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The San Diego Supercomputer The San Diego Supercomputer

The San Diego Supercomputer - PDF document

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The San Diego Supercomputer - PPT Presentation

La Jolla CA 92037 USAD WILD and S HWANGThe Keck Graduate Institute 535 Watson Drive Claremont CA 91711 USAZ GHAHRAMANIGatsby Computational Neuroscience Unit University College London 17 Quee ID: 958595

targets data cases folds data targets folds cases structures structure predictions sequence pdb considered conventional bias targetdb protein london

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The San Diego Supercomputer La Jolla, CA 92037 USAD. WILD and S. HWANGThe Keck Graduate Institute, 535 Watson Drive, Claremont, CA 91711 USAZ. GHAHRAMANIGatsby Computational Neuroscience Unit, University College London, 1

7 Queen Square, London, WC1N 3AR, UKL. CHEN and J. WESTBROOK Department of Chemistry, Rutgers University, 610 Taylor Road, The targetdatabase (TargetDB) maintained by the Protein Data Bank ( http://targetdb. pdb

.org ) reportsthis progress through This is interpreted to mean a components and with the potential to a totalof 3324 structures were deposited with the PDB. 10% of structures to the field of at Different Stage

s of Solution (April 1,2003)Slightly less than 50% of targets are selected for scrutiny. From these a highpercentage can be expressed, but the number purified and crystallized drops offdramatically, indicating these steps c

ontinue to register low success rates and shouldbe a focus of renewed are focusing on one or more of the characteristics of the targets as a whole is considered.A review of the over 30,000 targets in the database (April 1,

2003)indicates a 13% redundancy at the 100% sequence identity and 38% redundancy at the30% sequence identity level. This implies that either individual groups are operatingwithout regard for other groups, or there is inter

est in be up to three 3). In some cases this is the nature of theredundancy in the complete proteome under study, in other cases perhaps a desire toattempt to solve multiple instances of an important structure that, based

of sequence and how do database (NR) ordered in bins of both FFAS and iGAP provided predictions for the nearly all targets,Bayesian networks for about 10%, based on a smaller template library. Not onlydoes this highlight

internal consistency between proteins that to what is found bias in these data andhence they should be considered cautiously. The bias arises in that predictions aredone with a mix of fold prediction and homology modeli

ng. In both cases there is abias towards known folds since, nevertheless expected trends do occur.Immunoglobulin-like beta sandwiches (b1) are over represented in the PDB appear to be over represented in contributed by wit

h a peak in the 8-10 month range (data not shown). Data are notavailable for how this compares to conventional structure determination but it is be contributing twice thenumber of new folds as conventional structure determi

nation, but the numbers aretwo small to be considered statistically significant. An argument has been made thatstructure genomics might contribute less new folds that one might anticipate sincethe emphasis will be on determ

ining the maximum A.97(10), 5161 (2000)3. R.F Service Tapping DNA for Structures Produces and A. Godzik, A. Comparison ofSequence profiles. Strategies for Structural Predictions using SequenceInformation. Protein Scie