Thomas C. Terwilliger

PHENIX: a comprehensive Python-based system for macromolecular structure solution

The PHENIX software for macromolecular structure determination is described.

PHENIX: building new software for automated crystallographic structure determination

Structural genomics seeks to expand rapidly the number of protein structures in order to extract the maximum amount of information from genomic sequence databases. The advent of several large-scale projects worldwide leads to many new challenges in the field of crystallographic macromolecular structure determination. A novel software package called PHENIX (Python-based Hierarchical ENvironment for Integrated Xtallography) is therefore being developed. This new software will provide the necessary algorithms to proceed from reduced intensity data to a refined molecular model and to facilitate structure solution for both the novice and expert crystallographer.

Automated MAD and MIR structure solution.

A fully automated procedure for solving MIR and MAD structures has been developed using a scoring scheme to convert the structure-solution process into an optimization problem.

Towards automated crystallographic structure refinement with phenix.refine.

phenix.refine is a program within the PHENIX package that supports crystallographic structure refinement against experimental data with a wide range of upper resolution limits using a large repertoire of model parameterizations. This paper presents an overview of the major phenix.refine features, with extensive literature references for readers interested in more detailed discussions of the methods.

Maximum-likelihood density modification

A likelihood-based density modification approach is developed that can incorporate expected electron-density information from a wide variety of sources.

Iterative model building, structure refinement and density modification with the PHENIX AutoBuild wizard

The highly automated PHENIX AutoBuild wizard is described. The procedure can be applied equally well to phases derived from isomorphous/anomalous and molecular-replacement methods.

Automated main-chain model building by template matching and iterative fragment extension

A method for automated macromolecular main-chain model building is described.

SOLVE and RESOLVE: Automated structure solution and density modification

SOLVE and RESOLVE have shown that it is possible to automate a significant part of the macromolecular X-ray structure determination process. The key elements of seamless and compatible subprograms, scoring algorithms, and error-tolerant software systems have been important in implementing these programs. The principles used in SOLVE and RESOLVE can be applied to other aspects of structure determination as well, suggesting that full automation of the entire structure determination process from scaling diffraction data to a refined model will be possible in the near future.

Protein tagging and detection with engineered self-assembling fragments of green fluorescent protein

Existing protein tagging and detection methods are powerful but have drawbacks. Split protein tags can perturb protein solubility or may not work in living cells. Green fluorescent protein (GFP) fusions can misfold or exhibit altered processing. Fluorogenic biarsenical FLaSH or ReASH substrates overcome many of these limitations but require a polycysteine tag motif, a reducing environment and cell transfection or permeabilization. An ideal protein tag would be genetically encoded, would work both in vivo and in vitro, would provide a sensitive analytical signal and would not require external chemical reagents or substrates. One way to accomplish this might be with a split GFP, but the GFP fragments reported thus far are large and fold poorly, require chemical ligation or fused interacting partners to force their association, or require coexpression or co-refolding to produce detectable folded and fluorescent GFP. We have engineered soluble, self-associating fragments of GFP that can be used to tag and detect either soluble or insoluble proteins in living cells or cell lysates. The split GFP system is simple and does not change fusion protein solubility.

Decision-making in structure solution using Bayesian estimates of map quality: the PHENIX AutoSol wizard

Ten measures of experimental electron-density-map quality are examined and the skewness of electron density is found to be the best indicator of actual map quality. A Bayesian approach to estimating map quality is developed and used in the PHENIX AutoSol wizard to make decisions during automated structure solution.