Mapping targetable sites on the human surfaceome for the design of novel binders

P Petra E. M. Balbi (Institute of Bioengineering, École Polytechnique Fédérale de Lausanne) A Ahmed Sadek (Institute of Bioengineering, École Polytechnique Fédérale de Lausanne) A Anthony Marchand T Ta-Yi Yu (Novo Nordisk A/S, AI & Digital Innovation, R&D) J Jovan Damjanovic (Novo Nordisk A/S, AI & Digital Innovation, R&D) S Sandrine Georgeon J Joseph Schmidt S Simone Fulle (Novo Nordisk A/S, AI & Digital Innovation, R&D) C Che Yang (Novo Nordisk A/S, AI & Digital Innovation, R&D) H Hamed Khakzad (Centre Inria de l’Université de Lorraine, LORIA, CNRS) B Bruno E. Correia

Abstract

The human cell surfaceome, integral to cell communication and disease mechanisms, presents a prime target for therapeutic intervention. De novo protein binder design against these cell surface proteins offers a promising yet underexplored strategy for drug development. However, the vast search space and limited data on natural or competitive binders have historically limited experimental success. In this study, we systematically analyzed the entire human surfaceome, identifying approximately 4,500 targetable sites and introducing potential binding seeds for initiating protein design applications. To validate these seeds, we implemented two experimental approaches (protein scaffolding and peptide cyclization) on three representative targets (FGFR2, IFNAR2, and HER3). Our results revealed a high success rate, showing that seeds provide valuable starting points for binder design against our identified targetable sites, as well as the need for constant improvements of computational protein design pipelines utilizing machine learning and physics-based methods. Additionally, we present SURFACE-Bind, an interactive database offering open access to all generated data. The high-throughput computational design methods and target-specific binder seeds established here pave the way for a new generation of targeted therapeutics for the human surfaceome.

Article Details

Volume / Issue Vol. 123, Issue 5
Published February 03, 2026
ISSN 0027-8424
Publisher National Academy of Sciences

Authors (11)

P

Petra E. M. Balbi

Institute of Bioengineering, École Polytechnique Fédérale de Lausanne

A

Ahmed Sadek

Institute of Bioengineering, École Polytechnique Fédérale de Lausanne

A

Anthony Marchand

T

Ta-Yi Yu

Novo Nordisk A/S, AI & Digital Innovation, R&D

J

Jovan Damjanovic

Novo Nordisk A/S, AI & Digital Innovation, R&D

S

Sandrine Georgeon

J

Joseph Schmidt

S

Simone Fulle

Novo Nordisk A/S, AI & Digital Innovation, R&D

C

Che Yang

Novo Nordisk A/S, AI & Digital Innovation, R&D

H

Hamed Khakzad

Centre Inria de l’Université de Lorraine, LORIA, CNRS

B

Bruno E. Correia