Aleksandra B. Rakhmaninova

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.

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

BackgroundRecent 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.ResultsWe 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.ConclusionsSDPclust performs a simultaneous identification of specificity determinants and specificity groups in a statistically robust and phylogeny-independent manner.

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.

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. Proteins 2003;51:85–95.

Computational Method for Predicting Protein Functional Sites with the Use of Specificity Determinants

The currently available body of decoded amino acid sequences of various proteins exceeds manifold the experimental capabilities of their functional annotation. Therefore, in silico annotation using bioinformatics methods becomes increasingly important. Such annotation is actually a prediction; however, this can be an important starting point for further laboratory research. This work describes a new method for predicting functionally important protein sites, SDPsite, on the basis of identification of specificity determinants. The algorithm proposed utilizes a protein family aglinment and a phylogenetic tree to predict the conserved positions and specificity determinants, map them onto the protein structure, and search for clusters of the predicted positions. Comparison of the resulting predictions with experimental data and published predictions of functional sites by other methods demonstrates that the results of SDPsite agree well with experimental data and exceed the results obtained with the majority of previous methods. SDPsite is publicly available at http://bioinf.fbb.msu.ru/SDPsite.

Regulation of nitrate and nitrite respiration in γ-proteobacteria: a comparative genomics study

Nitrate and nitrite are the most preferable electron acceptors in the absence of molecular oxygen. In the γ-proteobacterium Escherichia coli, nitrate and nitrite respiration is regulated by two homologous transcription factors, NarL and NarP. Although this regulatory system was a subject of intensive research for more than 20 years, many key issues, including the structure of the NarL-binding site, are still unclear. Comparative genomics analysis showed that only NarP is responsible for regulation in most γ-proteobacteria. The NarP regulon was studied in ten genomes. Although its structure considerably differs among some genomes, the mechanism regulating the nitrate and nitrite reduction genes is highly conserved. A correlation was observed between the evolutionary changes in the nitrate and nitrite respiration system and the relevant regulatory system. Potential NarP-binding sites were found upstream of the gene for the global regulator FNR and the sydAB, mdh, and sucAB aerobic metabolism genes. It was assumed on the basis of this evidence that the role of NarP in regulating respiration changed during evolution. In total, 35 new operons were assigned to the generalized NarP regulon. Autoregulation of the narQP operon was suggested for bacteria of the family Vibrionaceae.

Machine learning study of DNA binding by transcription factors from the LacI family

We studied 1372 LacI-family transcription factors and their 4484 DNA binding sites using machine learning algorithms and feature selection techniques. The Naive Bayes classifier and Logistic Regression were used to predict binding sites given transcription factor sequences and to classify factor-site pairs on binding and non-binding ones. Prediction accuracy was estimated using 10-fold cross-validation. Experiments showed that the best prediction of nucleotide densities at selected site positions is obtained using only a few key protein sequence positions. These positions are stably selected by the forward feature selection based on the mutual information of factor-site position pairs.

Evolution of Regulatory Systems in Bacteria (Invited Keynote Talk)

Recent comparative studies indicate surprising flexibility of regulatory systems in bacteria. These systems can be analyzed on several levels, and I plan to consider two of them. At the level of regulon evolution, one can attempt to characterize the evolution of regulon content formed by loss, gain and duplications of regulators and regulated genes, as well as gain and loss of individual regulatory sites and horizontal gene transfer. At the level of transcription factor families, one can study co-evolution of DNA-binding proteins and the motifs they recognize. While this area is not yet ripe for fully automated analysis, the results of systematic comparative studies gradually start to coalesce into an understanding of how bacteria regulatory systems evolve.