Karla Montserrat Castro Gilabert

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Track: Machine Learning in Protein Science De Novo Design of Viral Epitope Mimetics with Non-Regular Structural Features

Karla Montserrat Castro Gilabert, Bruno Emanuel Ferreira De Sousa Correia, Anastassia Andreevna Vorobieva, Jue Wang

WILEY2023

Computational design of vaccine immunogens

Karla Montserrat Castro Gilabert, Bruno Emanuel Ferreira De Sousa Correia, Andreas Scheck, Shuhao Xiao

Computational protein engineering has enabled the rational design of customized proteins, which has propelled both sequence-based and structure-based immunogen engineering and delivery. By discerning antigenic determinants of viral pathogens, computational methods have been implemented to successfully engineer representative viral strains able to elicit broadly neutralizing responses or present antigenic sites of viruses for focused immune responses. Combined with improvements in customizable nanoparticle design, immunogens are multivalently displayed to enhance immune responses. These rationally designed immunogens offer unique and powerful approaches to engineer vaccines for pathogens, which have eluded traditional approaches.

CURRENT BIOLOGY LTD2022

Scaffolding protein functional sites using deep learning

Karla Montserrat Castro Gilabert, Bruno Emanuel Ferreira De Sousa Correia, Jue Wang, Basile Isidore Martin Wicky, Wei Yang

The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, "constrained hallucination," optimizes sequences such that their predicted structures contain the desired functional site. The second approach, "inpainting," starts from the functional site and fills in additional sequence and structure to create a viable protein scaffold in a single forward pass through a specifically trained RoseTTAFold network. We use these two methods to design candidate immunogens, receptor traps, metalloproteins, enzymes, and protein-binding proteins and validate the designs using a combination of in silico and experimental tests.

AMER ASSOC ADVANCEMENT SCIENCE2022