Protein structure prediction.

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Protein structure prediction.
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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.

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Protein structure prediction flowchart
Does sequence align with a protein of known
structure?
No
Database similarity search
Protein sequence
Protein family analysis
Yes
Relationship to known structure?
Yes
Three-dimensional comparative modeling
Predicted three-dimensional structural model
No
Yes
Three-dimensional structural analysis in
laboratory
Is there a predicted structure?
Structural analysis
No
From D.W.Mount
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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).

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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)

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Steps of homology modeling.

1. Template recognition initial alignment.
2. Backbone generation.
3. Loop modeling.
4. Side-chain modeling.
5. Model optimization.

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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.

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Two zones of protein structure prediction.
Sequence identity
100
Homology modeling zone
50
Fold recognition zone
50
100
150
200
Alignment length
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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.

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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.

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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
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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
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Classwork Homology modeling.

• Go to NCBI Entrez, search for gi461699
• Do Blast search against PDB
• Repeat the same for gi60494508
• Compare the results

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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/assumpti
  ons
• The overall number of different protein folds is
  limited (1000-3000 folds)
• The native protein structure is in its ground
  state (minimum energy)

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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.

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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

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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
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Protein structure prediction target sequence is
compared to structures using sequence-structure
alignment
Structural templates
Score3gtScore2gtScore1
Score1
Score2
Score3
Structural model of target
Target sequence
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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.

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Scoring function for threading.
Ala
Trp
Tyr
Ile
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
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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
Y -0.4 -0.2 -0.1 -0.2
I 0.3 0.2 0.4
G 0.4 0.2
L 0.3
1 2 3 4 5 6 7 8 9 10
1
2
3
4
5
6
7
8
9
10
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Evaluation of quality of structural model

• Correct bond length and bond angles
• Correct placement of functionally important sites
• Prediction of global topology, not partial
  alignment (minimum number of gaps)

gtgt 3.8 Angstroms
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Success and limitations of structure prediction

• Limitations
• Models of large and remotely related proteins are
  not very accurate
• Domain boundaries are difficult to define
• Models often do not provide details for
  functional annotation

• Success
• Accuracy scores almost doubled from CASP1 to
  CASP6, might be because of database size
• Models of small targets are very accurate

Adapted from Kryshtafovych et al 2005
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GenThreader http//bioinf.cs.ucl.ac.uk/psipred.

1. Predicts secondary structures for target
   sequence.
2. Makes sequence profiles (PSSMs) for each template
   sequence.
3. Uses threading scoring function to find the best
   matching profile.

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Protein-protein interactions.
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Common properties of protein-protein interactions.
rim

• Majority of protein complexes have a buried
  surface area 1600400 ?2 (standard size
  patch).
• Complexes of standard size do not involve large
  conformational changes while large complexes do.
• Protein recognition site consists of a completely
  buried core and a partially accessible rim.
• Trp and Tyr are abundant in the core, but Ser
  and Thr, Lys and Glu are particularly disfavored.
  

core
Top molecule
Bottom molecule
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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).

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Experimental methods
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Verification of experimental protein-protein
interactions.

• Protein localization method.
• Expression profile reliability method.
• Paralogous verification method.

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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

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Expression profile reliability method.
Deane, C. M. (2002) Mol. Cell. Proteomics 1
349-356
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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
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Interaction databases

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

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Protein interaction databases

• Protein-protein interaction databases
• Domain-domain interaction databases

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DIP database

• Documents protein-protein interactions from
  experiment
• Y2H, protein microarrays, TAP/MS, PDB
• 55,733 interactions between 19,053 proteins from
  110 organisms.

Organisms proteins interactions
Fruit fly 7052 20,988
H. pylori 710 1425
Human 916 1407
E. coli 1831 7408
C. elegans 2638 4030
Yeast 4921 18,225
Others 985 401
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DIP database

• Duan et al., Mol Cell Proteomics, 2002
• Assess quality
• Via proteins PVM, EPR
• Via domains DPV
• Search by BLAST or identifiers / text

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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

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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?

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Protein interaction databases

• Protein-protein interaction databases
• Domain-domain interaction databases

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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

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Pibase database

• Records domain interactions from PDB and PQS
• Domains defined with SCOP and CATH
• All inter-domain and inter-chain distances within
  6 ? are considered interacting domains
• From interacting domain pairs, create list of
  interfaces with buried solvent accessible area gt
  300 ?2

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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

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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

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Definition of CBM

• Interacting domain pair if at least 5
  residue-residue contacts between domains
  (contacts distance of less than 8 ?)
• Structure-structure alignments between all
  proteins corresponding to a given pair of
  interacting domains
• Clustering of interface similarity, those with
  gt50 equivalently aligned positions are clustered
  together
• Clusters with more than 2 entries define
  conserved binding mode.

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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
2 112 Jawed vertebrates
3 17 Clam,earthworm
4 4 lamprey
5 4 V.stercoraria
6 2 Rice,soybeans
7 2 human
8 2 lamprey

• Classify complicated domain pairs by CBMs
• Globin example
• 630 pairs
• 2 CBMs account for majority

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Classwork.

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