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Title: Progress in low-resolution ab initio phasing with CrowdPhase

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. Lastly, performances of this installment are discussed for two low-resolution test cases based on bona fide diffraction data.
Authors:
 [1] ;  [1] ;  [1]
  1. Univ. of California, Los Angeles, CA (United States)
Publication Date:
Grant/Contract Number:
FC02-02ER63421
Type:
Accepted Manuscript
Journal Name:
Acta Crystallographica. Section D. Structural Biology
Additional Journal Information:
Journal Volume: 72; Journal Issue: 3; Journal ID: ISSN 2059-7983
Publisher:
IUCr
Research Org:
Univ. of California, Los Angeles, CA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 97 MATHEMATICS AND COMPUTING; crowdsourcing; phase problem; CrowdPhase; direct methods
OSTI Identifier:
1345574

Jorda, Julien, Sawaya, Michael R., and Yeates, Todd O.. Progress in low-resolution ab initio phasing with CrowdPhase. United States: N. p., Web. doi:10.1107/s2059798316003405.
Jorda, Julien, Sawaya, Michael R., & Yeates, Todd O.. Progress in low-resolution ab initio phasing with CrowdPhase. United States. doi:10.1107/s2059798316003405.
Jorda, Julien, Sawaya, Michael R., and Yeates, Todd O.. 2016. "Progress in low-resolution ab initio phasing with CrowdPhase". United States. doi:10.1107/s2059798316003405. https://www.osti.gov/servlets/purl/1345574.
@article{osti_1345574,
title = {Progress in low-resolution ab initio phasing with CrowdPhase},
author = {Jorda, Julien and Sawaya, Michael R. and Yeates, Todd O.},
abstractNote = {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. Lastly, performances of this installment are discussed for two low-resolution test cases based on bona fide diffraction data.},
doi = {10.1107/s2059798316003405},
journal = {Acta Crystallographica. Section D. Structural Biology},
number = 3,
volume = 72,
place = {United States},
year = {2016},
month = {3}
}