Biology subjects

Bertoni, M.

Publications and source records attributed to Bertoni, M..

9 recordsLinked to original sources

bioRxiv2025

Phenotypic AI-based design of cell-specific small molecule cytotoxics

Drug discovery is being transformed by artificial intelligence, which enables the exploration of vast chemical spaces and the generation of novel compounds with tailored properties. While most generative approaches focus on optimizing physicochemical features or protein-binding affinities, their application to phenotypic drug discovery remains limited. Here we present an integrated framework to design small molecules with selective cytotoxic effects in pancreatic cancer cells that combines high-throughput cytotoxicity screening, bioactivity signature-based machine learning predictors, and reinforcement learning-driven generative models. We screened over 11,000 compounds across six pancreatic cancer and two control cell lines, confirming 392 hits, and used these data to train cell line-specific classifiers that outperformed fingerprint-based models. We embedded these predictors into the REINVENT platform to generate molecules with desired cytotoxicity profiles, including challenging cases of distinguishing between pancreatic cancer lines with highly similar molecular backgrounds. We generated between 37 and 137 high- scoring candidates in seven different cell selectivity exercises, which were structurally distant from the active compounds in the training set. We tested 45 close analogues to the AI-designed molecules, and 20 of them showed the desired cell-specific cytotoxic effects. Indeed, we validated at least one compound in 5 of the 7 exercises tested, showing over a large increase in hit discovery rate compared to the HTS. Additionally, we benchmarked our strategy against direct predictions on a library of over 150,000 compounds, skipping the generative step, finding that the fraction of hits, and more importantly the dose-response validation, is markedly higher in the AI- designed molecules. Overall, our study demonstrates the feasibility of combining predictive and generative AI methods to design molecules with complex phenotypic outcomes in a target- agnostic manner, paving the way for next-generation phenotype-driven drug discovery strategies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=68 SRC="FIGDIR/small/682546v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@119daf2org.highwire.dtl.DTLVardef@7579b9org.highwire.dtl.DTLVardef@8a62f8org.highwire.dtl.DTLVardef@167a6d5_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Integration of diverse bioactivity data into the Chemical Checker compound universe

Chemical signatures encode the physicochemical and structural properties of small molecules into numerical descriptors, forming the basis for chemical comparisons and search algorithms. The increasing availability of bioactivity data has improved compound representations to include biological effects, although bioactivity descriptors are often limited to a few well-documented molecules. To address this issue, we implemented a collection of deep neural networks able to leverage the experimentally determined bioactivity data associated to small molecules and infer the missing bioactivity signatures for any compound of interest. However, unlike static chemical descriptors, these bioactivity signatures dynamically evolve with new data and processing strategies. Here, we present a computational protocol to modify or generate novel bioactivity spaces and signatures, describing the main steps needed to leverage diverse bioactivity data with the current knowledge, as catalogued in the Chemical Checker (CC), using the predefined data curation pipeline. We illustrate the functioning of the protocol through four specific examples, including the incorporation of new compounds to an already existing bioactivity space, a change in the data pre-processing without altering the underlying experimental data, and the creation of two novel bioactivity spaces from scratch, which are completed in under 9 hours using GPU computing. Overall, this protocol offers a guideline for installing, testing and running the CC data integration approach on user-provided data, with the aim of extending the annotation presented for a limited number of small molecules to a larger chemical landscape. Key pointsO_LIThe Chemical Checker is a large collection of processed, harmonized and integrated bioactivity signatures for over 1 million small molecules. Data are organized into 5 levels of increasing biological complexity and curation degrees: from chemical properties to clinical outcomes and from raw data representing explicit knowledge to embedded signatures inferred from observed bioactivity patterns. C_LIO_LIThe Chemical Checker package provides a predefined and versatile framework to integrate user-provided data to the Chemical Checker universe of small molecules and generate novel customized bioactivity signatures. C_LI Key referencesDuran-Frigola et al. "Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker." Nature Biotechnology 38.9 (2020): 1087-1096. Bertoni et al. "Bioactivity descriptors for uncharacterized chemical compounds." Nature Communications 12.1 (2021): 3932.

bioinformatics↗

bioRxiv2024

Inhibitor-induced supercharging of kinase turnover via endogenous proteolytic circuits

Targeted protein degradation has emerged as a promising new pharmacological strategy. Traditionally, it relies on small molecules that induce proximity between a target protein and an E3 ubiquitin ligase to prompt target ubiquitination and degradation by the proteasome. Sporadic reports indicated that ligands designed to inhibit a target can also induce its destabilization. Among others, this has repeatedly been observed for kinase inhibitors. However, we lack an understanding of the frequency, generalizability, and mechanistic underpinnings of these phenomena. To address this knowledge gap, we generated dynamic abundance profiles of 98 kinases after cellular perturbations with 1570 kinase inhibitors, revealing 160 selective instances of inhibitor-induced kinase destabilization. Kinases prone to degradation are frequently annotated as HSP90 clients, thus affirming chaperone deprivation as an important route of destabilization. However, detailed investigation of inhibitor-induced degradation of LYN, BLK and RIPK2 revealed a differentiated, common mechanistic logic where inhibitors function by inducing a kinase state that is more efficiently cleared by endogenous degradation mechanisms. Mechanistically, effects can manifest by ligand-induced changes in cellular activity, localization, or multimerization which may be triggered by direct target engagement or network effects. Collectively, our data suggest that inhibitor-induced kinase degradation is a common event and positions supercharging of endogenous degradation circuits as an alternative to classical proximity-inducing degraders.

molecular biology↗

bioRxiv2023

BQsupports: systematic assessment of the support and novelty of new biomedical associations

Living a Big Data era in Biomedicine, there is an unmet need to systematically assess experimental observations in the context of available information. This assessment would offer a means for an unbiased validation of the results and provide an initial estimate of the potential novelty of the findings. Here we present BQsupports, a web-based tool built upon the Bioteque biomedical descriptors that systematically analyzes and quantifies the current support to a given set of observations. The tool relies on over 1,000 distinct types of biomedical descriptors, covering over 11 different biological and chemical entities, including genes, cell lines, diseases and small molecules. By exploring hundreds of descriptors, BQsupports provide support scores for each observation across a wide variety of biomedical contexts. These scores are then aggregated to summarize the biomedical support of the assessed dataset as a whole. Finally, the BQsupports also suggests predictive features of the given dataset, which can be exploited in downstream machine learning applications. Availability and implementationThe web application and underlying data are available online (https://bqsuppports.irbbarcelona.org). Contactpatrick.aloy@irbbarcelona.org

bioinformatics↗

bioRxiv2022

Integrating and formatting biomedical data in the Bioteque, a comprehensive repository of pre-calculated knowledge graph embeddings

Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge, so that multiple views of a given biological event can be considered simultaneously. Here we present the Bioteque, a resource of unprecedented size and scope that contains pre-calculated biomedical descriptors derived from a gigantic knowledge graph, displaying more than 450 thousand biological entities and 30 million relationships between them. The Bioteque integrates, harmonizes, and formats data collected from over 150 data sources, including 12 biological entities (e.g., genes, diseases, drugs) linked by 67 types of associations (e.g., drug treats disease, gene interacts with gene). We show how Bioteque descriptors facilitate the assessment of high-throughput protein-protein interactome data, the prediction of drug response and new repurposing opportunities, and demonstrate that they can be used off-the-shelf in downstream machine learning tasks without loss of performance with respect to using original data. The Bioteque thus offers a thoroughly processed, tractable, and highly optimized assembly of the biomedical knowledge available in the public domain.

bioinformatics↗

bioRxiv2020

A Community Challenge for Pancancer Drug Mechanism of Action Inference from Perturbational Profile Data

The Columbia Cancer Target Discovery and Development (CTD2) Center has developed PANACEA (PANcancer Analysis of Chemical Entity Activity), a collection of dose-response curves and perturbational profiles for 400 clinical oncology drugs in cell lines selected to optimally represent 19 cancer subtypes. This resource, developed to study tumor-specific drug mechanism of action, was instrumental in hosting a DREAM Challenge to assess computational models for de novo drug polypharmacology prediction. Dose-response and perturbational profiles for 32 kinase inhibitors were provided to 21 participating teams, who did not know the identity or nature of the compounds, and they were asked to predict high-affinity binding among ~1,300 possible protein targets. Best performing methods leveraged both gene expression profile similarity analysis, and deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessment of context-specific drug mechanism of action.

cancer biology↗

bioRxiv2020

Bioactivity descriptors for uncharacterized compounds

Chemical descriptors encode the physicochemical and structural properties of small molecules, and they are at the core of chemoinformatics. The broad release of bioactivity data has prompted enriched representations of compounds, reaching beyond chemical structures and capturing their known biological properties. Unfortunately, bioactivity descriptors are not available for most small molecules, which limits their applicability to a few thousand well characterized compounds. Here we present a collection of deep neural networks able to infer bioactivity signatures for any compound of interest, even when little or no experimental information is available for them. Our signaturizers relate to bioactivities of 25 different types (including target profiles, cellular response and clinical outcomes) and can be used as drop-in replacements for chemical descriptors in day-to-day chemoinformatics tasks. Indeed, we illustrate how inferred bioactivity signatures are useful to navigate the chemical space in a biologically relevant manner, unveiling higher-order organization in natural product collections, and to enrich mostly uncharacterized chemical libraries for activity against the drug-orphan target Snail1. Moreover, we implement a battery of signature-activity relationship (SigAR) models and show a substantial improvement in performance, with respect to chemistry-based classifiers, across a series of biophysics and physiology activity prediction benchmarks.

bioinformatics↗

bioRxiv2020

Emergence of stealth polymorphs that escape α-synuclein amyloid monitoring, take over and acutely spread in neurons

The conformational strain diversity characterizing -synuclein (-syn) amyloid fibrils is possibly at the origin of the different clinical presentations of synucleinopathies. Experimentally, various -syn fibril polymorphs have been obtained from distinct fibrillization conditions by altering the medium constituents and were selected by amyloid monitoring using the probe Thioflavin T (ThT). We report here that besides classical ThT positive products, fibrillization in saline simultaneously gives rise to competing fibril polymorphs that are invisible to ThT (stealth polymorphs), and that can take over. Due to competition, spontaneous generation of such stealth polymorphs bears on the apparent fibrillization kinetics and on the final plateau values. Their emergence has thus been ignored so far or mistaken for fibrillization inhibitions/failures. Compared to their ThT-positive counterparts, and as judged from their chemical shift resonances fingerprint, these new stealth polymorphs present a yet undescribed atomic organization and show an exacerbated propensity (approx. 20-fold) towards self-replication in cortical neurons. They also trigger a long distance synucleinopathic spread along nigro-striatal projections in vivo. In order to rapidly screen fibrillization products for the presence of such stealth polymorphs, we designed a simple multiplexed assay that can be easily and rapidly operated. This assay allows us to demonstrate the sustainability of the conformational replication of these novel and particularly invasive strains. It should also be of help to avoid erroneous upstream interpretations of fibrillization rates based on sole ThT, and to expedite further structural and functional characterization of stealth amyloid assemblies. One Sentence Summarystealth -synuclein fibrils take over

neuroscience↗

bioRxiv2019

Extending the small molecule similarity principle to all levels of biology

We present the Chemical Checker (CC), a resource that provides processed, harmonized and integrated bioactivity data on 800,000 small molecules. The CC divides data into five levels of increasing complexity, ranging from the chemical properties of compounds to their clinical outcomes. In between, it considers targets, off-targets, perturbed biological networks and several cell-based assays such as gene expression, growth inhibition and morphological profilings. In the CC, bioactivity data are expressed in a vector format, which naturally extends the notion of chemical similarity between compounds to similarities between bioactivity signatures of different kinds. We show how CC signatures can boost the performance of drug discovery tasks that typically capitalize on chemical descriptors, including target identification and library characterization. Moreover, we demonstrate and experimentally validate that CC signatures can be used to reverse and mimic biological signatures of disease models and genetic perturbations, options that are otherwise impossible using chemical information alone.

bioinformatics↗