Identification of Novel Intrinsically Disordered Proteins in Eukaryotes

Abstract

During the past decade, one of the most significant advances in the field of structural biology has been the paradigm-shifting recognition of proteins which are functional under physiological conditions without having a well-defined, rigid 3D structure. Contrary to Anfinsen's dogma indicating that a single unique native structure of a protein is the prerequisite of its function, intrinsically disordered proteins (IDPs) lack stable tertiary structure and exist as ensembles of time-fluctuating structural conformations, while still possessing the ability to carry out functions across a wide spectrum of cellular processes. Due to advantages arising from structural disorder, such as adaptability in partner binding, specific dynamics and enlarged binding surfaces, IDPs are often implicated in complex functions such as cell signaling and gene expression regulation. Bioinformatic predictions suggest that IDPs comprise a significant percentage of numerous eukaryotic proteomes which necessitates high-throughput studies focusing on experimental and computational characterization of such widely-predicted IDPs. In plants, IDPs play important roles in response to abiotic stress, which directly affects plant growth and yield. We have demonstrated through bioinformatic analyses that the genome of industrially important Beta vulgaris L. plant contains a large portion of IDPs. We confirmed the presence and roles in abiotic stress response for several known but also several previously undocumented IDPs using a combination of experimental methods. In order to gain further functional insights, our studies involve molecular dynamics simulations of the newly identified IDPs for ab initio prediction of pre-formed secondary structural elements in short binding regions termed Pre-Structured Motifs. The existence of functionally important transient secondary structure in some IDPs has previously been shown by others using NMR, advancing recent developments of suitable computational approaches for its prediction and thus enabling a new approach to understanding IDP function.