A sound strategy for homology modeling-based affinity maturation of a HIF-1α single-domain intrabody

A sound strategy for computer-aided binding affinity prediction was developed for in silico nanobody affinity maturation. Venn-intersection of multi-algorithm screening (VIMAS), an iterative computer-assisted nanobody affinity maturation virtual screening procedure, was designed. Homology modeling and protein docking methods were used to substitute the need for solution of a complex crystal structure, which is expanding the application of this platform. As a test case, an anti-HIF-1 nanobody, VHH212, was screened via a native ribosome display library with a 26.6 nM of KD value was used as the parent. A mutant with a 17.5-fold enhancement in binding affinity (1.52 nM) was obtained by using the VIMAS strategy. Furthermore, the protein-protein interaction of interface residues, which is important for binding affinity, was analyzed in-depth. Targeting HIF-1 can sensitize PDAC tumors to gemcitabine, which is a potential co-treatment method for pancreatic cancer patients. Under combined treatment, the cytotoxicity of gemcitabine on pancreatic cancer cell lines increased with the enhanced-affinity of an intrabody. Thus, this study provides a platform for universal, efficient and convenient in silico affinity maturation of nanobodies.