Application of multiple sequence alignment profiles to improve protein secondary structure prediction

By James A. Cuff, Geoffrey J. Barton. Proteins: Structure, Function, and Bioinformatics, 2000

830 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/042a5c25b5dc77cc5e1053261c38e7fbe67ffd5a

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In Materials Chemistry

In Molecular Biology

Citations in the corpus

Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure (2011, methodology): “In this table, a variety of secondary structure prediction methods – SVMpsi [27], JNET [28], YASSSP [29], DBNfinal [5], DBNpred (our method), DBNN [5], PSIPRED [30], SVM_D3 [31], DESTRUCT [32], DISSPred [6], and DSPRED (our method) – are evaluated with respect to the CB513 benchmark.”
Deep learning methods in protein structure prediction (2020, background): “PHD, PSIPRED, and JPred [69] are among the ﬁrst notable examples in which cascaded FFNN are used to predict 1D PSA, in particular secondary structure.”
JPred4: a protein secondary structure prediction server (2015, methodology): “As with JPred3, JPred4 makes secondary structure and residue solvent accessibility predictions by the JNet algorithm (11,31).”
Bioinformatics - a practical guide to the analysis of genes and proteins (2001, background): “…432 Standard Input and Output ......................................................... 433 Finding the Length of a Sequence File ........................................ 435 Pattern Matching .......................................................................”
Protein secondary structure prediction for a single-sequence using hidden semi-Markov models (2006, methodology): “Algorithms of protein secondary structure prediction frequently employ neural networks [1-7], support vector machines [8-13] and hidden Markov models [14-16].”
A Unified Multitask Architecture for Predicting Local Protein Properties (2012, methodology): “Early methods for solvent accessibility prediction used neural networks [10,29,30] or support vector machines [31].”
OXBench: A benchmark for evaluation of protein multiple sequence alignment accuracy (2003, neutral): “This finding is consistent with results for secondary structure prediction, where the addition of data rather than underlying algorithm appear to dominate improvements in accuracy [49,50].”
Small but versatile: the extraordinary functional and structural diversity of the β-grasp fold (2007, methodology): “All newly-identified clusters of domains distinct from previously identified sequence families containing the -GF were aligned and used to predict secondary structure with the JPRED program [53].”
ELM server: a new resource for investigating short functional sites in modular eukaryotic proteins (2003, methodology): “In many cases users will be able to investigate surface accessibility by examination of an available three-dimensional structure or by using a good quality two-dimensional structure prediction (23,24) or perhaps by using a homology modelling server such as SWISSMODEL or the Meta server (25,26).”
A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction (2014, methodology): “Neural networks have been effectively used in a variety of prediction algorithms, including speech and face recognition as well as the aforementioned usage in protein secondary structure prediction[11-22, 24, 33-36].”
A graphical model for protein secondary structure prediction (2004, methodology): “We removed the proteins that are shorter than 30 residues, or longer than 550 residues, following Cuff and Barton (2000), to leave 480 proteins for cross validation training.”
A simple and fast secondary structure prediction method using hidden neural networks (2005, methodology): “…methods till date, PHD (Rost and Sander, 1993), PHDpsi (Przybylski and Rost, 2002), PROFsec (B. Rost, unpublished data), SSPro2 (Pollastri et al., 2002), JNET (Cuff and Barton, 2000) and PSIPRED (Jones, 1999) employ various types of NNs to perform predictions using MSAs of homologous sequences.”