1. Protein Tertiary Structure Prediction
Structural Bioinformatics
Protein Tertiary Structure Prediction

2. The Different levels of Protein Structure
Primary: amino acid linear sequence.
Secondary: -helices, β-sheets and loops.
Tertiary: the 3D shape of the fully folded
polypeptide chain

3. Predicting 3D Structure
Outstanding difficult problem
Comparative modeling (homology)
Based on structural homology
Fold recognition (threading)
Based on sequence homology

4. Comparative Modeling Based on Sequence homology
Similar sequences suggests similar structure

5. Sequence and Structure alignments of two Retinol Binding Protein

6. Structure Alignments
There are many different algorithms for structural Alignment.
The outputs of a structural alignment are a superposition of the atomic coordinates and a minimal Root Mean Square Distance (RMSD) between the structures. The RMSD of two aligned structures indicates their divergence from one another.
Low values of RMSD mean similar structures

7. Based on Sequence homology
Comparative Modeling
Similar sequence suggests similar structure
Builds a protein structure model based on its alignment to one or more related protein structures in the database

8. Based on Sequence homology
Comparative Modeling
Accuracy of the comparative model is related to the sequence identity on which it is based
>50% sequence identity = high accuracy
30%-50% sequence identity= 90% modeled
<30% sequence identity =low accuracy (many errors)

9. Homology Threshold for Different Alignment Lengths
Threshold (t)
Alignment length (L)
A sequence alignment between two proteins is considered to imply
structural homology if the sequence identity is equal to or above the
homology threshold t in a sequence region of a given length L.
The threshold values t(L) are derived from PDB

10. Comparative Modeling Similarity particularly high in core
Alpha helices and beta sheets preserved
Even near-identical sequences vary in loops

11. Comparative Modeling Methods
Based on Sequence homology
Comparative Modeling Methods
MODELLER (Sali –Rockefeller/UCSF)
SCWRL (Dunbrack- UCSF )
SWISS-MODEL

12. Based on Sequence homology
Comparative Modeling
Modeling of a sequence based on known structures
Consist of four major steps :
Finding a known structure(s) related to the sequence to be modeled (template), using sequence comparison methods such as PSI-BLAST
2. Aligning sequence with the templates
3. Building a model
4. Assessing the model

13. Based on Structure homology
Fold Recognition

14. Based on Secondary Structure
Protein Folds: sequential and spatial arrangement of secondary structures
Hemoglobin
TIM

15. Similar folds usually mean similar function
Transcription
factors
Homeodomain

16. The same fold can have multiple functions
Rossmann
12 functions
31 functions
TIM barrel

17. Based on Structure homology
Fold Recognition
Methods of protein fold recognition attempt to detect similarities between protein 3D structure that have no significant sequence similarity.
Search for folds that are compatible with a particular sequence.
"the turn the protein folding problem on it's head” rather than predicting how a sequence will fold, they predict how well a fold will fit a sequence

18. Based on Structure homology
Basic steps in Fold Recognition :
Compare sequence against a Library of all known Protein Folds (finite number)
Query sequence
MTYGFRIPLNCERWGHKLSTVILKRP...
Goal: find to what folding template the sequence fits best
There are different ways to evaluate sequence-structure fit

19. Based on Secondary Structure homology
There are different ways to evaluate sequence-structure fit
Potential fold
1) ) n)
...
...
MAHFPGFGQSLLFGYPVYVFGD...

20. Programs for fold recognition
Based on Secondary Structure homology
Programs for fold recognition
TOPITS (Rost 1995)
GenTHREADER (Jones 1999)
SAMT02 (UCSC HMM)
3D-PSSM

21. Ab Initio Modeling
Compute molecular structure from laws of physics and chemistry alone
Theoretically Ideal solution
Practically nearly impossible
WHY ?
Exceptionally complex calculations
Biophysics understanding incomplete

22. Ab Initio Methods Rosetta (Bakers lab, Seattle)
Undertaker (Karplus, UCSC)

23. CASP - Critical Assessment of Structure Prediction
Competition among different groups for resolving the 3D structure of proteins that are about to be solved experimentally.
Current state -
ab-initio - the worst, but greatly improved in the last years.
Modeling - performs very well when homologous sequences with known structures exist.
Fold recognition - performs well.

24. What can you do? FOLDIT Solve Puzzles for Science
A computer game to fold proteins

25. Predicting function from structure
What’s Next
Predicting function from structure

26. Structural Genomics : a large scale structure determination project designed to cover all representative protein structures
ATP binding domain of protein MJ0577
Zarembinski, et al., Proc.Nat.Acad.Sci.USA, 99:15189 (1998)

27. Wanted ! As a result of the Structure Genomic
initiative many structures of proteins
with unknown function will be solved
Wanted !
Automated methods to predict function from the protein structures resulting from the structural genomic project.

28. Approaches for predicting function from structure
ConSurf - Mapping the evolution conservation on the protein structure

29. Approaches for predicting function from structure
PFPlus – Identifying positive electrostatic patches on the protein structure

30. A method to distinguish DNA from RNA-binding proteins
DNA binding interface
RNA binding interface

31. RNA and DNA binding interfaces tend to
have different geometric features
DNA binding interface
RNA binding interface
So further, in order to differentiate between RNA and DNA binding interfaces we needed a geometric method that would be able to characterize the different interfaces.
Landscapes as proteins have different type of surfaces.
Those two pictures describe two different types of landscapes.
When looking carefully on the surface of the landscape, a composition of polymorphic shapes can be found.
For example Peaks and Pits.
The composition of those shapes can help characterize different surfaces.
We thought that applying a similar approach for analyzing and characterizing the DNA-binding and RNA-binding interfaces would help distinguishing the two groups.

32. Applying Differential Geometry to
characterize DNA and RNA binding proteins
k1 - minimal curvature
K2- MAXIMAL CURVATURE
The first step in the new method we developed was to extract the geometric properties of each point on protein-binding interface.
A surface is composed of many points. Finding the geometric properties Mean and Gaussian curvatures can help classify a point as a part of local geometry shape for example a pit, a peak or a valley.
The Mean and Gaussian curvatures’ calculation is based on k1 and k2. k1 and k2 are the principle curvatures.
What are the principle curvatures k1 and k2? A curvature is equals to one over the radius.
For example lets look at a certain point on a top of a hill. Imagine ourselves standing on that point looking towards all the directions around for all the paths that pass throw this point.
The most moderate path is k1 the minimal curvature. The most steep path is k2 the maximal curvature.
The principle curvatures have directions or signs.
If the curvature climbs up from a point than the curvature is positive, on the other hand if the curvature goes down than it is negative. The curvatures on a plane are zero.
The signs of k1 and k2, the principle curvatures determine the signs of the Mean and the Gaussian curvatures.
H=(k1+k2)/2 Mean Curvature
K=k1*k2 Gaussian Curvature

33. Applying Differential Geometry to characterize DNA and RNA proteins
Flat
Peak
Pit
Minimal
Surface
The signs of the Mean and Gaussian curvatures can classify the surface type a certain point is belonging to.
The eight fundamental surface types are shown here.
After extracting the Mean and Gaussian curvatures, the next step of the method was to classify each point on protein binding interface to one of those eight fundamental surface types, based on the point’s K and H.
A point that has a positive K and negative H is considered as a part of a peak, while a point which has both positive K and H should be a part of a Pit.
Ridge
Saddle
ridge
Valley
Saddle
valley

34. Applying Differential Geometry for DNA and RNA function prediction
Frequency of points
The signs of the Mean and Gaussian curvatures can classify the surface type a certain point is belonging to.
The eight fundamental surface types are shown here.
After extracting the Mean and Gaussian curvatures, the next step of the method was to classify each point on protein binding interface to one of those eight fundamental surface types, based on the point’s K and H.
A point that has a positive K and negative H is considered as a part of a peak, while a point which has both positive K and H should be a part of a Pit.

35. RNA binding surfaces are distinguished
from DNA binding surfaces based on
Differential Geometric features
76% RNA-binding
78% DNA binding
The signs of the Mean and Gaussian curvatures can classify the surface type a certain point is belonging to.
The eight fundamental surface types are shown here.
After extracting the Mean and Gaussian curvatures, the next step of the method was to classify each point on protein binding interface to one of those eight fundamental surface types, based on the point’s K and H.
A point that has a positive K and negative H is considered as a part of a peak, while a point which has both positive K and H should be a part of a Pit.

36. Differential Geometry can correctly determine
whether a given binding domain binds
RNA or DNA
Frequency of points
RNA pattern
DNA pattern
Shazman et al, NAR 2011

37. How can we view the protein structure ?
Download the coordinates of the structure from the PDB
Launch a 3D viewer program
For example we will use the program Pymol
The program can be downloaded freely from
the Pymol homepage
Upload the coordinates to the viewer

38. Pymol example Launch Pymol Open file “1aqb” (PDB coordinate file)
Display sequence
Hide everything
Show main chain / hide main chain
Show cartoon
Color by ss
Color red
Color green, resi 1:40
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