Antibody Modeling and Structure Analysis. Application to biomedical problems.

Background The usefulness of antibodies and antibody derived artificial constructs in various medical and biochemical applications has made them a prime target for protein engineering, modelling, and structure analysis. The huge number of known antibody sequences, that far outpaces the number of solved structures, raises the need for reliable automatic methods of antibody structure prediction. Antibodies have a very characteristic molecular structure that is reflected in their modelling technique. Currently, the most accurate models are produced using a quite peculiar modelling strategy, developed among others by our group: the framework regions are modelled with a standard comparative modelling approach, whereas the hypervariable loops are predicted using the ad-hoc “canonical structure method”, historically based on expert analysis of the available antibody solved structures. More than thirty years passed since this modelling method was initially developed, nonetheless there is still a huge effort in the academic and pharmaceutical communities to improve its accuracy. The reason for this lies in several error sources in the current modelling process. First of all, given the large amount of available structures, it was impossible to manually update “canonical structure” classes and rules. Moreover, the lack of specific studies on the packing between the VL and the VH domains and on possible conformational changes occurring upon antigen binding was impairing the integration in the modelling techniques of such factors. Aim The general aim of this study is to carry out an extensive characterization and annotation of immunoglobulin molecules i.e. to deepen our understanding of the molecular basis of their specificity using a combination of bioinformatics sequence- and structure-based analysis. I carried out improvements to the antibody modelling protocols by revising the canonical structure definitions and by minimizing the errors arising from VL and the VH domain packing at the same time by taking care of the conformational changes occurring upon antigen binding. Results During the past years, we successfully improved the description of the structural repertoire of immunoglobulins with lambda light chains, which has both practical (design, engineering and humanization) and theoretical applications (improvement of the antibody modelling)[1]. Our large-scale analysis of the association of heavy and light chain variable domains in antibodies showed that there are essentially two different modes of interaction that can be identified by the presence of key amino acids in specific positions of the antibody sequences [2]. Interestingly, we also found that the different packing modes are related to the volume and type of recognized antigen. These findings are clearly relevant for the design of antibodies and of antibody libraries. The investigation of the antibody conformational changes upon antigen binding allowed us to identify sections on variable and constant regions that show significant flexibility when comparing the antigen bound/unbound forms of immunoglobulins. The results of all the above-mentioned analyses have been implemented in our in-house immunoglobulin structure prediction server (PIGS, automatic Prediction of ImmunoGlobulin Structure), thus helping to minimize the sources of errors in the current modelling process. Consequent to our results, we were asked to write a chapter in Encyclopaedia of Biophysics on antibody modelling [3]. A further step in the direction of improving the understanding of antibody recognition mechanisms was to put together all the annotations of immunoglobulins in a publicly available database. To this aim, we constructed a database of immunoglobulin sequences and integrated tools (DIGIT) [4], which is becoming an extensively used resource by the community. DIGIT stores sequences of annotated immunoglobulin variable domains and offers to the user several tools for searching and analysing them. Our experience in antibody modelling allowed us to approach two biomedical problems in collaboration with Prof. Arcaini (University of Pavia) and Prof. Fabio Ghiotto (University of Genova). More specifically, by applying the tools we developed and all our theoretical knowledge we successfully analysed the immunoglobulin repertoires of SMZL (splenic marginal zone lymphoma) and CLL (chronic lymphocytic leukaemia) patient data. Both the CLL and SMZL patients are known to have a biased usage of immunoglobulin (IG) heavy variable (IGHV) genes and stereotyped B-cell receptors (BCRs), used as a marker in disease prognosis. We extended these analyses by taking into account VL germlines, VL-VH pairing and structural information, thus giving a more detailed view of the immunoglobulin repertoire in terms of sequence, structure and function. Analysing the immunoglobulins of patients with CLL, we discovered statistically significant differences among immunoglobulins in patients with favourable and unfavourable prognosis. A paper describing this work has been submitted [5]. The poster describing the results of SMZL repertoire analysis was accepted at the 2012 American Society of Haematology (ASH) meeting and published as an abstract [6]. Reference: 1. Chailyan, A., P. Marcatili, et al. (2011). "Structural repertoire of immunoglobulin lambda light chains." Proteins 79(5): 1513-1524. 2. Chailyan, A., P. Marcatili, et al. (2011). "The association of heavy and light chain variable domains in antibodies: implications for antigen specificity." FEBS J 278(16): 2858-2866. 3. Marcatili P., A. Chailyan, D. Cirillo and A. Tramontano. Modelling of antibody structures. Encyclopaedia of Biophysics. Springer (2012). 4. Chailyan, A., A. Tramontano, et al. (2012). "A database of immunoglobulins with integrated tools: DIGIT." Nucleic Acids Res. doi:10.1093/nar/gkr806. 5. Marcatili P., F. Ghiotto, C. Tenca, A. Chailyan, A. N. Mazzarello, X. Yan, M. Colombo, E. Albesiano, D. Bagnara, G. Cutrona, F. Morabito, S. Bruno, M. Ferrarini, N. Chiorazzi, A. Tramontano, F. Fais. "Immunoglobulins produced by chronic lymphocytic leukaemia B cells show limited binding site structure variability." submitted 6. Marcatili P., S. Zibellini, S. Rattotti, A. Chailyan, M. Varettoni, L. Morello, E. Boveri, M. Lucioni, M. Bonfichi, M. Gotti, V. Fiaccadori, M. Paulli, A. Tramontano, L. Arcaini. "Hierarchical Clustering of B-Cell Receptor Structures in Splenic Marginal Zone Lymphoma", abstract, American Society of Haematology (ASH) meeting

Testing a MultiTEP-based combination vaccine to reduce Aβ and tau pathology in Tau22/5xFAD bigenic mice.

BackgroundAlzheimer disease (AD) is characterized by the accumulation of beta-amyloid (Aβ) plaques and neurofibrillary tangles composed of hyperphosphorylated tau, which together lead to neurodegeneration and cognitive decline. Current therapeutic approaches have primarily aimed to reduce pathological aggregates of either Aβ or tau, yet phase 3 clinical trials of these approaches have thus far failed to delay disease progression in humans. Strong preclinical evidence indicates that these two abnormally aggregated proteins interact synergistically to drive downstream neurodegeneration. Therefore, combinatorial therapies that concurrently target both Aβ and tau might be needed for effective disease modification.MethodsA combinatorial vaccination approach was designed to concurrently target both Aβ and tau pathologies. Tau22/5xFAD (T5x) bigenic mice that develop both pathological Aβ and tau aggregates were injected intramuscularly with a mixture of two MultiTEP epitope vaccines: AV-1959R and AV-1980R, targeting Aβ and tau, respectively, and formulated in AdvaxCpG, a potent polysaccharide adjuvant. Antibody responses of vaccinated animals were measured by ELISA, and neuropathological changes were determined in brain homogenates of vaccinated and control mice using ELISA and Meso Scale Discovery (MSD) multiplex assays.ResultsT5x mice immunized with a mixture of Aβ- and tau-targeting vaccines generated high Aβ- and tau-specific antibody titers that recognized senile plaques and neurofibrillary tangles/neuropil threads in human AD brain sections. Production of these antibodies in turn led to significant reductions in the levels of soluble and insoluble total tau, and hyperphosphorylated tau as well as insoluble Aβ42, within the brains of bigenic T5x mice.ConclusionsAV-1959R and AV-1980R formulated with AdvaxCpG adjuvant are immunogenic and therapeutically potent vaccines that in combination can effectively reduce both of the hallmark pathologies of AD in bigenic mice. Taken together, these findings warrant further development of this vaccine technology for ultimate testing in human AD

Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies

Motivation: Antibodies are able to recognize a wide range of antigens through their complementary determining regions formed by six hypervariable loops. Predicting the 3D structure of these loops is essential for the analysis and reengineering of novel antibodies with enhanced affinity and specificity. The canonical structure model allows high accuracy prediction for five of the loops. The third loop of the heavy chain, H3, is the hardest to predict because of its diversity in structure, length and sequence composition.Results: We describe a method, based on the Random Forest automatic learning technique, to select structural templates for H3 loops among a dataset of candidates. These can be used to predict the structure of the loop with a higher accuracy than that achieved by any of the presently available methods. The method also has the advantage of being extremely fast and returning a reliable estimate of the model quality.Availability and implementation: The source code is freely available at http://www.biocomputing.it/H3Loopred/Contact: [email protected] 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

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

Immunoglobulin G structure and rheumatoid factor epitopes

Antibodies are important for immunity and exist in several classes (IgM, IgD, IgA, IgG, IgE). They are composed of symmetric dimeric molecules with two antigen binding regions (Fab) and a constant part (Fc), usually depicted as Y-shaped molecules. Rheumatoid factors found in patients with rheumatoid arthritis are autoantibodies binding to IgG and paradoxically appear to circulate in blood alongside with their antigen (IgG) without reacting with it. Here, it is shown that rheumatoid factors do not react with native IgG in solution, and that their epitopes only become accessible upon certain physico-chemical treatments (e.g. heat treatment at 57 °C), by physical adsorption on a hydrophobic surface or by antigen binding. Moreover, chemical cross-linking in combination with mass spectrometry showed that the native state of IgG is a compact (closed) form and that the Fab parts of IgG shield the Fc region and thereby control access of rheumatoid factors and presumably also some effector functions. It can be inferred that antibody binding to pathogen surfaces induces a conformational change, which exposes the Fc part with its effector sites and rheumatoid factor epitopes. This has strong implications for understanding antibody structure and physiology and necessitates a conceptual reformulation of IgG models

abYsis: Integrated Antibody Sequence and Structure-Management, Analysis, and Prediction

abYsis is a web-based antibody research system that includes an integrated database of antibody sequence and structure data. The system can be interrogated in numerous ways-from simple text and sequence searches to sophisticated queries that apply 3D structural constraints. The publicly available version includes pre-analyzed sequence data from the European Molecular Biology Laboratory European Nucleotide Archive (EMBL-ENA) and Kabat as well as structure data from the Protein Data Bank. A researcher's own sequences can also be analyzed through the web interface. A defining characteristic of abYsis is that the sequences are automatically numbered with a series of popular schemes such as Kabat and Chothia and then annotated with key information such as complementarity-determining regions and potential post-translational modifications. A unique aspect of abYsis is a set of residue frequency tables for each position in an antibody, allowing "unusual residues" (those rarely seen at a particular position) to be highlighted and decisions to be made on which mutations may be acceptable. This is especially useful when comparing antibodies from different species. abYsis is useful for any researcher specializing in antibody engineering, especially those developing antibodies as drugs. abYsis is available at www.abysis.org

Novel structural parameters of Ig -Ag complexes yield a quantitative description of interaction specificity and binding affinity

Antibody-antigen complexes challenge our understanding, as analyses to datefailed to unveil the key determinants of binding affinity and interaction specificity. We par-tially fill this gap based on novel quantitative analyses using two standardized databases, theIMGT/3Dstructure-DB and the structure affinity benchmark.First, we introduce a statistical analysis of interfaces which enables the classification of ligand types(protein, peptide, chemical; cross-validated classification error of 9.6%), and yield binding affinitypredictions of unprecedented accuracy (median absolute error of 0.878 kcal/mol). Second, weexploit the contributions made by CDRs in terms of position at the interface and atomic packingproperties to show that in general, VH CDR3 and VL CDR3 make dominant contributions tothe binding affinity, a fact also shown to be consistent with the enthalpy - entropy compensationassociated with pre-configuration of CDR3.Our work suggests that the affinity prediction problem could be solved from databases of highresolution crystal structures of complexes with known affinity

AbDb: Antibody structure database - A database of PDB derived antibody structures

In order to analyse structures of proteins of a particular class, these need to be extracted from Protein Data Bank (PDB) files. In the case of antibodies, there are a number of special considerations: (i) identifying antibodies in the PDB is not trivial, (ii) they may be crystallized with or without antigen, (iii) for analysis purposes, one is normally only interested in the Fv region of the antibody, (iv) structural analysis of epitopes, in particular, requires individual antibody–antigen complexes from a PDB file which may contain multiple copies of the same, or different, antibodies and (v) standard numbering schemes should be applied. Consequently, there is a need for a specialist resource containing pre-numbered non-redundant antibody Fv structures with their cognate antigens. We have created an automatically updated resource, AbDb, which collects the Fv regions from antibody structures using information from our SACS database which summarizes antibody structures from the PDB. PDB files containing multiple structures are split and numbered and each antibody structure is associated with its antigen where available. Antibody structures with only light or heavy chains have also been processed and sequences of antibodies are compared to identify multiple structures of the same antibody. The data may be queried on the basis of PDB code, or the name or species of the antibody or antigen, and the complete datasets may be downloaded. Database URL: www.bioinf.org.uk/abs/abdb

A chromosome conformation capture ordered sequence of the barley genome

201