Computational Recipe for Designing Antibodies against the Ebola Virus
A conceptual basis for antiviral therapy is to deliver a synthetic antibody that binds to a viral surface protein, and thus prevents the virus from deploying its cell-entry mechanism. The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. In this paper, we devised a computational recipe to predict both the viral escape mutations and the possible inhibitory synthetic antibodies. We combined bioinformatics, structural biology, and molecular dynamics (MD) simulations to explore the most likely viral mutations and the candidate antibodies that can inhibit those escape mutations. Specifically, using the crystal structures of the Sudan and Zaire Ebola viral GPs in complex to their respective antibodies (ABs), we have performed an extensive set of MD simulations, both on the wild-type structures and on a large array of additional complexes designed and generated through combinatorial mutations. We discovered that our methods enabled the successful redesign of antibody sequences to essentially all likely glycoprotein mutations. Our findings and the computational methodology developed here for general antibody design can facilitate therapy of current and possibly next generations of viruses. Significance of the ManuscriptThis manuscript has high significance both methodologically and in potential biomedical application. In methodology, the manuscript combines molecular dynamics, Monte Carlo calculations, and bioinformatics in a novel way to simulate the evolutionary arms race between an evolving viral coat protein and a counter-evolving antibody against the virus. This simulation is shown to provide a method for designing a synthetic antibody against the newly emerging viral strains. This work is done in the context of ongoing work in other laboratories in which cells can be induced to produce synthetic antibodies and those synthetic antibodies can be edited (via, for example, CRISPR) to have an arbitrary sequence in the region that binds the viral coat protein. Putting those experimental methods together with the computational methods we present in this paper has the potential to provide a important approach to produce antibodies-on-demand against evolving viruses.