Nimrod D. Rubinstein

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.

The interface of protein structure, protein biophysics, and molecular evolution

Abstract The interface of protein structural biology, protein biophysics, molecular evolution, and molecular population genetics forms the foundations for a mechanistic understanding of many aspects of protein biochemistry. Current efforts in interdisciplinary protein modeling are in their infancy and the state‐of‐the art of such models is described. Beyond the relationship between amino acid substitution and static protein structure, protein function, and corresponding organismal fitness, other considerations are also discussed. More complex mutational processes such as insertion and deletion and domain rearrangements and even circular permutations should be evaluated. The role of intrinsically disordered proteins is still controversial, but may be increasingly important to consider. Protein geometry and protein dynamics as a deviation from static considerations of protein structure are also important. Protein expression level is known to be a major determinant of evolutionary rate and several considerations including selection at the mRNA level and the role of interaction specificity are discussed. Lastly, the relationship between modeling and needed high‐throughput experimental data as well as experimental examination of protein evolution using ancestral sequence resurrection and in vitro biochemistry are presented, towards an aim of ultimately generating better models for biological inference and prediction.

A machine-learning approach for predicting B-cell epitopes.

The immune activity of an antibody is directed against a specific region on its target antigen known as the epitope. Numerous immunodetection and immunotheraputics applications are based on the ability of antibodies to recognize epitopes. The detection of immunogenic regions is often an essential step in these applications. The experimental approaches used for detecting immunogenic regions are often laborious and resource-intensive. Thus, computational methods for the prediction of immunogenic regions alleviate this drawback by guiding the experimental procedures. In this work we developed a computational method for the prediction of immunogenic regions from either the protein three-dimensional structure or sequence when the structure is unavailable. The method implements a machine-learning algorithm that was trained to recognize immunogenic patterns based on a large benchmark dataset of validated epitopes derived from antigen structures and sequences. We compare our method to other available tools that perform the same task and show that it outperforms them.

Epitope mapping using combinatorial phage-display libraries: a graph-based algorithm

A phage-display library of random peptides is a combinatorial experimental technique that can be harnessed for studying antibody–antigen interactions. In this technique, a phage peptide library is scanned against an antibody molecule to obtain a set of peptides that are bound by the antibody with high affinity. This set of peptides is regarded as mimicking the genuine epitope of the antibodys interacting antigen and can be used to define it. Here we present PepSurf, an algorithm for mapping a set of affinity-selected peptides onto the solved structure of the antigen. The problem of epitope mapping is converted into the task of aligning a set of query peptides to a graph representing the surface of the antigen. The best match of each peptide is found by aligning it against virtually all possible paths in the graph. Following a clustering step, which combines the most significant matches, a predicted epitope is inferred. We show that PepSurf accurately predicts the epitope in four cases for which the epitope is known from a solved antibody–antigen co-crystal complex. We further examine the capabilities of PepSurf for predicting other types of protein–protein interfaces. The performance of PepSurf is compared to other available epitope mapping programs.

Stepwise Prediction of Conformational Discontinuous B-Cell Epitopes Using the Mapitope Algorithm

Mapping the epitope of an antibody is of great interest, since it contributes much to our understanding of the mechanisms of molecular recognition and provides the basis for rational vaccine design. Here we present Mapitope, a computer algorithm for epitope mapping. The algorithm input is a set of affinity isolated peptides obtained by screening phage display peptide‐libraries with the antibody of interest. The output is usually 1–3 epitope candidates on the surface of the atomic structure of the antigen. We have systematically tested the performance of Mapitope by assessing the effect of the algorithm parameters on the final prediction. Thus, we have examined the effect of the statistical threshold (ST) parameter, relating to the frequency distribution and enrichment of amino acid pairs from the isolated peptides and the D (distance) and E (exposure) parameters which relate to the physical parameters of the antigen. Two model systems were analyzed in which the antibody of interest had previously been co‐crystallized with the antigen and thus the epitope is a given. The Mapitope algorithm successfully predicted the epitopes in both models. Accordingly, we formulated a stepwise paradigm for the prediction of discontinuous conformational epitopes using peptides obtained from screening phage display libraries. We applied this paradigm to successfully predict the epitope of the Trastuzumab antibody on the surface of the Her‐2/neu receptor in a third model system. Proteins 2007.

Pepitope: Epitope mapping from affinity-selected peptides

Abstract Identifying the epitope to which an antibody binds is central for many immunological applications such as drug design and vaccine development. The Pepitope server is a web-based tool that aims at predicting discontinuous epitopes based on a set of peptides that were affinity-selected against a monoclonal antibody of interest. The server implements three different algorithms for epitope mapping: PepSurf, Mapitope, and a combination of the two. The rationale behind these algorithms is that the set of peptides mimics the genuine epitope in terms of physicochemical properties and spatial organization. When the three-dimensional (3D) structure of the antigen is known, the information in these peptides can be used to computationally infer the corresponding epitope. A user-friendly web interface and a graphical tool that allows viewing the predicted epitopes were developed. Pepitope can also be applied for inferring other types of protein–protein interactions beyond the immunological context, and as a general tool for aligning linear sequences to a 3D structure. Availability: http://pepitope.tau.ac.il/ Contact: talp@post.tau.ac.il

A likelihood framework to analyse phyletic patterns

Probabilistic evolutionary models revolutionized our capability to extract biological insights from sequence data. While these models accurately describe the stochastic processes of site-specific substitutions, single-base substitutions represent only a fraction of all the events that shape genomes. Specifically, in microbes, events in which entire genes are gained (e.g. via horizontal gene transfer) and lost play a pivotal evolutionary role. In this research, we present a novel likelihood-based evolutionary model for gene gains and losses, and use it to analyse genome-wide patterns of the presence and absence of gene families. The model assumes a Markovian stochastic process, where gains and losses are represented by the transition between presence and absence, respectively, given an underlying phylogenetic tree. To account for differences in the rates of gain and loss of different gene families, we assume among-gene family rate variability, thus allowing for more accurate description of the data. Using the Bayesian approach, we estimated an evolutionary rate for each gene family. Simulation studies demonstrated that our methodology accurately infers these rates. Our methodology was applied to analyse a large corpus of data, consisting of 4873 gene families spanning 63 species and revealed novel insights regarding the evolutionary nature of genome-wide gain and loss dynamics.

Evolutionary Modeling of Rate Shifts Reveals Specificity Determinants in HIV-1 Subtypes

A hallmark of the human immunodeficiency virus 1 (HIV-1) is its rapid rate of evolution within and among its various subtypes. Two complementary hypotheses are suggested to explain the sequence variability among HIV-1 subtypes. The first suggests that the functional constraints at each site remain the same across all subtypes, and the differences among subtypes are a direct reflection of random substitutions, which have occurred during the time elapsed since their divergence. The alternative hypothesis suggests that the functional constraints themselves have evolved, and thus sequence differences among subtypes in some sites reflect shifts in function. To determine the contribution of each of these two alternatives to HIV-1 subtype evolution, we have developed a novel Bayesian method for testing and detecting site-specific rate shifts. The RAte Shift EstimatoR (RASER) method determines whether or not site-specific functional shifts characterize the evolution of a protein and, if so, points to the specific sites and lineages in which these shifts have most likely occurred. Applying RASER to a dataset composed of large samples of HIV-1 sequences from different group M subtypes, we reveal rampant evolutionary shifts throughout the HIV-1 proteome. Most of these rate shifts have occurred during the divergence of the major subtypes, establishing that subtype divergence occurred together with functional diversification. We report further evidence for the emergence of a new sub-subtype, characterized by abundant rate-shifting sites. When focusing on the rate-shifting sites detected, we find that many are associated with known function relating to viral life cycle and drug resistance. Finally, we discuss mechanisms of covariation of rate-shifting sites.

Evolutionary Models Accounting for Layers of Selection in Protein-Coding Genes and their Impact on the Inference of Positive Selection

The selective forces acting on a protein-coding gene are commonly inferred using evolutionary codon models by contrasting the rate of nonsynonymous substitutions to the rate of synonymous substitutions. These models usually assume that the synonymous substitution rate, Ks, is homogenous across all sites, which is justified if synonymous sites are free from selection. However, a growing body of evidence indicates that the DNA and RNA levels of protein-coding genes are subject to varying degrees of selective constraints due to various biological functions encoded at these levels. In this paper, we develop evolutionary models that account for these layers of selection by allowing for both among-site variability of substitution rates at the DNA/RNA level (which leads to Ks variability among protein-coding sites) and among-site variability of substitution rates at the protein level (Ka variability). These models are constructed so that positive selection is either allowed or not. This enables statistical testing of positive selection when variability at the DNA/RNA substitution rate is accounted for. Using this methodology, we show that variability of the baseline DNA/RNA substitution rate is a widespread phenomenon in coding sequence data of mammalian genomes, most likely reflecting varying degrees of selection at the DNA and RNA levels. Additionally, we use simulations to examine the impact that accounting for the variability of the baseline DNA/RNA substitution rate has on the inference of positive selection. Our results show that ignoring this variability results in a high rate of erroneous positive-selection inference. Our newly developed model, which accounts for this variability, does not suffer from this problem and hence provides a likelihood framework for the inference of positive selection on a background of variability in the baseline DNA/RNA substitution rate.

The operonic location of auto-transcriptional repressors is highly conserved in bacteria.

Bacterial genes are commonly encoded in clusters, known as operons, which share transcriptional regulatory control and often encode functionally related proteins that take part in certain biological pathways. Operons that are coregulated are known to colocalize in the genome, suggesting that their spatial organization is under selection for efficient expression regulation. However, the internal order of genes within operons is believed to be poorly conserved, and hence expression requirements are claimed to be too weak to oppose gene rearrangements. In light of these opposing views, we set out to investigate whether the internal location of the regulatory genes within operons is under selection. Our analysis shows that transcription factors (TFs) are preferentially encoded as either first or last in their operons, in the two diverged model bacteria Escherichia coli and Bacillus subtilis. In a higher resolution, we find that TFs that repress transcription of the operon in which they are encoded (autorepressors), contribute most of this signal by specific preference of the first operon position. We show that this trend is strikingly conserved throughout highly diverged bacterial phyla. Moreover, these autorepressors regulate operons that carry out highly diverse biological functions. We propose a model according to which autorepressors are selected to be located first in their operons in order to optimize transcription regulation. Specifically, the first operon position helps autorepressors to minimize leaky transcription of the operon structural genes, thus minimizing energy waste. Our analysis provides statistically robust evidence for a paradigm of bacterial autorepressor preferential operonic location. Corroborated with our suggested model, an additional layer of operon expression control that is common throughout the bacterial domain is revealed.