Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries.

Therapeutic antibodies are an important and rapidly growing drug modality. However, the design and discovery of early-stage antibody therapeutics remain a time and cost-intensive endeavor. Here we present an end-to-end Bayesian, language model-based method for designing large and diverse libraries of high-affinity single-chain variable fragments (scFvs) that are then empirically measured.

A dataset comprised of binding interactions for 104,972 antibodies against a SARS-CoV-2 peptide.

The dataset presented here contains quantitative binding scores of scFv-format antibodies against a SARS-CoV-2 target peptide collected via an AlphaSeq assay that can be used in the development and benchmarking of machine learning models. Starting from three seed sequences identified from a phage display campaign using a human naïve library, four sets of 29,900 antibodies were designed in silico by creating all k = 1 mutations and random k = 2 and k = 3 mutations throughout the complementary-determining regions (CDRs). Of the 119,600 designs, 104,972 were successfully built in to the AlphaSeq library and target binding was subsequently measured with 71,384 designs resulting in a predicted affinity value for at least one of the triplicate measurements.

Massively multiplexed affinity characterization of therapeutic antibodies against SARS-CoV-2 variants.

Antibody therapies represent a valuable tool to reduce COVID-19 deaths and hospitalizations. Multiple antibody candidates have been granted emergency use authorization by the Food and Drug Administration and many more are in clinical trials. Most antibody therapies for COVID-19 are engineered to bind to the receptor-binding domain (RBD) of the SARS-CoV-2 Spike protein and disrupt its interaction with angiotensin-converting enzyme 2 (ACE2).

Defective nucleotide-dependent assembly and membrane fusion in Mfn2 CMT2A variants improved by Bax.

Mitofusins are members of the dynamin-related protein family of large GTPases that harness the energy from nucleotide hydrolysis to remodel membranes. Mitofusins possess four structural domains, including a GTPase domain, two extended helical bundles (HB1 and HB2), and a transmembrane region. We have characterized four Charcot-Marie-Tooth type 2A-associated variants with amino acid substitutions in Mfn2 that are proximal to the hinge that connects HB1 and HB2.

A catalytic domain variant of mitofusin requiring a wildtype paralog for function uncouples mitochondrial outer-membrane tethering and fusion.

Mitofusins (Mfns) are dynamin-related GTPases that mediate mitochondrial outer-membrane fusion, a process that is required for mitochondrial and cellular health. In Mfn1 and Mfn2 paralogs, a conserved phenylalanine (Phe-202 (Mfn1) and Phe-223 (Mfn2)) located in the GTPase domain on a conserved β strand is part of an aromatic network in the core of this domain. To gain insight into the poorly understood mechanism of Mfn-mediated membrane fusion, here we characterize a Mitofusin mutant variant etiologically linked to Charcot-Marie-Tooth syndrome.