Computational counterselection identifies nonspecific therapeutic biologic candidates.

Effective biologics require high specificity and limited off-target binding, but these properties are not guaranteed by current affinity-selection-based discovery methods. Molecular counterselection against off targets is a technique for identifying nonspecific sequences but is experimentally costly and can fail to eliminate a large fraction of nonspecific sequences. Here, we introduce computational counterselection, a framework for removing nonspecific sequences from pools of candidate biologics using machine learning models. We demonstrate the method using sequencing data from single-target affinity selection of antibodies, bypassing combinatorial experiments. We show that computational counterselection outperforms molecular counterselection by performing cross-target selection and individual binding assays to determine the performance of each method at retaining on-target, specific antibodies and identifying and eliminating off-target, nonspecific antibodies. Further, we show that one can identify generally polyspecific antibody sequences using a general model trained on affinity data from unrelated targets with potential affinity for a broad range of sequences.

Ultra-high-diversity factorizable libraries for efficient therapeutic discovery.

The successful discovery of novel biological therapeutics by selection requires highly diverse libraries of candidate sequences that contain a high proportion of desirable candidates. Here we propose the use of computationally designed factorizable libraries made of concatenated segment libraries as a method of creating large libraries that meet an objective function at low cost. We show that factorizable libraries can be designed efficiently by representing objective functions that describe sequence optimality as an inner product of feature vectors, which we use to design an optimization method we call stochastically annealed product spaces (SAPS). We then use this approach to design diverse and efficient libraries of antibody CDR-H3 sequences with various optimized characteristics.

Protective anti-prion antibodies in human immunoglobulin repertoires.

Prion immunotherapy may hold great potential, but antibodies against certain PrP epitopes can be neurotoxic. Here, we identified > 6,000 PrP-binding antibodies in a synthetic human Fab phage display library, 49 of which we characterized in detail. Antibodies directed against the flexible tail of PrP conferred neuroprotection against infectious prions. We then mined published repertoires of circulating B cells from healthy humans and found antibodies similar to the protective phage-derived antibodies. When expressed recombinantly, these antibodies exhibited anti-PrP reactivity. Furthermore, we surveyed 48,718 samples from 37,894 hospital patients for the presence of anti-PrP IgGs and found 21 high-titer individuals. The clinical files of these individuals did not reveal any enrichment of specific pathologies, suggesting that anti-PrP autoimmunity is innocuous. The existence of anti-prion antibodies in unbiased human immunological repertoires suggests that they might clear nascent prions early in life. Combined with the reported lack of such antibodies in carriers of disease-associated PRNP mutations, this suggests a link to the low incidence of spontaneous prion diseases in human populations.

Antibody complementarity determining region design using high-capacity machine learning.

MOTIVATION: The precise targeting of antibodies and other protein therapeutics is required for their proper function and the elimination of deleterious off-target effects. Often the molecular structure of a therapeutic target is unknown and randomized methods are used to design antibodies without a model that relates antibody sequence to desired properties. RESULTS: Here, we present Ens-Grad, a machine learning method that can design complementarity determining regions of human Immunoglobulin G antibodies with target affinities that are superior to candidates derived from phage display panning experiments. We also demonstrate that machine learning can improve target specificity by the modular composition of models from different experimental campaigns, enabling a new integrative approach to improving target specificity. Our results suggest a new path for the discovery of therapeutic molecules by demonstrating that predictive and differentiable models of antibody binding can be learned from high-throughput experimental data without the need for target structural data. AVAILABILITY AND IMPLEMENTATION: Sequencing data of the phage panning experiment are deposited at NIH's Sequence Read Archive (SRA) under the accession number SRP158510. We make our code available at https://github.com/gifford-lab/antibody-2019. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MAA868, a novel FXI antibody with a unique binding mode, shows durable effects on markers of anticoagulation in humans.

A large unmet medical need exists for safer antithrombotic drugs because all currently approved anticoagulant agents interfere with hemostasis, leading to an increased risk of bleeding. Genetic and pharmacologic evidence in humans and animals suggests that reducing factor XI (FXI) levels has the potential to effectively prevent and treat thrombosis with a minimal risk of bleeding. We generated a fully human antibody (MAA868) that binds the catalytic domain of both FXI (zymogen) and activated FXI. Our structural studies show that MAA868 traps FXI and activated FXI in an inactive, zymogen-like conformation, explaining its equally high binding affinity for both forms of the enzyme. This binding mode allows the enzyme to be neutralized before entering the coagulation process, revealing a particularly attractive anticoagulant profile of the antibody. MAA868 exhibited favorable anticoagulant activity in mice with a dose-dependent protection from carotid occlusion in a ferric chloride-induced thrombosis model. MAA868 also caused robust and sustained anticoagulant activity in cynomolgus monkeys as assessed by activated partial thromboplastin time without any evidence of bleeding. Based on these preclinical findings, we conducted a first-in-human study in healthy subjects and showed that single subcutaneous doses of MAA868 were safe and well tolerated. MAA868 resulted in dose- and time-dependent robust and sustained prolongation of activated partial thromboplastin time and FXI suppression for up to 4 weeks or longer, supporting further clinical investigation as a potential once-monthly subcutaneous anticoagulant therapy.