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 GNAAAAKKGSEQESVKEFLAKAKEDFLKKWENPA QNTAHLDQFERIKTLGTGSFGRVMLVKHKETGNH FAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPF LVKLEYSFKDNSNLYMVMEYVPGGEMFSHLRRIG RFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPE NLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEY LAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPF FADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNL LQVDLTKRFGNLKDGVNDIKNHKWFATTDWIAIY QRKVEAPFIPKFKGPGDTSNFDDYEEEEIRVSIN EKCGKEFSEF Sequence Assumption (protein A is Similar to protein B) Result (protein A is Similar to protein B)

Unknown protein GLLTTKFVSLLQEAKDGVLDLKL AADTLAVRQKRRIYDITNVLEGIG LIEKKSKNSIQW Well studied protein SRRSASHPTYSEMIAAAIRAEKS RGGSSRQSIQKYIKSHYKVGHN ADLQIKLSIRRLLAA similarity prediction How do we know that these proteins are similar?

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

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

Model quality Empirical energy based tools PSQS ( SwissPDB viewer Geometric quality Procheck, SFCHECK, etc. ( n/sv3.cgi) n/sv3.cgi

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

Challenges of comparative modeling Recognition Alignment Modeling Challenges Trivial SimpleLoop modeling TrivialEasySimpleLoop modeling SimpleChallenging Alignment, backbone shifts DifficultVery difficult Significant errors Alignment, backbone shifts Often impossible Significant errors Often impossible Recognition

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