Single-support serial isomorphous replacement phasing

The use of single isomorphous replacement (SIR) has become less widespread due to difficulties in sample preparation and the identification of isomorphous native and derivative data sets. Non-isomorphism becomes even more problematic in serial experiments, because it adds natural inter-crystal non-isomorphism to heavy-atom-soaking-induced non-isomorphism. Here, a method that can successfully address these issues (and indeed can benefit from differences in heavy-atom occupancy) and additionally significantly simplifies the SIR experiment is presented. A single heavy-atom soak into a microcrystalline slurry is performed, followed by automated serial data collection of partial data sets. This produces a set of data collections with a gradient of heavy-atom occupancies, which are reflected in differential merging statistics. These differences can be exploited by an optimized genetic algorithm to segregate the pool of data sets into 'native' and 'derivative' groups, which can then be used to successfully determine phases experimentally by SIR.

Automated summary

The use of single isomorphous replacement (SIR) has decreased due to difficulties in sample preparation and identification of isomorphous native and derivative data sets. This study presents a method that can address these issues and simplify the SIR experiment. By soaking a single heavy atom into a microcrystalline slurry and performing automated serial data collection, a set of data collections with varying heavy-atom occupancies is obtained. Differential merging statistics are utilized to segregate the data sets into 'native' and 'derivative' groups, enabling successful determination of phases experimentally by SIR.