pubmed-article:9669880 | rdf:type | pubmed:Citation | lld:pubmed |
pubmed-article:9669880 | lifeskim:mentions | umls-concept:C0035647 | lld:lifeskim |
pubmed-article:9669880 | lifeskim:mentions | umls-concept:C0033684 | lld:lifeskim |
pubmed-article:9669880 | lifeskim:mentions | umls-concept:C0597535 | lld:lifeskim |
pubmed-article:9669880 | lifeskim:mentions | umls-concept:C1442080 | lld:lifeskim |
pubmed-article:9669880 | lifeskim:mentions | umls-concept:C0681842 | lld:lifeskim |
pubmed-article:9669880 | lifeskim:mentions | umls-concept:C0678594 | lld:lifeskim |
pubmed-article:9669880 | lifeskim:mentions | umls-concept:C0205372 | lld:lifeskim |
pubmed-article:9669880 | pubmed:issue | 3 | lld:pubmed |
pubmed-article:9669880 | pubmed:dateCreated | 1998-7-10 | lld:pubmed |
pubmed-article:9669880 | pubmed:abstractText | Success in solving the protein structure prediction problem relies on the choice of an accurate potential energy function. for a single protein sequence, it has been shown that the potential energy function can be optimized for predictive success by maximizing the energy gap between the correct structure and the ensemble of random structures relative to the distribution of the energies of these random structures (the Z-score). Different methods have been described for implementing this procedure for an ensemble of database proteins. Here, we demonstrate a new approach. | lld:pubmed |
pubmed-article:9669880 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
pubmed-article:9669880 | pubmed:language | eng | lld:pubmed |
pubmed-article:9669880 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
pubmed-article:9669880 | pubmed:citationSubset | IM | lld:pubmed |
pubmed-article:9669880 | pubmed:status | MEDLINE | lld:pubmed |
pubmed-article:9669880 | pubmed:issn | 1359-0278 | lld:pubmed |
pubmed-article:9669880 | pubmed:author | pubmed-author:GoldsteinR... | lld:pubmed |
pubmed-article:9669880 | pubmed:author | pubmed-author:ChanP SPS | lld:pubmed |
pubmed-article:9669880 | pubmed:issnType | Print | lld:pubmed |
pubmed-article:9669880 | pubmed:volume | 3 | lld:pubmed |
pubmed-article:9669880 | pubmed:owner | NLM | lld:pubmed |
pubmed-article:9669880 | pubmed:authorsComplete | Y | lld:pubmed |
pubmed-article:9669880 | pubmed:pagination | 223-8 | lld:pubmed |
pubmed-article:9669880 | pubmed:dateRevised | 2007-11-14 | lld:pubmed |
pubmed-article:9669880 | pubmed:meshHeading | pubmed-meshheading:9669880-... | lld:pubmed |
pubmed-article:9669880 | pubmed:meshHeading | pubmed-meshheading:9669880-... | lld:pubmed |
pubmed-article:9669880 | pubmed:meshHeading | pubmed-meshheading:9669880-... | lld:pubmed |
pubmed-article:9669880 | pubmed:meshHeading | pubmed-meshheading:9669880-... | lld:pubmed |
pubmed-article:9669880 | pubmed:meshHeading | pubmed-meshheading:9669880-... | lld:pubmed |
pubmed-article:9669880 | pubmed:meshHeading | pubmed-meshheading:9669880-... | lld:pubmed |
pubmed-article:9669880 | pubmed:meshHeading | pubmed-meshheading:9669880-... | lld:pubmed |
pubmed-article:9669880 | pubmed:meshHeading | pubmed-meshheading:9669880-... | lld:pubmed |
pubmed-article:9669880 | pubmed:year | 1998 | lld:pubmed |
pubmed-article:9669880 | pubmed:articleTitle | Optimizing energy potentials for success in protein tertiary structure prediction. | lld:pubmed |
pubmed-article:9669880 | pubmed:affiliation | Department of Chemistry, University of Michigan, Ann Arbor, MI 48109-1055, USA. | lld:pubmed |
pubmed-article:9669880 | pubmed:publicationType | Journal Article | lld:pubmed |
pubmed-article:9669880 | pubmed:publicationType | Research Support, U.S. Gov't, P.H.S. | lld:pubmed |
pubmed-article:9669880 | pubmed:publicationType | Research Support, U.S. Gov't, Non-P.H.S. | lld:pubmed |
pubmed-article:9669880 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
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