New and Updated Features in Phenix for Macromolecular Structure Determination

TL;DR

Phenix 2.0 introduces new tools for improving accuracy in macromolecular structure determination using quantum calculations and machine learning.

Contribution

Phenix 2.0 introduces quantum refinement for proteins using machine learning and more accurate ligand restraints via quantum mechanical calculations.

Findings

Quantum refinement for proteins using machine learning has been implemented.

Improved tools for accessing RCSB web services are now available.

Simplified usage of reference models for reciprocal space refinement at low resolution is introduced.

Abstract

In this presentation, we will highlight the newest developments in Phenix. Our newest release, Phenix 2.0, includes the following new features: more accurate restraints for ligands in situ using quantum mechanical calculations, quantum refinement for proteins using machine learning, improved tools for accessing RCSB web services (such as the PDB validation report), and simplified usage of reference models for reciprocal space refinement which is especially useful at low resolution. We will describe how to use these new tools, discuss other updates in Phenix, and explore future developments.