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.

A method for generating user-defined circular single-stranded DNA from plasmid DNA using Golden Gate intramolecular ligation.

Construction of user-defined long circular single stranded DNA (cssDNA) and linear single stranded DNA (lssDNA) is important for various biotechnological applications. Many current methods for synthesis of these ssDNA molecules do not scale to multikilobase constructs. Here we present a robust methodology for generating user-defined cssDNA employing Golden Gate assembly, a nickase, and exonuclease degradation. Our technique is demonstrated for three plasmids with insert sizes ranging from 2.1 to 3.4 kb, requires no specialized equipment, and can be accomplished in 5 h with a yield of 33%-43% of the theoretical. To produce lssDNA, we evaluated different CRISPR-Cas9 cleavage conditions and reported a 52 ± 8% cleavage efficiency of cssDNA. Thus, our current method does not compete with existing protocols for lssDNA generation. Nevertheless, our protocol can make long, user-defined cssDNA readily available to biotechnology researchers.

Low-cost, open-source cell culture chamber for regulating physiologic oxygen levels.

The physiological oxygen levels for several mammalian cell types in vivo are considered to be hypoxic (low oxygen tension), but the vast majority of in vitro mammalian cell culture is conducted at atmospheric oxygen levels of around 21%. In order to understand the impact of low oxygen environments on cells, oxygen levels need to be regulated during in vitro culture. Two common methods for simulating a hypoxic environment are through the regulation of gas composition or chemical induction. Chemically mimicking hypoxia can have adverse effects such as reducing cell viability, making oxygen regulation in cell culture chambers crucial for long-term culture. However, oxygen-regulating cell culture incubators and commercial hypoxia chambers may not always be a viable option due to cost and limited customization. Other low-cost chambers have been developed, but they tend to lack control systems or are fairly small scale. Thus, the objective of this project was to design and develop a low-cost, open-source, controllable, and reproducible hypoxia chamber that can fit inside a standard cell culture incubator. This design allows for the control of O2 between 1 and 21%, while maintaining CO2 levels at 5%, as well as monitoring of temperature, pressure, and relative humidity. Testing showed our hypoxia chamber was able to maintain CO2 levels at 5% and hypoxic O2 levels at 1% and 5% for long-term cell culture. This simple and easy-to-manufacture design uses off the shelf components, and the total material cost was $832.47 (USD).