Title: Protein structure prediction.
1Protein structure prediction.
2Protein 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.
3Protein 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
4Protein 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).
5Homology 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)
6Steps of homology modeling.
- Template recognition initial alignment.
- Backbone generation.
- Loop modeling.
- Side-chain modeling.
- Model optimization.
71. 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.
8Two zones of protein structure prediction.
Sequence identity
100
Homology modeling zone
50
Fold recognition zone
50
100
150
200
Alignment length
92. 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.
103. 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.
114. 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
125. 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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14Classwork Homology modeling.
- Go to NCBI Entrez, search for gi461699
- Do Blast search against PDB
- Repeat the same for gi60494508
- Compare the results
15Fold 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)
16Fold 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.
17Threading 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
18Protein 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
19Protein structure prediction target sequence is
compared to structures using sequence-structure
alignment
Structural templates
Score3gtScore2gtScore1
Score1
Score2
Score3
Structural model of target
Target sequence
20Scoring 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 Ã…, these residues
make a contact.
21Scoring 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
22Classwork 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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24Evaluation 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
25Success 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
26GenThreader 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.
27Protein-protein interactions.
28Common 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
29Different 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).
30Experimental methods
31Verification of experimental protein-protein
interactions.
- Protein localization method.
- Expression profile reliability method.
- Paralogous verification method.
32Protein 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
33Expression profile reliability method.
Deane, C. M. (2002) Mol. Cell. Proteomics 1
349-356
34Paralogous 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
35Interaction databases
- Experiment (E)
- Structure detail (S)
- Predicted
- Physical (P)
- Functional (F)
- Curated (C)
- Homology modeling (H)
- IMEx consortium
36Protein interaction databases
- Protein-protein interaction databases
- Domain-domain interaction databases
37DIP 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
38DIP database
- Duan et al., Mol Cell Proteomics, 2002
- Assess quality
- Via proteins PVM, EPR
- Via domains DPV
- Search by BLAST or identifiers / text
39BIND 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
40Classwork.
- 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?
41Protein interaction databases
- Protein-protein interaction databases
- Domain-domain interaction databases
42InterDom 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
43Pibase 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
44Classwork.
- 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
45NCBI 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
46Definition 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.
47Number 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
48Classwork.
- Retrieve structures 1GY3, 1E9H, 1OL2
- Examine all interactions within and between
chains/domains. - How many CBMs do you find?