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

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

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

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

A chromosome conformation capture ordered sequence of the barley genome

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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

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. © 2011 Wiley-Liss, Inc

Antibody structural modeling with prediction of immunoglobulin structure (PIGS).

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