Biology subjects

Ferla, M.

Publications and source records attributed to Ferla, M..

3 recordsLinked to original sources

bioRxiv2025

Leveraging crystallographic fragment screening, algorithmic merging and digital chemistry to target NCS-1 protein-protein interactions for treatment of neurological disorders

Efficient drug discovery relies on workflows that integrate structural insights with rapid and cost-effective exploration of chemical space. Here, we present a data-driven fragment-based lead discovery approach to target Neuronal Calcium Sensor 1 (NCS-1) protein-protein interactions (PPIs). This study represents a complete implementation of a single high-value design-make-test-analyze cycle that directly yields compounds with micromolar affinity with the potential to modulate NCS-1 interactions with key targets, including the G-protein chaperone Ric-8A and the dopamine D2 and cannabinoid CB1 receptors. X-ray crystallographic fragment screening (CFS) revealed diverse interaction patterns within the NCS-1 hydrophobic crevice. Algorithmically guided fragment evolution and automated synthesis enabled the rapid generation of over 250 derivatives, with biophysical validation using LC-MS and Grating-coupled interferometry. Structural analyses highlighted key pharmacophores, with selected compounds exhibiting favorable drug-like properties and potential blood-brain barrier penetration, making them promising candidates for neurodegenerative and neurodevelopmental disorders. Our results demonstrate the feasibility of accelerated hit-to-lead development at synchrotrons, demonstrating a robust, scalable platform for PPI-targeting drug discovery. The generated chemically diverse scaffolds provide a strong foundation for future therapeutic optimization.

biophysics↗

bioRxiv2020

COVID Moonshot: Open Science Discovery of SARS-CoV-2 Main Protease Inhibitors by Combining Crowdsourcing, High-Throughput Experiments, Computational Simulations, and Machine Learning

We report the results of the COVID Moonshot, a fully open-science, crowd sourced, structure-enabled drug discovery campaign targeting the SARS-CoV-2 main protease. We discovered a non-covalent, non-peptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>840 ligand-bound X-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2,400 compounds) for this campaign were shared rapidly and openly, creating a rich open and IP-free knowledgebase for future anti-coronavirus drug discovery.

biochemistry↗

bioRxiv2020

Pervasive cooperative mutational effects on multiple catalytic enzyme traits emerge via long-range conformational dynamics

Multidimensional fitness landscapes provide insights into the molecular basis of laboratory and natural evolution. Yet such efforts are rare and focus only on limited protein families and a single enzyme trait, with little concern about the relationship between protein epistasis and conformational dynamics. Here, we report the first multiparametric fitness landscape for a cytochrome P450 monooxygenase that was engineered for the regio- and stereoselective hydroxylation of a steroid. We developed a computational program to automatically quantify non-additive effects among all possible mutational pathways, finding pervasive cooperative sign and magnitude epistasis on multiple catalytic traits. By using quantum mechanics and molecular dynamics simulations, we show that these effects are modulated by long-range interactions in loops, helices and beta-strands that gate the substrate access channel allowing for optimal catalysis. Our work highlights the importance of conformational dynamics on epistasis in an enzyme involved in secondary metabolism and offers lessons for engineering P450s.

evolutionary biology↗