Oldfield Christopher J, Xue Bin, Van Ya-Yue, Ulrich Eldon L, Markley John L, Dunker A Keith, Uversky Vladimir N
Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA.
Biochim Biophys Acta. 2013 Feb;1834(2):487-98. doi: 10.1016/j.bbapap.2012.12.003. Epub 2012 Dec 8.
Intrinsically disordered proteins (IDPs) and proteins with long disordered regions are highly abundant in various proteomes. Despite their lack of well-defined ordered structure, these proteins and regions are frequently involved in crucial biological processes. Although in recent years these proteins have attracted the attention of many researchers, IDPs represent a significant challenge for structural characterization since these proteins can impact many of the processes in the structure determination pipeline. Here we investigate the effects of IDPs on the structure determination process and the utility of disorder prediction in selecting and improving proteins for structural characterization. Examination of the extent of intrinsic disorder in existing crystal structures found that relatively few protein crystal structures contain extensive regions of intrinsic disorder. Although intrinsic disorder is not the only cause of crystallization failures and many structured proteins cannot be crystallized, filtering out highly disordered proteins from structure-determination target lists is still likely to be cost effective. Therefore it is desirable to avoid highly disordered proteins from structure-determination target lists and we show that disorder prediction can be applied effectively to enrich structure determination pipelines with proteins more likely to yield crystal structures. For structural investigation of specific proteins, disorder prediction can be used to improve targets for structure determination. Finally, a framework for considering intrinsic disorder in the structure determination pipeline is proposed.
内在无序蛋白质(IDP)和具有长无序区域的蛋白质在各种蛋白质组中高度丰富。尽管它们缺乏明确的有序结构,但这些蛋白质和区域经常参与关键的生物学过程。尽管近年来这些蛋白质吸引了许多研究人员的关注,但IDP对结构表征来说是一项重大挑战,因为这些蛋白质会影响结构测定流程中的许多过程。在此,我们研究了IDP对结构测定过程的影响以及无序预测在选择和改进用于结构表征的蛋白质方面的效用。对现有晶体结构中内在无序程度的检查发现,相对较少的蛋白质晶体结构包含广泛的内在无序区域。虽然内在无序不是结晶失败的唯一原因,而且许多结构化蛋白质也无法结晶,但从结构测定目标列表中筛选出高度无序的蛋白质仍可能具有成本效益。因此,希望从结构测定目标列表中避免高度无序的蛋白质,并且我们表明无序预测可以有效地应用于用更有可能产生晶体结构的蛋白质丰富结构测定流程。对于特定蛋白质的结构研究,无序预测可用于改进结构测定的目标。最后,提出了一个在结构测定流程中考虑内在无序的框架。