SUMO - In Silico Sequence Assessment Using Multiple Optimization Parameters

Evers, A.; Malhotra, S.; Bolick, W.-G.; Najafian, A.; Borisovska, M.; Warszawski, S.; Fomekong Nanfack, Y.; Kuhn, D.; Rippmann, F.; Crespo, A.; Sood, V.

doi:10.1101/2022.11.19.517175

bioRxiv · 10.1101/2022.11.19.517175

SUMO - In Silico Sequence Assessment Using Multiple Optimization Parameters

Evers, A.·Malhotra, S.·Bolick, W.-G.·Najafian, A.·Borisovska, M.·Warszawski, S.·Fomekong Nanfack, Y.·Kuhn, D.·Rippmann, F.·Crespo, A.·Sood, V.

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Abstract

To select the most promising screening hits from antibody and VHH display campaigns for subsequent in-depth profiling and optimization, it is highly desirable to assess and select sequences on properties beyond only their binding signals from the sorting process. In addition, developability risk criteria, sequence diversity and the anticipated complexity for sequence optimization are relevant attributes for hit selection and optimization. Here, we describe an approach for the in silico developability assessment of antibody and VHH sequences. This method not only allows for ranking and filtering multiple sequences with regard to their predicted developability properties and diversity, but also visualizes relevant sequence and structural features of potentially problematic regions and thereby provides rationales and starting points for multi-parameter sequence optimization.

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Evers, A., Malhotra, S., Bolick, W.-G., Najafian, A., Borisovska, M., Warszawski, S., Fomekong Nanfack, Y., Kuhn, D., Rippmann, F., Crespo, A., Sood, V.. 2022-11-22. SUMO - In Silico Sequence Assessment Using Multiple Optimization Parameters. https://doi.org/10.1101/2022.11.19.517175

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