1. Bioinformatics how to …
use publicly available free tools to predict protein structure by comparative modeling

2. Proteins are 3D objects with complex shapes
Over 60,000 protein structures have been determined, mostly by X-ray crystallography (PDB)
3D structure of ~70% of bacterial and 50% of human proteins can be predicted (comparative modeling)

3. A predicted model simply illustrates our assumptions
No assumptions, this
is nature telling us
how it is
QNTAHLDQFERIKTLGTGSFGRVMLVKHKETGNH
GNAAAAKKGSEQESVKEFLAKAKEDFLKKWENPA
FAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPF
LVKLEYSFKDNSNLYMVMEYVPGGEMFSHLRRIG
RFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPE
LAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPF
NLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEY
FADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNL
LQVDLTKRFGNLKDGVNDIKNHKWFATTDWIAIY
QRKVEAPFIPKFKGPGDTSNFDDYEEEEIRVSIN
EKCGKEFSEF
Assumption
(protein A is Similar to protein B)
Result
(protein A is Similar to protein B)
Sequence

4. How do we know that these proteins are similar?
Well studied protein
SRRSASHPTYSEMIAAAIRAEKSRGGSSRQSIQKYIKSHYKVGHNADLQIKLSIRRLLAA
Unknown protein
GLLTTKFVSLLQEAKDGVLDLKLAADTLAVRQKRRIYDITNVLEGIGLIEKKSKNSIQW
similarity
prediction

5. How can we make such assumptions?
Statistical reliability of the prediction
E-value - the number of hits one can "expect" to see just by chance when searching a database of a particular size (closer to zero the better)
Z-score – score expressed as a distance from the mean calculated in standard deviations (the bigger the better)

6. Similar, but not homologous
phosphoribosyltransferase and viral coat protein, identity: 42%, different folds, different functions
99 IRLKSYCNDQSTGDIKVIGGDDLSTLTGKNVLIVEDIIDTGKTMQTLLSLVRQY.NPKMVKVASLLVKRTPRSVGY 173
: ||. ||| || |. || | : | | | | || | || |:| | ||.| |
214 VPLKTDANDQ.IGDSLY....SAMTVDDFGVLAVRVVNDHNPTKVT..SKVRIYMKPKHVRV...WCPRPPRAVPY 279

7. Different, but homologous
Histone H5 and transcription factor E2F4, identity 7%, similar fold, similar function (DNA binding)
PTYSEMIAAAIRAEKSRGGSSRQSIQKYIKSHYKVGHNADLQIKLSIRRLLAAGVLKQTKGVGASGSFRL | | | | |
GLLTTKFVSLLQEAKD-GVLDLKLAADTLA------VRQKRRIYDITNVLEGIGLIEKKS----KNSIQW

8. Steps in comparative modeling
Are there any well characterized
proteins similar to my protein?
Recognition
What is the position-by-position
target/template equivalence
Alignment
What is the detailed 3D structure of my proteins
Modeling
Model analysis
Is my model any good?

9. Recognition BLAST, PSI-BLAST or PFAM, FFAS, metaserver (bioinfo)
Name (PDB code) of the template
Statistical significance of the match (Z-score, e.value, p.value, points)

10. Alignment
The same tools as in recognition (perhaps with different parameters), editing by hand
Position by position equivalence table

11. Modeling Commercial programs Freeware/shareware/servers
Accelrys (Insight)
Tripos (Sybyl) …
Freeware/shareware/servers
Modeller (Andrej Sali)
Jackal (Barry Honig)
SCRWL (Roland Dunbrack)
SwissModel

12. Model quality Empirical energy based tools Geometric quality
PSQS (
SwissPDB viewer
Geometric quality
Procheck, SFCHECK, etc. (

13. Expectations of comparative modeling
Easy – % sequence id - strong sequence
similarity, strong structure similarity, obvious function analogy 75
Difficult – 40%-25% - twilight zone
sequence similarity, increasing structure divergence, function diversification 50 25
Fold prediction – below 25% seq id. no apparent sequence similarity extreme function divergence

14. Challenges of comparative modeling
Recognition
Alignment
Modeling
Challenges
Trivial
Simple
Loop modeling
Easy
Challenging
Alignment, backbone shifts
Difficult
Very difficult
Significant errors
Often impossible
100 80 60 40 20

15. Hands-on Activity http://bioinformatics.burnham.org/
Click below for a hands-on, “bioinformatics how to” activity
Go to
Click Structure Biology Course - “Protein Modeling Tutorial” Link in the homepage.
OR Go to….

16. Models and Simulation
Computational Biology

17. Models and Simulation Chapter Goals Complex Systems
Continuous and Discrete simulation
Object-oriented design and building models
Queuing systems
Weather and Seismic models

18. Computational Biology
Chapter Goals
Computational Biology
Bioinformatics
Computational Biomodeling
Protein Modeling
Molecular Modeling

19. Computer Graphics Chapter Goals The CREATION of complex images CAD
Fractal and Other Techniques
Light and Rendering
Movement

20. What is a Model? An Abstraction of a Real World System
A representation of the objects or quantities within the system (Noun, the Data)
and the rules that govern the interactions between them (Verb, the Code & Algorithms)
Systems that are best suited to being simulated are dynamic, interactive, and complicated

21. What Is Simulation?
Simulation is RUNNING a model to PREDICT the results of experimental CHANGES in the system
Doing “What If” analysis
“What happens if I change this?
“What happens if I don’t?”

22. Kinds of Models

23. Kinds of Models There are 2 Big Slices: Discrete Models
Continuous Models

24. Kinds of Models Discrete event simulation
Made up of entities, attributes, and events
Entity The representation of some object in the real system
Attribute Some characteristic of a particular entity
Event An interaction between entities

25. Air Traffic – A Discrete Model
Air Traffic in counrty Planes are objects
Attributes include speed
Events are planes entering and leaving airspace

26. Kinds of Models Continuous simulation Treats time as continuous
Expresses changes in terms of a set of differential equations that reflect the relationships among the quantities in the model
Meteorological models falls into this category

27. Hurricanes – A Continuous Model

28. A Continuous Models and FEA
Finite Element Analysis (FEA): Dividing a volume of space into small cubes, which contain our quantities of interest
Many Continuous Models Use FEA

29. Meteorological Models

30. Weather – A Continuous Model

31. Meteorological Models
Models based on the time-dependent partial differential equations of fluid mechanics and thermodynamics
Initial values for the variables are entered from observation, and the equations are solved to define the values of the variables at some later time

32. Weather – A Continuous Model

33. Computational Biology

34. Computational Biology
The application of computer science to problems in biology
(or is it the other way around??  )
Encompasses:
bioinformatics
computational biomodeling
molecular modeling
protein structure prediction 34 34

35. Computational Biology
Bioinformatics
Discovering and Processing DNA sequences
Human Genome Project and Others 35 35

36. Computational Biology
Computational Biomodeling
The simulation of biological systems
Knees
Cell Wall Protein
Cell Metabolism 36 36

37. Computational Biology
Protein Structure Modeling
Simulating 3-Dimensional Structure and Function of Protein Molecules 37 37

38. Computational Biology
Molecular Modeling
Simulating Structure and Function of Chemical Molecules (usually drug discovery) 38 38

39. “Cell” Models

40. Earth, Wind, Fire and Water
Cell-Based Models
Like continuous FEA models
Uses quantities and laws from physics
“How is a hurricane like a glass of water?”
Or a Cloud?
Or Fire?
Or Smoke?

41. Figure 14.7 Water pouring into a glass
Cell Models
Figure Water pouring into a glass

42. Figure 14.8 Cellular automata-based clouds
Cell Models
Figure 14.8 Cellular automata-based clouds

43. Modeling Complex Objects
Cell Models
What do clouds, smoke and
fire have in common?
Figure 14.9 A campfire