DISULFIND: a disulfide bonding state and cysteine connectivity prediction server
By Alessio Ceroni, Andrea Passerini, Alessandro Vullo, Paolo Frasconi. Nucleic Acids Res., 2006
348 citations recorded by the source corpus.
DISULFIND is a server for predicting the disulfide bonding state of cysteines and their disulfide connectivity starting from sequence alone. Optionally, disulfide connectivity can be predicted from sequence and a bonding state assignment given as input. The output is a simple visualization of the assigned bonding state (with confidence degrees) and the most likely connectivity patterns. The server is available at .
Subfields: Computational Theory and Mathematics, Molecular Biology, Organic Chemistry
View Paper: https://www.semanticscholar.org/paper/a356047fa58ee109fd928bda8b3fd6d52ac321e6
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In Computational Theory and Mathematics
- Yana Bromberg
- Avner Schlessinger
- Quan Le
- Mirko Torrisi
- Guy Yachdav
- Nir Ben-Tal
- Cristian Rocha-Roa
- M. Guerrero
In Molecular Biology
- Burkhard Rost
- Yana Bromberg
- Guy Yachdav
- Lukasz Kurgan
- Avner Schlessinger
- Marco Punta
- László Kaján
- Quan Le
Citations in the corpus
- PredictProtein—an open resource for online prediction of protein structural and functional features (2014, positive): “Prediction of structural features: predicted aspects of structure include PROFphd secondary structure and solvent accessibility (15,16), PROFtmb transmembrane strands (17), TMSEG transmembrane helices, COILS coiled-coil regions (18), DISULFIND disulfide bonds (19) and SEG low-complexity regions (20)”
- Improved Disorder Prediction by Combination of Orthogonal Approaches (2009, methodology): “DISULFIND [71] is a method that predicts cysteine pairs found in disulfide bridges.”
- Deep learning methods in protein structure prediction (2020, methodology): “…Edmonds-Gabow algorithm and Monte Carlo simulation annealing [102], or hybrid solutions such as Hidden Markov Models and FFNN [103], and multi-stage FFNN, SVM and BRNN [104], alongside classic Machine Learning models such as SVM [105], pure Deep Learning models such as BRNN [106], and FFNN [107].”
- Design of a specific peptide against phenolic glycolipid-1 from Mycobacterium leprae and its implications in leprosy bacilli entry (2022, methodology): “it/) was used to predict the disulfide bridges between cysteines and their connectivity pattern.(19) A prediction for N-glycosylation was performed in NetNGlyc 1.”