An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries.

Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of nine different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries.

Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of ten different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.

Facile Assembly of Combinatorial Mutagenesis Libraries Using Nicking Mutagenesis.

Combinatorial mutagenesis is a method where multiple user-defined mutations are encoded at defined positions in a sequence. Combinatorial mutagenic libraries can be used in a variety of applications including evaluating fundamental questions about molecular evolution, directed evolution workflows for enzyme engineering, and in better understanding of biological processes like antibody affinity maturation. Here, we show a method of combinatorial mutagenesis utilizing the template-based nicking mutagenesis with several modifications. We show an example for generating a combinatorial library with 14 mutated positions, a total of 16,384 library variants, and a protocol for the generation of large, user-defined combinatorial libraries. The reader can use this protocol to create such libraries in 2 days.

Optimization of multi-site nicking mutagenesis for generation of large, user-defined combinatorial libraries.

Generating combinatorial libraries of specific sets of mutations are essential for addressing protein engineering questions involving contingency in molecular evolution, epistatic relationships between mutations, as well as functional antibody and enzyme engineering. Here we present optimization of a combinatorial mutagenesis method involving template-based nicking mutagenesis, which allows for the generation of libraries with >99% coverage for tens of thousands of user-defined variants. The non-optimized method resulted in low library coverage, which could be rationalized by a model of oligonucleotide annealing bias resulting from the nucleotide mismatch free-energy difference between mutagenic oligo and template. The optimized method mitigated this thermodynamic bias using longer primer sets and faster annealing conditions. Our updated method, applied to two antibody fragments, delivered between 99.0% (32451/32768 library members) to >99.9% coverage (32757/32768) for our desired libraries in 2 days and at an approximate 140-fold sequencing depth of coverage.

One-shot identification of SARS-CoV-2 S RBD escape mutants using yeast screening.

The potential emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) escape mutants is a threat to the efficacy of existing vaccines and neutralizing antibody (nAb) therapies. An understanding of the antibody/S escape mutation landscape is urgently needed to preemptively address this threat. Here we describe a rapid method to identify escape mutants for nAbs targeting the S receptor binding site. We identified escape mutants for five nAbs, including three from the public germline class VH3-53 elicited by natural coronavirus disease 2019 (COVID-19) infection. Escape mutations predominantly mapped to the periphery of the angiotensin-converting enzyme 2 (ACE2) recognition site on the RBD with K417, D420, Y421, F486, and Q493 as notable hotspots. We provide libraries, methods, and software as an openly available community resource to accelerate new therapeutic strategies against SARS-CoV-2.

One-shot identification of SARS-CoV-2 S RBD escape mutants using yeast screening.

The potential emergence of SARS-CoV-2 Spike (S) escape mutants is a threat to reduce the efficacy of existing vaccines and neutralizing antibody (nAb) therapies. An understanding of the antibody/S escape mutations landscape is urgently needed to preemptively address this threat. Here we describe a rapid method to identify escape mutants for nAbs targeting the S receptor binding site. We identified escape mutants for five nAbs, including three from the public germline class VH3-53 elicited by natural COVID-19 infection. Escape mutations predominantly mapped to the periphery of the ACE2 recognition site on the RBD with K417, D420, Y421, F486, and Q493 as notable hotspots. We provide libraries, methods, and software as an openly available community resource to accelerate new therapeutic strategies against SARS-CoV-2.

User-defined single pot mutagenesis using unamplified oligo pools.

User-defined mutagenic libraries are fundamental for applied protein engineering workflows. Here we show that unamplified oligo pools can be used to prepare site saturation mutagenesis libraries from plasmid DNA with near-complete coverage of desired mutations and few off-target mutations. We find that oligo pools yield higher quality libraries when compared to individually synthesized degenerate oligos. We also show that multiple libraries can be multiplexed into a single oligo pool, making preparation of multiple libraries less expensive and more convenient. We provide software for automatic oligo pool design that can generate mutagenic oligos for saturating or focused libraries.