Biology subjects

Gabrielli, N.

Publications and source records attributed to Gabrielli, N..

3 recordsLinked to original sources

bioRxiv2020

Bioactivity assessment of natural compounds using machine learning models based on drug target similarity

Natural compounds constitute a rich resource of potential small-molecule therapeutics. While experimental access to this resource is limited due to its vast diversity and difficulties in systematic purification, computational assessment of structural similarity with known therapeutic molecules offers a scalable approach. Here, we assessed functional similarity between natural compounds and approved drugs by combining multiple chemical similarity metrics and physicochemical properties through a random forest model. As a training set, we used pair-wise similarity between 1410 drugs in terms of their shared protein targets. The resulting model featured high performance metrics (matthews correlation coefficient of 0.81, and balanced accuracy of 0.91) suggesting that it well-captured the structure-activity relation. The model was then used to predict protein targets of circa 11k natural compounds by comparing them with the drugs. This revealed therapeutic potential of several natural compounds, including those with support from previously published sources as well as those hitherto unexplored. We experimentally validated one of the predicted links activities, viz., Cox-1 inhibition by 5-methoxysalicylic acid, a molecule commonly found in tea, herbs and spices. In contrast, another natural compound, 4-isopropylbenzoic acid, which showed a higher similarity when considering the most weighted similarity metric but was not picked by the random forest model, did not inhibit Cox-1. Our results demonstrate the utility of a machine-learning approach combining multiple chemical features for uncovering protein binding potential of natural compounds.

bioinformatics↗

bioRxiv2019

The HIV-1 ribonucleoprotein dynamically regulates its condensate behavior and drives acceleration of protease activity through membrane-less granular phase-separation

A growing number of studies indicate that mRNAs and long ncRNAs can affect protein populations by assembling dynamic ribonucleoprotein (RNP) granules. These phase separated molecular sponges, stabilized by quinary (transient and weak) interactions, control proteins involved in numerous biological functions. Retroviruses such as HIV-1 form by self-assembly when their genomic RNA (gRNA) traps Gag and GagPol polyprotein precursors. Infectivity requires extracellular budding of the particle followed by maturation, an ordered processing of ~2400 Gag and ~120 GagPol by viral protease (PR). This leads to a condensed gRNA-NCp7 nucleocapsid and a CAp24-self-assembled capsid surrounding the RNP. The choreography by which all of these components dynamically interact during virus maturation is one of the missing milestones to fully depict the HIV life cycle. Here, we describe how HIV-1 has evolved a dynamic RNP granule with successive weak-strong-moderate quinary NC-gRNA networks during the sequential processing of the GagNC domain. We also reveal two palindromic RNA-binding triads on NC, KxxFxxQ and QxxFxxK, that provide quinary NC-gRNA interactions. Consequently, the nucleocapsid complex appears properly aggregated for capsid reassembly and reverse transcription, mandatory processes for viral infectivity. We show that PR is sequestered within this RNP and drives its maturation/condensation within minutes, this process being most effective at the end of budding. We anticipate such findings will stimulate further investigations of quinary interactions and emergent mechanisms in crowded environments throughout the wide and growing array of RNP granules.

molecular biology↗

bioRxiv2019

Multiplexed electron microscopy by fluorescent barcoding allows screening for ultrastructural phenotype

Genetic screens performed using high-throughput fluorescent microscopes have generated large datasets that have contributed many insights into cell biology. However, such approaches typically cannot tackle questions requiring knowledge of ultrastructure below the resolution limit of fluorescent microscopy. Electron microscopy (EM) is not subject to this resolution limit, generating detailed images of cellular ultrastructure, but requires time consuming preparation of individual samples, limiting its throughput. Here we overcome this obstacle and describe a robust method for screening by high-throughput electron microscopy. Our approach uses combinations of fluorophores as barcodes to mark the genotype of each cell in mixed populations, and correlative light and electron microscopy to read the fluorescent barcode of each cell before it is imaged by electron microscopy. Coupled with an easy-to-use software workflow for correlation, segmentation and computer image analysis, our method allows to extract and analyze multiple cell populations from each EM sample preparation. We demonstrate the method on several organelles with samples that each contain up to 15 different yeast variants. The methodology is not restricted to yeast, can be scaled to higher-throughput, and can be utilized in multiple ways to enable electron microscopy to become a powerful screening methodology.

cell biology↗