RESUMO
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.
Assuntos
Sequenciamento de Nucleotídeos em Larga Escala , Fragmentos Fab das Imunoglobulinas , Mutação , Humanos , Fragmentos Fab das Imunoglobulinas/genética , Fragmentos Fab das Imunoglobulinas/química , Fragmentos Fab das Imunoglobulinas/imunologia , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Engenharia de Proteínas/métodos , Anticorpos Neutralizantes/imunologia , Anticorpos Neutralizantes/química , Anticorpos Neutralizantes/genética , Regiões Determinantes de Complementaridade/genética , Regiões Determinantes de Complementaridade/química , Afinidade de Anticorpos , Antígenos/imunologia , Antígenos/genéticaRESUMO
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.
RESUMO
The yeast Saccharomyces cerevisiae is commonly used to interrogate and screen protein variants and to perform directed evolution studies to develop proteins with enhanced features. While several techniques have been described that help enable the use of yeast for directed evolution, there remains a need to increase their speed and ease of use. Here we present yDBE, a yeast diversifying base editor that functions in vivo and employs a CRISPR-dCas9-directed cytidine deaminase base editor to diversify DNA in a targeted, rapid, and high-breadth manner. To develop yDBE, we enhanced the mutation rate of an initial base editor by employing improved deaminase variants and characterizing several scaffolded guide constructs. We then demonstrate the ability of the yDBE platform to improve the affinity of a displayed antibody scFv, rapidly generating diversified libraries and isolating improved binders via cell sorting. By performing high-throughput sequencing analysis of the high-activity yDBE, we show that it enables a mutation rate of 2.13 × 10-4 substitutions/bp/generation over a window of 100 bp. As yDBE functions entirely in vivo and can be easily programmed to diversify nearly any such window of DNA, we posit that it can be a powerful tool for facilitating a variety of directed evolution experiments.