title: Improving the accuracy of high-throughput protein-protein affinity prediction may require better training data.
abstract: BACKGROUND
  One goal of structural biology is to understand how a protein's 3-dimensional conformation determines its capacity to interact with potential ligands. In the case of small chemical ligands, deconstructing a static protein-ligand complex into its constituent atom-atom interactions is typically sufficient to rapidly predict ligand affinity with high accuracy (>70% correlation between predicted and experimentally-determined affinity), a fact that is exploited to support structure-based drug design. We recently found that protein-DNA/RNA affinity can also be predicted with high accuracy using extensions of existing techniques, but protein-protein affinity could not be predicted with >60% correlation, even when the protein-protein complex was available.
  
  
  METHODS
  X-ray and NMR structures of protein-protein complexes, their associated binding affinities and experimental conditions were obtained from different binding affinity and structural databases. Statistical models were implemented using a generalized linear model framework, including the experimental conditions as new model features. We evaluated the potential for new features to improve affinity prediction models by calculating the Pearson correlation between predicted and experimental binding affinities on the training and test data after model fitting and after cross-validation. Differences in accuracy were assessed using two-sample t test and nonparametric Mann-Whitney U test.
  
  
  RESULTS
  Here we evaluate a range of potential factors that may interfere with accurate protein-protein affinity prediction. We find that X-ray crystal resolution has the strongest single effect on protein-protein affinity prediction. Limiting our analyses to only high-resolution complexes (≤2.5 Å) increased the correlation between predicted and experimental affinity from 54 to 68% (p = 4.32x10-3). In addition, incorporating information on the experimental conditions under which affinities were measured (pH, temperature and binding assay) had significant effects on prediction accuracy. We also highlight a number of potential errors in large structure-affinity databases, which could affect both model training and accuracy assessment.
  
  
  CONCLUSIONS
  The results suggest that the accuracy of statistical models for protein-protein affinity prediction may be limited by the information present in databases used to train new models. Improving our capacity to integrate large-scale structural and functional information may be required to substantively advance our understanding of the general principles by which a protein's structure determines its function.
authors.affiliation: Department of Microbiology and Cell Science, University of Florida, Gainesville, FL, USA.
authors.forename: Raquel
authors.initials: R
authors.lastname: Dias
authors.affiliation: Department of Microbiology and Cell Science, University of Florida, Gainesville, FL, USA. bryank@ufl.edu.
authors.forename: Bryan
authors.initials: B
authors.lastname: Kolaczkowski
mesh_terms: D000818:Animals; D000068598:Data Accuracy; D006801:Humans; D000069550:Machine Learning; D008958:Models, Molecular; D015233:Models, Statistical; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*
chemical_list: D011506:Proteins
keywords: Binding affinity; Intermolecular interactions; Machine learning; Protein-protein; Scoring functions