Therapeutic monoclonal antibodies bind to specific regions of proteins called epitopes, which elicit cellularresponses that treat or cure disease. Discovering therapeutic antibodies traditionally requires laborious andexpensive screening experiments, so computational approaches that select which antibodies bind an epitopebest and have the most desirable pharmaceutical properties are in high demand. Structure-based antibodydesign is also important to the modern drug discovery and development process. This approach requires a high-resolution quaternary (3D) protein complex structure, whose experimental determination is often a slow processthat is not always successful. Protein structure and binding interface prediction algorithms are poised to impacthuman health by accelerating the construction of high-confidence structural models of drug targets andbiopharmaceuticals, which will help identify new therapeutic strategies. However, the current algorithms arelimited in their ability to distinguish stronger-binding antibodies from weaker ones, which is preventing thediscovery of broad classes of therapeutics. In addition, technologies are needed to predict if a candidate antibodywill fail as early as possible in the development process. With improvements in simulating removal of molecularliabilities without damaging function, computer-aided antibody design can be used to lower drug developmentcosts and focus experiments on the most promising drug candidates. Here we propose to advance antibody discovery by developing highly accurate software tools built on thesuccess of DNASTAR's NovaFold Antibody program for antibody structure prediction, NovaDock for flexibleprotein-protein docking, and Lasergene Protein Design for protein engineering. The aims of the project focus 1)on developing more accurate and effective immune complex (an interacting antibody and antigen) structurepredictions through better modeling of the challenging complementarity-determining regions (CDR), which playa critical role in antibody affinity and selectivity; and 2) on predicting antibody sequences that reduce chemicaland energetic liabilities that prove detrimental to an antibody's manufacturing process or therapeutic effect in apatient. In particular, overall predictive capability will be improved by incorporating computational accelerationtechniques to support the virtual screening of tens of thousands of antibody sequences. Finally, and for the firsttime, this project will develop a "virtual immune system" to approach human antibody discovery, where antibodieswill be modeled from germline sequences and selected for best recognizing an antigen of interest. The overallproject goal is to deliver an advanced antibody screening pipeline that is powerful, accurate, and produces fastresults, which will accelerate antibody discovery by enabling detailed and accurate immune complex structurepredictions and structure-based liability detection at a high-throughput scale.
Public Health Relevance Statement: Antibodies are invaluable biotechnology tools for diagnosing and treating human diseases; they have been used
to detect drugs, toxins, hormones, or pathogens; perform tissue typing; suppress organ transplant rejection; and
therapeutically treat viral infections, cancer, and autoimmune diseases. In this project, we will develop a pipeline
to replace a large portion of costly, time-consuming experimental work required today in antibody drug discovery
with a software pipeline based on improvement in and integration of protein structure prediction, protein-protein
docking and protein engineering tools we currently provide to the market. Our proposed pipeline will be poised
to accelerate antibody drug development and reduce costs of antibody drug discovery by 80 - 90%, literally
revolutionizing the way antibody discovery and development is performed by biotech and pharmaceutical
companies and third-party contract research organizations.
Project Terms: 