Inhibition of a malaria host-pathogen interaction by a computationally designed inhibitor.

Malaria is a substantial global health burden with 229 million cases in 2019 and 450,000 deaths annually. Plasmodium vivax is the most widespread malaria-causing parasite putting 2.5 billion people at risk of infection. P. vivax has a dormant liver stage and therefore can exist for long periods undetected. Its blood-stage can cause severe reactions and hospitalization. Few treatment and detection options are available for this pathogen. A unique characteristic of P. vivax is that it depends on the Duffy antigen/receptor for chemokines (DARC) on the surface of host red blood cells for invasion. P. vivax employs the Duffy binding protein (DBP) to bind to DARC. We first de novo designed a three helical bundle scaffolding database which was screened via protease digestions for stability. Protease-resistant scaffolds highlighted thresholds for stability, which we utilized for selecting DARC mimetics that we subsequentially designed through grafting and redesign of these scaffolds. The optimized design small helical protein disrupts the DBP:DARC interaction. The inhibitor blocks the receptor binding site on DBP and thus forms a strong foundation for a therapeutic that will inhibit reticulocyte infection and prevent the pathogenesis of P. vivax malaria.

Sampling of structure and sequence space of small protein folds.

Nature only samples a small fraction of the sequence space that can fold into stable proteins. Furthermore, small structural variations in a single fold, sometimes only a few amino acids, can define a protein's molecular function. Hence, to design proteins with novel functionalities, such as molecular recognition, methods to control and sample shape diversity are necessary. To explore this space, we developed and experimentally validated a computational platform that can design a wide variety of small protein folds while sampling shape diversity. We designed and evaluated stability of about 30,000 de novo protein designs of eight different folds. Among these designs, about 6,200 stable proteins were identified, including some predicted to have a first-of-its-kind minimalized thioredoxin fold. Obtained data revealed protein folding rules for structural features such as helix-connecting loops. Beyond serving as a resource for protein engineering, this massive and diverse dataset also provides training data for machine learning. We developed an accurate classifier to predict the stability of our designed proteins. The methods and the wide range of protein shapes provide a basis for designing new protein functions without compromising stability.