Protein flexibility and rigidity predicted from sequence

By Avner Schlessinger, Burkhard Rost. 2005

189 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

In Molecular Biology

Citations in the corpus

Modeling aspects of the language of life through transfer-learning protein sequences (2019, methodology): “The idea was applied to other tasks, including the prediction of transmembrane regions [11 – 13], solvent accessibility [14], residue flexibility (B-values) [15, 16], inter-residue contacts [17] and protein disorder [15, 18 – 20].”
Modeling the language of life – Deep Learning Protein Sequences (2019, methodology): “The idea was 21 applied to other tasks, including the prediction of transmembrane regions [11-13], solvent 22 accessibility [14], residue flexibility (B-values) [15, 16], inter-residue contacts [17] and protein 23 disorder [15, 18-20].”
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]]”
PROFbval: predict flexible and rigid residues in proteins (2006, methodology): “Interestingly, surface residues in enzymes that are rigid often have a functional role (Schlessinger and Rost, 2005; Yuan, 2003).”
PredictProtein—an open resource for online prediction of protein structural and functional features (2014, unknown): “Disordered regions are predicted by a set of tools: UCON (21), NORSnet (22), PROFbval (23,24) and Meta-Disorder (25).”
SNAP: predict effect of non-synonymous polymorphisms on function (2007, background): “A study (57) of this enzyme, described in PMD, showed that set of 18 nsSNPs of thermolysin increase its activity (‘gain of function’).”
DFLpred: High-throughput prediction of disordered flexible linker regions in protein sequences (2016, unknown): “The concept of the sliding window has been adopted in other relevant predictors such as MoRFpred, fMoRFpred, DisoRDPbind, PROFbval, FlexPred, PredBF, PredyFlexy and DynaMine.”
iFC2: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content (2010, background): “... 2004; Garg et al. 2005; Nguyen and Rajapakse 2006; Dor and Zhou 2007; Chen et al. 2008c; Mooney and Pollastri 2009), the residue depth (Yuan and Wang 2008; Zhang et al. 2008a), the contact order and number (Pollastri et al. 2001; Kinjo et al. 2005; Yuan 2005; Song and Burrage 2006; Shi et al. 20”
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.”
Natively Unstructured Loops Differ from Other Loops (2007, methodology): “, from proteins without natively unstructured regions (states T, S, L from the Dictionary of Secondary Structure of Proteins; in blue); unstructured loops as predicted by NORSnet (in green); ‘‘flexible loops’’ from regular structures (TSL states with normalized B-factors 1 [82]; in red); and unstruc”