Download homology modeling

Protein Modeling
Protein Structure Prediction
3D Protein Structure
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Cα
Cα
Cα
Cα
LEU
PRO
VAL
?
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ALA
Cα
…
ARG
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backbone
sidechain
The Protein Folding Problem
• we know that the function of a protein is determined in
large part by its 3D shape (fold, conformation)
• can we predict the 3D shape of a protein given only its
amino-acid sequence?
Motivation
• Want to identify the function of genes we find,
and what different mutations/alleles do
• One gene = one protein (sort of)
– Function of protein = function of gene
• Function can be determined in many ways
– Gene expression, knockouts, etc
– But these take time, and are prone to mistakes
• Goal: If we can structure every protein, learning
their functions isn’t too far away
Thornton et al 2000 (Nature)
Protein Architecture
• proteins are polymers consisting of amino acids linked by
peptide bonds
• each amino acid consists of
– a central carbon atom
– an amino group NH 2
– a carboxyl group COOH
– a side chain
• differences in side chains distinguish different amino acids
3D Protein Structure
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Peptide Bonds
amino
group
side
chain
carboxyl
group
 carbon (common reference point for coordinates of a structure)
Amino Acid Side Chains
• side chains vary in: shape, size, charge, polarity
Levels of Description
• protein structure is often described at four different scales
– primary structure
– secondary structure
– tertiary structure
– quaternary structure
Levels of Description
Secondary Structure
• secondary structure refers to certain common repeating
structures
• it is a “local” description of structure
• two common secondary structures
 helices
 strands/sheets
• a third category, called coil or loop, refers to everything
else
 Helices
 carbon
individual
amino acid
hydrogen
bond
 Sheets
Ribbon Diagram Showing
Secondary Structures
Levels of Description
What Determines Conformation?
• in general, the amino-acid sequence of a protein determines
the 3D shape of a protein [Anfinsen et al., 1950s]
• but some exceptions
– all proteins can be denatured
– some proteins are inherently disordered (i.e. lack a regular
structure)
– some proteins get folding help from chaperones
– there are various mechanisms through which the
conformation of a protein can be changed in vivo
– post-translational modifications such as
phosphorylation
– prions
– etc.
What Determines Conformation?
• what physical properties of the protein determine its fold?
– rigidity of the protein backbone
– interactions among amino acids, including
• electrostatic interactions
• van der Waals forces
• volume constraints
• hydrogen, disulfide bonds
– interactions of amino acids with water
Determining Protein Structures
• protein structures can be determined
experimentally (in many cases) by
– x-ray crystallography
– nuclear magnetic resonance (NMR)
Myoglobin
From www.inst.bnl.gov/GasDetectorLab/x-rays/SRI94.htm
Myoglobin
S.E.V. Phillips. "Structure and refinement of oxymyoglobin at 1.6 Å
resolution.", J. Mol. Biol. 1980, 142, 531.
X-ray Crystallography
x-ray
beam
protein
crystal
collection
plate
diffraction
pattern
electron
density map
(“3D picture”)
Electron Density Map Interpretation
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GIVEN: 3D Electron Density Map
Electron Density Map Interpretation
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FIND:
All-atom Protein Model
NMR
• Nuclear Magnetic Resonance Spectroscopy
• Cannot handle large proteins like X-ray
• Exploits the chemical environment to return
distances between atoms
– Can use knowledge of restraints to identify
positions of atoms that produce peaks
Protein structure determination in solution by NMR spectroscopy Wuthrich K. J Biol Chem. 1990 December 25;265(36):22059-62
Experimental Methods
• Very expensive and time-consuming
– Computational methods can help with time
• Many proteins still cannot be done in this
manner
More motivation
• there is a large sequence-structure gap
≈300K protein sequences in SwissProt
database
≈50K protein structures in PDB database
• key question: can we predict structures by
computational means instead?
Approaches to Protein
Structure Prediction
• prediction in 1D
– secondary structure
– solvent accessibility (which residues are exposed to
water, which are buried)
– transmembrane helices (which residues span
membranes)
• prediction in 2D
– inter-residue/strand contacts
• prediction in 3D
– homology modeling
– fold recognition (e.g. via threading)
– ab initio prediction (e.g. via molecular dynamics)
Prediction in 1D, 2D and 3D
predicted secondary structure and solvent accessibility
known secondary structure (E = beta strand) and solvent accessibility
Figure from B. Rost, “Protein Structure in 1D, 2D, and 3D”, The Encyclopaedia of Computational Chemistry, 1998
2D Prediction Approaches
• use secondary structure predictions
to predict short-range contacts (e.g.
hydrogen bonds in α helices)
• use secondary structure predictions
to predict β strand alignments
• use correlated mutations to predict
contacts
Prediction in 3D
• homology modeling
given: a query sequence Q, a database of protein structures
do:
• find protein P has high sequence similarity to Q
• return P’s structure as an approximation to Q’s structure
• fold recognition (threading)
given: a query sequence Q, a database of known folds
do:
• find fold F such that Q can be aligned with F in a highly
compatible manner
• return F as an approximation to Q’s structure
Prediction in 3D
•
fragment assembly(Rosetta)
given: a query sequence Q, a database of structure fragments
do:
• find a set of fragments that Q can be aligned with in a highly
compatible manner
• return the combined fragments as an approximation
•
molecular dynamics
given: a query sequence Q
do:
• use laws of physics to to simulate folding of Q
Homology Modeling
• most pairs of proteins with similar structure are remote
homologs (< 25% sequence identity)
• homology modeling usually doesn’t work for remote
homologs ; most pairs of proteins with < 25% sequence
identity are unrelated
probably
unrelated
0%
remote
homologs
homologs
20% 30%
pairwise sequence identity
100%
Homology-based Prediction
Raw model
Loop modeling
Side chain
placement
Refinement
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
Examples of Fold Classes
Threading
Ab initio Prediction – ROSETTA
1.
PSI-BLAST – homology
search
Discard sequences with >25%
homology
2.
PHD
For each 3-long and each 9-long
sequence fragment, get 25
structure fragments that match
“well”
? ?
?
ai.stanford.edu/~serafim/CS262_2006/Slides
Ab initio Prediction – CASP
results
Summary of current state of the
art
Open Ended
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Ab Initio is the goal, far from it
Sidechain prediction
Contact Map prediction
Search space reduction
Parallelization (GPUs)
Surface Accessibility
Other areas
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Protein-Protein Interaction
Drug Design
Protein Engineering
Ligand Docking/Inhibition
Function Prediction