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)
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.
Freya (Klepsch) Trasischker, Senior Applications Scientist, Chemical Computing Group (AT)
Anette Henriksen, Principal Scientist, Novo Nordisk A/S (DK)
The development of protein therapeutics can be time consuming and cumbersome and often requires a trade-off between enhancing the biological effect of a protein and its stability and solubility. More specifically, the biophysical properties often highlighted as important for the developability of proteins are aggregation propensity, viscosity, adsorption, stability and solubility.
This presentation discusses how in-vitro assay guided optimization of antibody developability correlates with developability indicators obtained using in-silico tools.
Platformization of Multi-Specific Protein Engineering: Learning from High-Throughput Screening Data
Norbert Furtmann, Section Head Data Science & Computational Design, Biologics Research, Sanofi Deutschland GmbH (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). Here we report on our visualization and data analysis workflows to improve the understanding of our complex molecules and guide the engineering process.
Despite the fact that anti-TNF-alpha antibodies have significantly improved the treatment of rheumatic diseases, anti-drug antibody immune response pushes a fraction of patients to discontinue treatment due to ineffectiveness or adverse reactions.
Here is presented how rational design strategy can be used to modify variable region of the fully human IgG1 Adalimumab and reduce immunogenicity risk while retaining TNF-alpha binding activity.
Ultimately, germlining engineering improved mAb biophysical properties, leading to generation of Adalimumab biobetter.
Towards More Accurate Property Prediction and Developability Profiling for Biologics
David Thompson, Senior Applications Scientist, Chemical Computing Group (US)
The use of descriptors averaged over an ensemble of molecular conformations has improved the accuracy of property predictions key to biologics’ utility and developability as therapeutics. Using an increasing database of clinical stage therapeutics, we present some useful guidelines for developable biologicals, similar to the Lipinski rules for small molecules. We also describe recent efforts to develop realistic descriptors for use in reactive liability prediction.
The Cellular Membrane as a Major Platform for Drug Interaction with the Cell
Emad Tajkhorshid, Director of NIH Biotechnology Center for Macromolecular Modeling and Bioinformatics, Beckman Institute, University of Illinois (US)
The cellular membrane is the first cellular compartment that is encountered by any exogenous molecular species entering the body. The cellular membrane and how the molecular species interact with it determine both pharmacokinetics and pharmacodynamic properties of the molecules. Hosting a large number of functionally diverse proteins associated with this key metabolic compartment, the membrane not only directly controls the traffic of various molecules in and out of the cell, it also participates in important processes such as signal transduction and chemical processing of incoming molecular species. In this talk, I will present a number of cases where details of interaction of small molecular species such as drugs with the membrane, which are often experimentally inaccessible, have been studied using advanced molecular simulation techniques. Examples include systems where the partitioning of the drug in the membrane constitutes a key step for its final biological function, e.g., binding to and interacting with a protein associated with the membrane. I will also discuss some of the novel approaches we have developed to characterize binding of ligands to different functional intermediates of protein, which we refer to as extended ensemble docking. These examples demonstrate that membrane is not only important for the overall distribution of drugs and other small molecules into different compartments of the body, it may also play a key role in determining the efficiency and the mode of interaction of the drug with its target protein.
Site-specific bioconjugation technologies are frequently employed to generate homogeneous
antibody-drug conjugates (ADCs) and are generally considered superior to stochastic approaches like
lysine coupling. However, most of the technologies developed so far require undesired manipulation
of the antibody sequence or its glycan structures. Microbial transglutaminase is enabling efficient,
site-specific conjugation of drug-linker constructs to position HC-Q295 of native, fully glycosylated
IgG-type antibodies. Using computational protein design with Rosetta, we engineered a stable and
soluble enzyme with higher activity. This allows to generate ADCs in their native form with excellent
stability in vitro as well as strong efficacy in vitro and in vivo.
First-generation gene therapies for Haemophilia A exhibited large patient-to-patient variations in FVIII levels and a lack of durability that may at least in part be caused by the large size of the FVIII expression cassette, resulting in vector genome sizes exceeding the optimal capacity of the wild type AAV genome.
Here we present the design and preliminary characterization of FLT210, a next generation AAV-FVIII cassette, which we believe possesses the attributes required to become the best-in-class vector to treat Haemophilia A patients. It includes the optimisation to the ‘FVIII-SQ’ protein and coding sequence by in-silico design and the enhancement of the level of FVIII expression and secretion by de-novo design of the regulatory regions.
Intrinsic Physicochemical Properties of Currently Marketed Biologic Medicines
Sandeep Kumar, Senior Research Fellow (Biotherapeutics) and Group Leader, Boehringer Ingelheim (US)
In an application of Biopharmaceutical Informatics, we recently collected amino acid sequences of currently market antibody-based biologic medicines. These sequences were used to derive their homology-based structural models. Availability of both sequence and structural models of these biotherapeutics afforded us an opportunity to analyze their physicochemical attributes from a perspective of developability. These analyses have resulted in a profile that can be used to estimate ‘medicine-likeness’ of the biologic drug candidates currently in discovery and development. In this talk, I will present this profile and describe its potential uses.