PROSPERO: online prediction of crystallographic success from experimental results and sequence.

J Appl Crystallogr

Biomolecular Structure Center, Department of Biochemistry, University of Washington, Seattle, WA 98195-7742, USA.

Published: June 2012

AI Article Synopsis

  • PROSPERO is a web server designed to help structural biologists manage and analyze experimental data related to the crystallization of purified proteins, which is often a challenging step in determining their structures.
  • It offers guidance for researchers facing difficulties in crystallizing proteins, especially for high-priority eukaryotic proteins, by suggesting various rescue options based on their limited resources.
  • The server utilizes the HyGX1 predictor, trained on samples from pathogenic protozoa, and allows users to store, analyze, group samples into projects, and share results with collaborators.

Article Abstract

The growth of diffracting crystals from purified proteins is often a major bottleneck in determining structures of biological and medical interest. The PROSPERO web server, http://skuld.bmsc.washington.edu/prospero, is intended both to provide a means of organizing the potentially large numbers of experimental characterizations measured from such proteins, and to provide useful guidance for structural biologists who have succeeded in purifying their target protein but have reached an impasse in the difficult and poorly understood process of turning purified protein into well diffracting crystals. These researchers need to decide which of many possible rescue options are worth pursuing, given finite resources. This choice is even more crucial when attempting to solve high-priority but relatively difficult structures of eukaryotic proteins. The site currently uses the HyGX1 predictor, which was trained and validated on protein samples from pathogenic protozoa (eukaryotes) using results from six types of experiment. PROSPERO allows users to store, analyze and display multiple results for each sample, to group samples into projects, and to share results and predictions with collaborators.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3359727PMC
http://dx.doi.org/10.1107/S002188981201775XDOI Listing

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