Structural classification of Proteins
SCOP Classification: consists of a database
Family
Evolutionarily related with a significant sequence identity
Superfamily
Different families whose structural and functional features suggest common
evolutionary origin
Fold
Different superfamilies having same major secondary structures in same
arrangement and with same topological connections
Class
Secondary structure composition.
Databases of Structural Classification
SCOP:
Structural Classification of Proteins
CATH:
Class, Architecture, Topology and
Homologous superfamily
FSSP:
Families of Structurally Similar Proteins
The Protein Folding Problem
Given a particular sequence of amino acid residues
(primary structure), what will the tertiary/quaternary
structure of the resulting protein be?
Input: AAVIKYGCAL…
Output: 11, 22…
=> backbone conformation (no side chains yet)
 But what about the tertiary structure?
Folding intermediates
• Levinthal’s paradox – Consider a 100 residue
protein. If each residue can take only 3 positions,
there are 3100 = 5  1047 possible conformations.
• Finding a native folded state among all possible
configurations can take an enormously long time
• Folding must proceed by progressive stabilization
of intermediates for fast folding
– Molten globules – most secondary structure formed,
but much less compact than “native” conformation.
– It is an intermediate between the native state and
denatured state.
Forces driving protein folding
It is believed that hydrophobic collapse is a
key driving force for protein folding fast
reaction; produces molten globule state
Hydrophobic core
Polar surface interacting with solvent
Minimum volume (no cavities)
Disulfide bond formation stabilizes
Hydrogen bonds
Polar and electrostatic interactions
Forces driving protein folding
Proteins are, in fact, only marginally stable
Native state is typically only 5 to 10
kcal/mole more stable than the unfolded
form
Many proteins help in folding
Protein disulfide isomerase – catalyzes
shuffling of disulfide bonds
Chaperones – break up aggregates and (in
theory) unfold misfolded proteins
Recall: secondary and tertiary structure of proteins, and quaternary structure
Determining Protein structure
Coordinates are determined by X-ray crystallography
The interaction of x-rays with electrons arranged in a
crystal can produce electron-density map, which can be
interpreted to an atomic model. Crystal is very hard to
grow.
Nuclear magnetic resonance (NMR)
•Some atomic nuclei have a magnetic spin.
•Probe the molecule by radio frequency and get
the distances between atoms.
•Only applicable to small molecules.
PDB: Protein Data Bank
Three-dimensional structures of large biological molecules, including proteins and
nucleic acids.
 Contain sequence details, atomic coordinates, crystallization conditions
Ab initio Structure Prediction
Deriving structures, approximate or otherwise,
from sequence.
An free energy function to describe the protein
•bond energy
•bond angle energy
•dihedral angle energy
•van der Waals energy
•electrostatic energy
Minimize the function and obtain the structure
•An algorithm capable of finding the global minimum of the
energy function is be used
Not practical in general
•Computationally too expensive
•Accuracy is poor
Ab initio Structure Prediction
contd.
• It should be kept in mind that native
structures exist in a certain solvent
environment
• The native conformation need not necessarily
correspond to the global minimum of free
energy.
Template based protein structure prediction
or also known as: comparative modeling, homology modeling
Used where there is a clear sequence relationship between the
target structure and one or more known structures.
Template based…(contd)
The most reliable technique for
predicting protein structure
Comparing the sequence of the new
protein with the sequences of proteins of
known structure
Strong similarity
No strong similarities  comparative
modeling cannot be used.
Components
•Protein Structure library (Template library)
From PDB, FSSP, SCOP
•Scoring Function
Sequence compatibility
Structure compatibility
•Alignment Algorithm
•Fold Recognition
•Confidence Assessment
3D Model Building
 Programs:
MODELLER, SWISS-MOD, SCWRL, etc
Similar performances (from CASP)
 Best Template?
Closest biological function?
Environmental factors (pH, ligand, etc)
Resolution
Choosing family of proteins
Using multiple templates
 Alignment accuracy is everything!
Cannot recover from an incorrect alignment
Gap placement
Try many plausible alignments, and build multiple models
 Build as many models as possible, and determine the best
Consensus
Using protein structure analysis program
Prediction of protein structure
includes:
• Protein secondary structure prediction
• Protein Phi-Psi angle prediction
• Predicting disulphide Bridges
• Predicting beta-turns
• Domain recognition
• Domain boundary detection
• Protein structural classification
• Mining structural motifs