AI-based antibody discovery platform identifies novel, diverse, and pharmacologically active therapeutic antibodies against multiple SARS-CoV-2 strains.

Background: We are entering a new era of antibody discovery and optimization where machine learning (ML) processes will become indispensable for the design and development of therapeutics.

Methods: We have constructed a Humanoid Antibody Library for the discovery of therapeutics that is an initial step towards leveraging the utility of artificial intelligence and ML. We describe how we began our validation of the library for antibody discovery by isolating antibodies against a target of pandemic concern, SARS-CoV-2.

Adapting the chemical unfolding assay for high-throughput protein screening using experimental and spectroscopic corrections.

The chemical unfolding (denaturation) assay can be used to calculate the change in the Gibbs free energy of unfolding, ΔG, and inflection point of unfolding, to collectively inform on molecule stability. Here, we evaluated methods for calculating the ΔG across 23 monoclonal antibody sequence variants. These methods are based on how the measured output (intrinsic fluorescence intensity) is treated, including utilizing (a) a single wavelength, (b) a ratio of two wavelengths, (c) a ratio of a single wavelength to an area, and (d) a scatter correction plus a ratio of a single wavelength to an area.

CHO cell production and sequence improvement in the 13C6FR1 anti-Ebola antibody.

From March 2014 through February 2015, the Ebola virus spread rapidly in West Africa, resulting in almost 30,000 infections and approximately 10,000 deaths. With no approved therapeutic options available, an experimental antibody cocktail known as ZMapp™ was administered to patients on a limited compassionate-use basis. The supply of ZMapp™ was highly constrained at the time because it was in preclinical development and a novel production system (tobacco plants) was being used for manufacturing.

Remediating agitation-induced antibody aggregation by eradicating exposed hydrophobic motifs.

Therapeutic antibodies must encompass drug product suitable attributes to be commercially marketed. An undesirable antibody characteristic is the propensity to aggregate. Although there are computational algorithms that predict the propensity of a protein to aggregate from sequence information alone, few consider the relevance of the native structure.

Inhibition of kinesin motility by ADP and phosphate supports a hand-over-hand mechanism.

The motor protein kinesin couples a temporally periodic chemical cycle (the hydrolysis of ATP) to a spatially periodic mechanical cycle (movement along a microtubule). To distinguish between different models of such chemical-to-mechanical coupling, we measured the speed of movement of conventional kinesin along microtubules in in vitro motility assays over a wide range of substrate (ATP) and product (ADP and inorganic phosphate) concentrations. In the presence and absence of products, the dependence of speed on [ATP] was well described by the Michaelis-Menten equation.