10.1107/S2059798316012079

Merging of synchrotron serial crystallographic data by a genetic algorithm

Abstract

International audienceRecent advances in macromolecular crystallography have made it practical to rapidly collect hundreds of sub-data sets consisting of small oscillations of incomplete data. This approach, generally referred to as serial crystallography, has many uses, including an increased effective dose per data set, the collection of data from crystals without harvesting (in situ data collection) and studies of dynamic events such as catalytic reactions. However, selecting which data sets from this type of experiment should be merged can be challenging and new methods are required. Here, it is shown that a genetic algorithm can be used for this purpose, and five case studies are presented in which the merging statistics are significantly improved compared with conventional merging of all data

Similar works

Full text

HAL-CEAProvided a free PDF (195.62 KB)

hal-01474368v1oai:HAL:hal-01474368v1
Last time updated on April 13, 2017

This paper was published in HAL-CEA.

Having an issue?

Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.