Sensitive recovery of recombinant antibody clones after their in silico identification within NGS datasets

Spiliotopoulos, Anastasios; Owen, Jonathan P.; Maddison, Ben C.; Dreveny, Ingrid; Rees, Helen C.; Gough, Kevin C.

doi:10.1016/j.jim.2015.03.005

Cited by 24 publications

(16 citation statements)

References 10 publications

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“…Using currently available NGS tools, it is not possible to sequence the entire scFv gene, which is about 750 bp in length, without error. Our study and the other previous studies 6, 13 have proved that the scFv gene can be amplified by PCR primers designed based on HCDR3/FR4 sequences. However, there is always the possibility of cross-priming between clones during the PCR process.…”

Section: Discussionsupporting

confidence: 72%

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Next-generation sequencing enables the discovery of more diverse positive clones from a phage-displayed antibody library

et al. 2017

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Phage display technology provides a powerful tool to screen a library for a binding molecule via an enrichment process. It has been adopted as a critical technology in the development of therapeutic antibodies. However, a major drawback of phage display technology is that because the degree of the enrichment cannot be controlled during the bio-panning process, it frequently results in a limited number of clones. In this study, we applied next-generation sequencing (NGS) to screen clones from a library and determine whether a greater number of clones can be identified using NGS than using conventional methods. Three chicken immune single-chain variable fragment (scFv) libraries were subjected to bio-panning on prostate-specific antigen (PSA). Phagemid DNA prepared from the original libraries as well as from the Escherichia coli pool after each round of bio-panning was analyzed using NGS, and the heavy chain complementarity-determining region 3 (HCDR3) sequences of the scFv clones were determined. Subsequently, through two-step linker PCR and cloning, the entire scFv gene was retrieved and analyzed for its reactivity to PSA in a phage enzyme immunoassay. After four rounds of bio-panning, the conventional colony screening method was performed for comparison. The scFv clones retrieved from NGS analysis included all clones identified by the conventional colony screening method as well as many additional clones. The enrichment of the HCDR3 sequence throughout the bio-panning process was a positive predictive factor for the selection of PSA-reactive scFv clones.

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Section: Discussionsupporting

confidence: 72%

“…Then, the entire scFv gene was retrieved by inverse PCR using primers based on the HCDR3/FR4 sequences. 13 …”

Section: Introductionmentioning

confidence: 99%

Next-generation sequencing enables the discovery of more diverse positive clones from a phage-displayed antibody library

et al. 2017

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“…Currently, over 80 antibodies derived from phage display libraries have entered clinical studies with 10 of these granted marketing authorization [44]. Since Ravn U et al demonstrated the potential for NGS analysis in the phage-displayed antibody repertoire in 2010, numerous groups have leveraged similar strategies for discovering antibodies reactive to specific antigens [16,[45][46][47][48][49][50][51][52][53]. The next hurdle to overcome after the identification of in silico antibody sequences in NGS data was the low-throughput nature of chemically synthesizing all antibody sequences and individually testing their reactivity.…”

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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“…Our latest approach to overcome this problem, described here, has clear advantages compared to previous methods. For instance, compared to the previous methodology described by our group and others, based on inverse PCR, 15,16,31 the present strategy requires the design and synthesis of asingle primer, no purification of the inverse PCR products and subsequent enzymatic ligation to reconstitute aworking plasmid, and no bacterial transformation and subsequent cloning into yeast cells for validation of the binding activity. The isolation limit when using 60 base primers is~0.14%, decreasing to 0.044% when longer primers are used.…”

Section: Discussionmentioning

confidence: 99%

“…Since generating clones from NGS sequence represents the primary bottleneck in the full exploitation of NGS for invitro antibody selection, we 15 and others 16 have pioneered the development of efficient methods to rescue even low abundance scFv sequences identified after NGS analysis. Here, we describe arapid, novel, straightforward method to generate antibody clones identified by NGS that can reach deep into the abundance rank.…”

Section: Introductionmentioning

confidence: 99%

Exploiting next-generation sequencing in antibody selections – a simple PCR method to recover binders

Ferrara

Teixeira

Naranjo

et al. 2020

mAbs

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Antibody discovery using invitro display technologies such as phage and/or yeast display has become acornerstone in many research and development projects, including the creation of new drugs for clinical use. Traditionally, after the selection phase, random clones are isolated for binding validation and Sanger sequencing. More recently, next-generation sequencing (NGS) technology has allowed deeper insight into the antibody population after aselection campaign, enabling the identification of many more specific binders. However, this approach only provides the DNA sequences of potential binders, the properties of which need to be fully elucidated by obtaining corresponding clones and expressing them for further validation. Here we present arapid novel method to harvest potential clones identified by NGS that uses asimple PCR and yeast recombination approach. The protocol was tested in selections against three different targets and was able to recover clones at an abundance level that would be impractical to identify using traditional methods.

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Sensitive recovery of recombinant antibody clones after their in silico identification within NGS datasets

Cited by 24 publications

References 10 publications

Next-generation sequencing enables the discovery of more diverse positive clones from a phage-displayed antibody library

Next-generation sequencing enables the discovery of more diverse positive clones from a phage-displayed antibody library

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

Exploiting next-generation sequencing in antibody selections – a simple PCR method to recover binders

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