FunFOLDQA: a quality assessment tool for protein-ligand binding site residue predictions

• Published in: PloS one 2012-05, Vol.7 (5), p.e38219-e38219
• Main Authors: Roche, Daniel B, Buenavista, Maria T, McGuffin, Liam J
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Binding Sites; Bioinformatics; Biology; Carbohydrates; Computational Biology - methods; Correlation; Correlation coefficient; Correlation coefficients; Ligands; Linear Models; Methods; Models, Molecular; Neural networks; Neural Networks (Computer); Protein Binding; Protein Conformation; Protein structure; Protein structure prediction; Proteins; Proteins - chemistry; Proteins - metabolism; Quality; Quality assessment; Quality Control; Quality management; Regression analysis; ROC Curve; Statistical analysis
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0038219

The estimation of prediction quality is important because without quality measures, it is difficult to determine the usefulness of a prediction. Currently, methods for ligand binding site residue predictions are assessed in the function prediction category of the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment, utilizing the Matthews Correlation Coefficient (MCC) and Binding-site Distance Test (BDT) metrics. However, the assessment of ligand binding site predictions using such metrics requires the availability of solved structures with bound ligands. Thus, we have developed a ligand binding site quality assessment tool, FunFOLDQA, which utilizes protein feature analysis to predict ligand binding site quality prior to the experimental solution of the protein structures and their ligand interactions. The FunFOLDQA feature scores were combined using: simple linear combinations, multiple linear regression and a neural network. The neural network produced significantly better results for correlations to both the MCC and BDT scores, according to Kendall's τ, Spearman's ρ and Pearson's r correlation coefficients, when tested on both the CASP8 and CASP9 datasets. The neural network also produced the largest Area Under the Curve score (AUC) when Receiver Operator Characteristic (ROC) analysis was undertaken for the CASP8 dataset. Furthermore, the FunFOLDQA algorithm incorporating the neural network, is shown to add value to FunFOLD, when both methods are employed in combination. This results in a statistically significant improvement over all of the best server methods, the FunFOLD method (6.43%), and one of the top manual groups (FN293) tested on the CASP8 dataset. The FunFOLDQA method was also found to be competitive with the top server methods when tested on the CASP9 dataset. To the best of our knowledge, FunFOLDQA is the first attempt to develop a method that can be used to assess ligand binding site prediction quality, in the absence of experimental data.