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The formation mechanism of operons remains controversial despite the proposal of many models. Although acquisition of genes from other species, horizontal gene transfer, is considered to occur, definitive concrete cases have been unavailable. It is desirable to select horizontally transferred genes reliably and examine their relationship to operons. We here developed a method to identify candidates of horizontally transferred genes based on minimization of gene cluster insertions/deletions. To select a benchmark set of positively horizontally transferred genes against which the candidate set can be appraised, we devised another procedure using intergenetic alignments. Comparison with the benchmark set of horizontally transferred genes demonstrated the absence of a significant number of false positives in the candidates, showing that the method identifies horizontally transferred genes with a high degree of confidence. Horizontally transferred genes constitute at least 5.5% of the genes in Escherichia, Shigella, and Salmonella and ~46% of which originate from other gamma-proteobacteria. Not only informational genes, but also operational genes (those involved in housekeeping) are horizontally transferred less frequently than expected. A gene-cluster analysis of Escherichia coli K-12 operons revealed that horizontal transfer produced four entire operons and expanded two operons, but deletion of intervening genes accounts for the formation of no operons. We propose that operons generally form by horizontal gene transfer. We further suggest that genes with related essential functions tend to reside in conserved operons, while genes in nonconserved operons generally confer slight advantage to the organisms and frequently undergo horizontal transfer and decay., Comment: 22 pages, 7 figures

Background: One-dimensional protein structures such as secondary structures or contact numbers are useful for three-dimensional structure prediction and helpful for intuitive understanding of the sequence-structure relationship. Accurate prediction methods will serve as a basis for these and other purposes. Results: We implemented a program CRNPRED which predicts secondary structures, contact numbers and residue-wise contact orders. This program is based on a novel machine learning scheme called critical random networks. Unlike most conventional one-dimensional structure prediction methods which are based on local windows of an amino acid sequence, CRNPRED takes into account the whole sequence. CRNPRED achieves, on average per chain, Q3 = 81% for secondary structure prediction, and correlation coefficients of 0.75 and 0.61 for contact number and residue-wise contact order predictions, respectively. Conclusion: CRNPRED will be a useful tool for computational as well as experimental biologists who need accurate one-dimensional protein structure predictions., Comment: 10 pages, 1 figure, 2 tables

This paper was withdrawn by the authors., Comment: This paper has been withdrawn

Prediction of one-dimensional protein structures such as secondary structures and contact numbers is useful for the three-dimensional structure prediction and important for the understanding of sequence-structure relationship. Here we present a new machine-learning method, critical random networks (CRNs), for predicting one-dimensional structures, and apply it, with position-specific scoring matrices, to the prediction of secondary structures (SS), contact numbers (CN), and residue-wise contact orders (RWCO). The present method achieves, on average, $Q_3$ accuracy of 77.8% for SS, correlation coefficients of 0.726 and 0.601 for CN and RWCO, respectively. The accuracy of the SS prediction is comparable to other state-of-the-art methods, and that of the CN prediction is a significant improvement over previous methods. We give a detailed formulation of critical random networks-based prediction scheme, and examine the context-dependence of prediction accuracies. In order to study the nonlinear and multi-body effects, we compare the CRNs-based method with a purely linear method based on position-specific scoring matrices. Although not superior to the CRNs-based method, the surprisingly good accuracy achieved by the linear method highlights the difficulty in extracting structural features of higher order from amino acid sequence beyond that provided by the position-specific scoring matrices., Comment: 20 pages, 1 figure, 5 tables; minor revision; accepted for publication in BIOPHYSICS

Residue-wise contact order (RWCO) is a new kind of one-dimensional protein structures which represents the extent of long-range contacts. We have recently shown that a set of three types of one-dimensional structures (secondary structure, contact number, and RWCO) contains sufficient information for reconstructing the three-dimensional structure of proteins. Currently, there exist prediction methods for secondary structure and contact number from amino acid sequence, but none exists for RWCO. Also, the properties of amino acids that affect RWCO is not clearly understood. Here, we present a linear regression-based method to predict RWCO from amino acid sequence, and analyze the regression parameters to identify the properties that correlates with the RWCO. The present method achieves the significant correlation of 0.59 between the native and predicted RWCOs on average. An unusual feature of the RWCO prediction is the remarkably large optimal half window size of 26 residues. The regression parameters for the central and near-central residues of the local sequence segment highly correlate with those of the contact number prediction, and hence with hydrophobicity., Comment: 22 pages, 5 figures, 2 tables, manuscript submitted

Protein one-dimensional (1D) structures such as secondary structure and contact number provide intuitive pictures to understand how the native three-dimensional (3D) structure of a protein is encoded in the amino acid sequence. However, it has not been clear whether a given set of 1D structures contains sufficient information for recovering the underlying 3D structure. Here we show that the 3D structure of a protein can be recovered from a set of three types of 1D structures, namely, secondary structure, contact number and residue-wise contact order which is introduced here for the first time. Using simulated annealing molecular dynamics simulations, the structures satisfying the given native 1D structural restraints were sought for 16 proteins of various structural classes and of sizes ranging from 56 to 146 residues. By selecting the structures best satisfying the restraints, all the proteins showed a coordinate RMS deviation of less than 4\AA{} from the native structure, and for most of them, the deviation was even less than 2\AA{}. The present result opens a new possibility to protein structure prediction and our understanding of the sequence-structure relationship., Comment: Corrected title. No Change In Contents