Progress in low-resolution ab initio phasing with CrowdPhase.

Julien Jorda,Todd O Yeates,Michael R Sawaya

doi:10.1107/s2059798316003405

Julien Jorda, Todd O Yeates + Show 1 more

Open Access

https://doi.org/10.1107/s2059798316003405

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Abstract

Ab initio phasing by direct computational methods in low-resolution X-ray crystallography is a long-standing challenge. A common approach is to consider it as two subproblems: sampling of phase space and identification of the correct solution. While the former is amenable to a myriad of search algorithms, devising a reliable target function for the latter problem remains an open question. Here, recent developments in CrowdPhase, a collaborative online game powered by a genetic algorithm that evolves an initial population of individuals with random genetic make-up (i.e. random phases) each expressing a phenotype in the form of an electron-density map, are presented. Success relies on the ability of human players to visually evaluate the quality of these maps and, following a Darwinian survival-of-the-fittest concept, direct the search towards optimal solutions. While an initial study demonstrated the feasibility of the approach, some important crystallographic issues were overlooked for the sake of simplicity. To address these, the new CrowdPhase includes consideration of space-group symmetry, a method for handling missing amplitudes, the use of a map correlation coefficient as a quality metric and a solvent-flattening step. Performances of this installment are discussed for two low-resolution test cases based on bona fide diffraction data.

Highlights

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
We recently demonstrated that the patternrecognition abilities of a group of players could be harnessed to attack the low-resolution phase problem in X-ray crystallography (Jorda et al, 2014)
One of the first modifications in CrowdPhase was to integrate the handling of crystallographic symmetry

Summary

Introduction

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. Each individual presents a unique phenotype, here manifested in the form of an electrondensity map

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