During the production of our manuscript [1], an incorrect Figure ​Figure11 was put in place of the one we submitted, and we erred in not appropriately informing the production staff of this mistake. The corrected figure and the text describing this figure, as well as the figure legend, are present herein. Figure 1. Charge-Hydropathy plots. In (A) the IDP-Hydropathy scale was used, in (B) the Guy (1985) Hydropathy scale was used, and in (C) the Kyte-Doolittle (1981) hydropathy scale was used. Red circles indicate disordered proteins, blue circles indicate structured ... As stated in our manuscript [1], “The C-H plots generated using scale SVM parameters scale, Kyte-Doolittle hydropathy scale, and Guy hydropathy scale for whole protein prediction are shown in Figure ​Figure1.1. Figure ​Figure1A,1A, which is derived by SVM parameters scale, shows many fewer misclassified disordered proteins on the ordered side, compared to Figure ​Figure1B1B and ​and1C1C.” As further stated in reference [1]: “Figure 1. Charge-Hydropathy plots. In (A) the IDP-Hydropathy scale was used, in (B) the Guy (1985) Hydropathy scale was used, and in (C) the Kyte-Doolittle (1981) hydropathy scale was used. Red circles indicate disordered proteins, blue circles indicate structured proteins. For these plots, each scale was normalized to be in the interval of 0 to 1. The Guy’s scale is multiplied by -1 prior to normalization to conform to the energy rule set by Kyte-Doolittle scale. In (A) the function describing the boundary is:  = 3.31  -0.97. In (B) the function describing the boundary is:  = 2.32  -0.93. In (C), the function describing the boundary is  = 1.35  -0.49.” For more details, the reader is referred to the published manuscript [1].

BACKGROUND: The earliest whole protein order/disorder predictor (Uversky et al., Proteins, 41: 415-427 (2000)), herein called the charge-hydropathy (C-H) plot, was originally developed using the Kyte-Doolittle (1982) hydropathy scale (Kyte & Doolittle., J. Mol. Biol, 157: 105-132(1982)). Here the goal is to determine whether the performance of the C-H plot in separating structured and disordered proteins can be improved by using an alternative hydropathy scale. RESULTS: Using the performance of the CH-plot as the metric, we compared 19 alternative hydropathy scales, with the finding that the Guy (1985) hydropathy scale (Guy, Biophys. J, 47:61-70(1985)) was the best of the tested hydropathy scales for separating large collections structured proteins and intrinsically disordered proteins (IDPs) on the C-H plot. Next, we developed a new scale, named IDP-Hydropathy, which further improves the discrimination between structured proteins and IDPs. Applying the C-H plot to a dataset containing 109 IDPs and 563 non-homologous fully structured proteins, the Kyte-Doolittle (1982) hydropathy scale, the Guy (1985) hydropathy scale, and the IDP-Hydropathy scale gave balanced two-state classification accuracies of 79%, 84%, and 90%, respectively, indicating a very substantial overall improvement is obtained by using different hydropathy scales. A correlation study shows that IDP-Hydropathy is strongly correlated with other hydropathy scales, thus suggesting that IDP-Hydropathy probably has only minor contributions from amino acid properties other than hydropathy. CONCLUSION: We suggest that IDP-Hydropathy would likely be the best scale to use for any type of algorithm developed to predict protein disorder.

About 10 years ago we published our first predictor of intrinsically disordered protein residues in another IEEE journal, theProceedingsoftheIEEEInternationalConferenceonNeural Networks. Others call such proteins "natively unfolded" and "intrinsically unstructured." Since then, we and others have substantially improved the prediction of intrinsically disordered residues. The prediction of protein intrinsic disorder is similar to the prediction of secondary structure in terms of methodology, but, at the structural level, secondary structure (especially random coil) and intrinsic disorder differ completely in their dynamic motion. First, we will briefly describe the prediction of protein disorder, show the progress from ~ 70 % to ~ 85 % per residue prediction accuracy, and show that intrinsically disordered proteins are common over the three domains of life, but are especially common among the eukaryotes. Next we will discuss our methods for deducing functions that are associated with disordered rather than structured proteins. In brief, structured proteins have advantages for catalysis while disordered proteins and regions have advantages for the reversible, weak binding often observed in signaling, control, and regulation. After that we will discuss how disorder facilitates binding diversity in protein-protein interaction networks, both for single disordered regions binding to many partners and for many disordered regions with different sequences binding to a common site on the surface of one structured protein. Part three presents data indicating that alternative splicing is more prevalent in regions of RNA that code for disorder than those that code for structure, thus providing a means for evolving tissue-specific signaling networks. Finally, we will present a novel approach to drug discovery based on disordered protein.

Just over 10 years ago, in June, 1997, in the Proceedings of the IEEE International Conference on Neural Networks, we published our first predictor of intrinsically disordered protein [1]. Since then, we have substantially improved our predictors, and more than 20 other laboratory groups have joined in efforts to improve the prediction of protein disorder. At the algorithmic level, prediction of protein intrinsic disorder is similar to the prediction of secondary structure, but, at the structural level, secondary structure and intrinsic disorder are entirely different. The secondary structure class called random coil or irregular differs from intrinsic disorder due to very different dynamic properties, with the secondary structure class being much less mobile than the region of disorder. At the biological level, unlike the prediction of secondary structure, the prediction of intrinsic disorder has been revolutionary. That is, for many years, experimentalists have provided evidence that some proteins lack fixed structure or are disordered (or unfolded) under physiological conditions. Experimentalists further are showing that, for some proteins, functions depended on the unstructured rather than structured state. However, these examples have been mostly ignored. To our knowledge, not one disordered protein or disorder-associated function is discussed in any biochemistry textbook, even though such examples began to be discovered more than 50 years ago. Disorder prediction has been important for showing that the few experimentally characterized examples represent a very large cohort that is found all across all three domains of life.

MOTIVATION: The prediction of transcription units (TUs, which are similar to operons) is an important problem that has been tackled using many different approaches. The availability of complete microbial genomes has made genome-wide TU predictions possible. Pathway-genome databases (PGDBs) add metabolic and other organizational (i.e. protein complexes) information to the annotated genome, and are able to capture TU organization information. These characteristics of PGDBs make them a suitable framework for the development and implementation of TU predictors. RESULTS: We implemented a TU predictor that uses only intergenic distance and functional classification of genes to predict TU boundaries, and applied it to EcoCyc, our PGDB of Escherichia coli. To this original predictor, we added information on metabolic pathways, protein complexes and transporters, all readily available in EcoCyc, in order to generate an enhanced predictor. The enhanced predictor correctly predicted 80% of the known E.coli TUs (69% of the known operons), a moderate improvement over the original predictor's performance (75% of TUs and 65% of operons correctly predicted), demonstrating that the extra information available in the PGDB does indeed improve prediction performance. Performance of this E.coli-based predictor on a genome other than that of E.coli was tested on BsubCyc, our computationally generated PGDB for Bacillus subtilis, for which a set of 100 known operons is available. Prediction accuracy decreased substantially (46% of the known operons correctly predicted). This was due in part to missing information in BsubCyc, which prevented full use of the predictor's features. The augmented predictor has been implemented as part of our Pathway Tools software suite, and can be used to populate a PGDB with predicted TUs. AVAILABILITY: The TU predictor is included in version 7.0 of the Pathway Tools software suite. Pathway Tools 7.0 is available free of charge to academic institutions and for a fee to commercial enterprises. It runs on Sun Solaris 8, Linux and Windows. TUs predicted on the Caulobacter crescentus and Mycobacterium tuberculosis (H37Rv) genomes are available in our CauloCyc and MtbrvCyc databases, available at the BioCyc web site (http://biocyc.org). To obtain version 7.0 of Pathway Tools, follow the directions in our web site, http://biocyc.org/download.shtml.

Abstract Motivation: Bioinformatics requires reusable software tools for creating model-organism databases (MODs). Results: The Pathway Tools is a reusable, production-quality software environment for creating a type of MOD called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc (see http://ecocyc.org) integrates our evolving understanding of the genes, proteins, metabolic network, and genetic network of an organism. This paper provides an overview of the four main components of the Pathway Tools: The PathoLogic component supports creation of new PGDBs from the annotated genome of an organism. The Pathway/Genome Navigator provides query, visualization, and Web-publishing services for PGDBs. The Pathway/Genome Editors support interactive updating of PGDBs. The Pathway Tools ontology defines the schema of PGDBs. The Pathway Tools makes use of the Ocelot object database system for data management services for PGDBs. The Pathway Tools has been used to build PGDBs for 13 organisms within SRI and by external users. Availability: The software is freely available to academics and is available for a fee to commercial institutions. Contact [email protected] for information on obtaining the software. Contact: [email protected] Keywords: Bioinformatics; model organism database; genome analyses; metabolic pathways. *To whom correspondence should be addressed.