Biology subjects

Zaliani, A.

Publications and source records attributed to Zaliani, A..

At least 19 recordsLinked to original sources

bioRxiv2025

Morphological cell profiling for drug repurposing against SARS-CoV-2 infection

Antiviral drug discovery has traditionally focused on targeting viral proteins, while host-directed strategies remain largely underexplored. Here, present a systematic drug repurposing strategy leveraging morphological profiling to identify host-targeting antivirals. Our image-based approach combines viral protein immunostaining with high-content Cell Painting analysis to simultaneously assess viral replication and provide in-depth analysis of host cell responses. By screening 5,275 repurposable drugs against SARS-CoV-2 infected cells, we identified compounds that reversed the infected cell phenotype, including ones not detected by conventional cytopathicity and antibody-based assays. A counter-screen excluded compounds whose antiviral activity was likely driven by drug-induced phospholipidosis (DIPL). Pathway enrichment analysis of compounds validated by both Cell Painting dose-response and DIPL assays, revealed host processes frequently hijacked by viruses, including innate immune responses and kinases. Among the top hits, both novel candidates, such as serdemetan, and previously reported broad-spectrum antivirals, such as sunitinib, were identified. Our approach constitutes an adaptable and scalable platform suited for diverse viral pathogens and cell systems. We provide a resource of open access screening data, images, and automated analysis pipelines to advance both antiviral discovery and pandemic preparedness.

microbiology↗

bioRxiv2025

From Library to Landscape: Integrative Annotation Workflows for Compound Libraries in Drug Repurposing

In the rapidly advancing landscape of drug discovery and repurposing, efficient access and integration of chemical and bioactivity data from public repositories has become essential. We implemented two complementary annotation pipelines (KNIME- and Python-based) designed to automate the extraction and integration of curated chemical and bioactivity data from public repositories. These pipelines are adaptable to any user-provided compound library, allowing reproducible workflows to integrate data from heterogeneous sources (e.g., ChEMBL and PubChem). As part of the REMEDi4ALL project, which aims to establish a European platform for drug repurposing, we validated our framework on a harmonized subset of the Specs repurposing collection (over 5000 compounds, available in-house). Additionally, we developed two interactive dashboards that support multilayered analyses and visualization by integrating chemical properties, bioactivity profiles, and relational data. We show how this framework streamlines the collection of harmonized data and facilitates analyses that are critical in drug repurposing efforts, while remaining versatile for broader applications in drug discovery. Both pipeline protocols are publicly available online, and the dashboards are open access.

bioinformatics↗

bioRxiv2025

Interdisciplinary Study on Drug-Induced-Phospholipidosis of Repurposing Libraries through Machine Learning and Experimental Evaluation in Different Cell Lines

Bigger PicturePhospholipidosis is a cellular condition characterized by the excessive accumulation of phospholipids within cells, that also can be induced by medications--especially those known as cationic amphiphilic drugs. While this phenomenon is not always harmful in itself, it can interfere with how cells respond in laboratory experiments and complicate the interpretation of drug screening results, leading to potential delays or failures in the development of new therapies. As the pharmaceutical industry increasingly relies on high-throughput screening and machine learning to accelerate early-stage drug discovery, there is growing interest in identifying compounds that may cause phospholipidosis before they move too far through the development pipeline. In response to this challenge, we at the EU-Horizon HLTH Remedi4ALL project compiled and analyzed one of the most comprehensive datasets to date--over 5,000 repurposed drugs tested across multiple cell lines--to better understand which compounds are likely to induce phospholipidosis. Using this dataset, we developed a machine learning model capable of predicting the risk of phospholipidosis based on a drugs chemical structure. By validating our approach across diverse experimental settings, our goal is to provide the broader scientific and pharmaceutical communities with a practical tool to flag problematic compounds early, ultimately helping bring safer, more effective treatments to patients faster. Highlights- A comprehensive dataset for a repurposing collection, comprising 5,228 compounds, for induction of phospholipidosis in A549-ACE2 and Vero-E6 cells -A machine learning model developed using in-house experimental phospholipidosis data across multiple cell lines -Exploration of chemical features that induce phospholipidosis and investigation of the relationships underlying different cell line susceptibilities -A case study: correlation of drug-induced phospholipidosis with antiviral compound effects against SARS-CoV-2 using phenotypic screening data Phospholipidosis (PLD), a cellular adverse effect that is, among others, caused by numerous cationic amphiphilic drugs. Interest is raised within pharma discovery to predict this phenomenon, as it can impact the outcome of phenotypic cellular screens and significantly delay drug development processes. The development of accurate and validated machine learning models for predicting drug-induced PLD across different cell lines and research centers could provide a valuable early application tool for the pharmaceutical industry, potentially accelerating drug discovery and reducing the risk of late-stage failures. We report here the assembly, curation, testing and modeling of one of the largest datasets of repurposed drugs (5K+) tested for PLD induction on different cell lines. A machine-learning classification method was developed and validated to predict whether molecules are prone to induce PLD effects when applied in cell-based screens. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=187 SRC="FIGDIR/small/663638v1_ufig1.gif" ALT="Figure 1"> View larger version (67K): org.highwire.dtl.DTLVardef@1c4760org.highwire.dtl.DTLVardef@92084dorg.highwire.dtl.DTLVardef@15effbaorg.highwire.dtl.DTLVardef@1e73f4d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG

molecular biology↗

bioRxiv2025

Optimization and evaluation of complementary degrader discovery assays for application in screening

Targeted protein degradation (TPD) mediated by molecular glues is an innovative pharmaceutical paradigm. By binding to and modulating the surface of an E3-ligase component, molecular glue degraders can facilitate the recruitment of a specific target protein (or vice versa) and, ultimately, invoke target degradation. This mode of action results in specific challenges for the development of rational discovery strategies, and complex hit validation workflows may be required to reliably eliminate compounds that elicit non-specific effects. With the aim to guide screening efforts, we optimized two orthogonal cell-based, target-centric assays for degrader discovery: (1) a time-resolved FRET assay directly quantifying levels of target protein and its degradation (signal inhibition), and (2) an assay coupling TPD to cell growth (signal rescue). To enable a deeper understanding of the individual assays strengths and limitations, we compared their statistical performance as well as respective hit populations by screening a specifically designed collection of about 1000 compounds containing well annotated reference compounds and known frequent hitters. We found that the signal rescue format reliably and specifically captured active target degraders while it efficiently filtered out interfering or frequent hitter compounds. Importantly, this format achieved to retrieve lower potency hits, which might be desirable in order to confidently include as many diverse chemical starting points as possible at the start of a drug discovery project.

pharmacology and toxicology↗

bioRxiv2024

Predicting antimicrobial class specificity of small molecules using machine learning

Whilst the useful armory of antibiotic drugs is continually depleted due to the emergence of drug-resistant pathogens, the development of novel therapeutics has also slowed down. In the era of advanced computational methods, approaches like machine learning (ML) could be one potential solution to help reduce the high costs and complexity of antibiotic drug discovery and attract collaboration across organizations. In our work, we developed a large antimicrobial knowledge graph (AntiMicrobial-KG) as a repository for collecting and visualizing public in-vitro antibacterial assay. Utilizing this data, we build ML models to efficiently scan compound libraries to identify compounds with the potential to exhibit antimicrobial activity. Our strategy involved training seven classic ML models across six compound fingerprint representations, of which the Random Forest trained on the MHFP6 fingerprint outperformed, demonstrating an accuracy of 75.9% and Cohens Kappa score of 0.68. Finally, we illustrated the models applicability for predicting the antimicrobial properties of two small molecule screening libraries. Firstly, the EU-OpenScreen library was tested against a panel of Gram-positive, Gram-negative, and Fungal pathogens. Here, we unveiled that the model was able to correctly predict more than 30% of active compounds for Gram-positive, Gram-negative, and Fungal pathogens. Secondly, with the Enamine library, a commercially available HTS compound collection with claimed antibacterial properties, we predicted its antimicrobial activity and pathogen class specificity. These results may provide a means for accelerating research in AMR drug discovery efforts by carefully filtering out compounds from commercial libraries with lower chances of being active.

bioinformatics↗

bioRxiv2024

Morphological profiling in human dopaminergic neurons identifies mitochondrial uncoupling as a neuroprotective effect

Parkinsons disease (PD) involves multiple pathological processes in midbrain dopaminergic (mDA) neurons, including protein degradation defects, vesicular trafficking disruption, endolysosomal dysfunction, mitochondrial issues, and oxidative stress. Current PD models often lack complexity and focus on single phenotypes. We used patient-derived SNCA triplication (SNCA-4x) and isogenic control (SNCA-corr) mDA neurons, applying high-content imaging-based morphological profiling to identify and rescue multiple phenotypes. Screening 1,020 compounds, we identified top-scoring compounds that restored healthy profiles in SNCA-4x neurons, increasing Tyrosine hydroxylase (TH) and decreasing -synuclein (Syn) levels. Several hits were linked to mitochondrial biology. Tyrphostin A9, a mitochondrial uncoupler, and several of its structural analogues decreased ROS levels, normalized mitochondrial membrane potential, and increased respiration. Western blotting confirmed that Tyrphostin A9 reduces Syn levels. Our study highlights the neuroprotective potential of mild mitochondrial uncoupling in mDA neurons.

neuroscience↗

bioRxiv2024

Deep learning-driven neuromorphogenesis screenings identify repurposable drugs for mitochondrial disease

Mitochondrial disease encompasses untreatable conditions affecting tissues with high energy demands. A severe manifestation of mitochondrial disease is Leigh syndrome (Leigh), which causes defects in basal ganglia and midbrain regions, psychomotor regression, lactic acidosis, and early death. We previously generated isogenic pairs of Leigh cerebral organoids and uncovered defects in neuromorphogenesis. Here, we leveraged on this disease feature to devise drug discovery pipelines. We developed a deep learning algorithm tailored for cell type-specific drug repurposing to identify drugs capable of promoting neuronal commitment. In parallel, we performed a survival drug screen in yeast and validated the repurposable hits on branching capacity in Leigh neurons. The two approaches independently highlighted azole compounds, Talarozole and Sertaconazole, both of which lowered lactate release and improved neurogenesis and neurite organization in Leigh midbrain organoids. Hence, targeting neuromorphogenesis has led to identify potential new drugs for mitochondrial disease and could prove an effective strategy for further drug discovery.

neuroscience↗

bioRxiv2023

Medicinal chemistry lens on patent data in SureChEMBL

In the pharmaceutical industry, the patent protection of drugs and medicines is accorded importance because of the high costs involved in the development of novel drugs. Over the years, researchers have analyzed patent documents to identify freedom-to-operate spaces for novel drug candidates. To assist this, several well established public patent document data repositories have enabled automated methodologies for extracting information on therapeutic agents. In this study, we delve into one such publicly available patent database, SureChEMBL, which catalogues patent documents related to life sciences. Our exploration begins by identifying patent compounds across public chemical data resources, followed by pinpointing sections in patent documents where the chemical annotations were found. Next, we exhibit the potential of compounds to serve as drug candidates by evaluating their conformity to drug-likeness criteria. Lastly, we examine the drug development stage reported for these compounds to understand their clinical success. In summary, our investigation aims at providing a comprehensive overview of the patent compounds catalogued in SureChEMBL, assessing their relevance to pharmaceutical drug discovery.

bioinformatics↗

bioRxiv2023

Resolving the pharmacological redox-sensitivity of SARS-CoV-2 PLpro in drug repurposing screening enabled identification of the competitive GRL-0617 binding site inhibitor CPI-169

The SARS CoV-2 Papain-Like protease has multiple roles in the viral replication cycle, related to both its polypeptide cleavage function and its capacity to antagonize host immune response. Targeting PLpro function is recognized as a promising mechanism to modulate viral replication whilst supporting host immune responses. However, development of PLpro specific inhibitors remains challenging. Upcoming studies revealed the limitation of reported inhibitors by profiling them through a pipeline of enzymatic, binding and cellular activity assays showing unspecific activity. GRL-0617 remained the only validated molecule with demonstrated anti-viral activity in cells. In this study we refer to the pitfalls of redox-sensitivity of PLpro. Using a screening-based approach to identify inhibitors of PLpro proteolytic activity, we made extensive efforts to validate the active compounds over a range of conditions and readouts, emphasising the need for comprehensive orthogonal data when profiling putative PLpro inhibitors. The remaining active compound CPI-169, showed to compete with GRL-0617 in NMR-based experiments, suggesting to share a similar binding mode, opening novel design opportunities for further developments as antiviral agents. Author summaryThe increasing knowledge about SARS-CoV-2 allowed the development of multiple strategies to contain the spread of COVID-19 infection. Nevertheless, effective antiviral pharmacological treatments are still rare and viral evolution allowed a fast adaptation and escape from available containment methods. The papain like protease (PLpro) has now become the next most promising SARS-CoV-2 therapeutic due to its multiple functions in virus replication cycle and antagonization of host immune response. However, due to inherent flexibility and sensitivity of this enzyme specific inhibitors are rare. Here we report on a screening strategy using repurposing of known drugs that takes into account PLpro characteristics to identify new inhibitors, showing the success of the approach by identifying CPI-169 that competitive targets the well described GRL-0617 inhibitor binding pocket of PLpro and helping to design further antiviral agents.

molecular biology↗

bioRxiv2023

Pharmacophoric-based ML model to filter candidate E3 ligands and predict E3 Ligase binding probabilities

Among the plethora of E3 Ligases, only a few have been utilized for the novel PROTAC technology. However, extensive knowledge of the preparation of E3 ligands and their utilization for PROTACs is already present in several databases. Here we provide, together with an analysis of functionalized E3 ligands, a comprehensive list of trained ML models to predict the probability to be an E3 ligase binder. We compared the different algorithms based on the different description schemes used and identified that the pharmacophoric-based ML approach was the best. Due to the peculiar pharmacophores present in E3 ligase binders and the presence of an explainable model, we were able to show the capability of our ErG model to filter compound libraries for fast virtual screening or focused library design. A particular focus was also given to target E3 ligase prediction and to find a subset of candidate E3 ligase binders within known public and commercial compound collections.

bioinformatics↗

bioRxiv2023

The novel coronary artery disease risk factor ADAMTS-7 modulates atherosclerotic plaque formation by degradation of TIMP-1

BackgroundThe ADAMTS7 locus was genome-wide significantly associated with coronary artery disease (CAD). Lack of the extracellular matrix (ECM) protease ADAMTS-7 was shown to reduce atherosclerotic plaque formation. ObjectiveTo identify molecular mechanisms and downstream targets of ADAMTS-7 mediating risk of atherosclerosis. MethodsTargets of ADAMTS-7 were identified by high-resolution mass spectrometry of atherosclerotic plaques from Apoe-/- and Apoe-/-Adamts7-/- mice. ECM proteins were identified using solubility profiling. Putative targets were validated using immunofluorescence, in vitro degradation assays, co-immunoprecipitation, and Forster resonance energy transfer (FRET)-based protein-protein interaction assays. ADAMTS7 expression was measured in fibrous caps of human carotid artery plaques. ResultsIn humans, ADAMTS7 expression was higher in caps of unstable as compared to stable carotid plaques. Compared to Apoe-/- mice, atherosclerotic aortas of Apoe-/- mice lacking Adamts-7 (Apoe-/-Adamts7-/-) contained higher protein levels of tissue inhibitor of metalloproteases 1 (Timp-1). In co-immunoprecipitation experiments, the catalytic domain of ADAMTS-7 bound to TIMP-1, which was degraded in the presence of ADAMTS-7 in vitro. ADAMTS-7 reduced the inhibitory capacity of TIMP-1 at its canonical target matrix metalloprotease 9 (MMP-9) As a downstream mechanism, we investigated collagen content in plaques of Apoe-/- and Apoe-/-Adamts7-/- mice after Western diet. Picrosirius red staining of the aortic root revealed less collagen as a readout of higher MMP-9 activity in Apoe-/- as compared to Apoe-/- Adamts7-/- mice. In order to facilitate high-throughput screening for ADAMTS-7 inhibitors with the aim to decrease TIMP-1 degradation, we designed a FRET-based assay targeting the ADAMTS-7 catalytic site. ConclusionADAMTS-7, which is induced in unstable atherosclerotic plaques, decreases TIMP-1 stability reducing its inhibitory effect on MMP-9, which is known to promote collagen degradation and is likewise genome-wide significantly associated with CAD. Disrupting the interaction of ADAMTS-7 and TIMP-1 might be a strategy to increase collagen content and plaque stability for reduction of atherosclerosis-related events.

molecular biology↗

bioRxiv2023

Pharmaceutical patent landscaping: A novel approach to understand patents from the drug discovery perspective

Patents play a crucial role in the drug discovery process by providing legal protection for discoveries and incentivising investments in research and development. By identifying patterns within patent data resources, researchers can gain insight into the market trends and priorities of the pharmaceutical and biotechnology industries, as well as provide additional perspectives on more fundamental aspects such as the emergence of potential new drug targets. In this paper, we used the patent enrichment tool, PEMT, to extract, integrate, and analyse patent literature for rare diseases (RD) and Alzheimers disease (AD). This is followed by a systematic review of the underlying patent landscape to decipher trends and applications in patents for these diseases. To do so, we discuss prominent organisations involved in drug discovery research in AD and RD. This allows us to gain an understanding of the importance of AD and RD from specific organisational (pharmaceutical or university) perspectives. Next, we analyse the historical focus of patents in relation to individual therapeutic targets and correlate them with market scenarios allowing the identification of prominent targets for a disease. Lastly, we identified drug repurposing activities within the two diseases with the help of patents. This resulted in identifying existing repurposed drugs and novel potential therapeutic approaches applicable to the indication areas. The study demonstrates the expanded applicability of patent documents from legal to drug discovery, design, and research, thus, providing a valuable resource for future drug discovery efforts. Moreover, this study is an attempt towards understanding the importance of data underlying patent documents and raising the need for preparing the data for machine learning-based applications.

scientific communication and education↗

bioRxiv2022

MultiGML: Multimodal Graph Machine Learning for Prediction of Adverse Drug Events

Adverse drug events constitute a major challenge for the success of clinical trials. Several computational strategies have been suggested to estimate the risk of adverse drug events in preclinical drug development. While these approaches have demonstrated high utility in practice, they are at the same time limited to specific information sources and thus neglect a wealth of information that is uncovered by fusion of different data sources, including biological protein function, gene expression, chemical compound structure, cell-based imaging, etc. In this work we propose an integrative and explainable Graph Machine Learning approach (MultiGML), which fuses knowledge graphs with multiple further data modalities to predict drug related adverse events. MultiGML demonstrates excellent prediction performance compared to alternative algorithms, including various knowledge graph embedding techniques. MultiGML distinguishes itself from alternative techniques by providing in-depth explanations of model predictions, which point towards biological mechanisms associated with predictions of an adverse drug event. MotivationAdverse drug events are a major risk for failure of late-stage clinical trials. Attempts to prevent adverse drug events in preclinical drug development include experimental procedures for measuring liver-toxicity, cardio-toxicity, etc. Yet these procedures are costly and cannot fully guarantee success in later clinical studies, specifically in situations without a reliable animal model. Computational approaches developed for adverse event prediction have shown to be valuable, but are mostly limited to single data sources. Our approach successfully integrates various data sources on protein functions, gene expression, chemical compound structures and more, into the prediction of adverse events. A main distinguishing characteristic is the explainability of our model predictions which allow further insight into biological mechanisms.

bioinformatics↗

bioRxiv2022

Monkeypox Knowledge Graph: A comprehensive representation embedding chemical entities and associated biology of Monkeypox

SummaryThe outbreak of Monkeypox virus (MPXV) infection in May 2022 is declared a global health emergency by WHO. As of 28th July, 21067 cases have been confirmed and the numbers are on the rise. Unfortunately, MPXV pathophysiology and its underlying mechanisms are not yet understood. Likewise, the knowledge of biochemicals and drugs used against MPXV and their downstream effects is sparse. In this work, using Knowledge Graph (KG) representations we have depicted chemical and biological aspects of MPXV. To achieve this, we have collected and rationally assembled several biological study results, assays, drug candidates, and preclinical evidence to form a dynamic and comprehensive network. The KG is compliant with FAIR annotations allowing seamless transformation and integration to/with other formats and infrastructures. Availability and implementationThe Monkeypox knowledge graph is publicly available at https://github.com/Fraunhofer-ITMP/mpox-kg Supplementary informationSupplementary data are available at Bioinformatics online. ContactReagon.Karki@itmp.fraunhofer.de

systems biology↗

bioRxiv2022

PEMT: A patent enrichment tool for drug discovery

MotivationDrug discovery practitioners in industry and academia use semantic tools to extract information from online scientific literature to generate new insights into targets, therapeutics and diseases. However, due to complexities in access and analysis, patent-based literature is often overlooked as a source of information. As drug discovery is a highly competitive field, naturally, tools that tap into patent literature can provide any actor in the field an advantage in terms of better informed decision making. Hence, we aim to facilitate access to patent literature through the creation of an automatic tool for extracting information from patents described in existing public resources. ResultsHere, we present PEMT, a novel patent enrichment tool, that takes advantage of public databases like ChEMBL and SureChEMBL to extract relevant patent information linked to chemical structures and/or gene names described through FAIR principles and metadata annotations. PEMT aims at supporting drug discovery and research by establishing a patent landscape around genes of interest. The pharmaceutical focus of the tool is mainly due to the subselection of International Patent Classification (IPC) codes, but in principle, it can be used for other patent fields, provided that a link between a concept and chemical structure is investigated. Finally, we demonstrate a use-case in rare diseases by generating a gene-patent list based on the epidemiological prevalence of these diseases and exploring their underlying patent landscapes. Availability and implementationPEMT is an open-source Python tool and its source code and PyPi package are available at https://github.com/Fraunhofer-ITMP/PEMT and https://pvpi.org/project/PEMT/ respectively. Contactyojana.gadiya@itmp.fraunhofer.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Identification of inhibitors of SARS-CoV-2 3CL-Pro enzymatic activity using a small molecule in-vitro repurposing screen

Compound repurposing is an important strategy for the identification of effective treatment options against SARS-CoV-2 infection and COVID-19 disease. In this regard, SARS-CoV-2 main protease (3CL-Pro), also termed M-Pro, is an attractive drug target as it plays a central role in viral replication by processing the viral polyproteins pp1a and pp1ab at multiple distinct cleavage sites. We here report the results of a repurposing program involving 8.7 K compounds containing marketed drugs, clinical and preclinical candidates, and small molecules regarded as safe in humans. We confirmed previously reported inhibitors of 3CL-Pro, and have identified 62 additional compounds with IC50 values below 1 M and profiled their selectivity towards Chymotrypsin and 3CL-Pro from the MERS virus. A subset of 8 inhibitors showed anti-cytopathic effect in a Vero-E6 cell line and the compounds thioguanosine and MG-132 were analysed for their predicted binding characteristics to SARS-CoV-2 3CL-Pro. The X-ray crystal structure of the complex of myricetin and SARS-Cov-2 3CL-Pro was solved at a resolution of 1.77 [A], showing that myricetin is covalently bound to the catalytic Cys145 and therefore inhibiting its enzymatic activity. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=154 SRC="FIGDIR/small/422677v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@17ca2aeorg.highwire.dtl.DTLVardef@19c5159org.highwire.dtl.DTLVardef@1a0adf6org.highwire.dtl.DTLVardef@1fd05cd_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOAbstract Figure.C_FLOATNO Workflow for identification and profiling of inhibitors of SARS-CoV-2 3CL-Pro using a large scale repurposing and bioactive compound collection (rhs). Primary assay principle based on quenched FRET peptide substrate of SARS-CoV-2 3CL-Pro (lhs). Inhibiting compounds reduce fluorescence signal relative to DMSO controls. Hit profiling using X-ray. C_FIG

molecular biology↗

bioRxiv2020

A blueprint for high affinity SARS-CoV-2 Mpro inhibitors from activity-based compound library screening guided by analysis of protein dynamics

The SARS-CoV-2 coronavirus outbreak continues to spread at a rapid rate worldwide. The main protease (Mpro) is an attractive target for anti-COVID-19 agents. Unfortunately, unexpected difficulties have been encountered in the design of specific inhibitors. Here, by analyzing an ensemble of ~30,000 SARS-CoV-2 Mpro conformations from crystallographic studies and molecular simulations, we show that small structural variations in the binding site dramatically impact ligand binding properties. Hence, traditional druggability indices fail to adequately discriminate between highly and poorly druggable conformations of the binding site. By performing ~200 virtual screenings of compound libraries on selected protein structures, we redefine the proteins druggability as the consensus chemical space arising from the multiple conformations of the binding site formed upon ligand binding. This procedure revealed a unique SARS-CoV-2 Mpro blueprint that led to a definition of a specific structure-based pharmacophore. The latter explains the poor transferability of potent SARS-CoV Mpro inhibitors to SARS-CoV-2 Mpro, despite the identical sequences of the active sites. Importantly, application of the pharmacophore predicted novel high affinity inhibitors of SARS-CoV-2 Mpro, that were validated by in vitro assays performed here and by a newly solved X-ray crystal structure. These results provide a strong basis for effective rational drug design campaigns against SARS-CoV-2 Mpro and a new computational approach to screen protein targets with malleable binding sites.

biophysics↗

bioRxiv2020

Minimal information for Chemosensitivity assays (MICHA): A next-generation pipeline to enable the FAIRification of drug screening experiments

Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. We report here the launching of MICHA (Minimal Information for Chemosensitivity Assays), accessed via https://micha-protocol.org. Distinguished from existing efforts that are often lacking support from data integration tools, MICHA can automatically extract publicly available information to facilitate the assay annotation including: 1) compounds, 2) samples, 3) reagents, and 4) data processing methods. For example, MICHA provides an integrative web server and database to obtain compound annotation including chemical structures, targets, and disease indications. In addition, the annotation of cell line samples, assay protocols and literature references can be greatly eased by retrieving manually curated catalogues. Once the annotation is complete, MICHA can export a report that conforms to the FAIR principle (Findable, Accessible, Interoperable and Reusable) of drug screening studies. To consolidate the utility of MICHA, we provide FAIRified protocols from five major cancer drug screening studies, as well as six recently conducted COVID-19 studies. With the MICHA webserver and database, we envisage a wider adoption of a community-driven effort to improve the open access of drug sensitivity assays.

bioinformatics↗