SPINE-D: Accurate Prediction of Short and Long Disordered Regions by a Single Neural-Network Based Method

By Tuo Zhang, Eshel Faraggi, Bin Xue, A. Keith Dunker, Vladimir N Uversky, Yaoqi Zhou. 2012

172 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019, methodology): “These datasets were the subsets from the established SL477 [16], MobiDB [43], and DisProt Complement [44] sets, respectively, after removing long proteins (>700 residues) and homologous proteins in our training dataset (25% sequence identity cutoff with BLASTClust).”
Intrinsically Semi-disordered State and Its Role in Induced Folding and Protein Aggregation (2013, methodology): “SPINE-D Server SPINE-D is a neural-network-based predictor trained on a non-redundant set of 4157 X-ray structures and 72 fully disordered proteins from the Disprot database v5.”
Improving protein disorder prediction by deep bidirectional long short‐term memory recurrent neural networks (2016, methodology): “Supplementary Table S1 compares SPOT-disorder against a number of 12 other predictors in addition to SPINE-D in SL329.”
Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions (2017, unknown): “SPINE-D 2012 [81] WS ? SP No http://sparks-lab.”
Computational Identification of MoRFs in Protein Sequences Using Hierarchical Application of Bayes Rule (2015, methodology): “Several computational tools are available to predict the locations of disordered segments in proteins, including DISOPRED [20], ESpritz [21], IUPred [22, 23], and SPINE-D [24].”
Natural protein sequences are more intrinsically disordered than random sequences (2016, methodology): “This happens because only native structures and disordered regions were employed for training SPINE-D [26].”
Energy functions in de novo protein design: current challenges and future prospects. (2013, methodology): “We employ SPINE-D (120) for this task because it was one of the top disorder predictors in critical assessments of structure prediction techniques in 2010 (CASP 9) (79).”
MFSPSSMpred: identifying short disorder-to-order binding regions in disordered proteins based on contextual local evolutionary conservation (2013, unknown): “Spine-D [26] 0.”