Prediction of the secondary structure of proteins from their amino acid sequence.

By P. Y. Chou, G. Fasman. 2006

1,697 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2d9771548da61fd0722eb48eb9164db9c29d7e8c

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In Molecular Biology

Citations in the corpus

Protein secondary structure prediction for a single-sequence using hidden semi-Markov models (2006, background): “These propensities are in the heart of many secondary structure prediction methods [51,52,55-62].”
Machine Learning Methods for Protein Structure Prediction (2008, methodology): “Early secondary structure prediction methods [47] were based on extracting the statistical correlations between a window of consecutive amino acids in a protein sequence and the secondary structure classification of the amino acid in the center of the window.”
The Jpred 3 secondary structure prediction server (2008, neutral): “Early techniques to predict protein secondary structure worked from a single sequence (13–15).”
Why similar protein sequences encode similar three-dimensional structures? (2010, background): “exposed hydrophilic ones [11]; some residue pairs are often in contact [12] and some sequences have preferences for different types of secondary structures [13, 14].”
The Immune Epitope Database and Analysis Resource in Epitope Discovery and Synthetic Vaccine Design (2017, background): “Historically, physicochemical properties such as hydrophilicity (57), surface accessibility (58), beta-turns (59, 60), and flexibility (61) were correlated with the occurrence of B cell epitopes in proteins.”
Functional Analysis of an Arabidopsis Transcription Factor, DREB2A, Involved in Drought-Responsive Gene Expression[W][OA] (2006, positive): “According to the results of the Chou–Fasman analysis for prediction of secondary structure (Chou and Fasman, 1978), the C-terminal half of the DREB2A protein, from just behind the DNAbinding domain to the C-terminal end of the protein (amino acid residues 136 to 335), is divided into five parts.”
A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features (2004, methodology): “Three of the most widely used approaches to this problem are those of Robson (Garnier et al., 1978), Chou and Fasman (1978) , and Lim (1974), which produce classification accuracies ranging from 48% to 58%.”
Specificity prediction of adenylation domains in nonribosomal peptide synthetases (NRPS) using transductive support vector machines (TSVMs) (2005, background): “� the number of hydrogen bond donors (28), � polarity (three different indices) (29‐31), � volume (32), � secondary structure preferences for beta-turns, beta-sheets and alpha-helices ( 33 ), � hydrophobicity with a three-dimensional vector (27) and � the isoelectric point (29).”
Improved method for predicting linear B-cell epitopes (2006, methodology): “They compared the scales on a data set consisting of nine of the sequences and found that the scales by Parker et al. [6], Chou and Fasman [7], Levitt [8] and of Emini et al. [9] gave slightly better results than the other scales tested.”
Graph kernels for chemical informatics (2005, background): “, 1996] (see also Chou and Fasman [1978] for a related early application to protein secondary structure prediction).”
iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences (2018, background): “Applications include predicting protein structural and function classes (Chou and Fasman, 1978), protein-protein interactions, protein–ligand interactions (Cao et al.”