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

Abstract

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.

TAPO: A combined method for the identification of tandem repeats in protein structures

“…Residues were determined to be in contact with the ligand if the distance between a ligand atom and a residue atom was less than sum of their Van der Waals radii plus 0.5 Å [50,51]. After that, we scan at either side these sites moving one residue at a time, using two adjacent windows of length l, which corresponds to the length between the equivalent residue positions of the binding sites (Fig.…”

“…The list of ligands corresponding to the PDB entries was downloaded from PDBsum [49] (https://www.ebi.ac.uk/pdbsum/) and combined with an extended set of the ligands utilized by the Fun-FOLD algorithm [50,51]. Residues were determined to be in contact with the ligand if the distance between a ligand atom and a residue atom was less than sum of their Van der Waals radii plus 0.5 Å [50,51].…”

Recognizing metal and acid radical ion-binding sites by integrating ab initio modeling with template-based transferals

“…Therefore, accurate identification of the protein-ion-binding sites is important for understanding the mechanism of protein function and for new drug discovery. Many computational methods have been proposed in the last two decades for predicting general ligand-protein binding sites, which can be roughly grouped into two categories of sequencebased (Capra and Singh, 2007;Chen et al, 2014Chen et al, , 2016Magliery and Regan, 2005;Rausell et al, 2010) and structure-based (Brylinski and Skolnick, 2008;Capra et al, 2009;Hendlich et al, 1997;Laskowski, 1995;Roche et al, 2011;Roy and Zhang, 2012;Wass et al, 2010;Yang et al, 2013b) approaches. The sequence-based methods mostly rely on residue conservation analyses under the assumption that ligand-binding residues are functionally important and therefore should be conserved in the evolution.…”

Normal Modes Expose Active Sites in Enzymes

“…From these data it is clear, that one should rely on sequence and structure homology when possible, and over the past decade, multiple methods to detect binding sites and functional pockets based on geometric, structural, and genetic data were developed [35–39]. Several webservers of ligand binding sites have also been constructed and may be used to infer unknown ligand binding sites based on homology and other attributes such as Pocketome [40], FunFold [41], scPDB [42], IBIS [43], Multibind [44], fPop [45], and FINDSITE [46]. To date however, no comprehensive study comparing geometry based techniques has been performed.…”