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.
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.
Bispecific Antibody-Based Molecules Show Multiple Faces: Developability Challenges and Multi-Dimensional Optimization
Guy Georges, Expert Scientist, Roche Diagnostics GmbH
The antibody developability criteria are evolving as well as the format complexity (multispecificity, multivalency, different conformations). Our aim is to be able to predict, design, register, produce and test molecules as fast as possible.
Over the past years, many prediction tools were introduced in our collection. Biophysical properties are predicted by analyzing sequences, computing parameters on structural models, and performing molecular dynamics. However, while progressing in analyzing single binders, the behavior of bi- or tri-specific formats in different flavors remains challenging. Shape, size, and linkers have their importance.
MOE's Antibody Database: Structural Bioinformatics for Antibody Research
Prabakaran Ponraj, Senior Principal Scientist, Sanofi
The MOE Antibody Database contains the three-dimensional (3D) structures of antibodies and antigen-antibody complexes derived from the Protein Data Bank (PDB) and provides powerful custom features enabling structural bioinformatics analysis useful for antibody research. We recently used the MOE Antibody Database to identify and analyze the 3D structures of antibodies and complexes that contain disulfide motifs in their CDR-H3s. We present the discovery of disulfide-bonded motifs (CXnC) in the CDR-H3s from the human antibody repertoire sequence data and compared them with structurally known motifs found in antibody structures. These results provide the fundamental understanding on the role of non-canonical cysteines in shaping the complex paratope diversity in human antibodies with potential applications in antibody design and engineering (Prabakaran and Chowdhury, Cell Reports 2020).
In-silico rational strategy of designing single-domain antibody drugs
Che Yang, Specialist Modelling Scientist, Novo Nordisk
Nanobodies, also known as single-domain antibodies, have emerged as a promising class of therapeutics due to their small size, stability, and versatility. However, optimization and design of nanobodies for drug development can be challenging. One of the main aspects is achieving high binding affinity and specificity for the target antigen while maintaining stability and ideal developability. To overcome these challenges, a combination of computational modeling, and experimental validation can be utilized to rationally screen large numbers of candidates and rapidly identify those with the desired properties. During this talk, we will present few use cases of applying general molecular modelling approach and emerging trends in design of nanobody modalities towards the development of nanobody-based therapeutics.
Predicting Antibody Developability Profiles Through Early Stage Discovery Screening
Essam Metwally, Principal Scientist, Merck Research Labs
Monoclonal antibodies play an increasingly important role for the development of new drugs across multiple therapy areas. The term ‘developability’ encompasses the feasibility of molecules to successfully progress from discovery to development via evaluation of their physicochemical properties. These properties include the tendency for self-interaction and aggregation, thermal stability, colloidal stability, and optimization of their properties through sequence engineering. Selection of the best antibody molecule based on biological function, efficacy, safety, and developability allows for a streamlined and successful CMC phase. An efficient and practical high-throughput developability workflow (100 s-1,000 s of molecules) implemented during early antibody generation and screening is crucial to select the best lead candidates. This involves careful assessment of critical developability parameters, combined with binding affinity and biological properties evaluation using small amounts of purified material (<1 mg), as well as an efficient data management and database system. Herein, a panel of 152 various human or humanized monoclonal antibodies was analyzed in biophysical property assays. Correlations between assays for different sets of properties were established. We demonstrated in two case studies that physicochemical properties and key assay endpoints correlate with key downstream process parameters. The workflow allows the elimination of antibodies with suboptimal properties and a rank ordering of molecules for further evaluation early in the candidate selection process. This enables any further engineering for problematic sequence attributes without affecting program timelines.
Charlotte Deane, Professor of Structural Bioinformatics, Dept. of Statistics, University of Oxford, and Chief Scientist, Biologics AI, ExScientia
Since the publication of Alphafold demonstrating a “solution” to the protein structure prediction problem the computational biology literature has been awash with novel methods and ideas that promise to revolutionise antibody discovery. In this talk I will discuss our work in areas from accurate rapid antibody structure prediction to binding site prediction and discuss ways to properly assess the wealth of new machine learning methods that are becoming available.
Antibody Paratope States Improve Structure Prediction to Elucidate Antibody-Antigen Recognition
Monica Fernández-Quintero, Postgraduate Research Scientist, Leopold-Franzens Universität Innsbruck
Describing an antibody’s binding site using only one single static structure limits the understanding of the antibody’s function. This limitation is even more pronounced when no experimentally determined structure is available or the crystal structure is distorted by packing effects, which can result in misleading antibody paratope structures. To improve antibody structure prediction and to take the strongly correlated loop and interface movements into account, antibody paratopes should be described as interconverting states in the solution. Therefore, the definition of kinetically and functionally relevant states can be successfully used to improve the accuracy and enhance the understanding of antibody-antigen recognition.
Accelerating Therapeutics Discovery with Disruptive Digital Innovation
Peter Clark, Head, Computational Sciences and Engineering, Janssen R&D
Significant advances in computational methods and hardware accelerated scientific computing have enabled the dawn of a new era of medicine in which lifesaving therapeutic molecules can be designed and optimized with greater speed and precision than ever before. At Johnson & Johnson, we are leveraging data from across the pharmaceutical value chain, manifested in a knowledge graph to inform novel computational, deep learning models to drive innovation and disrupt the therapeutic research and development lifecycle; building and leveraging our collective institutional knowledge across therapeutic programs and indications in order to inform novel AI/ML models to accelerate the development of lifesaving therapies for patients across the globe.
Introduction to Prescient Design: Lab in the Loop - large molecule drug discovery from optimization to de novo design
Franziska Seeger, Principal Scientist, Prescient Design / Genentech
Prescient Design, a Genentech accelerator, is developing integrated methods for optimizing antibody affinity and multiple developability parameters. Key to our efforts are a close integration of antibody engineering, machine learning, and structural biology. Our ML frameworks allow us to integrate later stages of optimization into the earliest stages of discovery, while our high throughput experimental systems allow rapid improvement of all methods and molecules.
This integration starts with the integration of people and scientific culture and ends with tightly integrated computational and experimental systems.
During this talk, I will give an overview of our generative modeling approach, multi-objective optimization, and active learning framework.