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

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)

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

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

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)

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

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

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?

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)

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

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

Model quality Empirical energy based tools Geometric quality PSQS (http://www1.jcsg.org/psqs/psqs.cgi) SwissPDB viewer Geometric quality Procheck, SFCHECK, etc. (http://www.jcsg.org/scripts/prod/validation/sv3.cgi)

Expectations of comparative modeling Easy – 100-40% 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

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

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

Models and Simulation Computational Biology

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

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

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

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

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?”

Kinds of Models

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

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

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

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

Hurricanes – A Continuous Model

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

Meteorological Models

Weather – A Continuous Model

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

Weather – A Continuous Model

Computational Biology

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

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

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

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

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

“Cell” Models

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?

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

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

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