ArticleProtein science : a publication of the Protein Society2003
Accurate and automated classification of protein secondary structure with PsiCSI.
Article in Protein science : a publication of the Protein Society, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed.
- CSI-LSTM: a web server to predict protein secondary structure using bidirectional long short term memory and NMR chemical shifts.Journal of biomolecular NMR · 2021Article
- Recent developments in solution nuclear magnetic resonance (NMR)-based molecular biology.Journal of molecular medicine (Berlin, Germany) · 2018Review
- Predicting the redox state and secondary structure of cysteine residues using multi-dimensional classification analysis of NMR chemical shifts.Journal of biomolecular NMR · 2016Article
- Exploring Polypharmacology in Drug Discovery and Repurposing Using the CANDO Platform.Current pharmaceutical design · 2016Review
- Application of data mining tools for classification of protein structural class from residue based averaged NMR chemical shifts.Biochimica et biophysica acta · 2015Article
- CSI 3.0: a web server for identifying secondary and super-secondary structure in proteins using NMR chemical shifts.Nucleic acids research · 2015Article
- CSI 2.0: a significantly improved version of the Chemical Shift Index.Journal of biomolecular NMR · 2014Article
- Type I and II β-turns prediction using NMR chemical shifts.Journal of biomolecular NMR · 2014Article
- Modeling proteins using a super-secondary structure library and NMR chemical shift information.Structure (London, England : 1993) · 2013Article
- Identification of helix capping and b-turn motifs from NMR chemical shifts.Journal of biomolecular NMR · 2012Article
- Uncovering symmetry-breaking vector and reliability order for assigning secondary structures of proteins from atomic NMR chemical shifts in amino acids.Journal of biomolecular NMR · 2011Article
- TALOS+: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts.Journal of biomolecular NMR · 2009Article
- Characterization of protein secondary structure from NMR chemical shifts.Progress in nuclear magnetic resonance spectroscopy · 2009Article
- Probabilistic interaction network of evidence algorithm and its application to complete labeling of peak lists from protein NMR spectroscopy.PLoS computational biology · 2009Article
- Nearest-neighbor effects on backbone alpha and beta carbon chemical shifts in proteins.Journal of biomolecular NMR · 2007Article
- Use of secondary structural information and C alpha-C alpha distance restraints to model protein structures with MODELLER.Journal of biosciences · 2007Article
- A Bayesian-probability-based method for assigning protein backbone dihedral angles based on chemical shifts and local sequences.Journal of biomolecular NMR · 2007Article
- An automated assignment-free Bayesian approach for accurately identifying proton contacts from NOESY data.Journal of biomolecular NMR · 2006Article
- PROTINFO: new algorithms for enhanced protein structure predictions.Nucleic acids research · 2005Article
- Protein energetic conformational analysis from NMR chemical shifts (PECAN) and its use in determining secondary structural elements.Journal of biomolecular NMR · 2005Article
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2 authors.
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Abstract
PsiCSI is a highly accurate and automated method of assigning secondary structure from NMR data, which is a useful intermediate step in the determination of tertiary structures. The method combines information from chemical shifts and protein sequence using three layers of neural networks. Training and testing was performed on a suite of 92 proteins (9437 residues) with known secondary and tertiary structure. Using a stringent cross-validation procedure in which the target and homologous proteins were removed from the databases used for training the neural networks, an average 89% Q3 accuracy (per residue) was observed. This is an increase of 6.2% and 5.5% (representing 36% and 33% fewer errors) over methods that use chemical shifts (CSI) or sequence information (Psipred) alone. In addition, PsiCSI improves upon the translation of chemical shift information to secondary structure (Q3 = 87.4%) and is able to use sequence information as an effective substitute for sparse NMR data (Q3 = 86.9% without (13)C shifts and Q3 = 86.8% with only H(alpha) shifts available). Finally, errors made by PsiCSI almost exclusively involve the interchange of helix or strand with coil and not helix with strand (<2.5 occurrences per 10000 residues). The automation, increased accuracy, absence of gross errors, and robustness with regards to sparse data make PsiCSI ideal for high-throughput applications, and should improve the effectiveness of hybrid NMR/de novo structure determination methods. A Web server is available for users to submit data and have the assignment returned.
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