Anna Chailyan

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.

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.

A database of immunoglobulins with integrated tools: DIGIT

The DIGIT (Database of ImmunoGlobulins with Integrated Tools) database (http://biocomputing.it/digit) is an integrated resource storing sequences of annotated immunoglobulin variable domains and enriched with tools for searching and analyzing them. The annotations in the database include information on the type of antigen, the respective germline sequences and on pairing information between light and heavy chains. Other annotations, such as the identification of the complementarity determining regions, assignment of their structural class and identification of mutations with respect to the germline, are computed on the fly and can also be obtained for user-submitted sequences. The system allows customized BLAST searches and automatic building of 3D models of the domains to be performed.

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.

Igs Expressed by Chronic Lymphocytic Leukemia B Cells Show Limited Binding-Site Structure Variability

Ag selection has been suggested to play a role in chronic lymphocytic leukemia (CLL) pathogenesis, but no large-scale analysis has been performed so far on the structure of the Ag-binding sites (ABSs) of leukemic cell Igs. We sequenced both H and L chain V(D)J rearrangements from 366 CLL patients and modeled their three-dimensional structures. The resulting ABS structures were clustered into a small number of discrete sets, each containing ABSs with similar shapes and physicochemical properties. This structural classification correlates well with other known prognostic factors such as Ig mutation status and recurrent (stereotyped) receptors, but it shows a better prognostic value, at least in the case of one structural cluster for which clinical data were available. These findings suggest, for the first time, to our knowledge, on the basis of a structural analysis of the Ab-binding sites, that selection by a finite quota of antigenic structures operates on most CLL cases, whether mutated or unmutated.

Human MHC-II with Shared Epitope Motifs Are Optimal Epstein-Barr Virus Glycoprotein 42 Ligands—Relation to Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder of unknown etiology, which is characterized by inflammation in the synovium and joint damage. Although the pathogenesis of RA remains to be determined, a combination of environmental (e.g., viral infections) and genetic factors influence disease onset. Especially genetic factors play a vital role in the onset of disease, as the heritability of RA is 50–60%, with the human leukocyte antigen (HLA) alleles accounting for at least 30% of the overall genetic risk. Some HLA-DR alleles encode a conserved sequence of amino acids, referred to as the shared epitope (SE) structure. By analyzing the structure of a HLA-DR molecule in complex with Epstein-Barr virus (EBV), the SE motif is suggested to play a vital role in the interaction of MHC II with the viral glycoprotein (gp) 42, an essential entry factor for EBV. EBV has been repeatedly linked to RA by several lines of evidence and, based on several findings, we suggest that EBV is able to induce the onset of RA in predisposed SE-positive individuals, by promoting entry of B-cells through direct contact between SE and gp42 in the entry complex.

3D Protein-Structure-Oriented Discovery of Clinical Relation Across Chronic Lymphocytic Leukemia Patients

Chronic lymphocytic leukemia (CLL) is the most common adult leukemia with still unclear etiology. Indications of antigenic pressure have been hinted, using sequence and structure-based reasoning. The accuracy of such approaches, and in particular of the ones derived from 3D models obtained from the patients’ antibody amino acid sequences, is intimately connected to both the reliability of the models and the quality of the methods used to compare and group them. The proposed work provides a sophisticated method for the classification of CLL patients based on clustering the amino acid sequences of the clonotypic B-cell receptor immunoglobulin, which is the ideal clone-specific marker, critical for clonal behavior and patient outcome. A novel CLL patient clustering method is hereby proposed, combining bioinformatics methods with the extraction of 3D object descriptors, used in machine learning applications. The proposed methodology achieved an efficient and highly informative grouping of CLL patients in accordance to their biological and clinical properties.

Structural landscape of immunoglobulin lambda light chains

The immunoglobulin lambda isotype family is present in nearly all vertebrates and plays a primary role in the human immune system. Despite its importance, few if any systematic studies have been performed to identify, as it has been the case for kappa and heavy chains, a set of structural conformations adopted by the loops forming its Antigen Binding Site (ABSs). The aim of this work is to perform such analysis on the vast majority of known antibody lambda chains by means of an integrative approach of bioinformatics (structural superpositions and sequence alignments), machine learning (clustering, random forest) and statistical techniques.