Review: protein secondary structure prediction continues to rise.

By Burkhard Rost. Journal of Structural Biology, 2001

581 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1efb0e16927b61ece8fd4335a5372d3a3d4ebfc0

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Citations in the corpus

QMEAN: A comprehensive scoring function for model quality assessment (2008, unknown): “The following targets were excluded in the target-specific optimization process (in brackets the number of models with GDT_TS > 20): T0202 (118), T0206 (94), T0228 (23), T0238 (129), T0242 (139), T0248 (5), T0262 (70), T0272 (4), T0273 (88), T0197 (51), T0198 (104), T0199 (12).”
Energy functions in de novo protein design: current challenges and future prospects. (2013, background): “On the other hand, secondary structures (or backbone torsion angles) are determined largely by a local sequence segment of 20 residues at about 80% accuracy for three-state secondary structures (32, 96) or 83% accuracy for both backbone φ and ψ torsion angles within 60° of their native values (31).”
Comprehensive large-scale assessment of intrinsic protein disorder (2015, background): “This difficulty in capturing distant sequential dependencies is a common problem in secondary structure prediction (Rost, 2001) and likely also true for disorder.”
Computational prediction of secondary and supersecondary structures. (2013, background): “summarized the most important advancements in this field, which were related to the use of sliding window, evolutionary information extracted from multiple sequence alignment, and machine-learning classifiers (53-55), and more recently due to the utilization of consensus-based approaches (56).”
Combining prediction of secondary structure and solvent accessibility in proteins (2005, background): “11,12 In this work, we focus our efforts on “local” ﬁngerprints of the 3D packing that may indicate sites where sequence propensities for secondary structure elements are likely to be inﬂuenced by long-range contacts.”
Local Protein Structures (2007, background): “much as one thousand [11], beginning from simple statistical methods [43] to complex”
A simple and fast secondary structure prediction method using hidden neural networks (2005, background): “The field of secondary structure prediction has a history of over 40 years and a wide range of different models has been applied to tackle the problem (for reviews see Heringa, 2000; Rost, 2001; Simossis and Heringa, 2004).”
Sixty-five years of the long march in protein secondary structure prediction: the final stretch? (2016, background): “As a result, helical residues are more accurately predicted than sheet residues as expected [47].”
The effect of long‐range interactions on the secondary structure formation of proteins (2005, methodology): “…can differ between different X-ray structures and NMR models of the same protein; second, there is inconsistency of secondary structure assignments by different methods, e.g., DSSP (Kabsch and Sander 1983) and STRIDE (Frishman and Argos 1995), and also of their parameters (Levin 1997; Rost 2001).”
Protein flexibility and intrinsic disorder (2004, background): “It is well known that adding evolutionary information in the form of sequence alignments leads to improved secondary structure prediction (Benner et al. 1992; Levin et al. 1993; Rost 2001).”
Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto‐encoder deep neural network (2014, background): “Protein local structure prediction is dominated by secondary structure prediction with its accuracy stagnant around 80% for more than a decade [3,4].”