Tal Pupko

ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids.

It is informative to detect highly conserved positions in proteins and nucleic acid sequence/structure since they are often indicative of structural and/or functional importance. ConSurf (http://consurf.tau.ac.il) and ConSeq (http://conseq.tau.ac.il) are two well-established web servers for calculating the evolutionary conservation of amino acid positions in proteins using an empirical Bayesian inference, starting from protein structure and sequence, respectively. Here, we present the new version of the ConSurf web server that combines the two independent servers, providing an easier and more intuitive step-by-step interface, while offering the user more flexibility during the process. In addition, the new version of ConSurf calculates the evolutionary rates for nucleic acid sequences. The new version is freely available at: http://consurf.tau.ac.il/.

ConSurf 2005: the projection of evolutionary conservation scores of residues on protein structures

Key amino acid positions that are important for maintaining the 3D structure of a protein and/or its function(s), e.g. catalytic activity, binding to ligand, DNA or other proteins, are often under strong evolutionary constraints. Thus, the biological importance of a residue often correlates with its level of evolutionary conservation within the protein family. ConSurf () is a web-based tool that automatically calculates evolutionary conservation scores and maps them on protein structures via a user-friendly interface. Structurally and functionally important regions in the protein typically appear as patches of evolutionarily conserved residues that are spatially close to each other. We present here version 3.0 of ConSurf. This new version includes an empirical Bayesian method for scoring conservation, which is more accurate than the maximum-likelihood method that was used in the earlier release. Various additional steps in the calculation can now be controlled by a number of advanced options, thus further improving the accuracy of the calculation. Moreover, ConSurf version 3.0 also includes a measure of confidence for the inferred amino acid conservation scores.

GUIDANCE: a web server for assessing alignment confidence scores

Evaluating the accuracy of multiple sequence alignment (MSA) is critical for virtually every comparative sequence analysis that uses an MSA as input. Here we present the GUIDANCE web-server, a user-friendly, open access tool for the identification of unreliable alignment regions. The web-server accepts as input a set of unaligned sequences. The server aligns the sequences and provides a simple graphic visualization of the confidence score of each column, residue and sequence of an alignment, using a color-coding scheme. The method is generic and the user is allowed to choose the alignment algorithm (ClustalW, MAFFT and PRANK are supported) as well as any type of molecular sequences (nucleotide, protein or codon sequences). The server implements two different algorithms for evaluating confidence scores: (i) the heads-or-tails (HoT) method, which measures alignment uncertainty due to co-optimal solutions; (ii) the GUIDANCE method, which measures the robustness of the alignment to guide-tree uncertainty. The server projects the confidence scores onto the MSA and points to columns and sequences that are unreliably aligned. These can be automatically removed in preparation for downstream analyses. GUIDANCE is freely available for use at http://guidance.tau.ac.il.

ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules

The degree of evolutionary conservation of an amino acid in a protein or a nucleic acid in DNA/RNA reflects a balance between its natural tendency to mutate and the overall need to retain the structural integrity and function of the macromolecule. The ConSurf web server (http://consurf.tau.ac.il), established over 15 years ago, analyses the evolutionary pattern of the amino/nucleic acids of the macromolecule to reveal regions that are important for structure and/or function. Starting from a query sequence or structure, the server automatically collects homologues, infers their multiple sequence alignment and reconstructs a phylogenetic tree that reflects their evolutionary relations. These data are then used, within a probabilistic framework, to estimate the evolutionary rates of each sequence position. Here we introduce several new features into ConSurf, including automatic selection of the best evolutionary model used to infer the rates, the ability to homology-model query proteins, prediction of the secondary structure of query RNA molecules from sequence, the ability to view the biological assembly of a query (in addition to the single chain), mapping of the conservation grades onto 2D RNA models and an advanced view of the phylogenetic tree that enables interactively rerunning ConSurf with the taxa of a sub-tree.

Selecton 2007: advanced models for detecting positive and purifying selection using a Bayesian inference approach

Biologically significant sites in a protein may be identified by contrasting the rates of synonymous (Ks) and non-synonymous (Ka) substitutions. This enables the inference of site-specific positive Darwinian selection and purifying selection. We present here Selecton version 2.2 (http://selecton.bioinfo.tau.ac.il), a web server which automatically calculates the ratio between Ka and Ks (ω) at each site of the protein. This ratio is graphically displayed on each site using a color-coding scheme, indicating either positive selection, purifying selection or lack of selection. Selecton implements an assembly of different evolutionary models, which allow for statistical testing of the hypothesis that a protein has undergone positive selection. Specifically, the recently developed mechanistic-empirical model is introduced, which takes into account the physicochemical properties of amino acids. Advanced options were introduced to allow maximal fine tuning of the server to the users specific needs, including calculation of statistical support of the ω values, an advanced graphic display of the proteins 3-dimensional structure, use of different genetic codes and inputting of a pre-built phylogenetic tree. Selecton version 2.2 is an effective, user-friendly and freely available web server which implements up-to-date methods for computing site-specific selection forces, and the visualization of these forces on the proteins sequence and structure.

GUIDANCE2: accurate detection of unreliable alignment regions accounting for the uncertainty of multiple parameters

Inference of multiple sequence alignments (MSAs) is a critical part of phylogenetic and comparative genomics studies. However, from the same set of sequences different MSAs are often inferred, depending on the methodologies used and the assumed parameters. Much effort has recently been devoted to improving the ability to identify unreliable alignment regions. Detecting such unreliable regions was previously shown to be important for downstream analyses relying on MSAs, such as the detection of positive selection. Here we developed GUIDANCE2, a new integrative methodology that accounts for: (i) uncertainty in the process of indel formation, (ii) uncertainty in the assumed guide tree and (iii) co-optimal solutions in the pairwise alignments, used as building blocks in progressive alignment algorithms. We compared GUIDANCE2 with seven methodologies to detect unreliable MSA regions using extensive simulations and empirical benchmarks. We show that GUIDANCE2 outperforms all previously developed methodologies. Furthermore, GUIDANCE2 also provides a set of alternative MSAs which can be useful for downstream analyses. The novel algorithm is implemented as a web-server, available at: http://guidance.tau.ac.il.

Genome-Scale Identification of Legionella pneumophila Effectors Using a Machine Learning Approach

A large number of highly pathogenic bacteria utilize secretion systems to translocate effector proteins into host cells. Using these effectors, the bacteria subvert host cell processes during infection. Legionella pneumophila translocates effectors via the Icm/Dot type-IV secretion system and to date, approximately 100 effectors have been identified by various experimental and computational techniques. Effector identification is a critical first step towards the understanding of the pathogenesis system in L. pneumophila as well as in other bacterial pathogens. Here, we formulate the task of effector identification as a classification problem: each L. pneumophila open reading frame (ORF) was classified as either effector or not. We computationally defined a set of features that best distinguish effectors from non-effectors. These features cover a wide range of characteristics including taxonomical dispersion, regulatory data, genomic organization, similarity to eukaryotic proteomes and more. Machine learning algorithms utilizing these features were then applied to classify all the ORFs within the L. pneumophila genome. Using this approach we were able to predict and experimentally validate 40 new effectors, reaching a success rate of above 90%. Increasing the number of validated effectors to around 140, we were able to gain novel insights into their characteristics. Effectors were found to have low G+C content, supporting the hypothesis that a large number of effectors originate via horizontal gene transfer, probably from their protozoan host. In addition, effectors were found to cluster in specific genomic regions. Finally, we were able to provide a novel description of the C-terminal translocation signal required for effector translocation by the Icm/Dot secretion system. To conclude, we have discovered 40 novel L. pneumophila effectors, predicted over a hundred additional highly probable effectors, and shown the applicability of machine learning algorithms for the identification and characterization of bacterial pathogenesis determinants.

Selecton: a server for detecting evolutionary forces at a single amino-acid site

UNLABELLED We present an algorithmic tool for the identification of biologically significant amino acids in proteins of known three dimensional structure. We estimate the degree of purifying selection and positive Darwinian selection at each site and project these estimates onto the molecular surface of the protein. Thus, patches of functional residues (undergoing either positive or purifying selection), which may be discontinuous in the linear sequence, are revealed. We test for the statistical significance of the site-specific scores in order to obtain reliable and valid estimates. AVAILABILITY The Selecton web server is available at: http://selecton.bioinfo.tau.ac.il SUPPLEMENTARY INFORMATION More information is available at http://selecton.bioinfo.tau.ac.il/overview.html. A set of examples is available at http://selecton.bioinfo.tau.ac.il/gallery.html.

A structural EM algorithm for phylogenetic inference

A central task in the study of evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. A well supported approach to tree reconstruction performs maximum likelihood (ML) analysis. Unfortunately, searching for the maximum likelihood phylogenetic tree is computationally expensive. In this paper, we describe a new algorithm that uses Structural-EM for learning maximum likelihood trees. This algorithm is similar to the standard EM method for estimating branch lengths, except that during iterations of this algorithms the topology is improved as well as the branch length. The algorithm performs iterations of two steps. In the E-Step, we use the current tree topology and branch lengths to compute expected sufficient statistics, which summarize the data. In the M-Step, we search for a topology that maximizes the likelihood with respect to these expected sufficient statistics. As we show, searching for better topologies inside the M-step can be done efficiently, as opposed to standard search over topologies. We prove that each iteration of this procedure increases the likelihood of the topology, and thus the procedure must converge. We evaluate our new algorithm on both synthetic and real sequence data, and show that it is both dramatically faster and finds more plausible trees than standard search for maximum likelihood phylogenies.

Epitopia: a web-server for predicting B-cell epitopes

BackgroundDetecting candidate B-cell epitopes in a protein is a basic and fundamental step in many immunological applications. Due to the impracticality of experimental approaches to systematically scan the entire protein, a computational tool that predicts the most probable epitope regions is desirable.ResultsThe Epitopia server is a web-based tool that aims to predict immunogenic regions in either a protein three-dimensional structure or a linear sequence. Epitopia implements a machine-learning algorithm that was trained to discern antigenic features within a given protein. The Epitopia algorithm has been compared to other available epitope prediction tools and was found to have higher predictive power. A special emphasis was put on the development of a user-friendly graphical interface for displaying the results.ConclusionEpitopia is a user-friendly web-server that predicts immunogenic regions for both a protein structure and a protein sequence. Its accuracy and functionality make it a highly useful tool. Epitopia is available at http://epitopia.tau.ac.il and includes extensive explanations and example predictions.