"High-throughput characterization, prediction, and applications of protein disorder"

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Proteins are bio-macromolecules composed of one or more amino acids chains which participate in virtually every process within cells. For years, scientists were convinced that these chains must fold into precise, rigid molecules to allow proteins to function correctly. This view is changing now. The intrinsically disordered proteins have at least some disordered (also called unfolded/highly flexible) parts and many of them carry out their function without ever fully folding into a rigid molecule. This flexibility possibly helped life on earth get started and is crucial for cellular signal transduction, regulation of cell division, transcription, translation, phosphorylation, and many other cellular processes. The disorder is highly abundant in nature and its prevalence was shown in several human diseases. This means that the roles of disorder in determining protein function cannot be ignored. However, the annotation and characterization of protein disorder is lagging behind the rapidly growing number of known proteins. Experimental annotations of disorder are time consuming and difficult and thus computational methods that predict disorder from protein sequences have emerged as a viable alternative to bridge the annotation gap and to investigate the disorder. Although the quality of these predictors continues to rise, more accurate methods and novel methods that address specific characteristics of disorder are urgently needed. Moreover, there is a pressing need to understand and characterize disorder in various proteomes and functional classes of proteins. To this end, our objectives include (1) development of a comprehensive computational platform for accurate, fast, and multi-objective prediction of disorder; and (2) applications and experimental validation of disorder predictions. This work facilitates a more complete understanding of the protein disorder, principles of protein folding, and molecular mechanisms of protein function. Our methods provide a cost and time effective solution to guide experimentalists, and they are crucial for modern research and development in several areas, including rational drug design, structural genomics, and systems biology.