A new approach to protein fold recognition

By David C. Jones, William R. Taylor, Janet M. Thornton. 1992

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Citations in the corpus

Improving homology modeling from low-sequence identity templates in Rosetta: A case study in GPCRs (2020, methodology): “This alignment maps the target sequence onto the template structure in a process called threading [9].”
A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction (2014, background): “secondary structure, and the use of these intermediate features has been shown to facilitate ab initio tertiary structure prediction [1], [3], [4].”
Template-based protein structure modeling using the RaptorX web server (2012, methodology): “The numbered labels indicate the location of the following screen features: (1) the rank of currently selected model; (2) the quality score of the model; (3) the PDB IDs for the set templates used for modeling; (4) a drop-down menu for selecting alternative structure models; (5) tabs for switching b”
Enhanced genome annotation using structural profiles in the program 3D-PSSM. (2000, methodology): “Solvation potential was modelled using the approach of Jones et al. (1992).”
QMEAN: A comprehensive scoring function for model quality assessment (2008, background): “…–20 Alternatively, statistical potentials have been derived for other structural features such as torsion angles21–25 and solvent accessibility.25 –27 Knowledge-based energy functions are based on the inverse Boltzmann equation, although their physical basis has been questioned and is still not…”
Template‐based modeling and free modeling by I‐TASSER in CASP7 (2007, background): “…structures at a resolution level useful for virtual ligand screening9,10 and biological function inference5,11; (2) for the sequences with weakly/distant homologous templates, how to identify the correct templates12,13 and how to refine the templates closer to native by computational simulations.”
An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction. (1998, background): “…knowledge-based discriminatory functions have gained popularity in their application to the ``fold recognition'' problem, i.e. recognising the fold an amino acid belongs to in the absence of detectable sequence homology (Sippl, 1990; Bowie et al., 1991; Jones et al., 1992; Bryant & Lawrence, 1993).”
MUSTER: Improving protein sequence profile–profile alignments by using multiple sources of structure information (2008, background): “It combines various sequence and structure information into singlebody terms which can be conveniently used in dynamic programming search: (1) sequence profiles; (2) secondary structures; (3) structure fragment profiles; (4) solvent accessibility; (5) dihedral torsion angles; (6) hydrophobic scoring”
How to derive a protein folding potential? A new approach to an old problem. (1996, methodology): “Sampling techniques that are more constrained to protein-like conformations (Finkelstein & Reva, 1991; Jones et al., 1992; Wodak & Rooman, 1993) can, however, recognize the native and native-like folds among a small enough pool of alternative conformations.”
The PSIPRED protein structure prediction server (2000, methodology): “fold similarities resulting from common ancestry), and exploits a conventional sequence profile-based alignment algorithm to generate sequence-structure alignments, which are then analysed by a set of statistical potentials previously used for threading (Jones et al., 1992).”
TASSER: An automated method for the prediction of protein tertiary structures in CASP6 (2005, background): “First, despite considerable progress on the ab initio/new fold (NF) predictions, 3–5 comparative modeling (CM), 6 and threading/fold-recogni-tion (FR) 7,8 remain the only methods that provide reliable and accurate models.”
Progress and challenges in protein structure prediction. (2008, background): “Template structure identification Since its invention in the early 1990s [6,7], threading has become one of the most active areas in proteins structure prediction.”
A distance‐dependent atomic knowledge‐based potential for improved protein structure selection (2001, background): “15 On the other hand, with today’s computer resources, knowledge-based potentials can be quite successful at fold recognition 16–19 and ab initio structure prediction 20,21 see also Proteins special issue, 2000, on CASP3).”
Protein structure comparison using iterated double dynamic programming (2008, methodology): “…and Orengo~1989b!, and its later developments, both in structure comparison~see Orengo and Taylor~1996! for a review! and sequence0structure comparison~Jones et al., 1992; Taylor, 1997!, is based on the dynamic programming algorithm used to compare protein sequences~Needleman & Wunsch, 1970!.”