B. Rost, "PHD: predicting one-dimensional protein structure by profile-based neural networks", Methods Enzymol., 266, 525 (1996). Home/Search Document Not in Database Summary Related Articles Check |
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Molecular Structure Computation from Multiple Data Sources - Chen (2000) (Correct)
....approximation, leaving dihedral angles as free variables in the structure of a protein. Statistical analyses of known, related structures can also tell us something about the molecule of interest itself; an example is the prediction of secondary structures of a protein from its primary sequence [25, 80, 82]. Finally, a multitude of experimental techniques yields different kinds of structural data. Examples include spatial frequencies of structural features from X ray crystallography [10] distances from nuclear magnetic resonance (NMR) 88] fluorescent energy transfer (FRET) 23] and ....
....can be made, in the second set of experiments. In the second experiment, we augment each of the baseline data set with secondary structure information. Three different predictions are used (see Figure 19) Actual secondary structure predictions are generated with the SSPAL [82] and the PHDsec [80] programs. In the case of 1CTF, the SSPAL prediction is almost perfect, while the PHDsec prediction is about 80 correct. In addition, we have manually created a Constructed prediction with a relatively large number of errors (about 45 correct) so that we can test the multimodal algorithm over a ....
B. Rost, "PHD: Predicting One-Dimensional Protein Structure by Profile-Based Neural Networks", Methods Enzymol., vol. 266, pp. 525-539, 1996.
Predicting protein structure using hidden Markov models - Karplus, Sjölander.. (1997) (8 citations) (Correct)
....matches in our structure library and produced the respective pairwise alignments. Each potential match was then inspected for the quality of the alignment, similarity in biological function, and consistency with other structural assessments. We checked PhD secondary structure 5 predictions [20] for consistency with our structure predictions. The alignments were inspected with Leslie Grate s SAE, a graphical tool combining RASMOL and an alignment viewer. This allowed us to see if insertions and deletions occurred in reasonable regions of the structure and whether the resulting protein ....
B. Rost. Phd: predicting one-dimensional protein structure by profile-based neural networks. Methods Enzymol., 2666:525--39, 1996.
Predicting protein structure using hidden Markov models - Karplus, Sjölander.. (1997) (8 citations) (Correct)
....potential matches in our structure library and produced the respective pairwise alignments. Each potential match was then inspected for the quality of the alignment, similarity in biological function, and consistency with other structural assessments. We checked PhD secondary structure predictions [20] for consistency with our structure predictions. The alignments were inspected with Leslie Grate s SAE, a graphical tool combining RASMOL and an alignment viewer. 3 This allowed us to see if insertions and deletions occurred in reasonable regions of the structure and whether the resulting ....
B. Rost. Phd: predicting one-dimensional protein structure by profile-based neural networks. Methods Enzymol., 2666:525--39, 1996.
Constructing Biological Knowledge Bases by Extracting.. - Craven, Kumlien (1999) (40 citations) (Correct)
....biological information such as sequences, structures, molecular interactions and expression patterns. Moreover, there are servers that perform such tasks as identifying genes in DNA sequences (e.g. GRAIL, Xu et al. 1996) and predicting protein secondary structures (e.g. PredictProtein, Rost, 1996). And there are systems that integrate information from various sources (e.g. The Genome Channel, Genome Annotation Consortium, 1999) provide interoperability among distributed databases (e.g. Entrez, National Center for Biotechnology Information, 1999) and support knowledge based reasoning (e.g. ....
Rost, B. 1996. PHD: Predicting one-dimensional protein structure by profile based neural networks. Methods in Enzymology 266:525--539.
Review: Protein Secondary Structure Prediction Continues to Rise - Rost (2001) (6 citations) Self-citation (Rost) (Correct)
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Rost, B. (1996) PHD: Predicting one-dimensional protein structure by profile based neural networks, Methods Enzymol. 266, 525--539.
Better 1D Predictions by Experts With Machines - Rost (1997) Self-citation (Rost) (Correct)
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Rost, B. PHD: Predicting one-dimensional protein structure by profile based neural networks. Methods Enzymol. 266:525--539, 1996.
Exploring Bias in the Protein Data Bank Using Contrast.. - Peng, Obradovic, Vucetic (Correct)
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B. Rost, "PHD: predicting one-dimensional protein structure by profile-based neural networks", Methods Enzymol., 266, 525 (1996).
Protein Sequence Threading: Averaging over Structures - Russell, Torda (2002) (Correct)
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Rost B. PHD: Predicting one-dimensional protein structure by profile-based neural networks. Meth Enzymol 1996;266:525--539.
Designing Repeat Proteins: Well-expressed, Soluble .. - Binz, Stumpp.. (2003) (Correct)
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Rost, B. (1996). PHD: predicting one-dimensional protein structure by profile-based neural networks. Methods Enzymol. 266, 525 -- 539.
The PredictProtein Server - Rost, Liu (2003) (Correct)
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Rost,B. (1996) PHD: predicting one-dimensional protein structure by profile based neural networks. Methods Enzymol., 266, 525--539.
EVA: Large-Scale Analysis of Secondary Structure Prediction - Rost, Eyrich (2001) (2 citations) (Correct)
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Rost B. PHD: predicting one-dimensional protein structure by profile based neural networks. Methods Enzymol 1996;266:525-- 539.
PEP: Predictions fo Entire Proteomes - Carter, Liu, Rost (2003) (Correct)
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Rost,B. (1996) PHD: predicting one-dimensional protein structure by profile-based neural networks. Methods Enzymol., 266, 525--539.
Bioinformatics in Support of Molecular Medicine - Altman (Correct)
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Rost B. PHD: predicting one-dimensional protein structure by profile-based neural networks. Methods Enzymol 1996;266:525-39.
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