural alignment program
  • Multiprospector
  • Global Optimum Protein Threading with Gapped
    Alignment
  • Quality measures for protein threading models
  • Improvements on threading-based models

24
Gapped Alignment
25
Review
  • Homology Modeling
  • Based on the reasonable assumption that two
    homologous proteins will share very similar
    structures.
  • Threading
  • Modeled as a set of inter-atomic distances
  • NP-Hard (if include interactions of multiple
    residues)
  • Build a score function based on energies in order
    to correctly recognize arrangements of residues
  • Threading via multiple structural alignment
  • Score function based upon alignment matrix

26
Specifics of Protein Threading
  • Different Threading Types
  • Multiprospector Predictions of Protein-Protein
    Interaction by Multimeric Threading
  • Global Optimum Protein Threading with Gapped
    Alignment
  • Quality measures for protein threading models
  • Improvements on threading-based models

27
MULTIPROSPECTOR
  • An algorithm for the prediction of
    protein-protein interactions by multimeric
    threading
  • Proteinprotein interactions are fundamental to
    cellular function and are associated with
    processes such as enzymatic activity,
    immunological recognition, DNA repair and
    replication, and cell signaling.
  • Function can be inferred from the nature of the
    protein with its interactants
  • Use properties related to the topology of the
    interface, solvent-accessible surface area and
    hydrophobicity
  • Addressed limitations of existing approaches

28
Method Basis
  • Thread the sequences through a representative
    structure template library that, in addition to
    monomers, also includes each of the chains in
    representative protein dimer structures.
  • Compute the interaction energy between a pair of
    protein chains for those protein structures
    involved in dimeric complexes.
  • Stable complex formation determined by the
    magnitude of the interfacial potentials and the
    Z-scores of the complex structures relative to
    that of the monomers.

29
Interfacial Statistical Potentials
  • Interfacial pair potentials
  • P(i, j), (i1, , 20 j 1, ,20),
  • Calculated by examining each interface of the
    selected dimers
  • Nobs(i, j) is the observed number of interacting
    pairs of i, j between two chains.
  • Nexp(i, j) is the expected number of interacting
    pairs of i, j Nexp (i, j) Xi Xj Ntotal
  • Apply Boltzman Principal to the ratio to obtain
    potential of mean force between 2 residues

30
Multimeric Threading Strategy and Z-Score
  • Z-score of the score for each probe-template
    alignment is used to decide if a correct fold is
    found
  • is the standard deviation of energies Ei is the
    energy of the i-th sequence of M alternative
    folds (i 1, , M).

31
Multimeric Threading
32
Results
33
Global Optimum Protein Threading with Gapped
Alignment and Empirical Pair Score Functions
  • The structural model corresponds to an annotated
    backbone trace of the secondary structure
    segments in the conserved core fold.
  • Loops are not considered part of the conserved
    fold, and are modeled by an arbitrary
    sequence-specific loop score function.
  • Alignment gaps are confined to the connecting
    non-core loop regions
  • Each distinct threading is assigned a score by an
    assumed score function
  • Exponentially large search space of possible
    threadings
  • NP-hard search spaces as large as 9.6x1031 at
    rates ranging as high as 6.8 x1028 equivalent
    threadings per second

34
Gapped Protein Threading Methodology
  • Common core of four secondary structure segments
  • Spatial interactions. Small circles represent
    amino acid residue positions (core elements), and
    thin lines connect neighbors in the folded core.
  • Thread through model by placing successive
    sequence amino acid residues into adjacent core
    elements. Tax indexes the sequence residue placed
    into the first element of segment X. Sequence
    regions between core segments become connecting
    turns or loops.
  • Sets used in the branch-and-bound search are
    defined by lower and upper limits (dark arrows,
    labeled bax and dax for segment X)

35
General Pairwise Score Function
  • For any threading t, let fv(v, t) be the score
    assigned to core element or vertex v
  • fe(u, v, t) the score assigned to interaction
    or edge u, v
  • f1(?i , t) the score assigned to loop region ?i
  • Then the total score of the threading is
  • Rewrite function of threading pairs of core
    segments

36
Branch-and-Bound Search Algorithm
  • branch-and-bound search requires the ability to
  • represent the entire search space as a set of
    possibilities
  • split any set into subsets
  • compute a lower bound on the best score
    achievable within any subset
  • After some finite number of steps, the chosen set
    will contain only one threading (equals its lower
    bound)

37
Splitting the Search Space
  • The set of all legal threadings is represented by
    the hyper-rectangle
  • lower bound on the score f(t) attainable by any
    threading t in the set T
  • summing lower bounds on each term separately

The enclosing mint?T ensures that the lower bound
will be instantiated on a specific legal
threading tlb?T. This will be used in splitting
T, below. The equation further ensures that the
singleton term, in g1(i, ti ), remains consistent
both with the terms that reflect loop scores, in
g2(i - 1, i, ti-1, ti ), and with the other
(non-loop) pairwise terms, in g2(i, j, ti , uj ).
The inner minu?T allows a different vector u for
each i, but requires u to be a legal threading.
38
Search Space Results
39
Threading Results
40
Quality Measures for Protein Threading Models
  • Evaluation of different prediction methods for
    protein threading
  • Purpose
  • determine if one method to build a model is
    better than another
  • optimize the performance of existing methods.
  • Threading Assessment
  • ability to predict the correct fold
  • the similarity of the model to the correct
    structure

41
Methods of Comparison Defined
  • Global
  • consider all residues in both the model and the
    correct structure in an "alignment dependent
    fashion
  • Alignment Dependent
  • based on an exact match between the residues in
    the model and the correct structure
  • Alignment Independent
  • based on a structural superposition between the
    model and the correct structure
  • Template Based
  • available for models that are created from the
    sequence being aligned onto a single structural
    template.

42
Methods of Comparison
43
Comparison Results
  • Most methods correlate to each other
  • 0.51 model-normalized
  • 0.41 template-normalized
  • High quality homology-models correlate less with
    the rest of the data
  • Measures of same type correlate well and tend to
    cluster

44
A Need for Improvement
  • Resulting models obtained from threading
    approaches are usually of very low quality, with
    gaps and insertions in threading alignments that
    somehow have to be connected or closed
  • Various threading methods and their associated
    scoring functions only focus on aspects of
    protein structure and a subset of their possible
    interactions.

45
Method of Improvement
  • Employs a lattice model
  • SICHO (Side Chain Only)
  • The model has been refined by incorporating
    evolutionary information into the interaction
    scheme.
  • a Monte Carlo annealing procedure attempts to
    find a conformation that maintains some (but not
    all) features of the original template
  • optimizes packing and intra-protein interactions

46
Lattice Model
  • The model chain consists of a string of virtual
    bonds connecting the interaction centers that
    correspond to the center of mass of the side
    chains and the backbone alpha carbons.
  • These interaction centers are projected onto an
    underlying cubic lattice with a lattice spacing
    of 1.45 A
  • A cluster of excluded volume points is associated
    with each bead of the model chain.
  • Each cluster consists of 19 lattice points
  • Closest approach distance from another cluster
    labels smallest inter-residue distance

47
Interaction Scheme
  • Starting Model takes on a tube form
  • Energy potentials.
  • generic, sequence-independent, biases that
    penalize against non protein-like conformations
  • two-body and multibody potentials extracted from
    a statistical analysis of known protein
    structures.
  • Evolutionary information extracted from multiple
    sequence alignments.
  • The stiffness/secondary structure bias term has
    the following form
  • Estiff - ?gen S min0.5, max (0, wi ? wi2)
  • - ?gen S min0.5, max (0, wi ? wi4)

48
Interaction Scheme
  • A weak bias being introduced towards helix-type
    and beta-type expanded states
  • Estruct SdH1(i) d H2(i) d E1(i) d E2(i)
  • d H1 and d H2 contributions defined as a broad
    range of helical/turn conformations
  • d E1 and d E2 as expanded conformations
  • Generic packing interactions
  • Short range interactions
  • Pairwise Interactions
  • Multi-body Interactions
  • statistical potential for residue type A having
    np parallel and na anti-parallel contacts.
  • Emulti SEm(A,np,na)
  • Total energy
  • Etotal Estiff Emap 0.875EH-bond
    0.75Eshort 1.25Epair 0.5Esurface
    0.5Emulti

49
Threading Model Refinement
  • a) Generate the threading alignment between the
    unknown sequence and the template structure.
  • b) Derive the sequence similarity-based short and
    long range pairwise potentials.
  • multiple alignments with homologous sequences of
    unknown structures were used in the potential
    derivation procedures.)
  • c) Build the starting continuous model chain onto
    the lattice-projected template structure.
  • d) Build the tube around the aligned fragments of
    the template structure. Then, perform the first
    stage of Monte Carlo refinement.
  • e) Refinement of the structure
  • assume to be the new template
  • Narrow restraints
  • Select lowest energy structures
  • All atom models using MODELLER.24

50
RESULTS
  • 12 targets/template proteins of low sequence
    similarity
  • 3 models used for tuning
  • 6 of 9 yield lower rmsd than original
  • Effective parameters
  • Neglecting part of threading alignment
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