Research Article: Progress in low-resolution ab initio phasing with CrowdPhase

Date Published: March 01, 2016

Publisher: International Union of Crystallography

Author(s): Julien Jorda, Michael R. Sawaya, Todd O. Yeates.

http://doi.org/10.1107/S2059798316003405

Abstract

New developments in CrowdPhase, a collaborative online game for tackling the low-resolution phase problem, are presented. The new features address several crystallographic issues and extend the reach of CrowdPhase to a broader range of experimental data sets.

Partial Text

In just a few years, crowdsourcing and gamification have become important actors in efforts to solve challenging scientific problems, owing in part to the emergence of cloud computing and social networks. Crowdsourced initiatives such as Foldit (Khatib, Cooper et al., 2011 ▸; Khatib, DiMiao et al., 2011 ▸), EteRNA (Lee et al., 2014 ▸) and numerous others (Gardner et al., 2011 ▸; Kelder et al., 2012 ▸; Loguercio et al., 2013 ▸) are convincing examples illustrating that, in certain cases, nontrivial scientific problems can be subdivided into elementary tasks and effectively distributed to a collective workforce. Along these lines, we recently demonstrated that the pattern-recognition abilities of a group of players could be harnessed to attack the low-resolution phase problem in X-ray crystallography (Jorda et al., 2014 ▸). Specifically, we involved non-expert users in a collaborative game called CrowdPhase in an attempt to determine ab initio the best sets of phases for low-resolution data sets. At its core, CrowdPhase is driven by a modified genetic algorithm that evolves a population of candidate solutions. Each candidate solution (or individual) comprises a set of phases for the observed reflections; in the language of genetic algorithms, each phase is a gene in the complete genome of an individual. Each individual presents a unique phenotype, here manifested in the form of an electron-density map.

A series of additions and improvements have been made to the CrowdPhase program. One of the main modifications was the consideration of crystal symmetry, enabling the system to handle a complete range of problem cases. Additional modifications such as the special treatment of unmeasured reflections, a solvent-flattening step and a map correlation coefficient were also implemented, with similar objectives.

 

Source:

http://doi.org/10.1107/S2059798316003405

 

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