Biology subjects

Pellegrino, J.

Publications and source records attributed to Pellegrino, J..

3 recordsLinked to original sources

bioRxiv2026

Real-world results from a Machine Learning-guided, phenotypic High-Throughput Screen for novel antibiotics

Antimicrobial resistance is an urgent global health threat, with over 2.8 million multidrug-resistant infections killing over 35,000 annually in the US. Machine Learning (ML) has emerged as a potential solution to improve efficiency of antibiotic high-throughput screens (HTS). We report ML-guided high-throughput screening against E. coli. Large-scale Learning-to-Rank models were trained on public and proprietary datasets to maximize phenotypic inhibition and minimize human cell cytotoxicity. We evaluated several pre-plated compound libraries and a set of "cherry-picked", structurally novel compounds. We screened against a hyperpermeable lptD- mutant, followed by hit confirmation, profiling, cytotoxicity counter-screening, and MOA determination. Results demonstrated a doubled hit rate and 3X fewer toxic hits. Additionally, activity improved against both Wild Type E. coli and the lptD- mutant. ML models showed robust predictive power on structurally dissimilar compounds. The combination of large-scale HTS, ML innovation, and both library-wise selection and cherry-picking strategies distinguishes this study in the antibiotic discovery field.

microbiology↗

bioRxiv2020

Robust Sequence Determinants of α-Synuclein Toxicity in Yeast Implicate Membrane Binding

Protein conformations are shaped by cellular environments, but how environmental changes alter the conformational landscapes of specific proteins in vivo remains largely uncharacterized, in part due to the challenge of probing protein structures in living cells. Here, we use deep mutational scanning to investigate how a toxic conformation of -synuclein, a dynamic protein linked to Parkinsons disease, responds to perturbations of cellular proteostasis. In the context of a course for graduate students in the UCSF Integrative Program in Quantitative Biology, we screened a comprehensive library of -synuclein missense mutants in yeast cells treated with a variety of small molecules that perturb cellular processes linked to -synuclein biology and pathobiology. We found that the conformation of -synuclein previously shown to drive yeast toxicity--an extended, membrane-bound helix--is largely unaffected by these chemical perturbations, underscoring the importance of this conformational state as a driver of cellular toxicity. On the other hand, the chemical perturbations have a significant effect on the ability of mutations to suppress -synuclein toxicity. Moreover, we find that sequence determinants of -synuclein toxicity are well described by a simple structural model of the membrane-bound helix. This model predicts that -synuclein penetrates the membrane to constant depth across its length but that membrane affinity decreases toward the C terminus, which is consistent with orthogonal biophysical measurements. Finally, we discuss how parallelized chemical genetics experiments can provide a robust framework for inquiry-based graduate coursework.

biochemistry↗

bioRxiv2019

Computational design of a modular protein sense/response system

Sensing and responding to signals is a fundamental ability of living systems, but despite remarkable progress in computational design of new protein structures, there is no general approach for engineering arbitrary new protein sensors. Here we describe a generalizable computational strategy for designing sensor/actuator proteins by building binding sites de novo into heterodimeric protein-protein interfaces and coupling ligand sensing to modular actuation via split reporters. Using this approach, we designed protein sensors that respond to farnesyl pyrophosphate, a metabolic intermediate in the production of valuable compounds. The sensors are functional in vitro and in cells, and the crystal structure of the engineered binding site matches the design model with atomic accuracy. Our computational design strategy opens broad avenues to link biological outputs to new signals.\n\nOne Sentence SummaryAn engineering strategy to design modular synthetic signaling systems that respond to new small molecule inputs.

biophysics↗