The effect of long‐range interactions on the secondary structure formation of proteins

By Daisuke Kihara. 2005

113 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

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In Materials Chemistry

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Citations in the corpus

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, background): “It is well known that structural properties of protein sequences are reﬂected in long-range dependencies (Kihara, 2005).”
Protein knots and fold complexity: Some new twists (2007, background): “Indeed, it has also been shown recently hat for non-local interactions, where the most accurate predicions for secondary structure are needed, is exactly where they re least accurate (Kihara, 2005).”
Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto‐encoder deep neural network (2014, background): “This arbitrariness has limited the theoretically achievable accuracy of three-state prediction to 88-90% [4,5].”
PONDR-FIT: A Meta-Predictor of Intrinsically Disordered Amino Acids (2010, background): “In prediction of secondary structure, interactions between the segment of interest and the rest of the protein can override the structural tendencies of the segment of interest and thereby reduce prediction accuracy [81].”
Sixty-five years of the long march in protein secondary structure prediction: the final stretch? (2016, background): “negatively correlated with residue contact order, or number of non-local contacts [115].”
Next-Step Conditioned Deep Convolutional Neural Networks Improve Protein Secondary Structure Prediction (2017, background): “There is a natural upper-bound on the maximum possible Q3 accuracy from inconsistencies in secondary structure assignment likely due to the coarseness and inherent arbitrariness of three-class labels (Kihara, 2005).”
Analysis of molecular recognition features (MoRFs). (2006, background): “34 Here, we take the view that different proteins, and likely different regions in the same protein, vary in the relative contributions of local and non-local interaction to local structure, but we make no attempt to contribute to the debate over the magnitude of these contributions.”
Critical assessment of high-throughput standalone methods for secondary structure prediction (2011, unknown): “[15,18,19].”
DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment (2015, background): “However, some disagreement in secondary structure is anticipated due to both intrinsic difficulties in assigning secondary structure [54] and challenges inherent to aligning distantly related tertiary structures [21, 55].”