The document provides an overview of protein structural prediction, emphasizing the hierarchical nature of protein structure, which is determined by the amino acid sequence and various interactions. It discusses methodologies for determining protein structure, including experimental techniques like X-ray crystallography and computational approaches such as homology modeling and ab initio prediction. Additionally, it highlights the challenges of protein folding, the importance of energy interactions, and the role of databases in classifying protein structures.

1. Protein Structural Prediction

2. Protein Structure is Hierarchical

3. Structure Determines Function
What determines structure?
• Energy
• Kinematics
How can we determine structure?
• Experimental methods
• Computational predictions
The Protein Folding Problem

4. Primary Structure: Sequence
• The primary structure of a protein is the amino acid sequence

5. Primary Structure: Sequence
• Twenty different amino acids
have distinct shapes and
properties

6. Primary Structure: Sequence
A useful mnemonic for the hydrophobic amino acids is "FAMILY VW"

7. Secondary Structure: , , & loops
•  helices and  sheets are stabilized by hydrogen bonds between backbone
oxygen and hydrogen atoms

8. Secondary Structure:  helix

9. Secondary Structure:  sheet
 sheet
 buldge

10. Second-and-a-half-ary Structure:
Motifs
beta helix
beta barrel
beta trefoil

11. Tertiary Structure: Domains

12. Mosaic Proteins

13. Tertiary Structure: A Protein Fold

14. Protein Folds Composed of , , other

15. Quaternary Structure: Multimeric Proteins or
Functional Assemblies
• Multimeric Proteins
• Macromolecular Assemblies
Ribosome:
Protein Synthesis
Replisome:
DNA copying
Hemoglobin:
A tetramer

16. Protein Folding
• The amino-acid sequence of a protein determines the 3D fold [Anfinsen et
al., 1950s]
Some exceptions:
– All proteins can be denatured
– Some proteins have multiple conformations
– Some proteins get folding help from chaperones
• The function of a protein is determined by its 3D fold
• Can we predict 3D fold of a protein given its amino-acid sequence?

17. The Leventhal Paradox
• Given a small protein (100aa) assume 3 possible conformations/peptide
bond
• 3100 = 5 × 1047 conformations
• Fastest motions 10- 15 sec so sampling all conformations would take 5 ×
1032 sec
• 60 × 60 × 24 × 365 = 31536000 seconds in a year
• Sampling all conformations will take 1.6 × 1025 years
• Each protein folds quickly into a single stable native conformation the
Leventhal paradox

18. Quick Overview of Energy
Strength
(kcal/mole)
Bond
3-7
H-bonds
10
Ionic bonds
1-2
Hydrophobic
interactions
1
Van der vaals
interactions
51
Disulfide bridge

19. The Hydrophobic Effect
• Important for folding, because every amino acid participates!
Trp
2.25
Ile
1.80
Phe
1.79
Leu
1.70
Cys
1.54
Met
1.23
Val
1.22
Tyr
0.96
Pro
0.72
Ala
0.31
Thr
0.26
His
0.13
Gly
0.00
Ser
-0.04
Gln
-0.22
Asn
-0.60
Glu
-0.64
Asp
-0.77
Lys
-0.99
Arg
-1.01
Experimentally Determined Hydrophobicity Levels
Fauchere and Pilska (1983).
Eur. J. Med. Chem. 18, 369-75.

20. Protein Structure Determination
• Experimental
– X-ray crystallography
– NMR spectrometry
• Computational – Structure Prediction
(The Holy Grail)
Sequence implies structure, therefore in principle we
can predict the structure from the sequence alone

21. Protein Structure Prediction
• ab initio
– Use just first principles: energy, geometry, and kinematics
• Homology
– Find the best match to a database of sequences with known 3D-
structure
• Threading
• Meta-servers and other methods

22. Ab initio Prediction
• Sampling the global conformation space
– Lattice models / Discrete-state models
– Molecular Dynamics
– Pre-set libraries of fragment 3D motifs
• Picking native conformations with an energy function
– Solvation model: how protein interacts with water
– Pair interactions between amino acids
• Predicting secondary structure
– Local homology
– Fragment libraries

23. Lattice String Folding
• HP model: main modeled force is hydrophobic attraction
– NP-hard in both 2-D square and 3-D cubic
– Constant approximation algorithms
– Not so relevant biologically

24. Lattice String Folding

25. ROSETTA
http://www.bioinfo.rpi.edu/~bystrc/hmmstr/server.php
http://depts.washington.edu/bakerpg/papers/Bonneau-ARBBS-v30-p173.pdf
• Monte Carlo based method
• Limit conformational search space by using sequence—structure motif I-
Sites library (http://isites.bio.rpi.edu/Isites/)
– 261 patterns in library
– Certain positions in motif favor certain residues
• Remove all sequences with <25% identity
• Find structures of the 25 nearest sequence neighbors of each 9-
mer
Rationale
– Local structures often fold independently of full protein
– Can predict large areas of protein by matching sequence to I-Sites
?
? ?

26. I-Sites Examples
• Non polar helix
– Abundance of alanine at all positions
– Non-polar side chains favored at positions 3, 6, 10
(methionine, leucine, isoleucine)
• Amphipathic helix
 Non-polar side chains favored at positions 6, 9, 13, 16
(methionine, leucine, isoleucine)
 Polar side chains favored at positions 1, 8, 11, 18 (glutamic acid,
lysine)

27. ROSETTA Method
• New structures generated by swapping
compatible fragments
• Accepted structures are clustered based on
energy and structural size
• Best cluster is one with the greatest number
of conformations within 4-Å rms deviation
structure of the center
• Representative structures taken from each of
the best five clusters and returned to the user
as predictions
?
? ?

28. Robetta & Rosetta

30. Rosetta results in CASP

31. Rosetta Results
• In CASP4, Rosetta’s best models ranged from 6–10 Å rmsd C
• For comparison, good comparative models give 2-5 Å rmsd C
• Most effective with small proteins (<100 residues) and structures with
helices

32. Only a few folds are found in
nature

33. The SCOP Database
Structural Classification Of Proteins
FAMILY: proteins that are >30% similar, or >15% similar and have similar
known structure/function
SUPERFAMILY: proteins whose families have some sequence and
function/structure similarity suggesting a common evolutionary origin
COMMON FOLD: superfamilies that have same secondary structures in same
arrangement, probably resulting by physics and chemistry
CLASS: alpha, beta, alpha–beta, alpha+beta, multidomain

34. Status of Protein Databases
SCOP: Structural Classification of Proteins. 1.67 release
24037 PDB Entries (15 May 2004). 65122 Domains.
Class
Number of
folds
Number of
superfamilies
Number of
families
All alpha proteins 202 342 550
All beta proteins 141 280 529
Alpha and beta proteins (a/b) 130 213 593
Alpha and beta proteins (a+b) 260 386 650
Multi-domain proteins 40 40 55
Membrane and cell surface
proteins
42 82 91
Small proteins 71 104 162
Total 887 1447 2630
EMBL
PDB

35. Evolution of Proteins – Domains
#members in different families obey power law
429 families common in all 14 eukaryotes;
80% of animal domains, 90% of fungi domains
80% of proteins are multidomain in eukaryotes;
domains usually combine pairwise in same order --
why?
Evolution of proteins happens
mainly through duplication,
recombination, and divergence
Chothia, Gough, Vogel, Teichmann, Science 300:1701-17-3, 2003

36. Homology-based Prediction
• Align query sequence with sequences of known structure,
usually >30% similar
• Superimpose the aligned sequence onto the structure
template, according to the computed sequence alignment
• Perform local refinement of the resulting structure in 3D
90% of new structures submitted to PDB in the
past three years have similar folds in PDB
The number of unique structural folds
is small (possibly a few thousand)

37. Examples of Fold Classes

38. Homology-based Prediction
Raw model
Loop modeling
Side chain placement
Refinement

39. Homology-based Prediction