A critical evaluation of in silico methods for detection of membrane protein intrinsic disorder.

Intrinsically disordered regions in proteins possess important biological roles including transcriptional regulation, molecular recognition, and provision of sites for posttranslational modification. In three-dimensional crystallization of both soluble and membrane proteins, identification and removal of disordered regions is often necessary for obtaining crystals possessing sufficient long-range order for structure determination. Disordered regions can be identified experimentally, with techniques such as limited proteolysis coupled with mass spectrometry, or computationally, by using disorder prediction programs, of which many are available. Although these programs use various methods to predict disorder from a protein's primary sequence, they all were developed using information derived from soluble protein structures. Therefore, their performance and accuracy when applied to integral membrane proteins remained an open question. We evaluated the performance of 13 disorder prediction programs on a dataset containing 343 membrane proteins, and upon subdatasets containing only α-helical or β-barrel proteins. These programs were ranked using multiple metrics, including metrics specifically created for membrane proteins. Analysis of these data shows a clear distinction between programs that accurately predict disordered regions in membrane proteins and programs which perform poorly, and allows for the robust integration of in silico disorder prediction into our PSI:Biology membrane protein structural genomics pipeline.