Paolo Marcatili

PIGS: automatic prediction of antibody structures

UNLABELLED We describe a web server for the automatic prediction of immunoglobulin variable domains based on the canonical structure model. The server is user-friendly and flexible. It allows the user to select the templates for the frameworks and the loops using different strategies. The final output is a full-fledged 3D model of the variable domains of the target immunoglobulin. AVAILABILITY The server is openly accessible to academic users at the address: http://arianna.bio.uniroma1.it/pigs. It does not require registration and there is no limit to the number of sequences that can be submitted.

The association of heavy and light chain variable domains in antibodies: implications for antigen specificity

The antigen‐binding site of immunoglobulins is formed by six regions, three from the light and three from the heavy chain variable domains, which, on association of the two chains, form the conventional antigen‐binding site of the antibody. The mode of interaction between the heavy and light chain variable domains affects the relative position of the antigen‐binding loops and therefore has an effect on the overall conformation of the binding site. In this article, we analyze the structure of the interface between the heavy and light chain variable domains and show that there are essentially two different modes for their interaction that can be identified by the presence of key amino acids in specific positions of the antibody sequences. We also show that the different packing modes are related to the type of recognized antigen.

NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data

Cytotoxic T cells are of central importance in the immune system’s response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC class I molecules. Peptide binding to MHC molecules is the single most selective step in the Ag-presentation pathway. Therefore, in the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has attracted widespread attention. In the past, predictors of peptide–MHC interactions have primarily been trained on binding affinity data. Recently, an increasing number of MHC-presented peptides identified by mass spectrometry have been reported containing information about peptide-processing steps in the presentation pathway and the length distribution of naturally presented peptides. In this article, we present NetMHCpan-4.0, a method trained on binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increase in predictive performance compared with state-of-the-art methods when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes.

Effects of Natural Selection and Gene Conversion on the Evolution of Human Glycophorins Coding for MNS Blood Polymorphisms in Malaria-Endemic African Populations

Malaria has been a very strong selection pressure in recent human evolution, particularly in Africa. Of the one million deaths per year due to malaria, more than 90% are in sub-Saharan Africa, a region with high levels of genetic variation and population substructure. However, there have been few studies of nucleotide variation at genetic loci that are relevant to malaria susceptibility across geographically and genetically diverse ethnic groups in Africa. Invasion of erythrocytes by Plasmodium falciparum parasites is central to the pathology of malaria. Glycophorin A (GYPA) and B (GYPB), which determine MN and Ss blood types, are two major receptors that are expressed on erythrocyte surfaces and interact with parasite ligands. We analyzed nucleotide diversity of the glycophorin gene family in 15 African populations with different levels of malaria exposure. High levels of nucleotide diversity and gene conversion were found at these genes. We observed divergent patterns of genetic variation between these duplicated genes and between different extracellular domains of GYPA. Specifically, we identified fixed adaptive changes at exons 3-4 of GYPA. By contrast, we observed an allele frequency spectrum skewed toward a significant excess of intermediate-frequency alleles at GYPA exon 2 in many populations; the degree of spectrum distortion is correlated with malaria exposure, possibly because of the joint effects of gene conversion and balancing selection. We also identified a haplotype causing three amino acid changes in the extracellular domain of glycophorin B. This haplotype might have evolved adaptively in five populations with high exposure to malaria.

FunClust: a web server for the identification of structural motifs in a set of non-homologous protein structures

BackgroundThe occurrence of very similar structural motifs brought about by different parts of non homologous proteins is often indicative of a common function. Indeed, relatively small local structures can mediate binding to a common partner, be it a protein, a nucleic acid, a cofactor or a substrate. While it is relatively easy to identify short amino acid or nucleotide sequence motifs in a given set of proteins or genes, and many methods do exist for this purpose, much more challenging is the identification of common local substructures, especially if they are formed by non consecutive residues in the sequence.ResultsHere we describe a publicly available tool, able to identify common structural motifs shared by different non homologous proteins in an unsupervised mode. The motifs can be as short as three residues and need not to be contiguous or even present in the same order in the sequence. Users can submit a set of protein structures deemed or not to share a common function (e.g. they bind similar ligands, or share a common epitope). The server finds and lists structural motifs composed of three or more spatially well conserved residues shared by at least three of the submitted structures. The method uses a local structural comparison algorithm to identify subsets of similar amino acids between each pair of input protein chains and a clustering procedure to group similarities shared among different structure pairs.ConclusionsFunClust is fast, completely sequence independent, and does not need an a priori knowledge of the motif to be found. The output consists of a list of aligned structural matches displayed in both tabular and graphical form. We show here examples of its usefulness by searching for the largest common structural motifs in test sets of non homologous proteins and showing that the identified motifs correspond to a known common functional feature.

On the Mechanism of Chloroquine Resistance in Plasmodium falciparum

Resistance to chloroquine of malaria strains is known to be associated with a parasite protein named PfCRT, the mutated form of which is able to reduce chloroquine accumulation in the digestive vacuole of the pathogen. Whether the protein mediates extrusion of the drug acting as a channel or as a carrier and which is the protonation state of its chloroquine substrate is the subject of a scientific debate. We present here an analytical approach that explores which combination of hypotheses on the mechanism of transport and the protonation state of chloroquine are consistent with available equilibrium experimental data. We show that the available experimental data are not, by themselves, sufficient to conclude whether the protein acts as a channel or as a transporter, which explains the origin of their different interpretation by different authors. Interestingly, though, each of the two models is only consistent with a subset of hypotheses on the protonation state of the transported molecule. The combination of these results with a sequence and structure analysis of PfCRT, which strongly suggests that the molecule is a carrier, indicates that the transported species is either or both the mono and di-protonated forms of chloroquine. We believe that our results, besides shedding light on the mechanism of chloroquine resistance in P. falciparum, have implications for the development of novel therapies against resistant malaria strains and demonstrate the usefulness of an approach combining systems biology strategies with structural bioinformatics and experimental data.

Prediction of site-specific interactions in antibody-antigen complexes: the proABC method and server

Motivation: Antibodies or immunoglobulins are proteins of paramount importance in the immune system. They are extremely relevant as diagnostic, biotechnological and therapeutic tools. Their modular structure makes it easy to re-engineer them for specific purposes. Short of undergoing a trial and error process, these experiments, as well as others, need to rely on an understanding of the specific determinants of the antibody binding mode. Results: In this article, we present a method to identify, on the basis of the antibody sequence alone, which residues of an antibody directly interact with its cognate antigen. The method, based on the random forest automatic learning techniques, reaches a recall and specificity as high as 80% and is implemented as a free and easy-to-use server, named prediction of Antibody Contacts. We believe that it can be of great help in re-design experiments as well as a guide for molecular docking experiments. The results that we obtained also allowed us to dissect which features of the antibody sequence contribute most to the involvement of specific residues in binding to the antigen. Availability: http://www.biocomputing.it/proABC. Contact: anna.tramontano@uniroma1.it or paolo.marcatili@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes

Abstract Antibodies have become an indispensable tool for many biotechnological and clinical applications. They bind their molecular target (antigen) by recognizing a portion of its structure (epitope) in a highly specific manner. The ability to predict epitopes from antigen sequences alone is a complex task. Despite substantial effort, limited advancement has been achieved over the last decade in the accuracy of epitope prediction methods, especially for those that rely on the sequence of the antigen only. Here, we present BepiPred-2.0 (http://www.cbs.dtu.dk/services/BepiPred/), a web server for predicting B-cell epitopes from antigen sequences. BepiPred-2.0 is based on a random forest algorithm trained on epitopes annotated from antibody-antigen protein structures. This new method was found to outperform other available tools for sequence-based epitope prediction both on epitope data derived from solved 3D structures, and on a large collection of linear epitopes downloaded from the IEDB database. The method displays results in a user-friendly and informative way, both for computer-savvy and non-expert users. We believe that BepiPred-2.0 will be a valuable tool for the bioinformatics and immunology community.

Structural repertoire of immunoglobulin λ light chains

The immunoglobulin λ isotype is present in nearly all vertebrates and plays an important role in the human immune system. Despite its importance, few systematic studies have been performed to analyze the structural conformation of its variable regions, contrary to what is the case for κ and heavy chains. We show here that an analysis of the structures of λ chains allows the definition of a discrete set of recurring conformations (canonical structures) of their hypervariable loops and, most importantly, the identification of sequence constraints that can be used to predict their structure. We also show that the structural repertoire of λ chains is different and more varied than that of the κ chains, consistently with the current view of the involvement of the two major light‐chain families in complementary strategies of the immune system to ensure a fine tuning between diversity and stability in antigen recognition. Proteins 2011;

Antibody modeling using the Prediction of ImmunoGlobulin Structure (PIGS) web server

Antibodies (or immunoglobulins) are crucial for defending organisms from pathogens, but they are also key players in many medical, diagnostic and biotechnological applications. The ability to predict their structure and the specific residues involved in antigen recognition has several useful applications in all of these areas. Over the years, we have developed or collaborated in developing a strategy that enables researchers to predict the 3D structure of antibodies with a very satisfactory accuracy. The strategy is completely automated and extremely fast, requiring only a few minutes (∼10 min on average) to build a structural model of an antibody. It is based on the concept of canonical structures of antibody loops and on our understanding of the way light and heavy chains pack together.