Priyadarshi, P. | Search bioRxiv

bioRxiv2026

Target-Specific Discovery of BMM_1567 Restores Aminoglycoside Activity Against Multidrug-Resistant Gram-Negative ESKAPE Pathogens

The global emergence of multidrug-resistant (MDR) ESKAPE pathogens has significantly reduced the effectiveness of existing antibiotics, highlighting the urgent need for new strategies to restore antimicrobial susceptibility. Here, we report the discovery and mechanism of BMM_1567, a peptide potentiator that enhances aminoglycoside efficacy against MDR pathogens. A genetically defined reporter-based screen identified BMM_1567 as a potent inhibitor of aminoglycoside resistance, potentiating spectinomycin activity against MDR Gram-negative ESKAPE isolates at low micromolar concentrations. Structural modeling and molecular dynamics simulations indicated that BMM_1567 interacts with residues lining the antibiotic-binding groove of aminoglycoside-modifying enzymes (ANT, APH, AAC), with highest affinity for ANT ({Delta}G_bind = -62.25 kcal/mol), suggesting competitive inhibition of substrate binding. Site-directed mutagenesis of key ANT residues identified critical amino acids involved in BMM_1567 binding, confirming their role in mediating spectinomycin potentiation. In murine abscess model using XDR E. coli, BMM_1567 in combination with spectinomycin significantly reduced bacterial burden and pro-inflammatory cytokine levels, comparable to colistin. Collectively, these findings establish BMM_1567 as a promising aminoglycoside potentiator that restores antibiotic activity against MDR pathogens through direct inhibition of resistance enzymes, while exhibiting in vivo efficacy and a remarkably low propensity for resistance development.

microbiology↗

bioRxiv2025

Protein Language Model Based Structure-guided Antibody Screening for Disordered Protein Targets

A crucial step in the pathogenesis of Parkinsons disease involves cell-to-cell transmission of -Synuclein proto-fibrils via endocytosis, driven primarily by the interaction of its disordered C-terminal peptide with domain 1 of Lymphocyte Activation Gene 3 (LAG3) neuronal receptors. High-affinity antibodies have been proposed as therapeutic modalities to delay this progression and subsequent amyloid formation. In our work, we develop an end-to-end computational pipeline to enable rapid screening of antibody sequences that have a high-affinity for the disordered C-terminal peptide of -Synuclein using no information of known binders. This de novo screening was enabled by a structural bioinformatics based in silico data generation pipeline combined with a deep learning framework. Our simple feed forward network model built upon sequence embeddings from a protein language model ranked the binding affinities ({Delta}G) of antibodies to -Synuclein with a high accuracy (Spearman{rho} = 0.86) when the training and the evaluation datasets contained sequences having some overlap in the complementarity determining regions (CDRs). However, for vastly different CDR sequences, a transformer encoder model trained using the antibody sequence embeddings showed a low Spearman rank correlation of{rho} = 0.18. The models have a mean Precision@100 of 38 and 12 respectively, significantly outperforming a random process. Overall, our work demonstrates a computational protocol for generating a high quality dataset of antibody-antigen complexes spanning a very large diversity in antibody sequences followed by training of a deep learning model for prediction of high-affinity antibody sequences for a specific protein target with no known binders.

bioinformatics↗