PHOENIX: A Scoring Function for Affinity Prediction Derived Using High-Resolution Crystal Structures and Calorimetry Measurements

• Published in: Journal of chemical information and modeling Vol. 51; no. 2; pp. 214 - 228
• Main Authors: Tang, Yat T, Marshall, Garland R
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 28.02.2011
• Subjects: Binding sites; Biochemistry; Biological and medical sciences; Calorimetry; Chemical Information; Computational Biology - methods; Crystallography, X-Ray; Databases, Protein; Drug Design; Entropy; Fundamental and applied biological sciences. Psychology; General pharmacology; Interactions. Associations; Intermolecular phenomena; Least-Squares Analysis; Medical sciences; Molecular biophysics; Molecular chemistry; Molecular structure; Molecules; Pharmaceutical technology. Pharmaceutical industry; Pharmacology. Drug treatments; Proteins - metabolism; Reproducibility of Results; Structure-Activity Relationship; Thermodynamics; High resolution; Energy function; Molecular interaction; Computational chemistry; Binding protein; Thermodynamics; Data structure; Computer aided design; Availability; Validation; Ligand binding; Pharmaceutical technology; Image retrieval; Pharmacology; Software component; Experimental study; Free energy; Scoring credit; Binding energy; Calorimetry; Content-based retrieval; Objective function; Molecular biology; Protein structure; Crystalline structure
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/ci100257s

Binding affinity prediction is one of the most critical components to computer-aided structure-based drug design. Despite advances in first-principle methods for predicting binding affinity, empirical scoring functions that are fast and only relatively accurate are still widely used in structure-based drug design. With the increasing availability of X-ray crystallographic structures in the Protein Data Bank and continuing application of biophysical methods such as isothermal titration calorimetry to measure thermodynamic parameters contributing to binding free energy, sufficient experimental data exists that scoring functions can now be derived by separating enthalpic (ΔH) and entropic (TΔS) contributions to binding free energy (ΔG). PHOENIX, a scoring function to predict binding affinities of protein−ligand complexes, utilizes the increasing availability of experimental data to improve binding affinity predictions by the following: model training and testing using high-resolution crystallographic data to minimize structural noise, independent models of enthalpic and entropic contributions fitted to thermodynamic parameters assumed to be thermodynamically biased to calculate binding free energy, use of shape and volume descriptors to better capture entropic contributions. A set of 42 descriptors and 112 protein−ligand complexes were used to derive functions using partial least-squares for change of enthalpy (ΔH) and change of entropy (TΔS) to calculate change of binding free energy (ΔG), resulting in a predictive r 2 (r pred 2) of 0.55 and a standard error (SE) of 1.34 kcal/mol. External validation using the 2009 version of the PDBbind “refined set” (n = 1612) resulted in a Pearson correlation coefficient (R p) of 0.575 and a mean error (ME) of 1.41 pK d. Enthalpy and entropy predictions were of limited accuracy individually. However, their difference resulted in a relatively accurate binding free energy. While the development of an accurate and applicable scoring function was an objective of this study, the main focus was evaluation of the use of high-resolution X-ray crystal structures with high-quality thermodynamic parameters from isothermal titration calorimetry for scoring function development. With the increasing application of structure-based methods in molecular design, this study suggests that using high-resolution crystal structures, separating enthalpy and entropy contributions to binding free energy, and including descriptors to better capture entropic contributions may prove to be effective strategies toward rapid and accurate calculation of binding affinity.