Stefano Menegatti

This paper develops a machine learning framework using Bayesian optimization to enhance the yield and purity of AAV vectors in gene therapy.

This paper develops an affinity-based purification process for adenoviral vectors using peptide-functionalized resins, achieving high yield and purity suitable for clinical applications.

The paper compares methods for quantifying GFPuv yield in E. coli bioreactors, identifying optimal harvest times and highlighting the strengths of imaging over other techniques.

This paper isolates and characterizes cyclic peptide binders using yeast surface display, showing they can bind targets like lysozyme and IL-17 with low micromolar to nanomolar affinities without chemical synthesis.