The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
DiANNA: a web server for disulfide connectivity prediction
Open accessAbstract
Correctly predicting the disulfide bond topology in a protein is of crucial importance for the understanding of protein function and can be of great help for tertiary prediction methods. The web server http://clavius.bc.edu/~clotelab/DiANNA/ outputs the disulfide connectivity prediction given input of a protein sequence. The following procedure is performed. First, PSIPRED is run to predict the protein's secondary structure, then PSIBLAST is run against the non-redundant SwissProt to obtain a multiple alignment of the input sequence. The predicted secondary structure and the profile arising from this alignment are used in the training phase of our neural network. Next, cysteine oxidation state is predicted, then each pair of cysteines in the protein sequence is assigned a likelihood of forming a disulfide bond--this is performed by means of a novel architecture (diresidue neural network). Finally, Rothberg's implementation of Gabow's maximum weighted matching algorithm is applied to diresidue neural network scores in order to produce the final connectivity prediction. Our novel neural network-based approach achieves results that are comparable and in some cases better than the current state-of-the-art methods.
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Jones, D. T. (1999) · Journal of Molecular Biology · doi:10.1006/jmbi.1999.3091 · Cited by 5,449
A general framework for adaptive processing of data structures
Frasconi, P., Gori, M., & Sperduti, A. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/72.712151 · Cited by 471
Amino acid neighbours and detailed conformational analysis of cysteines in proteins
Petersen, M. T. N., Jonson, P. H., & Petersen, S. B. (1999) · Protein Engineering, Design and Selection · doi:10.1093/protein/12.7.535 · Cited by 179
Role of evolutionary information in predicting the disulfide-bonding state of cysteine in proteins
Fariselli, P., Riccobelli, P., & Casadio, R. (1999) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(19990815)36:3<340::aid-prot8>3.0.co;2-d · Cited by 144
Disulfide connectivity prediction using secondary structure information and diresidue frequencies
Ferrè, F., & Clote, P. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti328 · Cited by 109
Self-citationPrediction of disulfide connectivity in proteins
Fariselli, P., & Casadio, R. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.10.957 · Cited by 99
Chen, Y., Lin, Y., Lin, C., & Hwang, J. (2004) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20079 · Cited by 66
Predicting the disulfide bonding state of cysteines using protein descriptors
Mucchielli-Giorgi, M.-H., Hazout, S., & Tufféry, P. (2002) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10047 · Cited by 65
Relationship between protein structures and disulfide‐bonding patterns
Chuang, C., Chen, C., Yang, J., Lyu, P. C., & Hwang, J. (2003) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10492 · Cited by 56
Cysteine separations profiles on protein sequences infer disulfide connectivity
Zhao, E., Liu, H.-L., Tsai, C.-H., Tsai, H., Chan, C., & Kao, C.-Y. (2004) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti179 · Cited by 42
Cited by
Chemistry and Enzymology of Disulfide Cross-Linking in Proteins
Fass, D., & Thorpe, C. (2018) · Chemical Reviews · doi:10.1021/acs.chemrev.7b00123 · Cited by 321
Deep learning methods in protein structure prediction
Torrisi, M., Pollastri, G., & Le, Q. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.12.011 · Cited by 284
Liu, Z., Zhang, Z., Faris, J. D., Oliver, R. P., Syme, R., McDonald, M. C., McDonald, B. A., Solomon, P. S., Lu, S., Shelver, W. L., et al. (2012) · PLoS Pathogens · doi:10.1371/journal.ppat.1002467 · Cited by 269
Genome-wide analysis of restriction-modification system in unicellular and filamentous cyanobacteria
Zhao, F., Zhang, X., Liang, C., Wu, J., Bao, Q., & Qin, S. (2006) · Physiological Genomics · doi:10.1152/physiolgenomics.00255.2005 · Cited by 47
I-Shaped Dimers of a Plant Chloroplast FOF1-ATP Synthase in Response to Changes in Ionic Strength
Osipov, S. D., Ryzhykau, Y. L., Zinovev, E. V., Minaeva, A. V., Ivashchenko, S. D., Verteletskiy, D. P., Sudarev, V. V., Kuklina, D. D., Nikolaev, M. Y., Semenov, Y. S., et al. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241310720 · Cited by 5
Metadata
- Authors
- Ferre, F., & Clote, P.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-07-01
- Volume
- 33
- Issue
- Web Server
- Pages
- W230-W232
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2072708496
- Cited by
- 375
- References
- 10
- FWCI
- 4.96
- Type
- journal article
- Language
- en
- Open access
- gold
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