Ranking SS Prediction Using CA Overlap

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Presentation transcript:

Ranking SS Prediction Using CA Overlap Chester Shiu CS273 May 31, 2005

Servers ROBETTA META-Basic Shotgun-INBGU

ROBETTA Server Implementation of ROSETTA Attempts Homology Modelling, then fills in gap with 3mers + simulated annealing Does not handle extremes very well!

Why? ROBETTA performs poorly at extremes Small – domain classification errors Large – low contact order clustering? Errors from poor homology identification and dependence on SA

META-Basic Not a Meta Server! Meta-Profile Sequence AND Structure 6 PSI-BLAST iterations + RPS BLAST High Specificity

Shotgun-INBGU Uses Consensus from linear weighing of parameters Sequence Multiple Alignment Profiles of Fold Libraries Consensus from linear weighing of parameters Can pick out weak signal

Methodology Pair-wise compare top ranked model from each algorithm. Select pair with highest score Rationale: If ROBETTA suffers homology error then other two should outweigh

Scoring cRMSD Livebench 3D Score: exp(-ln(2)*d*d/(3*3)) But only got 2/10 correct Number aligned Cα < 3A 9/10 correct!

The Erroneous Ranking 1rr9 – ATP-Dependent Protease cRMSD roughly equidistant (15.9 vs 16.2) Low Data: 4 versus 3 overlaps at <3A

Why is 3D Score so off? Lower penalty for high distance than cRMSD, but still major Sequence alignment issue?