Abstract
Traditional methods for macromolecular refinement often have limited success at low resolution (3.0-3.5 Å or worse), producing models that score poorly on crystallographic and geometric validation criteria. To improve low-resolution refinement, knowledge from macromolecular chemistry and homology was used to add three new coordinate-restraint functions to the refinement program phenix.refine. Firstly, a `reference-model' method uses an identical or homologous higher resolution model to add restraints on torsion angles to the geometric target function. Secondly, automatic restraints for common secondary-structure elements in proteins and nucleic acids were implemented that can help to preserve the secondary-structure geometry, which is often distorted at low resolution. Lastly, we have implemented Ramachandran-based restraints on the backbone torsion angles. In this method, a ϕ,ψ term is added to the geometric target function to minimize a modified Ramachandran landscape that smoothly combines favorable peaks identified from nonredundant high-quality data with unfavorable peaks calculated using a clash-based pseudo-energy function. All three methods show improved MolProbity validation statistics, typically complemented by a lowered R(free) and a decreased gap between R(work) and R(free).
Highlights
The productive refinement of atomic models at resolutions worse than 3–3.5 Aremains a major challenge in macromolecular crystallography
We introduce a ‘reference-model’ method in phenix.refine that uses a related model, ideally solved at higher resolution, to generate a set of torsion restraints that are added to the refinement energy target, conceptually similar to the local Noncrystallographic symmetry (NCS) restraints described by Sheldrick and coworkers (Uson et al, 1999)
The consistent success of our reference-model torsion restraints in arriving at an improved final model comparable to models of higher resolution quality demonstrate that these restraints are a viable option to improve refined models when faced with low-resolution data
Summary
The productive refinement of atomic models at resolutions worse than 3–3.5 Aremains a major challenge in macromolecular crystallography. Real-space and steric-based methods, conformation-dependent libraries (Tronrud et al, 2010) and NCS are very useful if the model is close to correct, but much less so for poorly built starting models with significant errors For such situations, which are common at low resolution, a number of methods have been developed to include information from higher resolution related structures or from homology models into the refinement target, thereby improving the data-to-parameter ratio by using external knowledge of the likely structure. We have added automatic generation of distance restraints for hydrogen bonds in protein and nucleic acid secondary structures, which can help to enforce correct geometry at lower resolution These can be defined automatically without user intervention, but a simple parameter syntax allows custom annotation without the need to specify individual bonding atoms for facile customization. The implications and possible pitfalls of using Ramachandran-based restraints are addressed in x5
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