EAGER: Computational Design of Peptide Ligands for the Bioseparation of "Fab" Antibody Fragments

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Developing a drug that will cure cancer is the holy grail of the pharmaceutical industry. One strategy is to produce antibodies that mimic, yet enhance, the body's natural ability to bind to, and eliminate, foreign agents (antigens) that lead to chronic and fatal diseases, including cancer. Improving upon the body's natural ability to self-heal requires producing high quantities of highly purified antibodies that are designed to seek out specific antigens while leaving healthy cells unharmed. Producing high purity, targeted synthetic antibodies requires isolation and growth of a culture of cells that produces that antibody, followed by recovery of the antibody from the cell culture. This process leads to monoclonal antibodies, reflecting that one (mono) immune cell able to produce the antibody of interest was "cloned" (reproduced and grown) in large quantity. Recovery and purification of monoclonal antibodies from the complex media that comprises the cell culture is difficult, and thus expensive. Furthermore, monoclonal antibodies are large and complex biological molecules, features that lead to sluggish penetration of the monoclonal antibody into biological tissues. Identifying the particular fragment of the antibody that binds to the targeted antigen and producing only that fragment of the antibody (Fab), thus increases tissue penetration and the potency of the treatment. Fragmentation addresses penetration concerns, but recovery of Fabs from isolated cell cultures remains a challenge. Current methods for Fab recovery are expensive and use potentially-immunogenic proteins. The goal of this project is thus to computationally design and synthesize new peptide ligands (short protein-derived molecules) that will selectively recover targeted Fabs, and can easily be produced in a laboratory rather than derived from cell cultures. This computational project will screen short novel peptides for affinity, selectivity, and reversible binding to FAbs. The screening will use a previously developed search algorithm that identifies short peptide sequences (less than 25 amino acids) that have high affinity for a target Fab at neutral pH, but release the Fab at mildly acidic conditions (4.0 pH 5.0). Moreover, the computations will identify short peptide sequences that distinguish between Fabs with kappa and lambda light chains, and allow fractionation of kappa-lambda mixtures. The project is 'high risk' and exploratory because a good initial guess for the sequence of the peptide binder is currently unavailable. The computational predictions will be validated with laboratory synthesis and testing. The project will train a post doctoral researcher, and enable further efforts to broaden participation of women and under-represented minorities in STEM via mentoring, seminars, and outreach efforts. If successful, this project will lead to the development of short chain (15 amino acids) synthetic peptides for Fab recovery and selective differentiation of kappa and lambda Fabs. These two outcomes represent a new paradigm for antibody purification, with foreseeable extension to fractionation of other antibodies, assay development, investigation of the individual therapeutic powers of antibody subclasses for immune protection, disease diagnosis, and design of novel antibody therapeutics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.