1.
A fragment-based approach to the SAMPL3 Challenge.
Kulp, JL, Blumenthal, SN, Wang, Q, Bryan, RL, Guarnieri, F
Journal of computer-aided molecular design. 2012;(5):583-94
Abstract
The success of molecular fragment-based design depends critically on the ability to make predictions of binding poses and of affinity ranking for compounds assembled by linking fragments. The SAMPL3 Challenge provides a unique opportunity to evaluate the performance of a state-of-the-art fragment-based design methodology with respect to these requirements. In this article, we present results derived from linking fragments to predict affinity and pose in the SAMPL3 Challenge. The goal is to demonstrate how incorporating different aspects of modeling protein-ligand interactions impact the accuracy of the predictions, including protein dielectric models, charged versus neutral ligands, ΔΔGs solvation energies, and induced conformational stress. The core method is based on annealing of chemical potential in a Grand Canonical Monte Carlo (GC/MC) simulation. By imposing an initially very high chemical potential and then automatically running a sequence of simulations at successively decreasing chemical potentials, the GC/MC simulation efficiently discovers statistical distributions of bound fragment locations and orientations not found reliably without the annealing. This method accounts for configurational entropy, the role of bound water molecules, and results in a prediction of all the locations on the protein that have any affinity for the fragment. Disregarding any of these factors in affinity-rank prediction leads to significantly worse correlation with experimentally-determined free energies of binding. We relate three important conclusions from this challenge as applied to GC/MC: (1) modeling neutral ligands--regardless of the charged state in the active site--produced better affinity ranking than using charged ligands, although, in both cases, the poses were almost exactly overlaid; (2) simulating explicit water molecules in the GC/MC gave better affinity and pose predictions; and (3) applying a ΔΔGs solvation correction further improved the ranking of the neutral ligands. Using the GC/MC method under a variety of parameters in the blinded SAMPL3 Challenge provided important insights to the relevant parameters and boundaries in predicting binding affinities using simulated annealing of chemical potential calculations.
2.
Quantitative prediction of the thermal motion and intrinsic disorder of protein cofactors in crystalline state: a case study on halide anions.
Ren, Y, Chen, X, Li, X, Lai, H, Wang, Q, Zhou, P, Chen, G
Journal of theoretical biology. 2010;(2):291-8
Abstract
The thermal motion and intrinsic disorder of protein cofactors are highly correlated with their biological functions and can be at least in part measured by atomic temperature factor or B-factor. However, this crystallographic parameter, which actually shares the equal importance with the atomic coordinate in describing the complete profile of crystal structures, has long been underappreciated in the field of biology. In the present study, we attempt to put the first step towards the quantitative prediction of the B-factor values of halide anions, which were recently found to play a fundamental role in conferring stability and specificity to the architecture of proteins and their complexes with nucleic acids and small ligands. In this procedure, the local nonbonding landscapes of halide anions bound in proteins are characterized by electrostatic and dispersion potentials, and then the resulting descriptors of the characterization are statistically correlated with experimentally measured B-factors by using both linear and nonlinear machine learning approaches. From the modeling results and the comparison of these results to those obtained previously for predicting protein B-factors, we demonstrate that the dynamic behavior of halide anions in protein crystals is primarily governed by the local features of nonbonding potential landscapes and, owing to the non-ignorable noise existing in experimental data, the relationship between the B-factor values and the local nonbonding landscapes can only be modeled at a moderate level of accuracy even using the complicated nonlinear methods. These findings are consistent well with that concluding from previous studies of protein B-factors.