MOEフォーラム2019開催のご案内

Database AutoPH4: pharmacophore analysis of multiple protein structures
Chris Williams (Chemical Computing Group ULC)

Abstract: An automated approach to summarize pocket shapes and binding hot-spots from a collection of protein structures is presented. Pocket shapes are described using pocket volumes derived from Alpha Sites and molecular surfaces. Binding hot-spots are located using pharmacophore features generated by AutoPH4. Collections of pocket volumes and pharmacophores are analyzed using feature densities which map onto a universal grid the fraction of structures that possess a given feature at each point in space. Regions with high pharmacophore feature densities identify the most persistent interaction binding hot-spots over the collection of structures. Pocket volume densities detect and classify binding site regions into core pockets and sub-pocket regions. Fingerprints that represent pocket shape, sub-pocket presence and pharmacophore feature presence are derived and used to cluster and classify multiple protein structures using standard fingerprint clustering tools. Application of the method to fragment-based drug design, minor pocket detection, selectivity mapping, binding-mode classification and custom docking scoring function creation is presented.

Modeling Protein Properties using pH-dependent Conformational Sampling
John Gunn (Chemical Computing Group ULC.)

Abstract: Proteins present particular challenges for property calculations due to their conformational flexibility in solution and the sensitivity of the structure to environmental parameters such as buffer strength and pH. We present a novel method for calculating thermodynamically averaged properties using a conformational ensemble which correctly takes into account the variability of both the structure and the charge state (protonation) of the protein.

The validity of this approach will be demonstrated using various benchmark calculations with experimental reference data, and additional applications will be shown with an emphasis toward modeling developability criteria for therapeutic antibodies.