33
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Intrinsic Disorder in Protein Interactions: Insights From a Comprehensive Structural Analysis

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          We perform a large-scale study of intrinsically disordered regions in proteins and protein complexes using a non-redundant set of hundreds of different protein complexes. In accordance with the conventional view that folding and binding are coupled, in many of our cases the disorder-to-order transition occurs upon complex formation and can be localized to binding interfaces. Moreover, analysis of disorder in protein complexes depicts a significant fraction of intrinsically disordered regions, with up to one third of all residues being disordered. We find that the disorder in homodimers, especially in symmetrical homodimers, is significantly higher than in heterodimers and offer an explanation for this interesting phenomenon. We argue that the mechanisms of regulation of binding specificity through disordered regions in complexes can be as common as for unbound monomeric proteins. The fascinating diversity of roles of disordered regions in various biological processes and protein oligomeric forms shown in our study may be a subject of future endeavors in this area.

          Author Summary

          Traditionally, protein structure is believed to determine function. Recently, it was observed that many proteins contain regions without well-defined structure (intrinsically disordered regions), including a large fraction of eukaryotic proteins. Intrinsic disorder has been associated with particular functions including cell regulation; signaling; and protein, DNA, and ligand binding. Many proteins are intrinsically disordered in native form and fold upon binding, following the conventional paradigm. Accordingly, disorder in a protein may facilitate binding to multiple partners. However, in some cases disorder has also been found in the bound state. To gain clearer insight into the functional importance of disorder regions in protein complexes, we perform a large-scale analysis of disorder using protein structures in complex and in unbound forms. We show that disorder in protein complexes is rather common and pinpoint changes that occur upon protein binding at interaction interfaces. By illustrating a variety of functional roles for disorder in specific proteins, we emphasize the versatility and importance of this phenomenon.

          Related collections

          Most cited references53

          • Record: found
          • Abstract: found
          • Article: not found

          Intrinsically unstructured proteins: re-assessing the protein structure-function paradigm.

          A major challenge in the post-genome era will be determination of the functions of the encoded protein sequences. Since it is generally assumed that the function of a protein is closely linked to its three-dimensional structure, prediction or experimental determination of the library of protein structures is a matter of high priority. However, a large proportion of gene sequences appear to code not for folded, globular proteins, but for long stretches of amino acids that are likely to be either unfolded in solution or adopt non-globular structures of unknown conformation. Characterization of the conformational propensities and function of the non-globular protein sequences represents a major challenge. The high proportion of these sequences in the genomes of all organisms studied to date argues for important, as yet unknown functions, since there could be no other reason for their persistence throughout evolution. Clearly the assumption that a folded three-dimensional structure is necessary for function needs to be re-examined. Although the functions of many proteins are directly related to their three-dimensional structures, numerous proteins that lack intrinsic globular structure under physiological conditions have now been recognized. Such proteins are frequently involved in some of the most important regulatory functions in the cell, and the lack of intrinsic structure in many cases is relieved when the protein binds to its target molecule. The intrinsic lack of structure can confer functional advantages on a protein, including the ability to bind to several different targets. It also allows precise control over the thermodynamics of the binding process and provides a simple mechanism for inducibility by phosphorylation or through interaction with other components of the cellular machinery. Numerous examples of domains that are unstructured in solution but which become structured upon binding to the target have been noted in the areas of cell cycle control and both transcriptional and translational regulation, and unstructured domains are present in proteins that are targeted for rapid destruction. Since such proteins participate in critical cellular control mechanisms, it appears likely that their rapid turnover, aided by their unstructured nature in the unbound state, provides a level of control that allows rapid and accurate responses of the cell to changing environmental conditions. Copyright 1999 Academic Press.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology.

            The Gene Ontology Annotation (GOA) database (http://www.ebi.ac.uk/GOA) aims to provide high-quality electronic and manual annotations to the UniProt Knowledgebase (Swiss-Prot, TrEMBL and PIR-PSD) using the standardized vocabulary of the Gene Ontology (GO). As a supplementary archive of GO annotation, GOA promotes a high level of integration of the knowledge represented in UniProt with other databases. This is achieved by converting UniProt annotation into a recognized computational format. GOA provides annotated entries for nearly 60,000 species (GOA-SPTr) and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. By integrating GO annotations from other model organism groups, GOA consolidates specialized knowledge and expertise to ensure the data remain a key reference for up-to-date biological information. Furthermore, the GOA database fully endorses the Human Proteomics Initiative by prioritizing the annotation of proteins likely to benefit human health and disease. In addition to a non-redundant set of annotations to the human proteome (GOA-Human) and monthly releases of its GO annotation for all species (GOA-SPTr), a series of GO mapping files and specific cross-references in other databases are also regularly distributed. GOA can be queried through a simple user-friendly web interface or downloaded in a parsable format via the EBI and GO FTP websites. The GOA data set can be used to enhance the annotation of particular model organism or gene expression data sets, although increasingly it has been used to evaluate GO predictions generated from text mining or protein interaction experiments. In 2004, the GOA team will build on its success and will continue to supplement the functional annotation of UniProt and work towards enhancing the ability of scientists to access all available biological information. Researchers wishing to query or contribute to the GOA project are encouraged to email: goa@ebi.ac.uk.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Length-dependent prediction of protein intrinsic disorder

              Background Due to the functional importance of intrinsically disordered proteins or protein regions, prediction of intrinsic protein disorder from amino acid sequence has become an area of active research as witnessed in the 6th experiment on Critical Assessment of Techniques for Protein Structure Prediction (CASP6). Since the initial work by Romero et al. (Identifying disordered regions in proteins from amino acid sequences, IEEE Int. Conf. Neural Netw., 1997), our group has developed several predictors optimized for long disordered regions (>30 residues) with prediction accuracy exceeding 85%. However, these predictors are less successful on short disordered regions (≤30 residues). A probable cause is a length-dependent amino acid compositions and sequence properties of disordered regions. Results We proposed two new predictor models, VSL2-M1 and VSL2-M2, to address this length-dependency problem in prediction of intrinsic protein disorder. These two predictors are similar to the original VSL1 predictor used in the CASP6 experiment. In both models, two specialized predictors were first built and optimized for short (≤30 residues) and long disordered regions (>30 residues), respectively. A meta predictor was then trained to integrate the specialized predictors into the final predictor model. As the 10-fold cross-validation results showed, the VSL2 predictors achieved well-balanced prediction accuracies of 81% on both short and long disordered regions. Comparisons over the VSL2 training dataset via 10-fold cross-validation and a blind-test set of unrelated recent PDB chains indicated that VSL2 predictors were significantly more accurate than several existing predictors of intrinsic protein disorder. Conclusion The VSL2 predictors are applicable to disordered regions of any length and can accurately identify the short disordered regions that are often misclassified by our previous disorder predictors. The success of the VSL2 predictors further confirmed the previously observed differences in amino acid compositions and sequence properties between short and long disordered regions, and justified our approaches for modelling short and long disordered regions separately. The VSL2 predictors are freely accessible for non-commercial use at
                Bookmark

                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                March 2009
                March 2009
                13 March 2009
                : 5
                : 3
                : e1000316
                Affiliations
                [1 ]National Center for Biotechnology Information, National Institutes of Health, Bethesda, Maryland, United States of America
                [2 ]Institute of Protein Research, Russian Academy of Sciences, Pushchino, Moscow Region, Russia
                Indiana University-Purdue University, United States of America
                Author notes

                Conceived and designed the experiments: OVG ARP. Performed the experiments: JHF BAS SOG MYL OVG. Analyzed the data: JHF BAS OVG ARP. Wrote the paper: ARP.

                Article
                08-PLCB-RA-0585R3
                10.1371/journal.pcbi.1000316
                2646137
                19282967
                50ab44c5-159a-45fc-8634-3a80811d2bfd
                This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
                History
                : 17 July 2008
                : 3 February 2009
                Page count
                Pages: 11
                Categories
                Research Article
                Biophysics/Biomacromolecule-Ligand Interactions
                Biophysics/Protein Folding
                Biophysics/Structural Genomics
                Computational Biology/Systems Biology
                Evolutionary Biology/Bioinformatics

                Quantitative & Systems biology
                Quantitative & Systems biology

                Comments

                Comment on this article