Computer-guided design of Z domain peptides with improved inhibition of VEGF

Computational protein design is becoming increasingly helpful in the development of new protein therapeutics with enhanced efficacy, specificity, and minimal side effects, for precise modulation of biological pathways. In vascular biology, the interaction between vascular endothelial growth factor A (VEGFA) and its receptors (VEGFR1-R3) is a pivotal process underlying blood vessel growth. Dysregulation of this pathway contributes to diseases such as cancer and diabetic retinopathy. Existing VEGFA inhibitors are effective but have limitations, driving interest in peptide-based therapeutics. Peptide inhibitors offer advantages, including reduced toxicity, improved formulation flexibility, and enhanced stability. This study leverages computational tools, particularly ProteinMPNN and Rosetta, to design optimized peptide-based VEGFA inhibitors. Building on the existing peptide templates mini-Z-1 and Z-1-2, new sequences were computationally predicted and experimentally validated. A novel peptide with improved affinity (KD = 6.2 {micro}M) compared to mini-Z-1 (KD = 9.3 {micro}M) was found, requiring only one round of design and testing. The integration of ProteinMPNN and Rosetta enabled a rapid and cost-effective pipeline for designing potent VEGFA inhibitors, underscoring the potential of computational peptide design in developing next-generation therapeutics targeting angiogenesis-dependent diseases.