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bioRxiv2022

Inhibition of a critical malaria host-pathogen interaction by a computationally designed inhibitor targeting Plasmodium vivax DBP

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. To address this need, we developed nanomolar inhibitor that could serve as a therapeutic and a diagnostic. 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. TeaserDe novo designed proteins present a new alternative for the development of therapeutics. They can be small, highly stable and easily manufactured. Here we designed a potential new therapeutic to inhibit entry of Plasmodium vivax into red blood cells by interfering its interactions of surface displayed DBP molecules with the host receptor DARC.

bioengineering↗

bioRxiv2021

Sampling of Structure and Sequence Space of Small Protein Folds

Nature only samples a small fraction in sequence space, yet many more amino acid combinations can fold into stable proteins. Furthermore, small structural variations in a single fold, which may only be a few amino acids different from the next homolog, define their molecular function. Hence, to design proteins with novel molecular 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 high shape diversity. We designed and evaluated about 30,000 de novo protein designs of 7 different folds. Among these designs, about 6,200 stable proteins were identified, with predicted structures having first-of-its-kind minimalized thioredoxin. Obtained data revealed more protein folding rules, such as helix connecting loops, which were in nature. Beyond providing a resource database for protein engineering, our data presents a large training data set for machine learning. We developed a high-accuracy classifier to predict the stability of our designed proteins. The methods and the wide range of new protein shapes provide a basis for the design of new protein function without compromising stability.

bioengineering↗