Biology subjects

Comajuncosa-Creus, A.

Publications and source records attributed to Comajuncosa-Creus, A..

4 recordsLinked to original sources

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↗

bioRxiv2024

Comprehensive detection and characterization of human druggable pockets through novel binding site descriptors

Druggable pockets are protein regions that have the ability to bind organic small molecules, and their characterization is essential in target-based drug discovery. However, strategies to derive pocket descriptors are scarce and usually exhibit limited applicability. Here, we present PocketVec, a novel approach to generate pocket descriptors for any protein binding site of interest through the inverse virtual screening of lead-like molecules. We assess the performance of our descriptors in a variety of scenarios, showing that it is on par with the best available methodologies, while overcoming some important limitations. In parallel, we systematically search for druggable pockets in the folded human proteome, using experimentally determined protein structures and AlphaFold2 models, identifying over 32,000 binding sites in more than 20,000 protein domains. Finally, we derive PocketVec descriptors for each small molecule binding site and run an all-against-all similarity search, exploring over 1.2 billion pairwise comparisons. We show how PocketVec descriptors facilitate the identification of druggable pocket similarities not revealed by structure- or sequence-based comparisons. Indeed, our analyses unveil dense clusters of similar pockets in distinct proteins for which no inhibitor has yet been crystalized, opening the door to strategies to prioritize the development of chemical probes to cover the druggable space.

bioinformatics↗

bioRxiv2024

Stereochemically-aware bioactivity descriptors for uncharacterized chemical compounds

We recently presented a set of deep neural networks to generate bioactivity descriptors associated to small molecules (i.e. Signaturizers), capturing their effects at increasing levels of biological complexity (i.e. from protein targets to clinical outcomes)1. However, such models were trained on 2D representations of molecules and are thus unable to capture key differences in the activity of stereoisomers. Now, we systematically assess the relationship between stereoisomerism and bioactivity on over 1M compounds, finding that a very significant fraction ([~]40%) of spatial isomer pairs show, to some extent, distinct bioactivities. We then used these data to train a second generation of Signaturizers, which are now stereochemically-aware, and provide an even more faithful description of complex small molecule bioactivity properties.

bioinformatics↗