Cross-species reactive monoclonal antibodies against the extracellular domains of the insulin receptor and IGF1 receptor

Ørstrup, Laura Hvidsten; Slaaby, Rita; Rasch, Morten Grønbech; Rasmussen, Nicolaj; Lund, Søren Peter; Brandt, Jakob; Schluckebier, G.; Wang, Zhe; Lützen, Anne; Pedersen, Thomas Åskov; Hvid, Henning; Hansen, Bo F.; Blume, Niels

doi:10.1016/j.jim.2018.11.014

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…Our scoring method is well suited for this task, as we demonstrated scoring of antibody variants for both antigen and non-specific binding and showed that these scores were predictive of co-optimal variants along the experimental Pareto frontier. This method could be expanded to select antibodies with various co-optimal sets of properties, including broadly neutralizing variants against multiple viruses ( Sok and Burton 2018 , Magar et al 2021 ) or species cross-reactive antibodies against orthologs of the same target for clinical translation ( Ørstrup et al 2019 , Li et al 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data

et al. 2023

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Motivation Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries. Results Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns. Availability All deep sequencing datasets and code to do the analyses presented within are available via https://github.com/Tessier-Lab-UMich/PSERM_paper Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Discussionmentioning

confidence: 99%

Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data

et al. 2023

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“…To explore the molecular mechanisms associated with INSR internalization, we conducted co-immunoprecipitation followed by mass-spectrometry to identify INSR interactors. We also used INSR knockout C2C12 myoblasts as an negative control and an INSR antibody that does not cross-react with Insulin-like growth factor 1 receptor (IGF1R) 23 to identify INSR interactors with higher specificity than any previously reported study. After filtering the detected proteins against the INSR knockout cell control lysates, we identified INSR and five high-confidence INSR-binding proteins (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…C2C12 myoblasts were serum-starved for 6 hours and were given 0 or 2 nM insulin for 15 min before collecting their lysates in RIPA buffer. INSRα antibody (NNC0276-3000, a gift from NovoNordisk compound sharing program) that does not cross-react with IGF1R 23 was covalently conjugated to Dynabeads M-270 Epoxy beads (Cat. #14301, Invitrogen) to immunoprecipitate INSR according to the manufacture’s protocol.…”

Section: Methodsmentioning

confidence: 99%

Insulin-dependent and -independent dynamics of insulin receptor trafficking in muscle cells

Cen

Rogalski

Abraham

et al. 2021

Preprint

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Insulin resistance contributes to type 2 diabetes and can be driven by hyperinsulinemia. Insulin receptor (INSR) internalization and cell-surface dynamics at rest and during insulin exposure are incompletely understood in muscle cells. Using surfacing labeling and live-cell imaging, we observed robust basal internalization of INSR in C2C12 myoblasts, without a robust effect of added insulin. Mass-spectrometry analysis of INSR-binding proteins identified potential molecular mechanisms associated with internalization. We confirmed known interactors, including IGF1R, but also identified underappreciated INSR-binding factors such as ANXA2. Protein-protein interaction network mapping suggested links between INSR and caveolin-mediated endocytosis. INSR interacted with both caveolin and clathrin heavy chain (CLTC) in mouse skeletal muscle and C2C12 myoblasts. Whole cell 2D super-resolution imaging revealed that high levels of insulin (20 nM) increased INSR colocalization with CAV1 but decreased its colocalization CLTC. Single particle tracking confirmed the plasma membrane colocalization of INSR with both over-expressed CAV1-mRFP and CLTC-mRFP. INSR tracks that colocalized with CAV1 tracks exhibited longer radii and lifetimes, regardless of insulin exposure, compared to non-colocalized tracks, whereas insulin further increased the lifetime of INSR/CLTC colocalized tracks. Overall, these data suggest that muscle cells utilize both CAV1 and CLTC-dependent pathways for INSR membrane dynamics and internalization.

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“…Our scoring method is well suited for this task, as we demonstrated scoring of antibody variants for both antigen and non-specific binding and showed that these scores were predictive of co-optimal variants along the experimental Pareto frontier. This method could be expanded to select antibodies with various co-optimal sets of properties, including broadly neutralizing variants against multiple viruses (Sok et al 2018;Magar et al 2021) or species cross-reactive antibodies against orthologs of the same target for clinical translation (Ørstrup et al 2019;Li et al 2020).…”

Section: Discussionmentioning

confidence: 99%

Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data

Smith

Case

Makowski

et al. 2023

Preprint

View full text Add to dashboard Cite

Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries. Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns.

show abstract

Cross-species reactive monoclonal antibodies against the extracellular domains of the insulin receptor and IGF1 receptor

Cited by 7 publications

References 23 publications

Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data

Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data

Insulin-dependent and -independent dynamics of insulin receptor trafficking in muscle cells

Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data

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