Biology subjects

Grisoni, F.

Publications and source records attributed to Grisoni, F..

4 recordsLinked to original sources

bioRxiv2025

Predicting DNA origami stability in physiological media by machine learning

DNA origami nanostructures offer substantial potential as programmable, biocompatible platforms for drug delivery and diagnostics. However, their structural stability under physiological conditions remains a major barrier to practical applications. Stability assessment of DNA origami nanostructures has traditionally relied on image-based and empirical approaches, which are time-consuming and difficult to generalize across conditions. To address these limitations, we developed a machine learning approach for DNA origami stability prediction, based on measurable physicochemical parameters. Using dynamic light scattering (DLS) to quantify diffusion coefficients as a proxy for structural integrity, we characterized over 1400 DNA origami samples under varying physiologically relevant variables: temperature, incubation time, MgCl2 concentration, pH, and DNase I concentrations. The predictive performance of the model was confirmed using an independent set of samples under previously untested conditions. This data-driven approach offers a scalable and generalizable framework to guide the design of robust DNA nanostructures for biomedical applications.

bioengineering↗

bioRxiv2025

"Visualize, describe, compare" - nanoinformatics approaches for material-omics

Bioinformatics and cheminformatics are established disciplines, but nanoinformatics, the development of computational tools for understanding and designing nanomaterials, is still in its infancy. In light of the new data-driven approaches for nanomaterials discovery, there is a growing need for in silico tools tailored to analyze nanomaterials datasets. This is particularly crucial for soft materials, where a crystalline structure cannot be obtained and therefore the characterization datasets are less structured, and there are no standard methods for data mining. Here we present a computational package capable of visualizing, describing, and comparing nanoparticle datasets obtained with super-resolution microscopy at the single-particle and single-molecule level. Our method allows us to: i) visualize multiparametric nanoparticle datasets to grasp material properties and heterogeneity; ii) have a quantitative evaluation of a material through a series of molecular descriptors, and iii) compare different materials quantitatively and globally, going beyond comparison of a single property. We applied this method to a library of targeted nanoparticles revealing particle heterogeneity, similarities, and correlations between the synthesis and the physicochemical properties of the different nanomaterials. Finally, we show the potential of this approach to reveal batch-to-batch variations in time and between users hidden in standard analysis.

bioinformatics↗

bioRxiv2024

A language model assistant for biocatalysis

Language model assistants have transformed how researchers interact with computational tools, offering unprecedented capabilities in understanding and generating complex scientific queries. We introduce a language model assistant for biocatalysis (LM-ABC), a computational tool designed to streamline workflows in enzyme engineering research. LM-ABC integrates a large language model with domain-specific modules to facilitate biocatalysis research through natural language inputs. Its architecture employs the Reasoning and Acting (ReACT) framework for dynamic tool selection and chaining, enabling functionalities like binding site extraction and molecular dynamics simulations. LM-ABC can interpret and process user queries in the form of natural language, and interface with existing computational resources to generate relevant results for enzyme engineering. Additionally, LM-ABC is available via both command-line and web-based interfaces, which lowers the barriers for its usage and integration in various disciplines. Provided as open-source software, the LM-ABC contributes to the application of language models in computational biology, potentially accelerating enzyme engineering research processes.

bioengineering↗

bioRxiv2024

nanoFeatures: a cross-platform application to characterize nanoparticles from super-resolution microscopy images

Super-resolution microscopy and Single-Molecule Localization Microscopy (SMLM) are a powerful tool to characterize synthetic nanomaterials used for many applications such as drug delivery. In the last decade, imaging techniques like STORM, PALM, and PAINT have been used to study nanoparticle size, structure, and composition. While imaging has progressed significantly, often image analysis did not follow accordingly and many studies are limited to qualitative and semi-quantitative analysis. Therefore, it is imperative to have a robust and accurate method to analyze SMLM images of nanoparticles and extract quantitative features from them. Here we introduce nanoFeatures, a cross-platform Matlab-based app for the automatic and quantitative analysis of super-resolution images. nanoFeatures makes use of clustering algorithms to identify nanoparticles from the raw data (localization list) and extract quantitative information about size, shape, and molecular abundance at the single-particle and single-molecule levels. Moreover, it applies a series of quality controls, increasing data quality and avoiding artifacts. nanoFeatures, thanks to its intuitive interface is also accessible to non-experts and will facilitate analysis of super-resolution microscopy for materials scientists and nanotechnologies. This easy accessibility to expansive feature characterization at the single particle level will bring us one step closer to understanding the relationship between nanostructure features and their efficiency. https://github.com/n4nlab/nanoFeatures

bioinformatics↗