PROFbval: predict flexible and rigid residues in proteins

By Avner Schlessinger, Guy Yachdav, Burkhard Rost. 2006

156 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

I-TASSER server: new development for protein structure and function predictions (2015, methodology): “The B-factor profile (BFP) prediction is evaluated by the Pearson’s correlation between the predicted and the experimental B-factors, which was also used in previous B-factor prediction studies (7,8).”
Better prediction of functional effects for sequence variants (2015, neutral): “We compiled the original SNAP features: biophysical amino acid properties, explicit sequence, PSIC profiles [37], secondary structure and solvent accessibility [38-40], residue flexibility [41], and SWISS-PROT annotations.”
Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions (2017, methodology): “Each of the three SVMs uses a rich set of inputs that are categorized into two types: 1) the outputs from three predictors of intrinsic disorder, IUPred [46], DISOPRED2 [71] and DISOclust [68]; and 2) other sequence-derived information including the input sequence, PSSM profiles generated with PSI-B”
Critical assessment of high-throughput standalone methods for secondary structure prediction (2011, background): “The differences in these predictive qualities would have implications in the applications of the predicted SS to the prediction of B-factors [63] and disordered residues [64].”
Predict impact of single amino acid change upon protein structure (2012, methodology): “In addition, we used the following predicted structural and functional features: secondary structure [32,33] and solvent accessibility [24,25,32], protein flexibility [34], protein disorder [35-38], protein-protein interaction hotspots [39-41] and DNA-binding residues [42].”
Natively Unstructured Loops Differ from Other Loops (2007, methodology): “For each residue, we used the evolutionary profile (from PSI-BLAST alignments according to our standard protocol [79]), the three-state secondary structure predicted by PROFsec [30–32], the two-state solvent accessibility state predicted by PROFacc (a profile-based neural network predictor of solven”
Improved Disorder Prediction by Combination of Orthogonal Approaches (2009, methodology): “Final method MD better than simple averaging We then input to neural networks the results from the above four servers along with the output of a method predicting flexibility (PROFbval), and sequence profiles.”
Performance of mutation pathogenicity prediction methods on missense variants (2011, background): “…relative solvent accessibility values predicted by PROFsec/ PROFacc [Rost, 1996; Rost and Sander, 1994], chain flexibility predicted by PROFbval [Schlessinger et al., 2006], protein family evolutionary information, and information about domain boundaries from Pfam [Finn et al., 2010], and…”
SNAP: predict effect of non-synonymous polymorphisms on function (2007, methodology): “We used predictions of chain flexibility from PROFbval (48), including a single scaled node containing the method’s actual output.”
Comprehensive comparative assessment of in-silico predictors of disordered regions. (2012, methodology): “predictors built utilizing machine learning classifiers, such as DISOPRED [[24]], DisEMBL [[25]], DISOPRED2 [[26]], DISpro [[27]], RONN [[28]], Spritz [[29]], ProfBval [[30],[31]], PONDR family of predictors [[10],[32]-[36]], DisPSSMP [[37]], DisPSSMP2 [[38]], POODLE family of predictors [[39],[40]]”
Improving protein disorder prediction by deep bidirectional long short‐term memory recurrent neural networks (2016, unknown): “ANNs have also enjoyed widespread success in many accurate predictors, such as DisEMBL (Linding et al. , 2003 a ), DISOPRED (Jones and Ward, 2003), DISpro (Cheng et al. , 2005), NORSnet (Schlessinger et al. , 2007 a ), the PONDR series (Romero et al. , 2001), PROFbval (Schlessinger et al. , 2006), R”