title: Binding affinity prediction for protein-ligand complexes based on β contacts and B factor.
abstract: Accurate determination of protein-ligand binding affinity is a fundamental problem in biochemistry useful for many applications including drug design and protein-ligand docking. A number of scoring functions have been proposed for the prediction of protein-ligand binding affinity. However, accurate prediction is still a challenging problem because poor performance is often seen in the evaluation under the leave-one-cluster-out cross-validation (LCOCV). We introduce a new scoring function named B2BScore to improve the prediction performance. B2BScore integrates two physicochemical properties for protein-ligand binding affinity prediction. One is the property of β contacts. A β contact between two atoms requires no other atoms to interrupt the atomic contact and assumes that the two atoms should have enough direct contact area. The other is the property of B factor to capture the atomic mobility in the dynamic protein-ligand binding process. Tested on the PDBBind2009 data set, B2BScore shows superior prediction performance to existing methods on independent test data as well as under the LCOCV evaluation framework. In particular, B2BScore achieves a significant LCOCV improvement across 26 protein clusters-a big increase of the averaged Pearson's correlation coefficients from 0.418 to 0.518 and a significant decrease of standard deviation of the coefficients from 0.352 to 0.196. We also identified several important and intuitive contact descriptors of protein-ligand binding through the random forest learning in B2BScore. Some of these descriptors are closely related to contacts between carbon atoms without covalent-bond oxygen/nitrogen, preferred contacts of metal ions, interfacial backbone atoms from proteins, or π rings. Some others are negative descriptors relating to those contacts with nitrogen atoms without covalent-bond hydrogens or nonpreferred contacts of metal ions. These descriptors can be directly used to guide protein-ligand docking.
authors.affiliation: Advanced Analytics Institute and Center for Health Technologies, University of Technology, Sydney , Sydney, New South Wales, NSW 2007 Australia.
authors.forename: Qian
authors.initials: Q
authors.lastname: Liu
authors.forename: Chee Keong
authors.initials: CK
authors.lastname: Kwoh
authors.forename: Jinyan
authors.initials: J
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001665:Binding Sites; D030562:Databases, Protein; D003663:Decision Trees; D015195:Drug Design; D006860:Hydrogen Bonding; D008024:Ligands; D062105:Molecular Docking Simulation; D011485:Protein Binding; D054730:Protein Interaction Domains and Motifs; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D012107:Research Design; D013816:Thermodynamics
chemical_list: D008024:Ligands; D011506:Proteins