Macmillan Dictionary of Microcomputing 1985
DOI: 10.1007/978-1-349-17843-8_13
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Cited by 6 publications
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“…Known structures are essential for comparative modeling and fold recognition. The secondary structure prediction techniques [32] are trained on libraries of known structures and many techniques that build structures from scratch either employ structural fragments derived from known folds [33,34] or employ knowledge based energy functions derived from the protein structure database [35^37]. Of course, the study and simulation of protein^ligand [38] and protein^protein interactions [39], and virtual screening depend on structures obtained from experiments.…”
Section: Resultsmentioning
confidence: 99%
“…Known structures are essential for comparative modeling and fold recognition. The secondary structure prediction techniques [32] are trained on libraries of known structures and many techniques that build structures from scratch either employ structural fragments derived from known folds [33,34] or employ knowledge based energy functions derived from the protein structure database [35^37]. Of course, the study and simulation of protein^ligand [38] and protein^protein interactions [39], and virtual screening depend on structures obtained from experiments.…”
Section: Resultsmentioning
confidence: 99%
“…It is also interesting to compare the conformation of thymosin p4 derived from the NMR experiment with that predicted by the method of knowledge-based mean fields derived from a data base of known protein structures (Sippl et al, 1992). This procedure calculated the thymosin structure to be very close to the NMR structure of thymosin in the alcoholic solution and not to that of thymosin p4 in pure water (Sippl et al, 1992). A similar result to that obtained in alcohol solution is found for thymosin p4 by Garnier-Osguthorpe-Robson secondary-structure prediction (Voelter, 1992).…”
Section: Discussionmentioning
confidence: 99%
“…An energy function was developed for coarse-grained molecular simulation that accommodates chemical interference using Boltzmann inversion 37,42,43 from the data derived from all-atomistic molecular dynamics simulations. The pair correlation function between any two amino acid types i and j at a distance r in solvent type α is ) (r g ij α .…”
Section: Boltzmann Inversionmentioning
confidence: 99%