FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins

By Daniel B. Roche, Stuart J. Tetchner, Liam J. McGuffin. 2011

69 citations recorded by the source corpus.

BackgroundThe accurate prediction of ligand binding residues from amino acid sequences is important for the automated functional annotation of novel proteins. In the previous two CASP experiments, the most successful methods in the function prediction category were those which used structural superpositions of 3D models and related templates with bound ligands in order to identify putative contacting residues. However, whilst most of this prediction process can be automated, visual inspection and manual adjustments of parameters, such as the distance thresholds used for each target, have often been required to prevent over prediction. Here we describe a novel method FunFOLD, which uses an automatic approach for cluster identification and residue selection. The software provided can easily be integrated into existing fold recognition servers, requiring only a 3D model and list of templates as inputs. A simple web interface is also provided allowing access to non-expert users. The method has been benchmarked against the top servers and manual prediction groups tested at both CASP8 and CASP9.ResultsThe FunFOLD method shows a significant improvement over the best available servers and is shown to be competitive with the top manual prediction groups that were tested at CASP8. The FunFOLD method is also competitive with both the top server and manual methods tested at CASP9. When tested using common subsets of targets, the predictions from FunFOLD are shown to achieve a significantly higher mean Matthews Correlation Coefficient (MCC) scores and Binding-site Distance Test (BDT) scores than all server methods that were tested at CASP8. Testing on the CASP9 set showed no statistically significant separation in performance between FunFOLD and the other top server groups tested.ConclusionsThe FunFOLD software is freely available as both a standalone package and a prediction server, providing competitive ligand binding site residue predictions for expert and non-expert users alike. The software provides a new fully automated approach for structure based function prediction using 3D models of proteins.

Subfields: Computational Theory and Mathematics, 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 Computational Theory and Mathematics

In Molecular Biology

Citations in the corpus

IntFOLD: an integrated web resource for high performance protein structure and function prediction (2019, methodology): “The IntFOLD server provides a single point of access to an integrated suite of six component methods: IntFOLD-TS, for tertiary structure prediction (9–11,18,19); ModFOLD, for 3D model Accuracy Self-Estimate (ASE) scoring (9,20); ReFOLD, for 3D model refinement (9,21); DISOclust, for disorder predict”
Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment (2013, background): “The second structure-based approach is to infer ligand-binding information from the known template proteins, which have similar global and/or local structure to the query (Brylinski and Skolnick, 2008; Roche et al., 2011; Roy et al., 2012; Roy and Zhang, 2012; Wass et al., 2010).”
The FunFOLD2 server for the prediction of protein–ligand interactions (2013, methodology): “The FunFOLD algorithm has been extensively benchmarked on both the CASP8 and the CASP9 data sets (2).”
Automated tertiary structure prediction with accurate local model quality assessment using the intfold‐ts method (2011, methodology): “…is now available via the IntFOLD server,17 which also fully integrates the latest versions of our automated disorder prediction (IntFOLD-DR, DISOclust), 28 domain prediction (IntFOLD-DP, DomFOLD), binding site residue prediction (IntFOLD-FN, FunFOLD),29 and model QA (IntFOLD-QA, ModFOLD)26 methods.”
IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences (2015, methodology): “The FunFOLD algorithm works by carrying out model-to-template superpositions, of the top ranked IntFOLD3-TS 3D models and related templates with bound ligands, in order to identify the 3D locations of binding site residues and putative interacting ligands (22).”