Comparative genomics and evolution of regulons of the LacI-family transcription factors.

DNA-binding transcription factors (TFs) are essential components of transcriptional regulatory networks in bacteria. LacI-family TFs (LacI-TFs) are broadly distributed among certain lineages of bacteria. The majority of characterized LacI-TFs sense sugar effectors and regulate carbohydrate utilization genes. The comparative genomics approaches enable in silico identification of TF-binding sites and regulon reconstruction. To study the function and evolution of LacI-TFs, we performed genomics-based reconstruction and comparative analysis of their regulons. For over 1300 LacI-TFs from over 270 bacterial genomes, we predicted their cognate DNA-binding motifs and identified target genes. Using the genome context and metabolic subsystem analyses of reconstructed regulons, we tentatively assigned functional roles and predicted candidate effectors for 78 and 67% of the analyzed LacI-TFs, respectively. Nearly 90% of the studied LacI-TFs are local regulators of sugar utilization pathways, whereas the remaining 125 global regulators control large and diverse sets of metabolic genes. The global LacI-TFs include the previously known regulators CcpA in Firmicutes, FruR in Enterobacteria, and PurR in Gammaproteobacteria, as well as the three novel regulators-GluR, GapR, and PckR-that are predicted to control the central carbohydrate metabolism in three lineages of Alphaproteobacteria. Phylogenetic analysis of regulators combined with the reconstructed regulons provides a model of evolutionary diversification of the LacI protein family. The obtained genomic collection of in silico reconstructed LacI-TF regulons in bacteria is available in the RegPrecise database (http://regprecise.lbl.gov). It provides a framework for future structural and functional classification of the LacI protein family and identification of molecular determinants of the DNA and ligand specificity. The inferred regulons can be also used for functional gene annotation and reconstruction of sugar catabolic networks in diverse bacterial lineages.

An automated stochastic approach to the identification of the protein specificity determinants and functional subfamilies.

BACKGROUND: Recent progress in sequencing and 3 D structure determination techniques stimulated development of approaches aimed at more precise annotation of proteins, that is, prediction of exact specificity to a ligand or, more broadly, to a binding partner of any kind. RESULTS: We present a method, SDPclust, for identification of protein functional subfamilies coupled with prediction of specificity-determining positions (SDPs). SDPclust predicts specificity in a phylogeny-independent stochastic manner, which allows for the correct identification of the specificity for proteins that are separated on a phylogenetic tree, but still bind the same ligand. SDPclust is implemented as a Web-server http://bioinf.fbb.msu.ru/SDPfoxWeb/ and a stand-alone Java application available from the website. CONCLUSIONS: SDPclust performs a simultaneous identification of specificity determinants and specificity groups in a statistically robust and phylogeny-independent manner.

SDPpred: a tool for prediction of amino acid residues that determine differences in functional specificity of homologous proteins.

SDPpred (Specificity Determining Position prediction) is a tool for prediction of residues in protein sequences that determine the proteins' functional specificity. It is designed for analysis of protein families whose members have biochemically similar but not identical interaction partners (e.g. different substrates for a family of transporters). SDPpred predicts residues that could be responsible for the proteins' choice of their correct interaction partners. The input of SDPpred is a multiple alignment of a protein family divided into a number of specificity groups, within which the interaction partner is believed to be the same. SDPpred does not require information about the secondary or three-dimensional structure of proteins. It produces a set of the alignment positions (specificity determining positions) that determine differences in functional specificity. SDPpred is available at http://math.genebee.msu.ru/~psn/.

Automated selection of positions determining functional specificity of proteins by comparative analysis of orthologous groups in protein families.

The increasing volume of genomic data opens new possibilities for analysis of protein function. We introduce a method for automated selection of residues that determine the functional specificity of proteins with a common general function (the specificity-determining positions [SDP] prediction method). Such residues are assumed to be conserved within groups of orthologs (that may be assumed to have the same specificity) and to vary between paralogs. Thus, considering a multiple sequence alignment of a protein family divided into orthologous groups, one can select positions where the distribution of amino acids correlates with this division. Unlike previously published techniques, the introduced method directly takes into account nonuniformity of amino acid substitution frequencies. In addition, it does not require setting arbitrary thresholds. Instead, a formal procedure for threshold selection using the Bernoulli estimator is implemented. We tested the SDP prediction method on the LacI family of bacterial transcription factors and a sample of bacterial water and glycerol transporters belonging to the major intrinsic protein (MIP) family. In both cases, the comparison with available experimental and structural data strongly supported our predictions.

The channel in transporters is formed by residues that are rare in transmembrane helices.

Transmembrane transport is an essential component of the cell life. Many genes encoding known or putative transport proteins are found in bacterial genomes. In most cases their substrate specificity is not experimentally determined and only approximately predicted by comparative genomic analysis. Even less is known about the 3D structure of transporters. Nevertheless, the published experimental data demonstrate that channel-forming residues determine the substrate specificity of secondary transporters and analysis of these residues would provide better understanding of the transport mechanism. We developed a simple computational method for identification of channel-forming residues in transporter sequences. It is based on the analysis of amino acids frequencies in bacterial secondary transporters. We applied this method to a variety of transmembrane proteins with resolved 3D structure. The predictions are in sufficiently good agreement with the real protein structure.

BATMAS30: amino acid substitution matrix for alignment of bacterial transporters.

Aligned amino acid sequences of three functionally independent samples of transmembrane (TM) transport proteins have been analyzed. The concept of TM-kernel is proposed as the most probable transmembrane region of a sequence. The average amino acid composition of TM-kernels differs from the published amino acid composition of transmembrane segments. TM-kernels contain more alanines, glycines, and less polar, charged, and aromatic residues in contrast to non-TM-proteins. There are also differences between TM-kernels of bacterial and eukaryotic proteins. We have constructed amino acid substitution matrices for bacterial TM-kernels, named the BATMAS (BActerial Transmembrane MAtrix of Substitutions) series. In TM-kernels, polar and charged residues, as well as proline and tyrosine, are highly conserved, whereas there are more substitutions within the group of hydrophobic residues, in contrast to non-TM-proteins that have fewer, relatively more conserved, hydrophobic residues. These results demonstrate that alignment of transmembrane proteins should be based on at least two amino acid substitution matrices, one for loops (e.g., the BLOSUM series) and one for TM-segments (the BATMAS series), and the choice of the TM-matrix should be different for eukaryotic and bacterial proteins.