Predicting protein crystallization propensity from protein sequence |
| |
Authors: | György Babnigg Andrzej Joachimiak |
| |
Institution: | (1) Midwest Center for Structural Genomics, Biosciences Division, Argonne National Laboratory, 9700 S Cass Ave., Argonne, IL 60439, USA |
| |
Abstract: | The high-throughput structure determination pipelines developed by structural genomics programs offer a unique opportunity
for data mining. One important question is how protein properties derived from a primary sequence correlate with the protein’s
propensity to yield X-ray quality crystals (crystallizability) and 3D X-ray structures. A set of protein properties were computed
for over 1,300 proteins that expressed well but were insoluble, and for ~720 unique proteins that resulted in X-ray structures.
The correlation of the protein’s iso-electric point and grand average hydropathy (GRAVY) with crystallizability was analyzed
for full length and domain constructs of protein targets. In a second step, several additional properties that can be calculated
from the protein sequence were added and evaluated. Using statistical analyses we have identified a set of the attributes
correlating with a protein’s propensity to crystallize and implemented a Support Vector Machine (SVM) classifier based on
these. We have created applications to analyze and provide optimal boundary information for query sequences and to visualize
the data. These tools are available via the web site . |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|