Combinatorial assembly and design of enzymes.

The design of structurally diverse enzymes is constrained by long-range interactions that are necessary for accurate folding. We introduce an atomistic and machine learning strategy for the combinatorial assembly and design of enzymes (CADENZ) to design fragments that combine with one another to generate diverse, low-energy structures with stable catalytic constellations. We applied CADENZ to endoxylanases and used activity-based protein profiling to recover thousands of structurally diverse enzymes. Functional designs exhibit high active-site preorganization and more stable and compact packing outside the active site. Implementing these lessons into CADENZ led to a 10-fold improved hit rate and more than 10,000 recovered enzymes. This design-test-learn loop can be applied, in principle, to any modular protein family, yielding huge diversity and general lessons on protein design principles.

Collective repacking reveals that the structures of protein cores are uniquely specified by steric repulsive interactions.

Protein core repacking is a standard test of protein modeling software. A recent study of six different modeling software packages showed that they are more successful at predicting side chain conformations of core compared to surface residues. All the modeling software tested have multicomponent energy functions, typically including contributions from solvation, electrostatics, hydrogen bonding and Lennard-Jones interactions in addition to statistical terms based on observed protein structures. We investigated to what extent a simplified energy function that includes only stereochemical constraints and repulsive hard-sphere interactions can correctly repack protein cores. For single residue and collective repacking, the hard-sphere model accurately recapitulates the observed side chain conformations for Ile, Leu, Phe, Thr, Trp, Tyr and Val. This result shows that there are no alternative, sterically allowed side chain conformations of core residues. Analysis of the same set of protein cores using the Rosetta software suite revealed that the hard-sphere model and Rosetta perform equally well on Ile, Leu, Phe, Thr and Val; the hard-sphere model performs better on Trp and Tyr and Rosetta performs better on Ser. We conclude that the high prediction accuracy in protein cores obtained by protein modeling software and our simplified hard-sphere approach reflects the high density of protein cores and dominance of steric repulsion.

Semiautomated reminder system for improving syphilis management.

This project utilized an automated record system, COSTAR, to assess and improve the quality of care in managing syphilis in a health maintenance organization. A scoring tool was developed to assess care. There were four experimental periods, each lasting one year. The periods were Baseline (no intervention), Education (publication of guidelines and an educational session), Reminder (deficiencies in care brought to the attention of providers in time to permit correction), and Post-reminder (no intervention). Scores for overall management of syphilis rose from 70.4 to 90.5% during the Reminder period and did not deteriorate significantly in the Post-reminder period. Scores in the Education period were not significantly higher than baseline. The cost of the system was $195 per year. An inexpensive reminder system was effective in bringing about a significant improvement in quality of care for syphilis, and the effect persisted for at least a year after the system was discontinued.