Protein structure prediction.ppt by pptfiles

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									Protein structure prediction.
                Protein folds.
• Fold definition: two folds are similar if they
  have a similar arrangement of SSEs
  (architecture) and connectivity (topology).
  Sometimes a few SSEs may be missing.

• Fold classification: structural similarity between
  folds is searched using structure-structure
  comparison algorithms.
Protein structure prediction flowchart

                                               Does
                      Database              sequence      No
 Protein                                                           Protein
sequence              similarity           align with a             family
                       search               protein of             analysis
                                              known
                                           structure?
                                    Yes


Predicted three-           Three-dimensional        Yes        Relationship
  dimensional                 comparative                        to known
structural model               modeling                         structure?

                                          Yes                      No

       Three-
                      No           Is there a                  Structural
   dimensional
                                   predicted                    analysis
structural analysis
                                   structure?
   in laboratory
                                                               From D.W.Mount
         Protein structure prediction.

Prediction of three-dimensional structure of a protein from its
  sequence. Different approaches:

- Homology modeling (query protein has a very close homolog
  in the structure database).

- Fold recognition (query protein can be mapped to template
  protein with the existing fold).

- Ab initio prediction (query protein has a new fold).
           Homology modeling.

Aims to produce protein models with accuracy
  close to experimental and is used for:

- Protein structure prediction
- Drug design
- Prediction of functionally important sites (active
  or binding sites)
     Steps of homology modeling.

•   Template recognition & initial alignment.
•   Backbone generation.
•   Loop modeling.
•   Side-chain modeling.
•   Model optimization.
           1. Template recognition.
Recognition of similarity between the target and template.

Target – protein with unknown structure.

Template – protein with known structure.

Main difficulty – deciding which template to pick, multiple
  choices/template structures.

Template structure can be found by searching for structures
  in PDB using pairwise sequence alignment methods.
       Two zones of protein structure prediction.

Sequence identity
100




                         Homology modeling zone
  50




        Fold recognition zone


                    50           100          150   200
                                                      Alignment length
       2. Backbone generation.


If alignment between target and template is ready,
   copy the backbone coordinates of those
   template residues that are aligned.

If two aligned residues are the same, copy their
   side chain coordinates as well.
         3. Insertions and deletions.
                      insertion
                   AHYATPTTT
                   AH---TPSS
                      deletion

Occur mostly between secondary structures, in the loop
  regions. Loop conformations – difficult to predict.

Approaches to loop modeling:
- Knowledge-based: search the PDB for loops with known
  structures
- Energy-based: an energy function is used to evaluate the
  quality of a loop. Energy minimization or Monte Carlo.
               4. Side chain modeling.
     Side chain conformations – rotamers. In similar proteins -
       side chains have similar conformations.

     If % identity is high - side chain conformations can be copied
        from template to target. If % identity is not very high -
        modeling of side chains using libraries of rotamers and
        different rotamers are scored with energy functions.
     Problem: side chain configurations depend on backbone
        conformation which is predicted, not real
      E2
                             E3
                                     E = min(E1, E2, E3)
E1
            5. Model optimization.

Energy optimization of entire structure.

Since conformation of backbone depends on
  conformations of side chains and vice versa -
  iteration approach:

      Predict rotamers             Shift in backbone
    Classwork: Homology modeling.

-   Go to NCBI Entrez, search for gi461699
-   Do Blast search against PDB
-   Repeat the same for gi60494508
-   Compare the results
                 Fold recognition.
Unsolved problem: direct prediction of protein structure from
  the physico-chemical principles.

Solved problem: to recognize, which of known folds are
  similar to the fold of unknown protein.

Fold recognition is based on observations/assumptions:
- The overall number of different protein folds is limited (1000
  -3000 folds)

- The native protein structure is in its ground state (minimum
  energy)
               Fold recognition.
Goal: to find protein with known structure which best
 matches a given sequence.

Since similarity between target and the closest
  template is not high, pairwise sequence alignment
  methods fail.

Solution: threading – sequence-structure alignment
  method.
      Threading – method for structure
                prediction.
Sequence-structure alignment, target sequence is
  compared to all structural templates from the
  database.

Requires:
- Alignment method (dynamic programming, Monte
  Carlo,…)
- Scoring function, which yields relative score for
  each alternative alignment
Protein structure prediction: target sequence is
   compared to structures using sequence-
              structure alignment
                       Structural templates




      Score1                        Score2                  Score3




                         Target sequence


               Concept of threading: D. Jones et al, 1993
Protein structure prediction: target sequence is
   compared to structures using sequence-
              structure alignment
             Structural templates       Score3>Score2>Score1




  Score1
                    Score2          Score3         Structural
                                                 model of target



           Target sequence
Scoring function for threading.

            • Contact-based scoring function
            depends on amino acid types of two
            residues and distance between
            them.
            • Sequence-sequence alignment
            scoring function does not depend on
            the distance between two residues.
            • If distance between two non-
            adjacent residues in the template is
            less than 8 Å, these residues make a
            contact.
    Scoring function for threading.

             Ala                  Trp


                   Ile      Tyr




“w” is calculated from the frequency of amino acid contacts in
PDB; ai – amino acid type of target sequence aligned with the
position “i” of the template; N- number of contacts
     Classwork: calculate the score for target sequence
     “ATPIIGGLPY” aligned to template structure which
             is defined by the contact matrix.
                                                   A      T      P      Y      I      G      L

                                               A   -0.2   -0.1   0      -0.1   0.5    -0.2   0.2

                                               T          0.3    -0.1   -0.2   -0.3   0.1    0

                                               P                 -0.2   -0.4   -0.1   0.1    -0.2
      1   2   3   4   5   6   7   8   9   10

1             *       *       *                Y                        -0.4   -0.2   -0.1   -0.2

2
                                               I                               0.3    0.2    0.4
3     *
                                               G                                      0.4    0.2
4                         *

5     *                                   *    L                                             0.3
6                 *

7     *

8                                         *

9

10                    *           *
 Evaluation of quality of structural model

• Correct bond length and bond angles


              >> 3.8 Angstroms




• Correct placement of functionally important sites

• Prediction of global topology, not partial alignment
  (minimum number of gaps)
    Success and limitations of structure prediction
                                        Limitations:
Success:                                •      Models of large and remotely
•    Accuracy scores almost doubled            related proteins are not very
     from CASP1 to CASP6, might be             accurate
     because of database size           •      Domain boundaries are difficult to
•    Models of small targets are very          define
     accurate                           •      Models often do not provide details
                                               for functional annotation




                                            Adapted from Kryshtafovych et al 2005
                GenThreader
     http://bioinf.cs.ucl.ac.uk/psipred.
•   Predicts secondary structures for target
    sequence.
•   Makes sequence profiles (PSSMs) for
    each template sequence.
•   Uses threading scoring function to find
    the best matching profile.
Protein-protein interactions.
Common properties of protein-protein interactions.
                                                                       rim
•   Majority of protein complexes have a buried
    surface area ~1600±400 Ǻ^2 (“standard size”
    patch).

                                                                       core
•   Complexes of “standard size” do not involve
    large conformational changes while large
    complexes do.                                       Top molecule


•   Protein recognition site consists of a completely
    buried core and a partially accessible rim.

•   Trp and Tyr are abundant in the core, but Ser       Bottom molecule
    and Thr, Lys and Glu are particularly disfavored.
   Different types of protein-protein interactions.

• Permanent and transient.

• External are between different chains; internal are within
  the same chain.

• Homo- and hetero-oligomers depending on the similarity
  between interacting subunits.

• Interface type can be predicted from amino acid
  composition (Ofran and Rost 2003).
Experimental methods
 Verification of experimental protein-protein interactions.


• Protein localization method.

• Expression profile reliability method.

• Paralogous verification method.
                   Protein localization method.

Sprinzak, Sattath, Margalit, J Mol Biol,
  2003

A – A3: Y2H
B: physical methods
C: genetics
E: immunological

True positives:
- Proteins which are localized in the
   same cellular compartment
- Proteins with a common cellular role
Expression profile reliability method.




        Deane, C. M. (2002)   Mol. Cell. Proteomics 1: 349-356
     Paralogous verification method.




PVM method is based on
observation that if two proteins
interact, their paralogs would
interact. Calculates the number
of interactions between two
families of paralogous proteins.




                Deane, C. M. (2002)   Mol. Cell. Proteomics 1: 349-356
                      Interaction databases

• Experiment (E)
• Structure detail (S)
• Predicted
   – Physical (P)
   – Functional (F)
• Curated (C)
• Homology
  modeling (H)
• *IMEx consortium
           Protein interaction databases


• Protein-protein interaction databases



• Domain-domain interaction databases
                         DIP database


• Documents protein-             Organisms    # proteins # interactions
  protein interactions from
                                  Fruit fly     7052        20,988
  experiment
   – Y2H, protein microarrays,    H. pylori      710         1425
     TAP/MS, PDB
                                  Human          916         1407
                                   E. coli      1831         7408
• 55,733 interactions
  between 19,053 proteins        C. elegans     2638         4030
  from 110 organisms.              Yeast        4921        18,225
                                  Others         985          401
                           DIP database


Duan et al., Mol Cell Proteomics, 2002
• Assess quality
    – Via proteins: PVM, EPR
    – Via domains: DPV
• Search by BLAST or
  identifiers / text
                           BIND database
Alfarano et al., Nucleic Acids Res, 2005
• Records experimental
  interaction data
• 83,517 protein-protein
  interactions
• 204,468 total interactions
• Includes small molecules,
  NAs, complexes
                  Classwork.


• Go to DIP webpage (http://dip.doe-
  mbi.ucla.edu)
• Retrieve all interactions for cytochrome C,
  tubulin, RNA-polymerase from yeast
• How many of them are confirmed by
  several experimental methods?
           Protein interaction databases


• Protein-protein interaction databases



• Domain-domain interaction databases
                  InterDom database
                             Ng et al., Nucleic Acids Res, 2003
• Predicts domain
  interactions (~30000)
  from PPIs
• Data sources:
  –   Domain fusions
  –   PPI from DIP
  –   Protein complexes
  –   Literature
• Scores interactions
                    Pibase database

• Records domain interactions from PDB and PQS

• Domains defined with SCOP and CATH

• All inter-domain and inter-chain distances within 6 Ǻ are
  considered interacting domains

• From interacting domain pairs, create list of interfaces
  with buried solvent accessible area > 300 Ǻ2
                  Classwork.


• Go to Pibase website
  http://salilab.org/pibase
• Select largest structural complexes, 1k73,
  1i6h
• Compare two complexes in terms of the
  number of interacting domains,
  #interactions per node
                    NCBI CBM database
 Shoemaker et al., Protein Sci,
 2006.
• CBM – database of interacting structural domains exhibiting
   Conserved Binding Modes


• To retrieve interactions:
   – Record interactions
   – Use VAST structural
     alignments to compare
     binding surfaces
   – Study recurring domain-
     domain interactions
                           Definition of CBM

•   Interacting domain pair – if at least 5
    residue-residue contacts between
    domains (contacts – distance of less
    than 8 Ǻ)

•   Structure-structure alignments
    between all proteins corresponding to
    a given pair of interacting domains

•   Clustering of interface similarity,
    those with >50% equivalently aligned
    positions are clustered together

•   Clusters with more than 2 entries
    define conserved binding mode.
           Number of interacting pairs and binding modes


•   833 conserved interaction types
•   1,798 total domain interaction types
•   Up to 24 CBMs per interaction type



     CBM   Structures   Species
     1     154          Jawed vertebrates
                                            •   Classify complicated domain
     2     112          Jawed vertebrates
                                                pairs by CBMs
     3     17           Clam,earthworm
     4     4            lamprey             •   Globin example:
     5     4            V.stercoraria            – 630 pairs
     6     2            Rice,soybeans
                                                 – 2 CBMs account for majority
     7     2            human
     8     2            lamprey
                Classwork.


• Retrieve structures 1GY3, 1E9H, 1OL2
• Examine all interactions within and
  between chains/domains.
• How many CBMs do you find?

								
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