JPred : a consensus secondary structure prediction server

By James A. Cuff, Michele E. Clamp, Asim S. Siddiqui, M. Finlay, Geoffrey J. Barton. 1999

How researchers see this paper

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

Citations in the corpus

NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning (2018, methodology): “As a practical application of it, several methods have been developed over the last decades to predict from sequence only several protein structural features, including solvent accessibility, secondary structure, backbone geometry, disorder, and interface residues (2-7).”
EVA: evaluation of protein structure prediction servers (2003, background): “... (36) Inter-residue contacts CORNET http://prion.biocomp.unibo.it/cornet.html P Fariselli, O Olmea and A Valencia, R Casadio (10,37,38) PDGCON http://www.pdg.cnb.uam.es:8081/ F Pazos, O Olmea and A Valencia (13) CONcons/CONhydro http://www.pdg.cnb.uam.es:8081/ F Pazos, O Olmea and A Valencia (37–”
M-Coffee: combining multiple sequence alignment methods with T-Coffee (2006, background): “In these contexts, Meta-methods, or Jury-based methods (19,20) have often proven to be superior to the constitutive methods.”
Recent developments in deep learning applied to protein structure prediction (2019, background): “Prominent examples of early NN methods that are still widely used today are PHD,(1,2) PSIPRED,(3) and JPred.(4) The field has recently seen a surge of interest relating”
Length-dependent prediction of protein intrinsic disorder (2006, background): “…a putative ANK repeat, i.e. the 1st repeat in the domain, which is comprised of two predicted a-helices connected by an unusually long loop of 16 residues [69] (note that the current Jpred [71] and PSIPRED [48] predictors both predicted another α -helix inside this region, but at low confidence).”
Properties and identification of human protein drug targets (2009, methodology): “The JPred program (http://www.compbio.dundee.ac.uk/∼www-jpred/) (Cuff et al., 1998) was used to predict the percentages of α-helix and β-sheet in 148 drug targets and a sample of 1064 non-targets.”
FIH-1 is an asparaginyl hydroxylase enzyme that regulates the transcriptional activity of hypoxia-inducible factor. (2002, methodology): “A multiple sequence alignment was generated using Jpred (Cuff et al. 1998) for amino acid residues 179–300 of human FIH-1.”
Improving protein secondary structure prediction based on short subsequences with local structure similarity (2010, methodology): “JPred [34] was the first meta-predictor developed for PSS prediction.”