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Biology subjects

Samad, T. S.

Publications and source records attributed to Samad, T. S..

3 recordsLinked to original sources

Microbial-inspired antidotes to repurpose toxic compounds as antibiotics

Antibiotic-resistant (AMR) bacterial infections are a major global health threat. Despite the critical need for new antimicrobials, progress is constrained by protracted development timelines, as well as the requirement for chemical novelty to avoid cross-resistance. Although advances in high-throughput screening, genome mining, and machine learning have greatly accelerated antimicrobial discovery, insufficient separation between antibacterial efficacy and host toxicity remains a bottleneck, precluding the clinical development of many promising compounds. Here, we establish a generalizable, two-component strategy to engineer antimicrobial safety and mobilize otherwise inaccessible chemical space for antimicrobial therapy, using calicheamicin, a potent cytotoxin with unacceptable host toxicity, as a proof of concept. In the first arm, we engineer a conditionally-active drug conjugate that limits calicheamicin activity to infected tissue, thereby reducing systemic toxicity. In the second arm, we co-administer a re-engineered self-resistance enzyme from Micromonospora echinospora, the natural producer of calicheamicin, as an "antidote" to neutralize calicheamicin present outside of infected tissue, further mitigating off-target toxicity. The conditionally-active conjugate exhibits activity against Gram-negative and Gram-positive pathogens in response to a protease present within the infected microenvironment. When delivered in combination with the antidote, antibacterial efficacy is maintained while off-target toxicity is reduced in mouse models of Gram positive and negative bacterial pneumonia. We anticipate that our dual strategy, which engineers, rather than selects for enhanced drug safety, by combining conditional drug activity with antidote-driven neutralization of off-target effects, provides a generalizable framework for mobilizing other promising but toxic compounds as antimicrobials.

bioengineering↗

Deep learning guided design of protease substrates

Proteases, a class of enzymes that play critical roles in health and disease, exert their function through the cleavage of peptide bonds. Identifying substrates that are efficiently and selectively cleaved by target proteases is essential for studying protease activity and for harnessing their activity in protease-activated diagnostics and therapeutics. However, the vast design space of possible substrates (c.a. 2010 unique amino acid combinations for a 10-mer peptide) and the limited accessibility of high-throughput activity profiling tools hinder the speed and success of substrate design. We present CleaveNet, an end-to-end AI pipeline for the design of protease substrates. Applied to matrix metalloproteinases, CleaveNet enhances the scale, tunability, and efficiency of substrate design. CleaveNet generates peptide substrates that exhibit sound biophysical properties and capture not only well-established but also novel cleavage motifs. To enable precise control over substrate design, CleaveNet incorporates a conditioning tag that enables generation of peptides guided by a target cleavage profile, enabling targeted design of efficient and selective substrates. CleaveNet-generated substrates were validated experimentally through a large-scale in vitro screen, even in the challenging case of designing highly selective substrates for MMP13. We envision that CleaveNet will accelerate our ability to study and capitalize on protease activity, paving the way for new in silico design tools across enzyme classes.

biochemistry↗

Engineering Multiplexed Synthetic Breath Biomarkers as Diagnostic Probes

Breath biopsy is emerging as a rapid and non-invasive diagnostic tool that links exhaled chemical signatures with specific medical conditions. Despite its potential, clinical translation remains limited by the challenge of reliably detecting endogenous, disease-specific biomarkers in breath. Synthetic biomarkers represent an emerging paradigm for precision diagnostics such that they amplify activity-based biochemical signals associated with disease fingerprints. However, their adaptation to breath biopsy has been constrained by the limited availability of orthogonal volatile reporters that are detectable in exhaled breath. Here, we engineer multiplexed breath biomarkers that couple aberrant protease activities to exogenous volatile reporters. We designed novel intramolecular reactions that leverage protease-mediated aminolysis, enabling the sensing of a broad spectrum of proteases, and that each release a unique reporter in breath. This approach was validated in a mouse model of influenza to establish baseline sensitivity and specificity in a controlled inflammatory setting and subsequently applied to diagnose lung cancer using an autochthonous Alk-mutant model. We show that combining multiplexed reporter signals with machine learning algorithms enables tumor progression tracking, treatment response monitoring, and detection of relapse after 30 minutes. Our multiplexed breath biopsy platform highlights a promising avenue for rapid, point-of-care diagnostics across diverse disease states.

bioengineering↗