Abstracts
Training-free framework to design protein-based binders
Presenter: Dr. Kateryna Maksymenko (kmaksymenko@tuebingen.mpg.de)
Authors:
Dr. Kateryna Maksymenko¹; Prof. Julia Skokowa; Prof. Andrei Lupas; Dr. Mohammad ElGamacy
¹ Max Planck Institute for Biology
Designing specific and high-affinity protein binders remains an attractive yet challenging goal. Recently, the combination of restrained diffusion, message-passing neural networks, and structure prediction models has shown significant improvements in binder design. However, as deep learning tools keep advancing, they remain constrained by the structural and binding patterns learnt from natural complexes. Therefore, we sought to develop a training-free solution to the binder design problem.
Our complementarity-centric framework first identifies suitable design scaffolds and binding poses utilizing an analytical surface fingerprinting method. Next, the selected scaffold is mutated to carve a binding site de novo. Sequence design is carried out using the in-house developed Damietta software (Grin et al., NAR, 2024), which employs tensorized energy calculations. Finally, design candidates undergo rigorous in silico validation through accelerated molecular dynamics.
We applied this framework to design binders against two oncogenic targets: vascular endothelial growth factor and interleukin-7 receptor-α. Experimental characterization of a small set of designed proteins (ranging from 2 to 8 per target) identified nanomolar binders for each target site. By relying on first principles, our generalizable framework is well-suited for handling ligands that are underrepresented in the PDB, and we are currently adapting it to design protein-based binders for small-molecule targets.