Protein Structural Prediction. Protein Structure is Hierarchical.
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Transcript of Protein Structural Prediction. Protein Structure is Hierarchical.
Protein Structural Prediction
Protein Structure is Hierarchical
Structure Determines Function
What determines structure?
• Energy• Kinematics
How can we determine structure?
• Experimental methods• Computational predictions
The Protein Folding Problem
Primary Structure: Sequence
• The primary structure of a protein is the amino acid sequence
Primary Structure: Sequence
• Twenty different amino acids have distinct shapes and properties
Primary Structure: Sequence
A useful mnemonic for the hydrophobic amino acids is "FAMILY VW"
Secondary Structure: , , & loops
helices and sheets are stabilized by hydrogen bonds between backbone oxygen and hydrogen atoms
Secondary Structure: helix
Secondary Structure: sheet
sheet
buldge
Second-and-a-half-ary Structure: Motifs
beta helix
beta barrel
beta trefoil
Tertiary Structure: Domains
Mosaic Proteins
Tertiary Structure: A Protein Fold
Protein Folds Composed of , , other
Quaternary Structure: Multimeric Proteins or Functional Assemblies
• Multimeric Proteins• Macromolecular Assemblies
Ribosome:Protein Synthesis
Replisome:DNA copying
Hemoglobin:A tetramer
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?
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
Quick Overview of Energy
Bond Strength (kcal/mole)
H-bonds 3-7
Ionic bonds 10
Hydrophobic interactions 1-2
Van der vaals interactions 1
Disulfide bridge 51
The Hydrophobic Effect
• Important for folding, because every amino acid participates!
2.25 Trp
1.80 Ile
1.79 Phe
1.70 Leu
1.54 Cys
1.23 Met
1.22 Val
0.96 Tyr
0.72 Pro
0.31 Ala
0.26 Thr
0.13 His
0.00 Gly
-0.04 Ser
-0.22 Gln
-0.60 Asn
-0.64 Glu
-0.77 Asp
-0.99 Lys
-1.01 Arg
Experimentally Determined Hydrophobicity Levels Fauchere and Pilska (1983). Eur. J. Med. Chem. 18, 369-75.
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
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
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
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
Lattice String Folding
ROSETTAhttp://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
?? ?
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)
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
?? ?
Robetta & Rosetta
Rosetta results in CASP
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
Only a few folds are found in nature
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
Status of Protein Databases
SCOP: Structural Classification of Proteins. 1.67 release24037 PDB Entries (15 May 2004). 65122 Domains.
ClassNumber 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
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
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)
Examples of Fold Classes
Homology-based Prediction
Raw model
Loop modeling
Side chain placement
Refinement
Homology-based Prediction