1. Protein Structure Prediction
Sequence +
Dr. G.P.S. Raghava
Structure

2. Protein Structure Prediction
Experimental Techniques
X-ray Crystallography
NMR
Limitations of Current Experimental Techniques
Protein DataBank (PDB) -> protein structures
SwissProt -> 100,000 proteins
Non-Redudant (NR) -> 10,00,000 proteins
Importance of Structure Prediction
Fill gap between known sequence and structures
Protein Engg. To alter function of a protein
Rational Drug Design

3. Different Levels of Protein Structure

5. Protein Architecture
Proteins consist of amino acids linked by peptide bonds
Each amino acid consists of:
a central carbon atom
an amino group
a carboxyl group and
a side chain
Differences in side chains distinguish the various amino acids

6. Amino Acid Side Chains
Vary in:
Size
Shape
Polarity

7. Peptide Bond

8. Peptide Bonds

9. Dihedral Angles

10. Conformation Flexibility
Backbone (main chain of atoms in peptide bonds, minus side chains)
conformation:
Torsion or rotation angles around:
C-N bond ()
C-C bond ()
Sterical hinderance:
Most – Pro
Least - Gly

11. Ramachandran Plot

12. Protein Secondary Structure
Regular Secondary
Structure
(-helices, -sheets)
Irregular
Secondary
Structure
(Tight turns,
Random coils, bulges)

13. Secondary Structure: Helices
ALPHA HELIX : a result of H-bonding between every fourth peptide bond (via amino and carbonyl groups) along the length of the polypeptide chain
Individual
Amino acid
H-bond

15. Helix formation is local
THYROID hormone receptor
(2nll)

16. Secondary Structure: Beta Sheets
BETA PLEATED SHEET: a result of H-bonding between polypeptide chains

17. b-sheet formation is NOT local

18. Definition of -turn
A -turn is defined by four consecutive residues i, i+1, i+2 and i+3 that do not form a helix and have a C(i)-C(i+3) distance less than 7Å and the turn lead to reversal in the protein chain. (Richardson, 1981).
The conformation of -turn is defined in terms of  and  of two central residues, i+1 and i+2 and can be classified into different types on the basis of  and .
i+1
i+2 i
H-bond
i+3
D <7Å

20. Tight turns 2 3 4 5 6 Type No. of residues H-bonding -turn
NH(i)-CO(i+1)
-turn 3
CO(i)-NH(i+2)
-turn 4
CO(i)-NH(i+3)
-turn 5
CO(i)-NH(i+4)
-turn 6
CO(i)-NH(i+5)

21. Secondary Structure shortcuts

22. Tertiary Structure: Hexokinase (6000 atoms, 48 kD, 457 amino acids)
polypeptides with a tertiary level of structure are usually referred to as
globular proteins, since their shape is irregular and globular in form

23. Quarternary Structure: Haemoglobin

24. What determines fold?
Anfinsen’s experiments in 1957 demonstrated that proteins can fold spontaneously into their native conformations under physiological conditions. This implies that primary structure does indeed determine folding or 3-D stucture.
Some exceptions exist
Chaperone proteins assist folding
Abnormally folded Prion proteins can catalyze misfolding of normal prion proteins that then aggregate

25. Levels of Description of Structural Complexity
Primary Structure (AA sequence)
Secondary Structure
Spatial arrangement of a polypeptide’s backbone atoms without regard to side-chain conformations
, , coil, turns (Venkatachalam, 1968)
Super-Secondary Structure
, , /, + (Rao and Rassman, 1973)
Tertiary Structure
3-D structure of an entire polypeptide
Quarternary Structure
Spatial arrangement of subunits (2 or more polypeptide chains)

26. Techniques of Structure Prediction
Computer simulation based on energy calculation
Based on physio-chemical principles
Thermodynamic equilibrium with a minimum free energy
Global minimum free energy of protein surface
Knowledge Based approaches
Homology Based Approach
Threading Protein Sequence
Hierarchical Methods

27. Energy Minimization Techniques
Energy Minimization based methods in their pure form, make no priori assumptions and attempt to locate global minma.
Static Minimization Methods
Classical many potential-potential can be construted
Assume that atoms in protein is in static form
Problems(large number of variables & minima and validity of potentials)
Dynamical Minimization Methods
Motions of atoms also considered
Monte Carlo simulation (stochastics in nature, time is not cosider)
Molecular Dynamics (time, quantum mechanical, classical equ.)
Limitations
large number of degree of freedom,CPU power not adequate
Interaction potential is not good enough to model

28. Molecular Dynamics
Provides a way to observe the motion of large molecules such as proteins at the atomic level – dynamic simulation
Newton’s second law applied to molecules
Potential energy function
Molecular coordinates
Force on all atoms can be calculated, given this function
Trajectory of motion of molecule can be determined

29. Knowledge Based Approaches
Homology Modelling
Need homologues of known protein structure
Backbone modelling
Side chain modelling
Fail in absence of homology
Threading Based Methods
New way of fold recognition
Sequence is tried to fit in known structures
Motif recognition
Loop & Side chain modelling
Fail in absence of known example

30. Homology Modeling Simplest, reliable approach
Basis: proteins with similar sequences tend to fold into similar structures
Has been observed that even proteins with 25% sequence identity fold into similar structures
Does not work for remote homologs (< 25% pairwise identity)

31. Homology Modeling Given:
A query sequence Q
A database of known protein structures
Find protein P such that P has high sequence similarity to Q
Return P’s structure as an approximation to Q’s structure

32. Threading Given: Find:
sequence of protein P with unknown structure
Database of known folds
Find:
Most plausible fold for P
Evaluate quality of such arrangement
Places the residues of unknown P along the backbone of a known structure and determines stability of side chains in that arrangement

33. Hierarcial Methods
Intermidiate structures are predicted, instead of predicting tertiary structure of protein from amino acids sequence
Prediction of backbone structure
Secondary structure (helix, sheet,coil)
Beta Turn Prediction
Super-secondary structure
Tertiary structure prediction
Limitation
Accuracy is only %
Only three state prediction

34. Thanks