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Protein structure prediction:The holy grail of bioinformatics
Proteins: Four levels of structural Proteins: Four levels of structural organization:organization:
Primary structurePrimary structure
Secondary structureSecondary structure
Tertiary structureTertiary structure
Quaternary structureQuaternary structure
Primary structure = the linear amino acid sequence
Secondary structure = spatial arrangement of amino-acid residues that are adjacent in the primary structure
helix = A helical structure, whose chain coils tightly as a right-handed screw with all the side chains sticking outward in a helical array. The tight structure of the helix is stabilized by same-strand hydrogen bonds between NH groups and CO groups spaced at four amino-acid residue intervals.
The -pleated sheet is made of loosely coiled strands are stabilized by hydrogen bonds between -NH and -CO groups from adjacent strands.
An antiparallel β sheet. Adjacent β strands run in opposite directions. Hydrogen bonds between NH and CO groups connect each amino acid to a single amino acid on an adjacent strand, stabilizing the structure.
A parallel β sheet. Adjacent β strands run in the same direction. Hydrogen bonds connect each amino acid on one strand with two different amino acids on the adjacent strand.
Silk fibroinSilk fibroin
helix sheet (parallel and antiparallel)tight turnsflexible loopsirregular elements (random coil)
Tertiary structure = three-dimensional structure of protein
The tertiary structure is formed by The tertiary structure is formed by the folding of secondary structures the folding of secondary structures by covalent and non-covalent forces, by covalent and non-covalent forces, such assuch as hydrogen bondshydrogen bonds,, hydrophobic interactionshydrophobic interactions,, salt salt bridgesbridges between positively and between positively and negatively charged residues, as well negatively charged residues, as well asas disulfide bondsdisulfide bonds between pairs of between pairs of cysteines.cysteines.
Quaternary structure = spatial arrangement of subunits Quaternary structure = spatial arrangement of subunits and their contacts.and their contacts.
Prosthetic groupProsthetic group
HoloproteinHoloprotein
Holoproteins & ApoproteinsHoloproteins & Apoproteins
ApoproteinApoprotein
Prosthetic groupProsthetic groupHoloproteinHoloprotein
Apohemoglobin = 2Apohemoglobin = 2 + 2 + 2
Prosthetic groupProsthetic group
HemeHeme
Hemoglobin = Apohemoglobin + 4HemeHemoglobin = Apohemoglobin + 4Heme
Sela M, White FH, & Anfinsen CB. 19591959. The reductive cleavage of disulfide bonds and its application to problems of protein structure. Biochim. Biophys. Acta. 31:417-426.
Christian B. AnfinsenChristian B. Anfinsen1916-19951916-1995
Not all proteins fold independently.Chaperones.
Reducing agents: Ammonium thioglycolate (alkaline) pH 9.0-10Glycerylmonothioglycolate (acid) pH 6.5-8.2
Oxidant
What do we need to know in order to What do we need to know in order to state that the tertiary structure of a state that the tertiary structure of a
protein has been solved?protein has been solved?
Ideally: We need to determine the position of all Ideally: We need to determine the position of all atoms and their connectivity.atoms and their connectivity.
Less Ideally: We need to determine the position Less Ideally: We need to determine the position of all Cof all Cbackbone structure).backbone structure).
Protein structure: Limitations and caveats
• Not all proteins or parts of proteins assume a well-defined 3D structure in solution.
• Protein structure is not static, there are various degrees of thermal motion for different parts of the structure.
• There may be a number of slightly different conformations in solution.
• Some proteins undergo conformational changes when interacting with STUFF.
Experimental Protein Structure Experimental Protein Structure DeterminationDetermination
• X-ray crystallography X-ray crystallography – most accuratemost accurate– in vitroin vitro– needs crystalsneeds crystals– ~$100-200K per structure~$100-200K per structure
• NMR NMR – fairly accuratefairly accurate– in vivoin vivo– no need for crystalsno need for crystals– limited to very small proteinslimited to very small proteins
• Cryo-electron-microscopyCryo-electron-microscopy– imaging technologyimaging technology– low resolutionlow resolution
Why predict protein structure?
• Structural knowledge = some understanding of function and mechanism of action
• Predicted structures can be used in structure-based drug design
• It can help us understand the effects of mutations on structure and function
• It is a very interesting scientific problem (still unsolved in its most general form after more than 50 years of effort)
Secondary structure prediction
• Historically first structure prediction methods predicted secondary structure
• Can be used to improve alignment accuracy
• Can be used to detect domain boundaries within proteins with remote sequence homology
• Often the first step towards 3D structure prediction
• Informative for mutagenesis studies
Secondary structure prediction
Protein Secondary Structures (Simplifications)
COIL (everything else)COIL (everything else)
-STRAND-STRAND
-HELIX-HELIX
Assumptions• The entire information for forming secondary structure is
contained in the primary sequence
• side groups of residues will determine structure
• examining windows of 13-17 residues is sufficient to predict secondary structure
-helices 5–40 residues long -strands 5–10 residues long
Predicting Secondary Structure From Primary Structure
• accuracy 64-75%• higher accuracy for -helices than for
sheets• accuracy is dependent on protein family• predictions of engineered (artificial) proteins
are less accurate
A surprising result!
ChameleonChameleonsequencessequences
The “Chameleon” sequence
TEAVDAATAEKVFKQYANDNGVDGEWTYDDATKTFTVTEK
TEAVDAWTVEKAFKTFANDNGVDGAWTVEKAFKTFTVTEK
sequence 1 sequence 2
Replace both sequences withan engineered peptide (“chameleon”)
Source: Minor and Kim. 1996. Nature 380:730-734
-helix -strand
Measures of prediction accuracy
• Qindex and Q3
• Correlation coefficient
Qindex
Qindex: (Qhelix, Qstrand, Qcoil, Q3) - percentage of residues correctly predicted as -helix, -strand,
coil, or for all 3 conformations.
Drawbacks:- even a random assignment of structure can achieve a high score
(Holley & Karpus 1991)
1003 observed
predicted
N
NQ
Correlation coefficient
True positive
pa
False positive
(overpredicted)
oa
True negative
na
False negative
(underpredicted)
ua
])][][[]([
opuponun
ounpC
C= 1 (=100%)
Methods of secondary structure prediction
Chou & Fasman (1974 & 1978) : Some residues have particular secondary-structure
preferences. Based on empirical frequencies of residues in -helices, -sheets, and coils.
Examples: Glu α-helix Val β-strand
First generation methods: single residue statistics
Chou-Fasman methodName P(H) P(E) P(turn) f(i) f(i+1) f(i+2) f(i+3)
Alanine 142 83 66 0.06 0.076 0.035 0.058
Arginine 98 93 95 0.07 0.106 0.099 0.085
Aspartic Acid 101 54 146 0.147 0.11 0.179 0.081
Asparagine 67 89 156 0.161 0.083 0.191 0.091
Cysteine 70 119 119 0.149 0.05 0.117 0.128
Glutamic Acid 151 37 74 0.056 0.06 0.077 0.064
Glutamine 111 110 98 0.074 0.098 0.037 0.098
Glycine 57 75 156 0.102 0.085 0.19 0.152
Histidine 100 87 95 0.14 0.047 0.093 0.054
Isoleucine 108 160 47 0.043 0.034 0.013 0.056
Leucine 121 130 59 0.061 0.025 0.036 0.07
Lysine 114 74 101 0.055 0.115 0.072 0.095
Methionine 145 105 60 0.068 0.082 0.014 0.055
Phenylalanine 113 138 60 0.059 0.041 0.065 0.065
Proline 57 55 152 0.102 0.301 0.034 0.068
Serine 77 75 143 0.12 0.139 0.125 0.106
Threonine 83 119 96 0.086 0.108 0.065 0.079
Tryptophan 108 137 96 0.077 0.013 0.064 0.167
Tyrosine 69 147 114 0.082 0.065 0.114 0.125
Valine 106 170 50 0.062 0.048 0.028 0.053
Amino Acid P P Pt Glu 1.51 0.37 0.74 Met 1.45 1.05 0.60 Ala 1.42 0.83 0.66 Val 1.06 1.70 0.50 Ile 1.08 1.60 0.50 Tyr 0.69 1.47 1.14 Pro 0.57 0.55 1.52 Gly 0.57 0.75 1.56
Chou-Fasman Method
• Accuracy: Q3 = 50-60%
Second generation methods: segment statistics
• Similar to single-residue methods, but incorporating additional information (adjacent residues, segmental statistics).
• Problems:– Low accuracy - Q3 below 66% (results).– Q3 of -strands (E) : 28% - 48%.– Predicted structures were too short.
The GOR method
• developed by Garnier, Osguthorpe & Robson• build on Chou-Fasman Pij values• evaluate each residue PLUS adjacent 8 N-
terminal and 8 carboxyl-terminal residues • sliding window of 17 residues• underpredicts -strand regions• GOR method accuracy Q3 = ~64%
Third generation methods
• Third generation methods reached 77% accuracy.• They consist of two new ideas:
1. A biological idea –
Using evolutionary information based on conservation analysis of multiple sequence alignments.
2. A technological idea –
Using neural networks.
Artificial Neural NetworksAn attempt to imitate the human brain (assuming that this is the way it works).
Neural network models
- machine learning approach - provide training sets of structures (e.g. -helices, non
-helices)- computers are trained to recognize patterns in known
secondary structures- provide test set (proteins with known structures)
- accuracy ~ 70 –75%
Reasons for improved accuracy
• Align sequence with other related proteins of the same protein family
• Find members that has a known structure
• If significant matches between structure and sequence assign secondary structures to corresponding residues
New and Improved Third-Generation Methods
Exploit evolutionary information. Based on conservation analysis of multiple sequence alignments.
• PHD (Q3 ~ 70%)
Rost B, Sander, C. (1993) J. Mol. Biol. 232, 584-599.
• PSIPRED (Q3 ~ 77%)
Jones, D. T. (1999) J. Mol. Biol. 292, 195-202.Arguably remains the top secondary structure prediction method(won all CASP competitions since 1998).
Secondary Structure PredictionSummary
1st Generation - 1970s• Q3 = 50-55%• Chou & Fausman, GOR
2nd Generation -1980s• Q3 = 60-65%• Qian & Sejnowski, GORIII
3rd Generation - 1990s• Q3 = 70-80%• PhD, PSIPRED
Many 3rd+ generation methods exist: PSI-PRED - http://bioinf.cs.ucl.ac.uk/psipred/ JPRED - http://www.compbio.dundee.ac.uk/~www-jpred/ PHD - http://www.embl-heidelberg.de/predictprotein/predictprotein.html NNPRED - http://www.cmpharm.ucsf.edu/~nomi/nnpredict.html
The sequence-structure gapThe sequence-structure gap
More than 13,137,813 known protein sequences, 76,495 experimentally determined structures.
2000040000
6000080000
100000120000
140000160000
0
Seq
uen
ces
Seq
uen
ces S
tructu
resS
tructu
res
180000200000
The gap is getting biggerThe gap is getting bigger..The sequence-structure gapThe sequence-structure gap
Protein Secondary Structures (Simplifications)
COIL (everything else)COIL (everything else)
-STRAND-STRAND
-HELIX-HELIX
Beyond Secondary StructureBeyond Secondary StructureBefore Tertiary StructureBefore Tertiary Structure
Supersecondary structures (motifs): small, discrete, commonly observed aggregates of secondary structures
helix-loop-helix
Domains: independent units of structure barrel four-helix bundle
The terms “domain” and “motif” are The terms “domain” and “motif” are
sometimes used interchangeably.sometimes used interchangeably.
Helix-loop-helixHelix-loop-helix
Beyond Secondary StructureBeyond Secondary StructureBefore Tertiary StructureBefore Tertiary Structure
Folds: Compact folding arrangements of a polypeptide chain (a protein or part of a protein).
The terms “domain” and “fold” are The terms “domain” and “fold” are
sometimes used interchangeably.sometimes used interchangeably.
EF Fold
Found in Calcium binding proteins such as Calmodulin
Leucine Zipper
•The beta-alpha-beta-alpha-beta subunit•Often present in nucleotide-binding proteins
Rossman Fold
sandwich barrel
horseshoe
Four helix bundleFour helix bundle
•24 amino acid peptide with a hydrophobic surface•Assembles into 4 helix bundle through hydrophobic regions•Maintains solubility of membrane proteins
TIM Barrel
PDB New Fold Growth
• The number of unique folds in nature is fairly small (possibly a few thousands)
• 90% of new structures submitted to PDB in the past three years have similar structural folds in PDB
New fold
Old fold
Protein data bank
• http://www.rcsb.org/pdb/
Protein 3D structure data: The structure of a protein consists of the 3D (X,Y,Z) coordinates of each non-hydrogen atom of the protein. Some protein structure also include coordinates of covalently linked prosthetic groups, non-covalently linked ligand molecules, or metal ions.For some purposes (e.g. structural alignment) only the Cα coordinates are needed.
Example of PDB format: X Y Z occupancy / temp. factor
ATOM 18 N GLY 27 40.315 161.004 11.211 1.00 10.11ATOM 19 CA GLY 27 39.049 160.737 10.462 1.00 14.18ATOM 20 C GLY 27 38.729 159.239 10.784 1.00 20.75ATOM 21 O GLY 27 39.507 158.484 11.404 1.00 21.88
Note: the PDB format provides no information about connectivity between atoms. The last two numbers (occupancy, temperature factor) relate to disorders of atomic positions in crystals.
Protein structure: Some computational tasksProtein structure: Some computational tasks
• Building a protein structure model from X-ray data
• Building a protein structure model from NMR data
• Computing the energy for a given protein structure (conformation)
• Energy minimization: Finding the structure with the minimal energy according to some empirical “force fields”.
• Simulating the protein folding process (molecular dynamics)
• Structure visualizationStructure visualization
• Computing secondary structure from atomic coordinates
• Protein superposition, structural alignmentProtein superposition, structural alignment
• Protein fold classificationProtein fold classification
• Threading: finding a fold (prototype structure) that fits to a sequenceThreading: finding a fold (prototype structure) that fits to a sequence
• Docking: fitting ligands onto a protein surface by molecular dynamics or energy minimization
• Protein 3D structure prediction from sequenceProtein 3D structure prediction from sequence
Viewing protein structures
When looking at a protein structure, we may ask the following types of questions:
• Is a particular residue on the inside or outside of a protein?• Which amino acids interact with each other?• Which amino acids are in contact with a ligand (DNA, peptide
hormone, small molecule, etc.)?• Is an observed mutation likely to disturb the protein structure?
Standard capabilities of protein structure software:• Display of protein structures in different ways (wireframe, backbone,
sticks, spacefill, ribbon.• Highlighting of individual atoms, residues or groups of residues• Calculation of interatomic distances• Advanced feature: Superposition of related structures
Example: c-abl oncoprotein SH2 domain, display wireframe
Example: c-abl oncoprotein SH2 domain, display sticks
Example: c-abl oncoprotein SH2 domain, display backbone
Example: c-abl oncoprotein SH2 domain, display spacefill
Example: c-abl oncoprotein SH2 domain, display ribbons
Predicting protein 3d structure
Goal: 3d structure from 1d sequence
Fold recognition
Homology modeling
ab-initio
An existing fold
A new fold
Homology modelingBased on the two major observations
(and some simplifications):
1. The structure of a protein is uniquely defined by its amino acid sequence.
2. Similar sequences adopt similar structures. (Distantly related sequences may still fold into similar structures.)
Homology modeling needs three items of input:
• The sequence of a protein with unknown 3D structure, the "target sequence."
• A 3D “template” – a structure having the highest sequence identity with the target sequence ( >30% sequence identity)
• An sequence alignment between the target sequence and the template sequence
Homology Modeling: How it works
o Find template
o Align target sequence with template
o Generate model:- add loops- add sidechains
o Refine model
[Rost, Protein Eng. 1999]
Two zones of homology modeling
Automated Web-Based Homology Modelling
SWISS Model : http://www.expasy.org/swissmod/SWISS-MODEL.html
WHAT IF : http://www.cmbi.kun.nl/swift/servers/
The CPHModels Server : http://www.cbs.dtu.dk/services/CPHmodels/
3D Jigsaw : http://www.bmm.icnet.uk/~3djigsaw/
SDSC1 : http://cl.sdsc.edu/hm.html
EsyPred3D : http://www.fundp.ac.be/urbm/bioinfo/esypred/
Fold recognition = Protein Threading
Which of the known folds is likely to be similar to the (unknown) fold of a new protein when only its amino-acid sequence is known?
Protein Threading• The goal: find the “correct” sequence-structure alignment
between a target sequence and its native-like fold in PDB
• Energy function – knowledge (or statistics) based rather than physics based – Should be able to distinguish correct structural folds from
incorrect structural folds– Should be able to distinguish correct sequence-fold alignment
from incorrect sequence-fold alignments
MTYKLILN …. NGVDGEWTYTE
Protein Threading
• Basic premise
• Statistics from Protein Data Bank (~2,000 structures)
• Chances for a protein to have a structural fold that already exists in PDB are quite good.
The number of unique structural (domain) folds in The number of unique structural (domain) folds in nature is fairly small (possibly a few thousand)nature is fairly small (possibly a few thousand)
90% of new structures submitted to PDB in the past 90% of new structures submitted to PDB in the past three years have similar structural folds in PDB three years have similar structural folds in PDB
Protein Threading
Basic components:– Structure database– Energy function– Sequence-structure alignment algorithm– Prediction reliability assessment
Protein Threading – structure database
• Build a template database
Process
• Threading - A protein fold recognition technique that involves incrementally replacing the sequence of a known protein structure with a query sequence of unknown structure. The new “model” structure is evaluated using a simple heuristic measure of protein fold quality. The process is repeated against all known 3D structures until an optimal fit is found.
Fold recognition methods
• 3D-PSSM http://www.sbg.bio.ic.ac.uk/~3dpssm/
• Fugue http://www-cryst.bioc.cam.ac.uk/~fugue/
• HHpred http://protevo.eb.tuebingen.mpg.de/toolkit/index.php?view=hhpred
ab-initio foldingGoal: Predict structure from “first principles”Requires:
– A free energy function, sufficiently close to the “true potential”
– A method for searching the conformational space
Advantages:– Works for novel folds– Shows that we understand the process
Disadvantages:– Applicable to short sequences only
Rosetta [Simons et al. 1997]
http://www.bioinfo.rpi.edu/~bystrc/hmmstr/server.php
Qian et al. (Nature: 2007) used distributed computing* to predict the 3D structure of a protein from its amino-acid sequence. Here, their predicted structure (grey) of a protein is overlaid with the experimentally determined crystal structure (color) of that protein. The agreement between the two is excellent.
*70,000 home computers for about two years.
Protein Sequence
Database SearchingMultiple Sequence
Alignment
Homologuein PDB
HomologyModelling
SecondaryStructurePrediction
No
Yes
3-D Protein Model
FoldRecognition
PredictedFold
Sequence-StructureAlignment
Ab-initioStructurePrediction
No
Yes
Overall Approach
ExPASy Proteomics Server:Expert Protein Analysis System
links to lots of protein prediction resources
http://expasy.org/
RMSDRMSDminmin
The root mean square deviation (RMSD) is the measure of the average distance between the backbones of superimposed proteins. In the study of globular protein conformations, one customarily measures the similarity in three-dimensional structure by the RMSD of the Cα atomic coordinates after optimal rigid body superposition.
A widely used way to compare the structures of biomolecules or solid bodies is to “translate” or rotate one structure with respect to the other to minimize the RMSD. This RMSDmin can be used as a distance measure between two proteins.