Mapping targetable sites on the human surfaceome for the design of novel binders
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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 high-quality binder seeds tailored for 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, emphasizing the precision and therapeutic potential of these seeds, 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.
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Optimized seeds were selected based on several criteria: computed binding energy (ddG), shape complementarity, the number of interface hydrogen bonds, the number of buried unsatisfied polar atoms, and the number of atoms in contact with the small molecule.
Could you consider for instance, including a table or brief flowchart showing the specific cutoff values (e.g., ddG < -X kcal/mol) and how many designs passed or failed at each step would help readers replicate your pipeline. This more detailed account of each criterion (and its relative weight in eliminating or selecting designs) would be helpful.
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Additionally, all scripts for executing the workflow on a large scale, defining unbound-state scores for binding sites, analyzing the results, and optimizing binders resulting from MaSIF-seed-search can be found at https://github.com/hamedkhakzad/SURFACE-Bind.
Congratulations on this comprehensive work. The referenced GitHub repository, a Zenodo data deposit, and the SURFACE-Bind database as key resources for replicating and extending the findings. However, the GitHub link appears inactive, the Zenodo record is unavailable, and the database link does not load a page. As these resources are essential for reproducibility and broader community use, please ensure that working links are provided or that readers are informed of the timeline for making them accessible.
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