Chalamalasetty, A. | bioRxiv Science

bioRxiv2026

De novo minibinders targeting RH5 achieve nanomolar inhibition of blood-stage malaria replication

Malaria kills {approx}600,000 people annually despite recently deployed vaccines. The parasite's blood-stage invasion of erythrocytes, driven by the essential, non-redundant interaction between Plasmodium falciparum RH5 and erythrocyte basigin, remains a leading therapeutic target. Here, we designed de novo mini-proteins (minibinders) to occlude this interface. From {approx}1,500 computationally generated designs, we synthesized 18 candidates ranging from 9.3 to 16.9 kDa which were easily expressed in E. coli as soluble, well-folded proteins. Of these, 7 minibinders blocked blood-stage replication of P. falciparum asexual stage parasites with greater potency than {approx}300nM IC50. Three potent candidate minibinders, mb-5, mb-7, and mb-21, were confirmed to bind RH5 via SPR and BLI and block the merozoite-to-ring transition in stage-synchronized parasite progression assays, with CryoEM of minibinder-RH5 complex confirming the predicted binding at RH5-basigin interface. Although pharmacological challenges remain, our data demonstrate that potent inhibitors of parasite growth, rivaling existing antimalarials, can be designed rather than discovered via extensive screening. Our data highlight the potential for artificial intelligence to change the development landscape of new antimalarial therapies.

microbiology↗

bioRxiv2026

TubercuProbe: A Cross-Attention Graph-Sequence Model for Cross-Species Chemoproteomic Discovery in Mycobacterium tuberculosis

Activity-based protein profiling (ABPP) and residue-specific chemoproteomics have transformed human chemical biology, yet applying these approaches to pathogens remains limited by biosafety constraints, low throughput, and the absence of reusable atlases. We present TubercuProbe, a cross-species machine learning framework that leverages large-scale human chemoproteomic knowledge to prioritize compound-protein interactions in Mycobacterium tuberculosis (Mtb) and other pathogens. Our model integrates a graph isomorphism network (GINE) for ligand encoding with frozen ESM-C (600M) protein embeddings via bidirectional cross-attention. Trained on >2M ChEMBL compound-protein pairs (predominantly human targets), TubercuProbe achieves R2=0.77 (MSE=0.45) for continuous affinity prediction and transfers effectively to binary cysteine reactivity prediction (CysDB AUPRC=0.63). Ablation studies reveal that pretrained features are highly transferable across freezing strategies ({Delta}AUPRC<0.03), suggesting the model captures fundamental protein-molecule interaction patterns. As a case study, we prioritize cysteine-reactive electrophiles and molecular glues for three Mtb virulence proteins (PtpB, SapM, Rv3671c), providing candidate probes for prospective ABPP validation. Orthogonal comparison with Boltz-2 structure predictions shows moderate correlation (Pearson r{approx}0.69). TubercuProbe provides a lightweight, sequence-driven first-pass ranker that enables pre-experimental prioritization--reducing the time, cost, and experimental burden of chemoproteomic discovery in biosafety-restricted systems. We discuss extensions toward multitask learning that jointly predicts non-covalent binding and covalent reactivity, recognizing that effective covalent probes must both reach their target and react once there.

biochemistry↗