Predicting novel protein folds by using FRAGFOLD

By David C. Jones. 2001

139 citations recorded by the source corpus.

The results of applying a fragment‐based protein tertiary structure prediction method to the prediction of 8 CASP4 targets are described. The method is based on the assembly of supersecondary structural fragments taken from highly resolved protein structures using a simulated annealing algorithm. Despite the significant degree of success in this case, there is clearly much more developmental work required before predictions with the accuracy of a good homology model, or even a good fold recognition model, can be made with use of this kind of approach. Proteins 2001;Suppl 5:127–132. © 2002 Wiley‐Liss, Inc.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Toward optimal fragment generations for ab initio protein structure assembly (2012, methodology): “The position-specific structural fragments were also directly used by FRAGFOLD,21 TASSER,22 and I-TASSER23 for structure assembly simulations.”
Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns (2014, methodology): “Using this strategy, methods based on fragment-based assembly of proteins can sometimes produce surprisingly accurate models, in particular for small a-helical proteins [1-3].”
DNCON2: improved protein contact prediction using two-level deep convolutional neural networks (2017, background): “In recent years, protein residue–residue contacts have been identified as a key feature for accurate de novo protein structure prediction (Jones, 2001; Mabrouk et al. , 2016; Marks et al. , 2012; Michel et al. , 2014, 2017a).”
Toward the solution of the protein structure prediction problem (2021, methodology): “Apart from the Rosetta, additional FM predictors, such as QUARK (23) and FragFold (137), were developed by other groups based on a similar idea of fragment assembly using variants of Monte Carlo simulations, but with different approaches for fragment generation and energy function design.”
Protein structure prediction on the Web: a case study using the Phyre server (2009, methodology): “The most successful of these approaches are based on a principle of fragment assembly, originally pioneered by David Jone”
De Novo Structure Prediction of Globular Proteins Aided by Sequence Variation-Derived Contacts (2014, methodology): “FRAGFOLD’s force field embodies pair-wise potentials determined by inverse Boltzmann equation, solvation potential, hydrogen bonding, structure compactness and steric terms [30].”
Multipass membrane protein structure prediction using Rosetta (2005, unknown): “…simulations and predicted the glycophorin A structure with an RMSD of 1.9 Å. Pellegrini-Calace et al. 60 adopted their knowledge-based FRAG-FOLD 65,66 method to model helical TM proteins by addition of membrane-speciﬁc energy terms derived from the known helical TM proteins structures using…”
ConEVA: a toolbox for comprehensive assessment of protein contacts (2016, methodology): “In addition, this similarity score can even be integrated into model building methods like FUSION [40], UniCon3D [41], and FRAGFOLD [42] to decide the weight of the contact energy term.”
Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction (2015, methodology): “We also compared the accuracy of ReRASREC-PLM with two other recently published methods (PconsFold [33] and FRAGFOLD [31]) on the subset of targets where each publication reported actual numbers on.”
Assembling novel protein folds from super‐secondary structural fragments (2003, methodology): “The method used in CASP5 (FRAGFOLD), is based on an earlier method(2) which was used in the CASP2 experiment held in 1996 and the CASP4 experiment held in 2000.(4) Here only the most recent alterations to the method”