Protein Engineering / Protein Properties / Developability / Hot Spot Analysis / Antibody Modeling / Humanization / Molecular Surfaces
The course covers approaches for structure-based antibody design and includes protein-protein interactions analysis, in silico protein engineering, affinity modeling and antibody homology modeling. The interaction of a co-crystallized antibody-antigen complex will be studied by generating and examining the molecular surfaces and visualizing protein-protein interactions in 3D and 2D. Antibody properties will be evaluated using specialized calculated protein property descriptors and analyzing protein patches. The application of protein engineering tools for homology modeling and conducting property optimization of antibodies in the context of developability will be studied. Antibody optimization examples will include identification of glycosylation sites and analysis of correlated pairs using a specialized antibody database. An approach for humanizing antibody homology models will be discussed. All the steps necessary for producing and assessing antibody homology models will be described.
Barbara Sander, Senior Applications Scientist, Chemical Computing Group (DE)
Biologics: Protein Alignments, Modeling and Docking
Protein Alignments and Superposition / Loop and Linker Modeling / Homology Modeling / Protein- Protein Docking
The course covers methods for aligning protein sequences, superposing structures, homology modeling fusion proteins and conducting protein-protein docking. In particular, an approach for aligning and superposing multiple structures will be described for determining structural and surface protein variations in relation to protein property modulation. A method for grafting and refining antibody CDR loops as well as using a knowledge-based approach to scFv fusion protein modeling using the MOE linker application will be described. An approach to generate homology models of a murine antigen structure from a human template as well as protein-protein docking of an antibody to an antigen will be discussed.
Sarah Witzke, Applications Scientist, Chemical Computing Group (UK)
February 24 - Scientific Presentations
CHAIR: Hubert Kettenberger
Digital Biologics Development Juan-Carlos Mobarec, Associate Director, AstraZeneca (UK)
Prediction of MHC Class II Antigen Presentation, Applications for The Assessment of Protein Drug Immunogenicity
Morten Nielsen, Professor, Department of Health Technology, The Technical University of Denmark (DK)
Methods for prediction of HLA antigen presentation have improved substantially over the last years in particular for HLA class II. This achievement is the result of a dedicated development of immunoinformatics methods refined to mine and extract information from complex MS-immunopeptidome data sets. In my talk, I will describe this development, illustrate how analysis and interpretation of MS-immunopeptidome data is reliant on immunoinformatics and demonstrate how the reliability of protein drug immunogenicity assessment can be boosted by the integral use of in-silico and in-vivo experimental techniques.
IgG1 Conformational Behavior: Elucidation of the N-glycosylation Role via Molecular Dynamics
Simona Saporiti, Research Assistant, Università degli studi di Milano (IT)
Simona Saporiti,1 Chiara Parravicini,1 Carlo Pergola,2 Uliano Guerrini,1 Mara Rossi,3 Fabio Centola,3 and Ivano Eberini4
1Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Milano, Italy; 2Analytical Development Biotech and 3Global Analytical Pharmaceutical Science and Innovation, Merck Serono S.p.A., Rome, Italy; 4Dipartimento di Scienze Farmacologiche e Biomolecolari & DSRC, Università degli Studi di Milano, Milano, Italy
Currently, monoclonal antibodies (mAbs) are the most used biopharmaceuticals for human therapy. One of the key aspects in their development is the control of effector functions mediated by the interaction between fragment crystallizable (Fc) and Fcg receptors, which is a secondary mechanism of action of biotherapeutics. N-glycosylation at the Fc portion can regulate these mechanisms, and much experimental evidence suggests that modifications of glycosidic chains can affect antibody binding to FcgRIIIa, consequently impacting the immune response. In this work, we try to elucidate via in silico procedures the structural role exhibited by glycans, particularly fucose, in mAb conformational freedom that can potentially affect the receptor recognition. By using adalimumab, a marketed IgG1, as a general template, after rebuilding its three-dimensional (3D) structure through homology modeling approaches, we carried out molecular dynamics simulations of three differently glycosylated species: aglycosylated, afucosylated, and fucosylated antibody. First, the use of AMBER10:EHT forcefield provided by MOE was validated, being a good parametrization method for the glycans used in our study. Then, trajectory analysis showed different dynamical behaviors and pointed out that sugars can influence the overall 3D structure of the antibody. As a result, we propose a putative structural mechanism by which the presence of fucose introduces conformational constraints in the whole antibody and not only in the Fc domain, preventing a conformation suitable for the interaction with the receptor. As secondary evidence, we observed a high flexibility of the antibodies that is translated into an asymmetric behavior of Fab portions shown by all the simulated biopolymers, making the dynamical asymmetry a new, to our knowledge, molecular aspect that may be further investigated. In conclusion, these findings can help understand the contribution of sugars on the structural architecture of mAbs, paving the way to novel strategies of pharmaceutical development.
Structure-Based Charge Calculations for Predicting Properties and Profiling Antibody Therapeutics
Nels Thorsteinson, Director of Biologics, Chemical Computing Group (CA)
In this work, we present a method for modeling antibodies and performing pH-dependent conformational sampling, which can enhance property calculations. Structure-based charge descriptors are evaluated for their predictive performance on recently published antibody pI, viscosity, and clearance data. From this, we devised four rules for therapeutic antibody profiling which address developability issues arising from hydrophobicity and charged-based solution behavior, PK, and the ability to enrich for those that are approved by the U.S. Food and Drug Administration. Differences in strategy for optimizing the solution behavior of human IgG1 antibodies versus the IgG2 and IgG4 isotypes and the impact of pH alterations in formulation are discussed.
Computational Prediction of Non-Specific Interactions of Antibodies
Michele Vendruscolo, Professor and Co-Director, Centre for Mis-folding Diseases, Dept. of Chemistry, University of Cambridge (UK)
There is an increasing interest in developing computational methods to predict the developability of antibodies in order to reduce the cost of antibody discovery programmes as well as late stage failures. Among the major causes of attrition in these programmes, poor specificity stands out, in part because it is challenging to assess it experimentally in a high-throughput manner. The availability of large databases of antibody sequences and data about antibody specificity is making it possible to develop computational methods to predict the specificity of antibodies from their amino acid sequences. I will present a novel method to achieve this goal.
From Data to Predictions: AI-Based Virtual Screening for Multi-Specific Protein Therapeutics
Norbert Furtmann, Head of Computational and High-Throughput Protein Engineering, Sanofi (DE)
Our novel, automated high-throughput engineering platform enables the fast generation of large panels of multi-specific variants (up to 10.000) giving rise to large data sets (more than 100.000 data points). By mining our data sets we were able to extract engineering patterns and to develop AI-based virtual screening workflows to guide the exploration of huge design spaces for multi-specific biologics drug discovery.
Using MOE's "Ensemble Protein Properties" in Early Developability Assessment of Therapeutic Antibodies
Hubert Kettenberger, Senior Principal Scientist Protein Engineering, Roche Diagnostics GmbH (DE)
During the lead identification and optimization phase, predicting and engineering biophysical properties of therapeutic antibody candidates is an important but challenging task. Properties such as aggregation propensity, folding stability, hydrophobicity, charge distribution, etc. are of particular interest because they may affect manufacturability, storage stability, pharmacokinetics and others. The presentation will focus on a comparison between different in-silico property calculation methods with MOE’s molecular dynamics-supported ensemble protein property method in terms of predictivity.
There is considerable enthusiasm in the pharmaceutical industry towards antibody-based biotherapeutics because they can bind diverse receptors highly selectively and often possess desirable pharmacology. Here, we have reviewed all approved antibody-based biotherapeutics from 1986 to Mid-2020 by gathering a wide range of public domain information on 89 marketed antibody-based biotherapeutics. Our analyses has documented ‘coming of age’ for therapeutic antibodies by revealing major trends in their establishment as the best-selling class of pharmaceuticals. For example, all therapeutic antibodies approved recently are either humanized or fully human, while the earlier ones were mostly chimeric. Before 2010, most therapeutic mAbs were developed to treat cancer and focused on merely fifteen receptors, with CD20 being the most common one. Over the next decade (2010-present), thanks to industrialization of antibody manufacturing technologies, their usage has blossomed to 38 additional targets distributed over 15 different therapeutic areas. Drug developers appear to be solidifying their choices in regard to source of antibodies, formulation, and routes of administration. The drivers for these trends are (a) minimize adverse effects such as immunogenicity; (b) improve the drug efficacy and pharmacology; and (c) improve patient convenience for greater compliance. However, humanized or fully human antibodies are merely 3% less likely to generate ADAs than chimeric ones. In spite of high concentration and greater likelihood for aggregation in vivo because of small volumes, the subcutaneous route of administration is ~4% less likely to generate ADAs than the intravenous one. These learnings should help us develop improved novel biotherapeutics.