Prediction of protein secondary structure and active sites using the alignment of homologous sequences.

By Marketa Zvelebil, Geoffrey J. Barton, William R. Taylor, Michael J.E. Sternberg. 1987

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Citations in the corpus

Protein sectors: evolutionary units of three-dimensional structure (2009, neutral): “Theory”
Improved prediction of protein-protein binding sites using a support vector machines approach. (2005, background): “Conserved residues are most likely to be found at important functional sites on a protein (Livingstone and Barton, 1993; Zvelebil et al., 1987), although recently it has been argued that residue conservation is rarely sufficient for complete and accurate prediction of a protein–protein interface…”
A family of evolution-entropy hybrid methods for ranking protein residues by importance. (2004, background): “) into a property called conservation number, which enabled them to distinguish poorly conserved loop regions from the rest of the protein structure.(1) For the current progress in this line of thought, see Valdar.”
Sixty-five years of the long march in protein secondary structure prediction: the final stretch? (2016, methodology): “This was followed by the inclusion of neighbouring residues within a window [68, 69, 71, 81], and later by a sequence evolution profile derived from multiple sequence alignment [75].”
KinasePhos 2.0: a web server for identifying protein kinase-specific phosphorylation sites based on sequences and coupling patterns (2007, background): “It was found that amino acids have a great variety of properties such as mass, polarity, hydrophobicity, so many groupings are possible (19).”
A strategy for the rapid multiple alignment of protein sequences. Confidence levels from tertiary structure comparisons. (1987, methodology): “…similar sequences generated by our algorithm have been used as the basis of an improved secondary structure and active site prediction algorithm (Zvelebil et al., 1987) and also to align four different strains of human immunodeficiency virus (HIV) enu (800 residues), gag (500 residues) and pol…”
The Jpred 3 secondary structure prediction server (2008, background): “(17) ﬁrst demonstrated that the information from a multiple sequence alignment gave an increase in accuracy of 9% (to 66”
Cascaded multiple classifiers for secondary structure prediction (2000, unknown): “... any of the GOR algorithms when analyzed at the residue level, while at the segment level the performance was rather similar ~Table 1!. However, the Sov should not be used to assess the performance of a classifier, but rather to assess the quality and the usefulness of a prediction as the Sov can”
Evaluation and improvement of multiple sequence methods for protein secondary structure prediction (1999, background): “Key words: protein; secondary structure prediction; combination of methods; benchmarks”
Protein secondary structure prediction. (1995, methodology): “[4] capitalized on new techniques for multiple sequence alignment [5] and the increased database, to extend the GOR method to use aligned sequence data.”
Scoring residue conservation (2002, background): “Fortunately, Zvelibil et al. 20 abridge it to something more convenient: a truth table of amino acids with 10 property descriptors (Fig.”
Bioinformatics - a practical guide to the analysis of genes and proteins (2001, methodology): “Notice the difference between this GUI-based program and the UNIX equivalent illustrated in Figure 1.4.”
JPred : a consensus secondary structure prediction server (1999, methodology): “…(DSC (King and Sternberg, 1996), PHD (Rost and Sander, 1993), NNSSP (Sala- mov and Solovyev, 1995), PREDATOR (Frishman and Argos, 1997), ZPRED (Zvelebil et al., 1987) and MULPRED (Barton, 1988, unpublished)) are then run, and the results from each method are combined into a simple file…”
Consensus protein design (2016, background): “In the case of consensus design, highly conserved residues tend to be found within the protein core, evolve slowly and are therefore unlikely to be detected by covariation analysis even in very large alignments (Zvelebil et al., 1987; Bartlett et al., 2002; Talavera et al., 2015).”