Predicting and Classifying Protein Structures

1
Predicting and Classifying Protein Structures

• Michel Dumontier, Ph.D.
• Carleton University
• michel_at_bioinfocg.com

2
Outline

• 3D Structure Determination
• Validation
• Structure Classification
• Structure Prediction
• Secondary Structure

3
Structure Validation

• A structure can (and often does) have mistakes
• A poor structure will lead to poor models of
  mechanism or relationship
• Unusual parts of a structure may indicate
  something important (or an error)

4
Famous bad structures

• Azobacter ferredoxin (wrong space group)
• Zn-metallothionein (mistraced chain)
• Alpha bungarotoxin (poor stereochemistry)
• Yeast enolase (mistraced chain)
• Ras P21 oncogene (mistraced chain)
• Gene V protein (poor stereochemistry)

5
Structure Validation

• Assess experimental fit
• look at Resolution, R-Factor or RMSD
• Assess correctness of overall fold
• look at disposition of hydrophobic residues
• Assess structure quality
• packing
• stereochemistry
• contacts...

6
X-Ray Resolution

• Resolution Meaning
• gt4.0 Coordinates meaningless.
• 3.0 - 4.0 Fold possibly correct, but errors
  are very likely. Many sidechains placed with
  wrong rotamer.
• 2.5 - 3.0 Fold likely correct except that some
  surface loops might be mis-modelled. Several
  long, thin sidechains (lys, glu, gln, etc) and
  small sidechains (ser, val, thr, etc) likely to
  have wrong rotamers.
• 2.0 - 2.5 As 2.5 - 3.0, but number of sidechains
  in wrong rotamer is considerably less. Many
  small errors can normally be detected. Fold
  normally correct and number of errors in
  surface loops is small.
• 1.5 - 2.0 Few residues have wrong rotamer. Many
  small errors can normally be detected. Fold
  always correct, also in surface loops.
• 0.5 - 1.5 Threonines may have wrong
  chirality on the C-beta.

7
A Good Protein Structure..
X-ray structure NMR structure

• R 0.59 random chain
• R 0.45 initial structure
• R 0.35 getting there
• R 0.25 typical protein
• R 0.15 best case
• R 0.05 small molecule

• RMSD 4 Å random
• RMSD 2 Å initial fit
• RMSD 1.5 Å OK
• RMSD 0.8 Å typical
• RMSD 0.4 Å best case
• RMSD 0.2 Å dream on

8
A Good Protein Structure..

• Minimizes disallowed torsion angles
• Maximizes number of hydrogen bonds
• Maximizes buried hydrophobic ASA
• Maximizes exposed hydrophilic ASA
• Minimizes interstitial cavities or spaces

9
A Good Protein Structure..

• Minimizes number of bad contacts
• Minimizes number of buried charges
• Minimizes radius of gyration
• Minimizes covalent and noncovalent (van der Waals
  and coulombic) energies

10
Structure Validation Servers

• WHAT IF
• http//swift.cmbi.kun.nl/WIWWWI/
• Verify3D
• http//www.doe-mbi.ucla.edu/Services/Verify_3D/
• VADAR
• http//redpoll.pharmacy.ualberta.ca

11
(No Transcript)
12
(No Transcript)
13
Structure Validation Programs

• PROCHECK
• http//www.biochem.ucl.ac.uk/roman/procheck/proch
  eck.html
• VADAR
• http//www.pence.ca/software/vadar/latest/vadar.ht
  ml
• DSSP
• http//www.cmbi.kun.nl/gv/dssp/

14
Procheck
15
Outline

• 3D Structure Determination
• Validation
• Structure Classification
• Structure Prediction
• Secondary Structure

16
Domains are ubiquitous in proteins
Large proteins are composed of compact,
semi-independent units - domains.
Reason Modularity Folding efficiency
2MCP.PDB
17
Protein Domains an alphabet of functional
modules
18
SCOP

• The SCOP database aims to provide a detailed and
  comprehensive description of the structural and
  evolutionary relationships between all proteins
  whose structure is known.
• Created by manual inspection and aided by
  automated methods
• Consists of four hierarchical categories
• Class, Fold, Superfamily and Family.
• http//scop.mrc-lmb.cam.ac.uk/scop

19
structural classification
The eight most frequent SCOP superfolds
20
Semi-automated consensus domain definition -
Structure (CATH)
Dehydrolipoamide dehydrogenase 1LPFA
http//www.biochem.ucl.ac.uk/bsm/cath/
Jones S et al. (1998) Domain assignment for
protein structures using a consensus approach
Chracterization and analysis. Protein Science
7233-242
21
CATH - Class
Class 4 Few Secondary Structures
Class 2 Mainly Beta
Class 3 Mixed Alpha/Beta

• Class 1 Mainly Alpha

Secondary structure content (automatic)
22
CATH - Architecture

• Roll

Super Roll
Barrel
2-Layer Sandwich
Orientation of secondary structures (manual)
23
CATH - Topology

• L-fucose Isomerase

Serine Protease
Aconitase, domain 4
TIM Barrel
Topological connection and number of secondary
structures
24
CATH - Homology

• Alanine racemase

Dihydropteroate (DHP) synthetase
FMN dependent fluorescent proteins
7-stranded glycosidases
Superfamily clusters of similar structures
functions
25
Conserved Domain Database
Automated (objective) domain definition using
sequence.
CDD from Smart and Pfam CDART from CDD and
Genbank
http//www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtm
l
26
Homologous domains have similar structures
1PLS/2DYN 23 ID
1PLS - PH domain (Human pleckstrin)
2DYN - PH domain (Human dynamin)
27
Homology and Structural Similarity
Proteins that diverge in evolution maintain their
global fold !
Russell et al. (1997) J Mol Biol 269 423-439
28
Superposition

• Important as a means to identify protein motifs
  and fold families
• Non-evolutionary structural relationships

Structural similarity between Calmodulin and
Acetylcholinesterase
29
RMSD metric
To calculate the RMSD, a pairwise correspondence
of points has to be defined first.
30
RMSDopt
RMSDopt min(RMSDcoord)
RMSDopt RMSDcoord(A, Rs x (B-Ts))
The translation vector Ts and the rotation matrix
Ms define a superposition of the vector set B on
A.
An analytic solution of the superposition problem
is available, but not straightforward (involves
an eigenvalue problem).
31
Superposition in practice

• Pre-aligned structures
• VAST www.ncbi.nlm.nih.gov/Structure/VAST/vast.shtm
  l
• FSSP www.bioinfo.biocenter.helsinki.fi8080/dali/i
  ndex.html
• Homstrad www-cryst.bioc.cam.ac.uk/homstrad/
• PDBsum www.biochem.ucl.ac.uk/bsm/pdbsum/
• DALI www.ebi.ac.uk/dali/
• On the fly
• CE cl.sdsc.edu/ce.html
• FAST biowulf.bu.edu/FAST/

32
Outline

• 3D Structure Determination
• Validation
• Structure Classification
• Structure Prediction
• Secondary Structure

33
Secondary (2o) Structure
34
Secondary Structure Prediction

• One of the first fields to emerge in
  bioinformatics (1967)
• Grew from a simple observation that certain amino
  acids or combinations of amino acids seemed to
  prefer to be in certain secondary structures
• Subject of hundreds of papers and dozens of
  books, many methods

35
2o Structure Prediction

• Statistical (Chou-Fasman, GOR)
• Homology or Nearest Neighbor (Levin)
• Physico-Chemical (Lim, Eisenberg)
• Pattern Matching (Cohen, Rooman)
• Neural Nets (Qian Sejnowski, Karplus)
• Evolutionary Methods (Barton, Niemann)
• Combined Approaches (Rost, Levin, Argos)

36
Secondary Structure Prediction
37
Chou-Fasman Statistics
38
Simplified C-F Algorithm

• Select a window of 7 residues
• Calculate average Pa over this window and assign
  that value to the central residue
• Repeat the calculation for Pb and Pc
• Slide the window down one residue and repeat
  until sequence is complete
• Analyze resulting plot and assign secondary
  structure (H, B, C) for each residue to highest
  value.

39
Simplified C-F Algorithm
helix
beta
coil
10 20 30 40
50 60
40
Limitations of Chou-Fasman

• Does not take into account
• long range information (gt3 residues away)
• structure class
• Does not include
• related sequences or alignments in prediction
  process
• Only about 55 accurate

41
The PhD Algorithm

• Search the SWISS-PROT database and select high
  scoring homologues
• Create a sequence profile from the resulting
  multiple alignment
• Include global sequence info in the profile
• Input the profile into a trained two-layer neural
  network to predict the structure and to
  clean-up the prediction

42
Prediction Performance
43
Best of the Best

• PredictProtein-PHD (72)
• http//cubic.bioc.columbia.edu/predictprotein/
• Jpred (73-75)
• http//www.compbio.dundee.ac.uk/www-jpred/submit.
  html
• SAM-T02 (75)
• http//www.cse.ucsc.edu/research/compbio/HMM-apps/
  T02-query.html
• PSIpred (77)
• http//bioinf.cs.ucl.ac.uk/psipred/psiform.html

44
(No Transcript)
45
Evaluating Secondary Structure Predictions

• Historically problematic due to tester bias
  (developer trains and tests their own
  predictions)
• Some predictions were up to 10 off
• Move to make testing independent and test sets as
  large as possible
• EVA evaluation of protein secondary structure
  prediction

46
EVA

• gt10 different methods evaluated as new structures
  are deposited in the PDB
• Results posted on the web and updated weekly
• http//cubic.bioc.columbia.edu/eva

47
EVA
48
Secondary Structure Evaluation

• Q3 score
• standard method in evaluating performance, 3
  states (H,C,B) evaluated like a multiple choice
  exam with 3 choices. Same as correct
• SOV (segment overlap score)
• more useful measure of how segments overlap and
  how much overlap exists

49
Homology Modeling

• Similar sequences usually share the same fold.
• Structure models can be constructed from
  alignments with proteins having a 3D structure.
• When no suitable template structure can be found,
  possible templates are found using threading
• More with Boris in 3.3 and 3.5

50
ab initio Protein Structure Prediction

• Predicting the 3D structure without any prior
  knowledge
• Used when homology modeling or threading have
  failed (no homologues are evident)
• Equivalent to solving the Protein Folding
  Problem
• Still an active research problem
• Howards Lecture 5.2

51
Conclusions

• Protein structures are now sufficiently abundant
  and well defined that they can be classified
  using well-developed rules of taxonomy
• Distant relationships and common rules of folding
  can be uncovered through fold classification
  comparison

52
Conclusions

• Structure prediction is still one of the key
  areas of active research in bioinformatics and
  computational biology
• Significant strides have been made over the past
  decade through the use of larger databases,
  machine learning methods and faster computers