Application of modified yeast surface display technologies for non-Antibody protein engineering

Mei, Meng; Yu, Zhe; Peng, Wenfang; Yu, Chan; Ma, Lixin; Zhang, Guimin; Li, Yang

doi:10.1016/j.micres.2016.12.002

Cited by 29 publications

(25 citation statements)

References 100 publications

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“…We believe our prediction model may be enhanced to better predict binding reactivity with multiple (high, mid, low) rather than binary (reactive/non-reactive) classifications. It is highly likely that this model can be applied to other display platforms that use bio-panning as the selection process, such as yeast display library for fluorescence-activated cell sorting screening [54]. Recently, artificial intelligence has been applied to predict the physicochemical properties of antibody sequences [55][56][57][58][59] and/or optimize them [60][61][62].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Guided Prediction of Antigen-Reactive In Silico Clonotypes Based on Changes in Clonal Abundance through Bio-Panning

Yoo

Lee

Jung³

et al. 2020

Biomolecules

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c-Met is a promising target in cancer therapy for its intrinsic oncogenic properties. However, there are currently no c-Met-specific inhibitors available in the clinic. Antibodies blocking the interaction with its only known ligand, hepatocyte growth factor, and/or inducing receptor internalization have been clinically tested. To explore other therapeutic antibody mechanisms like Fc-mediated effector function, bispecific T cell engagement, and chimeric antigen T cell receptors, a diverse panel of antibodies is essential. We prepared a chicken immune scFv library, performed four rounds of bio-panning, obtained 641 clones using a high-throughput clonal retrieval system (TrueRepertoireTM, TR), and found 149 antigen-reactive scFv clones. We also prepared phagemid DNA before the start of bio-panning (round 0) and, after each round of bio-panning (round 1–4), performed next-generation sequencing of these five sets of phagemid DNA, and identified 860,207 HCDR3 clonotypes and 443,292 LCDR3 clonotypes along with their clonal abundance data. We then established a TR data set consisting of antigen reactivity for scFv clones found in TR analysis and the clonal abundance of their HCDR3 and LCDR3 clonotypes in five sets of phagemid DNA. Using the TR data set, a random forest machine learning algorithm was trained to predict the binding properties of in silico HCDR3 and LCDR3 clonotypes. Subsequently, we synthesized 40 HCDR3 and 40 LCDR3 clonotypes predicted to be antigen reactive (AR) and constructed a phage-displayed scFv library called the AR library. In parallel, we also prepared an antigen non-reactive (NR) library using 10 HCDR3 and 10 LCDR3 clonotypes predicted to be NR. After a single round of bio-panning, we screened 96 randomly-selected phage clones from the AR library and found out 14 AR scFv clones consisting of 5 HCDR3 and 11 LCDR3 AR clonotypes. We also screened 96 randomly-selected phage clones from the NR library, but did not identify any AR clones. In summary, machine learning algorithms can provide a method for identifying AR antibodies, which allows for the characterization of diverse antibody libraries inaccessible by traditional methods.

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Section: Discussionmentioning

confidence: 99%

Machine Learning-Guided Prediction of Antigen-Reactive In Silico Clonotypes Based on Changes in Clonal Abundance through Bio-Panning

Yoo

Lee

Jung³

et al. 2020

Biomolecules

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“…To avoid these limitations, other display methods have been developed to display antibodies on the surface of yeast or mammalian cells, which can be sorted by flow cytometry according to antigen specificity. This is known as yeast surface display, which has become a powerful engineering tool for displaying recombinant proteins on the surface of Saccharomyces cerevisiae via genetic fusion (Traxlmayr and Shusta, 2017;Mei et al, 2017;Andreu and Del Olmo, 2017;Sheehan and Marasco, 2015;Gera et al, 2013;Boder et al, 2012). Yeast surface display is a eukaryotic expression system with the capability to induce post translational modifications on recombinant antibodies.…”

Section: Phage/yeast Displaymentioning

confidence: 99%

Brief introduction of current technologies in isolation of broadly neutralizing HIV-1 antibodies

Sun

Yan

Tang³

et al. 2018

Virus Research

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HIV/AIDS has become a worldwide pandemic. Before an effective HIV-1 vaccine eliciting broadly neutralizing monoclonal antibodies (bnmAbs) is fully developed, passive immunization for prevention and treatment of HIV-1 infection may alleviate the burden caused by the pandemic. Among HIV-1 infected individuals, about 20% of them generated cross-reactive neutralizing antibodies two to four years after infection, the details of which could provide knowledge for effective vaccine design. Recent progress in techniques for isolation of human broadly neutralizing antibodies has facilitated the study of passive immunization. The isolation and characterization of large panels of potent human broadly neutralizing antibodies has revealed new insights into the principles of antibody-mediated neutralization of HIV. In this paper, we review the current effective techniques in broadly neutralizing antibody isolation.

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“…Both C and N terminal fusions with Aga2p were used for successful display on the yeast surface (Uchański et al, 2019; Wang et al, 2005). Multiple Aga2p fusion partners, and their libraries, were screened and tailored to fulfill a plethora of tasks such as affinity reagents development (Gai and Wittrup, 2007; Simeon and Chen, 2018a; Mata-Fink et al, 2013), substrate specificity modulation (Cohen-Khait and Schreiber, 2016; Zhang et al, 2013), protein stability engineering (Traxlmayr and Obinger, 2012), and also for directed evolution of enzymes (Mei et al, 2017; Szczupak and Alfonta, 2015). Still, despite all of these developments, yeast display selection of optimal binders is challenging and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

An enhanced yeast display platform demonstrates the binding plasticity under various selection pressures

Zahradník

Dey

Marciano

et al. 2020

Preprint

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Yeast surface display is popularin vitroevolution method. Here, we enhanced the method by multiple rounds of DNA and protein engineering, resulting in increased protein stabilities, surface expression, and enhanced fluorescence. The pCTcon2 yeast display vector was rebuild, introducing surface exposure tailored reporters – eUnaG2 and DnbALFA, creating a new platform of C and N terminal fusion vectors. In addition to gains in simplicity, speed, and cost, new applications were included to monitor protein surface exposure and protein retention in the secretion pathway. The enhanced methodologies were applied to investigatede-novoevolution of protein-protein interaction sites. Selecting binding from a mix of 6 protein-libraries towards two targets using high stringency selection led to the isolations of single high-affinity binders to each of the targets, without the need for high complexity libraries. Conversely, low-stringency selection resulted in the creation of many solutions for weak binding, demonstrating the plasticity of weakde-novointeractions.

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Application of modified yeast surface display technologies for non-Antibody protein engineering

Cited by 29 publications

References 100 publications

Machine Learning-Guided Prediction of Antigen-Reactive In Silico Clonotypes Based on Changes in Clonal Abundance through Bio-Panning

Machine Learning-Guided Prediction of Antigen-Reactive In Silico Clonotypes Based on Changes in Clonal Abundance through Bio-Panning

Brief introduction of current technologies in isolation of broadly neutralizing HIV-1 antibodies

An enhanced yeast display platform demonstrates the binding plasticity under various selection pressures

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