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Please use this identifier to cite or link to this item: http://hdl.handle.net/10805/1909

Title: Antibody Modeling and Structure Analysis. Application to biomedical problems.
Authors: CHAILYAN, ANNA
Tutor: Tramontano, Anna
Marcatili, Paolo
Keywords: immunoglobulin
structure prediction
large-scale analysis
Issue Date: 27-Feb-2013
Abstract: 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.
URI: http://hdl.handle.net/10805/1909
Research interests: structure prediction, immunoglobulin sequence-stucture-function large-scale analysis, application of bioinformatics tools to biomedical problems
Appears in PhD:SCIENZE PASTEURIANE

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