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CrowdPhase: crowdsourcing the phase problem
#MMPMID24914965
Jorda J
; Sawaya MR
; Yeates TO
Acta Crystallogr D Biol Crystallogr
2014[Jun]; 70
(Pt 6
): 1538-48
PMID24914965
show ga
The human mind innately excels at some complex tasks that are difficult to solve
using computers alone. For complex problems amenable to parallelization,
strategies can be developed to exploit human intelligence in a collective form:
such approaches are sometimes referred to as `crowdsourcing'. Here, a first
attempt at a crowdsourced approach for low-resolution ab initio phasing in
macromolecular crystallography is proposed. A collaborative online game named
CrowdPhase was designed, which relies on a human-powered genetic algorithm, where
players control the selection mechanism during the evolutionary process. The
algorithm starts from a population of `individuals', each with a random genetic
makeup, in this case a map prepared from a random set of phases, and tries to
cause the population to evolve towards individuals with better phases based on
Darwinian survival of the fittest. Players apply their pattern-recognition
capabilities to evaluate the electron-density maps generated from these sets of
phases and to select the fittest individuals. A user-friendly interface, a
training stage and a competitive scoring system foster a network of well trained
players who can guide the genetic algorithm towards better solutions from
generation to generation via gameplay. CrowdPhase was applied to two synthetic
low-resolution phasing puzzles and it was shown that players could successfully
obtain phase sets in the 30° phase error range and corresponding molecular
envelopes showing agreement with the low-resolution models. The successful
preliminary studies suggest that with further development the crowdsourcing
approach could fill a gap in current crystallographic methods by making it
possible to extract meaningful information in cases where limited resolution
might otherwise prevent initial phasing.
free
free
free
