Powered by OpenAIRE graph
Found an issue? Give us feedback
Sciencearrow_drop_down
Science
Article . 1996 . Peer-reviewed
Data sources: Crossref
Science
Article . 1996 . Peer-reviewed
Data sources: Crossref
Science
Article . 1996 . Peer-reviewed
Data sources: Crossref
Science
Article . 1996 . Peer-reviewed
Data sources: Crossref
Science
Article . 2010
versions View all 5 versions
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

Protein Structure Prediction

Authors: S A, Benner; D L, Geroff; J D, Rozzell;

Protein Structure Prediction

Abstract

Elizabeth Pennisi's Research News article “Teams tackle protein prediction” ([26 July, p. 426][1]) describes an ongoing project, known as CASP (for Critical Assessment of Techniques for Protein Structure Prediction) ([1][2]), to provide researchers who model protein structures with the opportunity to jointly make bona fide predictions, announced before a structure is determined by experiment. The theme of the article, that collaboration is needed for progress, is indisputable. We do not agree, however, that, among those who participated in the ab initio part of the first prediction contest “nobody really came close to predicting an accurate structure,” that “predictions [of secondary structure] were no more accurate than ones made a decade ago with cruder methods,” and that the assembly of predicted secondary structural elements into “a complete 3D structure” “didn't work at all,” as John Moult alleges. One advantage of the CASP approach is that the predictions are independently judged and the judges publish their opinions so that they are available to the public. This was so for the ab initio session of CASP1. The evaluations of the predictions published by the judges differed greatly from those reported by Pennisi. “For phospho-βD-galactosidase,” wrote judges DeFay and Cohen ([2][3]), “Benner and Sader [both] correctly predicted this protein to be an α/β barrel.” The success came from “an exceptionally small number of ‘wrong’ predictions.” Further, the judges wrote, “it … would have been unlikely if a prediction was made from the [decade-old, cruder] GOR [method for] secondary structure prediction.” For synaptotagmin, the judges noted that “both Hubbard and Benner correctly predicted the first six strands,” missing only the final secondary structural element. Despite this error, three (out of 196) possible folds were chosen to represent the beta sandwich of this protein ([3][4]); one of them was correct. This sounds “close” to us. Predictions today are not simply contest entries; they are good enough to be applied to solve real biochemical problems. Progress has come in part through the recognition that the protein folding problem is a special example of a much older problem in organic chemistry, conformational analysis. Through this has come the realization that organic chemical approaches have something to contribute to protein folding. Science readers should therefore be encouraged to apply prediction tools to their own research problems. ### References 1. 1.[↵][5] 1. J. Moult, 2. J. T. Pedersen, 3. R. Judson, 4. K. Fidelis , Proteins Struct. Funct. Genet. 23, R2 (1995). [OpenUrl][6] 2. 2.[↵][7] 1. T. DeFay, 2. d F. E. Cohen , Proteins 23, 431 (1995). [OpenUrl][8][CrossRef][9][PubMed][10][Web of Science][11] 3. 3.[↵][12] 1. S. A. Bennder, 2. D. L. Gerloff, 3. G. Chelvanayagam , ibid. 446. [1]: /lookup/volpage/274/426 [2]: #ref-1 [3]: #ref-2 [4]: #ref-3 [5]: #xref-ref-1-1 "View reference 1. in text" [6]: {openurl}?query=rft.jtitle%253DProteins%2BStruct.%2BFunct.%2BGenet.%26rft.volume%253D23%26rft.spage%253DR2%26rft.atitle%253DPROTEINS%2BSTRUCT%2BFUNCT%2BGENET%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [7]: #xref-ref-2-1 "View reference 2. in text" [8]: {openurl}?query=rft.jtitle%253DProteins%26rft.stitle%253DProteins%26rft.aulast%253DDefay%26rft.auinit1%253DT.%26rft.volume%253D23%26rft.issue%253D3%26rft.spage%253D431%26rft.epage%253D445%26rft.atitle%253DEvaluation%2Bof%2Bcurrent%2Btechniques%2Bfor%2Bab%2Binitio%2Bprotein%2Bstructure%2Bprediction.%26rft_id%253Dinfo%253Adoi%252F10.1002%252Fprot.340230317%26rft_id%253Dinfo%253Apmid%252F8710836%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [9]: /lookup/external-ref?access_num=10.1002/prot.340230317&link_type=DOI [10]: /lookup/external-ref?access_num=8710836&link_type=MED&atom=%2Fsci%2F274%2F5292%2F1447.3.atom [11]: /lookup/external-ref?access_num=A1995TH80900016&link_type=ISI [12]: #xref-ref-3-1 "View reference 3. in text"

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!