Biology subjects

Lev, K. L.

Publications and source records attributed to Lev, K. L..

2 recordsLinked to original sources

bioRxiv2025

Advancing (-)-Premarineosin A as a Potent Antimalarial Therapeutic via Metabolic Engineering and Late-Stage Derivatization

Diversification of structurally complex natural products remains a key challenge in the discovery of next-generation therapeutics. Premarineosin A, a potent and selective antimalarial natural product, is a promising yet underexplored scaffold due to limited availability and synthetic complexity. In this work, we overcome both barriers by coupling metabolic engineering with late-stage derivatization, enabling the first systematic exploration of the premarineosin A scaffold. Rational engineering of Streptomyces eitanensis, encoding a (-)-premarineosin A biosynthetic gene cluster, increased titers over 200-fold. Sustainable production of (-)-premarineosin A enabled a unique semi-synthetic and biocatalytic derivatization campaign. In this first structure-activity relationship study of premarineosin A, we accessed a suite of novel analogs, including a C12-brominated derivative with nanomolar potency (EC50 < 5 nM). This work establishes (-)-premarineosin A as a tractable and evolvable antimalarial scaffold, demonstrating how chemical biology approaches can unlock new structural and pharmacological space from complex microbial metabolites.

microbiology↗

bioRxiv2024

Ultra-high-throughput screening of antimicrobial combination therapies using a two-stage transparent machine learning model

Here, we present M2D2, a two-stage machine learning (ML) pipeline that identifies promising antimicrobial drug combinations, which are crucial for combating drug resistance. M2D2 addresses key challenges in drug combination discovery by predicting drug synergies using computationally generated drug-protein interaction data, thereby circumventing the need for expensive omics data. The model improves the accuracy of drug target identification using high-throughput experimental and computational methods via feedback between ML stages. M2D2s transparent framework provides mechanistic insights into drug interactions and was benchmarked against chemogenomics, transcriptomics, and metabolomics datasets. We experimentally validated M2D2 using high-throughput screening of 946 combinations of Food and Drug Administration (FDA)- approved drugs and antibiotics against Escherichia coli. We discovered synergy between a cerebrovascular drug and a widely used penicillin antibiotic and validated predicted mechanisms of action using genome-wide CRISPR inhibition screens. M2D2 offers a transparent ML tool for rapidly designing combination therapies and guides repurposing efforts while providing mechanistic insights.

systems biology↗