1. G53BIO – Bioinformatics http://www.cs.nott.ac.uk/~jqb/G53BIO
Protein Structure Prediction Dr. Jaume Bacardit –
Some material taken from “Arthur Lesk Introduction to Bioinformatics 2nd edition Oxford University Press 2005” and “Introduction to Bioinformatics by Anna Tramontano”

2. Outline Introduction and motivation
Basic concepts of protein structure
PSP: A family of problems
Prediction of structural aspects of protein residues
Prediction of the 3D structure of proteins
Assessment of PSP quality: CASP
Summary

3. Protein Structure: Introduction
Proteins are molecules of primary importance for the functioning of life
Structural Proteins (collagen nails hair etc.)
Enzymes
Transmembrane proteins
Proteins are polypeptide chains constructed by joining a certain kind of peptides amino acids in a linear way
The chain of amino acids however folds to create very complex 3D structures
There is a general consensus that the end state of the folding process depends on the amino acid composition of the chain

4. Motivation for PSP
The function of a protein depends greatly on its structure
The structure that a protein adopts is vital to it’s chemistry
Its structure determines which of its amino acids are exposed to carry out the protein’s function
Its structure also determines what substrates it can react with
However the structure of a protein is very difficult to determine experimentally and in some cases almost impossible

5. Protein Structure Prediction
That is why we have to predict it
PSP aims to predict the 3D structure of a protein based on its primary sequence

6. Impact of PSP
PSP is an open problem. The 3D structure depends on many variables
It has been one of the main holy grails of computational biology for many decades
Impact of having better protein structure models are countless
Genetic therapy
Synthesis of drugs for incurable diseases
Improved crops
Environmental remediation

7. Protein Structure

8. Backbone and side chain
All amino acids have a common part: the backbone
Each amino acid type has a different side chain
The Cα atom connects the backbone and the side chain
The first carbon atom in the side chain is called Cβ (except for Gly)

9. Amino Acids

10. Protein Structure: Introduction
Different amino acids have different properties
These properties will affect the protein structure and function
Hydrophobicity, for instance, is the main driving force (but not the only one) of the folding process

11. Protein Structure: Hierarchical nature of protein structure
Primary Structure = Sequence of amino acids
MKYNNHDKIRDFIIIEAYMFRFKKKVKPEVDMTIKEFILLTYLFHQQENTLPFKKIVSDLCYKQSDLVQHIKVLVKHSYISKVRSKIDERNTYISISEEQREKIAERVTLFDQIIKQFNLADQSESQMIPKDSKEFLNLMMYTMYFKNIIKKHLTLSFVEFTILAIITSQNKNIVLLKDLIETIHHKYPQTVRALNNLKKQGYLIKERSTEDERKILIHMDDAQQDHAEQLLAQVNQLLADKDHLHLVFE
Secondary Structure
Tertiary
Local Interactions
Global Interactions

12. Protein Structure: Hierarchical nature of protein structure
The amino acid composition of a protein is called primary structure or primary sequence
The folding process of a protein involves several steps
The protein creates some patterns due to local interactions with the closest residues in the chain. These patters are called the protein secondary structure
Afterwards, the secondary structure motifs organise into stable patters, called tertirary structure
Finally, proteins can be composed of several subunits or monomers, forming the quaternary structure
Other, less used, levels of this hierarchy are
Supersecondary structure (recurrent patters of interaction between secondary structure elements close in sequence )
Domains (subunits within a protein with quasi-independent folding stability)

13. Backbone
The polypeptide chain of proteins in joined together in a very specific way
Two dihedral angles (phi and psi) define the torsion of each amino acid in the chain
Phi is the angle of the Cα –N bond and psi is the angle of the Cα-C bond.

14. Protein Structure: Hierarchical nature of protein structure
Residues form a loop of 3.6 residues/turn and 5.4Å wide
There are two main kinds of secondary structure motifs:
α helices
β sheets
Residues that do not fail in these two categories are said to be in coil state
Residues lay flat in parallell strands. Called parallell sheets if all strands have the same N-to-C orientation, and antiparallell if adjacent strands have opposed orientations

15. Protein Structure: Hierarchical nature of protein structure
Supersecondary structure elements
β hairpin
β-α-β unit

16. Protein Data Bank
Proteins for which scientists have been able to resolve the structure (using x-ray crystallography, NMR, etc.) are stored in the Protein Data Bank (PDB)
Each protein has a four letter ID code (PDB id)
A fifth letter (A, B, C, etc.) is used to identify the chain within the protein
Proteins are stored in a format called also PDB format
File for the 1A68 protein

17. Protein Structure: Ramachandran plots
We saw that the backbone of a residue is characterised by two angles: psi and phi.
Can they take any value?
Fortunately not
This effect was studied long ago by GN Ramachandran
He proposed a diagram to visualize these angles (phi in the X axis, psi in the Y axi) of amino acid residues
Different types of secondary structure are clustered in different regions of the diagram

18. Protein Structure: Ramachandran plots
In real proteins, these plots are not so clear
You can create the Ramachandran plot for any protein in PDB at
At the right there is the plot for a set of 80 proteins

19. Protein Structure: Classifications of protein structure
Several tertiary structure classification method exists, for instance, SCOP, CATH, and FSSP/DDD.
No method is perfect, hence was proposed.
SCOP is the most widespread of them
SCOP = Structural Classification Of Proteins
In its 1.73 release (November 2007) it catalogs proteins with known structure (that is, entries in the PDB archive)
It uses a hierarchical system to catalog the proteins, according to evolutionary origin and structural similarity
The levels of the hierarchy are: class, fold, superfamily, family, protein and species

20. Protein Structure: Classifications of protein structure
Main classes of SCOP (first level of hierarchy)
All α proteins – proteins that have (almost) only α helices
All β proteins – proteins that have (almost) only β sheets
α+β proteins – proteins that have both α helices and (mostly) antiparallell strands, but segregated in different parts of the protein
α/β proteins – proteins that have both α helices and (mostly) parallell strands, typically forming β+α+β units
Multidomains proteins – proteins having two or more domains belonging to different classes
Membrane and cell surface proteins
Small proteins (metal ligans, heme and proteins with disulfide bridges
Coiled coils proteins
Low resolution protein structure
Peptides
Designed proteins

21. Protein Structure: Classifications of protein structure
SCOP classification of Flavodoxin from Clostridium beijerinckii
Class: α/β
Fold: Flavodoxin-like: 3 layers, α/β/α; parallel β-sheet of 5 strands
Superfamily: Flavoproteins
Family: Flavodoxin-related binds FMN
Protein: Flavodoxin
Species: Clostridium beijerinckii
PDB ID: 5ULL

22. Prediction types of PSP
There are several kinds of prediction problems within the scope of PSP
The main one of course is to predict the 3D coordinates of all atoms of a protein (or at least the backbone) based on its primary sequence
There are many structural properties of individual residues within a protein that can be predicted for instance:
The secondary structure state of the residue
If a residue is buried in the core of the protein or exposed in the surface
Accurate predictions of these sub-problems can simplify the general 3D PSP problem

23. Prediction types of PSP
There is an important distinction between the two classes of prediction
The 3D PSP is generally treated as an optimisation problem
The prediction of structural aspects of protein residues are generally treated as machine learning problems

24. Optimisation
Given a problem for which you have a way of assessing how good is each possible solution
An evaluation function
Optimisation is the process of finding the best possible solution
Dynamic programming (as seen for sequence alignment) is an optimisation method
Genetic Algorithms are another examples of optimisation
The key differences between them is how they explore the space of candidate solutions

25. Machine Learning
Machine learning: How to construct programs that automatically learn from experience [Mitchell 1997]
ML is a Computer Science discipline part of the Artificial Intelligence field
Its goal is to construct automatically a description of some phenomenon given a set of data extracted from previous observations of the phenomenon because it would be beneficial to predict it in the future.

26. Flow of data in machine learning
Specifically we are concerned with supervised learning. That is when we know the solution for the training data
Training Set
Learning
Method
Theory
Unknown instance
Class

27. Types of machine learning
Rule learning 1
If (X<0.25 and Y>0.75) or
(X>0.75 and Y<0.25) then 
If (X>0.75 and Y>0.75) then 
If (X<0.25 and Y<0.25) then  Y
Everything else  1 X

28. Other machine learning techniques
Other methods that have also been used in PSP are
Artificial Neural Networks
Support Vector Machines
Hidden Markov Models
If you are interested in the technology side of PSP a good book is “Bioinformatics: The Machine Learning Approach” by Baldi and Brunak

29. Prediction of structural aspects of protein residues
Many of these features are due to local interactions of an amino acid and its immediate neighbours
Can it be predicted using information from the closest neighbours in the chain?
In this simplified example to predict the SS state of residue i we would use information from residues i-1 i and i+1. That is a window of ±1 residues around the target
Ri-5
SSi-5
Ri-4
SSi-4
Ri-3
SSi-3
Ri-2
SSi-2
Ri-1
SSi-1 Ri
SSi
Ri+1
SSi+1
Ri+2
SSi+2
Ri+3
SSi+3
Ri+4
SSi+4
Ri+5
SSi+5
Ri-1 Ri Ri+1  SSi
Ri Ri+1 Ri+2  SSi+1
Ri+1 Ri+2 Ri+3  SSi+2

30. What information do we include for each residue?
Early prediction methods used just the primary sequence  the AA types of the residues in the window
However the primary sequence has limited amount of information
It does not contain any evolutionary information it does not say which residues are conserved and which are not
Where can we obtain this information?
Position-Specific Scoring Matrices which is a product of a Multiple Sequence Alignment

31. Position-Specific Scoring Matrices (PSSM)
For each residue in the query sequence compute the distribution of amino acids of the corresponding residues in all aligned sequences (discarding those too similar to the query)
This distributions will tell us which mutations are likely and which mutations are less likely for each residue in the query sequence
In essence it’s similar to a substitution matrix but tailored for the sequence that we are aligning
A PSSM profile will also tell us which residues are more conserved and which residues are more subject to insertions or deletions

32. PSSM for the 10 first residues of 1n7lA
A R N D C Q E G H I L K M F P S T W Y V A: M: E: K: V: Q: Y: L: T: R:

33. Secondary Structure Prediction
The most usual way is to predict whether a residue belongs to an α helix a β sheet or is in coil state
Several programs can determine the actual SS state of a protein from a PDB file. The most common of them is DSSP
Typically, a window of ±7 amino acids (15 in total) is used

34. Secondary Structure Prediction
MSA R1 R2 R3
Rn-1 Rn
PSSM1
PSSM2
PSSM3
PSSMn-1
PSSMn
Primary sequence
PSSM profile of sequence
Prediction method
Windows generation
PSSMi-1
PSSMi
PSSMi+1
SSi?
Window of PSSM profiles
Prediction
The most popular public SS predictor is PSIPRED

35. Coordination Number Prediction
Two residues of a chain are said to be in contact if their distance is less than a certain threshold (e.g. 8Å)
CN of a residue : count of contacts that a certain residue has
CN gives us a simplified profile of the density of packing of the protein
Native State
Primary
Sequence
Contact

36. Other predictions
Other kinds of residue structural aspects that can be predicted
Solvent accessibility: Amount of surface of each residue that is exposed to solvent
Recursive Convex Hull: A metric that models a protein as an onion and assigns each residue to a layer. Formally each layer is a convex hull of points
These features (and others) are predicted in a similar was as done for SS or CN

37. Contact Map prediction
Prediction given two residues from a chain whether these two residues are in contact or not
This problem can be represented by a binary matrix. 1= contact 0 = non contact
Plotting this matrix reveals many characteristics from the protein structure
helices
sheets

38. Contact Map Prediction
Instead of a single window around the target now there are two windows around the pair of residues to be predicted to be in contact or not
Many methods also use a third window, placed in the middle point in the chain between the two target residues

39. Contact Map prediction at Nottingham
For each position in these 3 windows we include:
PSSM profile
Predicted SS, SA, RCH and CN
The whole connecting segment between the two targets is represented as
Distribution of AA and predicted SS, SA, RCH and CN

40. Contact Map prediction at Nottingham
Moreover, global protein information is also included
Sequence length
Separation between target residues
Contact propensity of target residues
Distribution of AA and predicted SS, SA, RCH and CN of the whole chain
Each instance is represented by 631 variables

41. Contact Map prediction at Nottingham
Training set
x50
x25
Consensus
Predictions
Samples
Rule sets
Training set of 2413 proteins selected to represent a broad set of sequences
32 million pairs of amino-acids (instances in the training set) with less than 2% of real contacts
Each instance is characterized by up to 631 attributes
50 samples of ~ examples are generated from the training set. Each sample contains two no-contact instances for each contact instance
The BioHEL GBML method (Bacardit et al., 2009) was run 25 times on each sample
An ensemble of 1250 rule sets (50 samples x 25 seeds) performs the contact maps predictions using simple consensus voting
Confidence is computed based on the votes distribution in the ensemble
(Bacardit et al., Bioinformatics (2012) 28 (19): )

42. 3D Protein Structure Prediction
Approaches for 3D PSP
Template-Based Modelling
Ab-Initio methods
State-of-the-Art methods
I-Tasser
Rosetta

43. Approaches for 3D PSP
Some PSP methods try to identify a template protein and then adapt the structure of the template to the target protein  Template-based Modelling
Other methods try to generate the structure of the protein from scratch (Ab Initio Modelling) optimizing some energy function that models the stability of the protein, in case that no template can be identified

44. Pipeline for Template-based Modelling
Typical steps
Identify the template (next slide)
Produce the final alignment between the residues of target and template
Determine main chain segments to represent the regions containing insertions and deletions (gaps in the alignment) and stitch them into the main chain of the template to create an initial model for the target
Replace the side chains of residues that have been mutated (mismatches in the alignment) although it is possible that the conformation in the template is still conserved
Examine the model to detect any serious atom collision and relieve them
Refine the model by energy minimization. This stage is meant to adapt the stitched segments to the conserved structure and to adjust the side chains so find the most stable conformation

45. Loop remodelling

46. Template identification
Can we find a sequence with known structure and high sequence identify with the target?
Homology Modelling
Still, there is a template (structure similar to that of the target) but it has poor sequence identity. We need to identify it by other means
Fold recognition
Profile-based methods
Threading methods

47. Profile-based Methods
Aim is to construct 1D representations (profiles) of the structures in our fold database
Afterwards, when a target sequence comes, we construct its profile and check our database for the most similar profile
That is, instead of aligning amino acid sequences, we align structural 1D profiles

48. How to construct the profile?
We choose a series of structural properties of residues
Most frequent secondary structure state
Alpha helix, Beta sheet, other
Solvent Accessibility
< 40Å2, >100Å2, intermediate
Hydrophobic/polar
For each amino acid, we decide to which category it belongs based on statistics computed on a large database of structures

49. How to construct the profile?
Alpha helix
Beta sheet
Other
<40Å2
Hydrophobic: a
Polar: d
Hydrophobic: b
Polar: e
Hydrophobic: c
Polar: f
>100Å2
Hydrophobic: g
Polar: j
Hydrophobic: h
Polar: k
Hydrophobic: i
Polar: l
intermediate
Hydrophobic: m
Polar: p
Hydrophobic: n
Polar: q
Hydrophobic: o
Polar: r
Now the sequence for each protein in our database will have a new structural representation
We need to predict SS and Acc for the template

50. Threading methods
We start with compiling a catalogue of unique folds (filtering out repeats)
Afterwards, we evaluate how likely it is that the target sequence adopts each of the folds, and how (alignment)
Name is a metaphor taken from tailoring, as we are are trying to fit the sequence (a thread) through a known structure
We will choose the template (and alignment) that has the lowest (estimated) energy

51. Threading methods Energy estimation needs to be simple and fast
As we need to evaluate all possible folds and alignments
Energy is the product of all the pair-wise interactions ocurring in a protein
Thus, the energy estimation will be computed as the sum of the energy terms for every pair of residues in the protein
How to compute the energy interaction for a given pair of amino acids?

52. Pair-wise Energy estimation
Boltzmann’s equation states that the probablity of observing a given event depends on its energy
P(x) = e(E(x)/KT)
If we reverse this equation we get:
E(x) = -KT ln[ P(x) ]
We can compute P(x), for each pair of amino acids from a database of known structures as the frequency in which these amino acids are observed to be in contact

53. Alignment within threading
We still need to solve the problem of the correspondence of the residues in our template with those of the target
This is a very difficult problem, as a change in an alignment can have impact in the interaction with many residues
There is an exact (but costly) solution
Instead, most methods adopt an approximate method called frozen approximation
When evaluating the possibility of assigning one of the amino acids of the target to a certain position in the template, instead of computing the interactions with the rest of the target residues, we will use those of the template

54. Frozen Apporximation

55. Aligning target and template
Crucial step before generating the initial model
It is possible, specially for homology modelling, that the best sequence alignment does not correspond to the best structural alignment
That is, finding the best correspondence between the coordinates of each amino acid of target and template
In this case, a better alignment process needs to be performed, to do se, we can use
Information derived from the template’s structure
Predicted for the target

56. Aligning Target and Template
Wrong alignment. Some atoms are
too close (big circle). Some atoms
are too far (small circle)
Correct alignment after shifting

57. The poor man approach to homology modelling
To find templates
PSI-BLAST
3D Jury. This program is a meta-server. That is it asks many other servers what templates would they choose and then produces a consensus decision based on the answers of the servers
To produce a model of a protein given a template
MODELLER. Very popular homology modelling package. Free for academic use
To refine the side-chain conformations
SCWRL

58. Ab-Initio modelling
In general this kind of modelling is still quite primitive when compared to homology modelling
However without a target it is the only choice
Pure ab-initio modelling is still very costly and ineffective but hybrid homology/ab-initio methods such as fragment assembly have better performance

59. Ab-Initio modelling
The most advanced ab-initio method is fragment assembly
Consists by breaking up the sequence in small subsegments of 3 to 9 residues and generating structure for these segments based on a large library of known fragments
Decoys are generated from all possible combinations of fragments
An energy minimization process is applied to all decoys.
Decoys are clustered and the final models are selected from the center of the largest clusters

60. Energy minimisation
Energy minimization is not easy. We may need to go uphill before we can
reach the lowest energy conformation

61. Energy functions for ab-initio methods
Energy function needs to take into account the interactions of all atoms of all amino acids
Many different types of energy sources
Covalent bonds
Angles and torsions of bonds between atoms
Van der Waals interactions (repulsion/attraction)
Energy of charged atoms
Interactions with solvent
Hydrogen bonds
Exact formulas are very costly, so generally PSP methods use knowledge-based potentials, computed from a large database of structures

62. I-Tasser Prediction method from Zhang’s group
Fully automated server, without any human intervention
Steps
Template identification
Structure assembly
Atomic model construction
Model selection

63. I-Tasser: Template Identification
MUSTER fold recognition method, used both for whole proteins (TBM) or for fragments (Ab Inition)
Profile-based fold recognition
Secondary structure
Structural frament profile
Solvent accessibility
Backbone torsion angle
Hydrophobicity
For the most difficult targets, a meta-server that combines the outputs of various methods is used

64. I-Tasser: Structure assembly
Generation of a preliminary model with only coordinates for Cα and sidechain positions
Using the template as starting point where possible and ab-initio methods for amino acids without alignment
Two iterations of refinement
1st based on templates
2nd based on clustering the models of the previous iteration and using the centroids of each cluster as starting points

65. I-Tasser energy function
Knowledge-based statistics of
Cα – sidechain correlation
H-bonds
Hydrophobicity
Spatial restraints of templates
Contact Map prediction from SVMSEQ
9 predictions included, combinations of
Contacts between Cα, Cβ or side chain centers
Contact cut-offs of 6, 7 or 8 Å

66. I-Tasser atomic model construction
Full-atom models are constructed from the approximate models produced by the cluster centroids
1st the backbone is matched with a large library of template fragments with high resolution structure
Then full-atom optimization occurs focusing on H-bonds, removing clashes and using the Charmm22 molecular dynamics force field

67. I-Tasser model selection
Several full-atom models are generated from each cluster centroid
Models need to be ranked to select the best one
I-Tasser uses a weighted sum of
Number of H-Bonds / target length
TM-score (metric to compare structures) between the full-atom model and the centroid cluster

68. Rosetta Predictor from David Baker’s group
It uses a massive distributed computing infrastructure
For CASP7 in 2006 it claimed to dedicate up to 104 cpu years/target
Template identification used a variety of methods depending on sequence identity between target and template
Different protocols for Template-Based Modelling and Free Modelling (fragment assembly)
3 variants of TBM depending on degree of homology between target and template

69. Rosetta Full-atom refinement protocol Energy function based on
Short-range interations: Van der Waals energe, H-bonds and solvent accessibility
Long range interactions (dampening of electrostatic interactions)
Minimization through Monte Carlo with the following steps:
Perturbation of a randomly selected angle from the backbone
Optimisation of side-chain rotamer conformations
Optimisation of both backbone and sidechain torsion angles

70. PSP and CASP
PSP has improved through the years. This improvement has been assessed mainly in CASP
CASP = Critical Assessment of Techniques for Protein Structure Prediction
It is a biannual community exercise to evaluate the state-of-the-art in PSP
Every day for about three months the organizers release some protein sequences for which nobody knows the structure (128 sequences were released in CASP8 in 2008)
Each prediction group is given three weeks to return their predictions. 24 hours are give to automated servers
Then at the end of the year experts meet in a place close to the sea to discuss the results of the experiment 

71. CASP categories Several categories of experiments are assessed in CASP
Template-Based Modeling (Homology and fold recognition)
Free Modeling (no template i.e. ab initio)
Contact Map prediction
Functional sites prediction
Domain prediction
Disordered regions
Quality assessment
Categories have changed through time
SS prediction is not assessed anymore after CASP4
Homology modeling and fold recognition merged into TBM

72. Progress through CASP 1. Computers help structure prediction:
(From Nick Grishin’s Humans vs Servers presentation in CASP8)
1. Computers help structure prediction:
no more paper models
2. Knowledge-based potentials work better.
3. Local “threading” and fragment assembly
(Baker)
4. Averaging and consensus methods work:
meta-servers (Ginalski-Rychlewski)
5. Sequence profile methods are as
(or more powerful) than threading: (Sốding)
6. Jamming poorly similar templates together
helps: (Skolnick-Zhang)

73. Assessment of 3D PSP How can we quantify how good is a model?
That is, how similar is a model structure to the actual (native) one?
We will see this in depth when we cover the protein structure comparison topic, later in the module
Now we are just going to describe the most popular metric, GDT-TS

74. GDT-TS Global Distance Test – Total Score
This measure tries to produce a balance between good local and global similarity of structures (unlike RMSD)
If a measure only takes a global point of view, good models that only fail badly in a few amino acids could be discarded

75. GDT-TS steps
All segments of 3, 5 and 7 consecutive amino acids from the model are superimposed to the actual structure.
Each of them will be iteratively extended while they are good enough
Good enough = Distance between all residue pairs (represented by their Cα atoms) is less than a certain threshold
A final superposition includes the set of segments covering as many residues as possible
Segments do not need to be continuous

76. GDT-TS metric
The process of superposition is performed four times, using thresholds of 1, 2, 4 and 8 Å
The reason for including 4 different thresholds is to have a metric which is good both for high accuracy models and for approximate models

77. GDT-HA HA = High Accuracy
Set of thresholds in GDT-TS changed to 0.5, 1, 2 and 4
For high accuracy GDT just provide a crude approximation (backbone). So other measures are taken into account
H-bonds
Position and rotation of sidechains
Clashes of atoms

78. Contact Map prediction in CASP
Contact Map is assessed using the targets in the Free Modelling category
Also, only long-range contacts (with a minimum chain separation of 24 residues) are evaluated
Predictor groups are asked to submit a list of predicted contacts and a confidence level for each prediction
The assessors then rank the predictions for each protein and take a look at the top L/x ones, where L is the length of the protein and x={5,10}

79. Contact Map prediction in CASP
From these L/x top ranked contacts two measures are computed
Accuracy: TP/(TP+FP)
Xd: difference between the distribution of predicted distances and a random distribution

80. CASP9 results
These two groups derived contact predictions from 3D models

81. Other CASP prediction categories
Functional sites prediction
Predicting which residues of a given sequence are those that perform the chemistry of the protein
Bind to other proteins/compounds
Methods can use whatever information they can infer to perform this prediction
However, most predictions can be performed simply by homology 
Domain prediction
Domains = quasi-independent subsets of a protein, that fold on their own
Their prediction follows a simple divide-and-conquer motivation
It is much easier to create separate models for the different domains of a protein

82. Disordered regions prediction
Regions of a protein that do not fold into a unique pattern (no coordinates in the PDB file)
75% of mammal signaling proteins are estimated to contain long (>30) disordered regions, and 25% of the total amount of proteins may be fully disordered
Thus, it is useful to predict from the sequence if that is the case

83. Disordered protein 2K5K

84. Quality assessment prediction
Given a model, can we predict how good it is (without comparing it to the native structure)?
Overall and per-residue model quality
Prediction was done based on the models from the server category
Two families of methods
That perform predictions for individual models
That take a set of models and give predictions based on consensus agreements

85. Summary of topic Importance of PSP
Many different types of prediction included in the PSP family
3D PSP
Prediction of amino acid structural features
Others
Families of 3D PSP
Template-based Modelling
Free modelling