Gampp, O. | bioRxiv Science

bioRxiv2025

FragmentScope - exploring the fragment space with learned surface representations

Exploring fragment chemical space for ligand design remains a major challenge in early stage drug discovery. This task is particularly challenging due to the small size, low specificity, and weak binding affinities of low molecular weight (MW) fragments. We present FragmentScope, a computational pipeline that uses learned protein surface fingerprints to guide fragment placement and small molecule generation. By using a contrastive learning model trained on protein-ligand interactions, we built a database of surface-fragment pairs, which enables fast and accurate fragment placement in the target protein pocket. We demonstrate its effectiveness on benchmark datasets, achieving robust placement accuracy. FragmentScope also enables the design of small molecules based on the predicted fragments ensuring synthetic accessibility. We experimentally validated Fragmentscope across 5 different targets with binding assays and structural characterization. Our approach shows high success rates in fragment discovery and yielded promising leads for designed ligands. FragmentScope offers a scalable, structure-guided approach for narrowing chemical space and identifying prospective scaffolds, accelerating the early stages of small molecule design.

bioinformatics↗

bioRxiv2023

Overall biomass yield of multiple nutrient sources

Microorganisms utilize nutrients primarily to generate biomass and replicate. When a single nutrient source is available, the produced biomass increases linearly with the initial amount of the available nutrient. This linear trend can be predicted to high accuracy by "black box models" that consider growth as a single chemical reaction with nutrients as substrates and biomass as a product. Since natural environments typically feature multiple nutrients, we here quantify the effect of co-utilization of multiple nutrients on bacterial biomass production. First, we demonstrate a mutual effect between the metabolism of different nutrient sources where the ability to utilize one is affected by the other. Second, we show that for some nutrient combinations, the produced biomass is no longer linear to the initial amount of nutrients. These observations cannot be explained by the traditional "black box models", presumably because the metabolism of one nutrient affects another, which is not accounted for by these models. To capture these observations, we extent "black box models" to include catabolism, anabolism, and biosynthesis of biomass precursors and phenomenologically add a mutual effect between the metabolism of the nutrient sources. The expanded model qualitatively recaptures the experimental observations and, unexpectedly, predicts that the produced biomass is not only dependent on the combination of nutrient sources but also on their relative initial amounts. We validate this prediction experimentally by demonstrating how measurement of the produced biomass can be used to determine how each nutrient effects the metabolic processes of another.

microbiology↗