Shaping the interaction landscape of bioactive molecules

Motivation: Most bioactive molecules perform their action by interacting with proteins or other macromolecules. However, for a significant fraction of them, the primary target remains unknown. In addition, the majority of bioactive molecules have more than one target, many of which are poorly characterized. Computational predictions of bioactive molecule targets based on similarity with known ligands are powerful to narrow down the number of potential targets and to rationalize side effects of known molecules. Results: Using a reference set of 224 412 molecules active on 1700 human proteins, we show that accurate target prediction can be achieved by combining different measures of chemical similarity based on both chemical structure and molecular shape. Our results indicate that the combined approach is especially efficient when no ligand with the same scaffold or from the same chemical series has yet been discovered. We also observe that different combinations of similarity measures are optimal for different molecular properties, such as the number of heavy atoms. This further highlights the importance of considering different classes of similarity measures between new molecules and known ligands to accurately predict their targets. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Shaping the interaction landscape of bioactive molecules.

MOTIVATION: Most bioactive molecules perform their action by interacting with proteins or other macromolecules. However, for a significant fraction of them, the primary target remains unknown. In addition, the majority of bioactive molecules have more than one target, many of which are poorly characterized. Computational predictions of bioactive molecule targets based on similarity with known ligands are powerful to narrow down the number of potential targets and to rationalize side effects of known molecules. RESULTS: Using a reference set of 224 412 molecules active on 1700 human proteins, we show that accurate target prediction can be achieved by combining different measures of chemical similarity based on both chemical structure and molecular shape. Our results indicate that the combined approach is especially efficient when no ligand with the same scaffold or from the same chemical series has yet been discovered. We also observe that different combinations of similarity measures are optimal for different molecular properties, such as the number of heavy atoms. This further highlights the importance of considering different classes of similarity measures between new molecules and known ligands to accurately predict their targets. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Protein homology reveals new targets for bioactive small molecules

Motivation: The functional impact of small molecules is increasingly being assessed in different eukaryotic species through large-scale phenotypic screening initiatives. Identifying the targets of these molecules is crucial to mechanistically understand their function and uncover new therapeutically relevant modes of action. However, despite extensive work carried out in model organisms and human, it is still unclear to what extent one can use information obtained in one species to make predictions in other species. Results: Here, for the first time, we explore and validate at a large scale the use of protein homology relationships to predict the targets of small molecules across different species. Our results show that exploiting target homology can significantly improve the predictions, especially for molecules experimentally tested in other species. Interestingly, when considering separately orthology and paralogy relationships, we observe that mapping small molecule interactions among orthologs improves prediction accuracy, while including paralogs does not improve and even sometimes worsens the prediction accuracy. Overall, our results provide a novel approach to integrate chemical screening results across multiple species and highlight the promises and remaining challenges of using protein homology for small molecule target identification. Availability and implementation: Homology-based predictions can be tested on our website http://www.swisstargetprediction.ch. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics onlin

2D and 3D similarity landscape analysis identifies PARP as a novel off-target for the drug Vatalanib

Background Searching for two-dimensional (2D) structural similarities is a useful tool to identify new active compounds in drug-discovery programs. However, as 2D similarity measures neglect important structural and functional features, similarity by 2D might be underestimated. In the present study, we used combined 2D and three-dimensional (3D) similarity comparisons to reveal possible new functions and/or side-effects of known bioactive compounds. Results We utilised more than 10,000 compounds from the SuperTarget database with known inhibition values for twelve different anti-cancer targets. We performed all-against-all comparisons resulting in 2D similarity landscapes. Among the regions with low 2D similarity scores are inhibitors of vascular endothelial growth factor receptor (VEGFR) and inhibitors of poly ADP-ribose polymerase (PARP). To demonstrate that 3D landscape comparison can identify similarities, which are untraceable in 2D similarity comparisons, we analysed this region in more detail. This 3D analysis showed the unexpected structural similarity between inhibitors of VEGFR and inhibitors of PARP. Among the VEGFR inhibitors that show similarities to PARP inhibitors was Vatalanib, an oral “multi-targeted” small molecule protein kinase inhibitor being studied in phase-III clinical trials in cancer therapy. An in silico docking simulation and an in vitro HT universal colorimetric PARP assay confirmed that the VEGFR inhibitor Vatalanib exhibits off-target activity as a PARP inhibitor, broadening its mode of action. Conclusion In contrast to the 2D-similarity search, the 3D-similarity landscape comparison identifies new functions and side effects of the known VEGFR inhibitor Vatalanib

Application of the SwissDrugDesign Online Resources in Virtual Screening.

SwissDrugDesign is an important initiative led by the Molecular Modeling Group of the SIB Swiss Institute of Bioinformatics. This project provides a collection of freely available online tools for computer-aided drug design. Some of these web-based methods, i.e., SwissSimilarity and SwissTargetPrediction, were especially developed to perform virtual screening, while others such as SwissADME, SwissDock, SwissParam and SwissBioisostere can find applications in related activities. The present review aims at providing a short description of these methods together with examples of their application in virtual screening, where SwissDrugDesign tools successfully supported the discovery of bioactive small molecules

Immune Response Modulation by Tumor-Secreted Glycosphingolipids

Although originally considered merely structural components of cellular membranes, glycosphingolipids (GSL) arenow recognized as having critical effects on cellular physiology, including proliferation, differentiation, viraltransformation and ontogenesis. In addition, a vast majority of human cancers have modified GSL compositioncompared to parental normal cells. These modifications may contribute to both tumor survival and exert strikingeffects on anti-tumor immunity. In this review, we discuss mechanisms of immune modulation by tumor-secreted GSL.Fil: Lardone, Ricardo Dante. John Wayne Cancer Institute at Providence Saint John’s Health Center. Santa Monica; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigaciones en Química Biológica de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Centro de Investigaciones en Química Biológica de Córdoba; ArgentinaFil: Cely, Ingrid. John Wayne Cancer Institute at Providence Saint John’s Health Center. Santa Monica; Estados UnidosFil: Sieling, Peter A.. John Wayne Cancer Institute at Providence Saint John’s Health Center. Santa Monica; Estados UnidosFil: Lee, Delphine. John Wayne Cancer Institute at Providence Saint John’s Health Center. Santa Monica; Estados Unido

Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery

Učinci imidazolijevih i kloriranih bispiridinijevih oksima povezani s njihovom toksičnosti na stanicama SH-SY5Y

Current research has shown that several imidazolium and chlorinated bispyridinium oximes are cytotoxic and activate different mechanisms or types of cell death. To investigate this further, we analysed interactions between these oximes and acetylcholine receptors (AChRs) and how they affect several signalling pathways to find a relation between the observed toxicities and their effects on these specific targets. Chlorinated bispyridinium oximes caused time-dependent cytotoxicity by inhibiting the phosphorylation of STAT3 and AMPK without decreasing ATP and activated ERK1/2 and p38 MAPK signal cascades. Imidazolium oximes induced a time-independent and significant decrease in ATP and inhibition of the ERK1/2 signalling pathway along with phosphorylation of p38 MAPK, AMPK, and ACC. These pathways are usually triggered by a change in cellular energy status or by external signals, which suggests that oximes interact with some membrane receptors. Interestingly, in silico analysis also indicated that the highest probability of interaction for all of our oximes is with the family of G-coupled membrane receptors (GPCR). Furthermore, our experimental results showed that the tested oximes acted as acetylcholine antagonists for membrane AChRs. Even though oxime interactions with membrane receptors need further research and clarification, our findings suggest that these oximes make promising candidates for the development of specific therapies not only in the field of cholinesterase research but in other fields too, such as anticancer therapy via altering the Ca2+ flux involved in cancer progression.Praćenjem učinka odabranih imidazolijevih i kloriranih bispiridinijevih oksima utvrđeno je da uzrokuju citotoksičnost i aktiviraju različite mehanizme ili tipove stanične smrti. Kako bismo to detaljnije istražili, analizirali smo aktivaciju nekoliko signalnih putova, kao i interakcije acetilkolinskih receptora (AChR) s navedenim oksimima te procijenili može li se opaženi toksični učinak objasniti njihovim utjecajem na ove specifične mete. Rezultati su pokazali da su klorirani bispiridinijevi oksimi prouzročili vremenski-ovisnu citotoksičnost, bez smanjenja razine ATP-a uz aktivaciju ERK1/2 i p38 MAPK-vezanih signalnih kaskada i inhibiciju fosforilacije STAT3 i AMPK proteina. Imidazolijevi oksimi djelovali su vremenski neovisno, uz značajno smanjenje razine ATP-a i inhibiciju ERK1/2 signalnog puta te fosforilaciju p38 MAPK, AMPK i ACC proteina. Navedeni signalni putovi obično se aktiviraju ili promjenom unutarnjega staničnog statusa, osobito energetskoga, ili vanjskim signalima, što upućuje na moguće interakcije oksima s nekim membranskim receptorima. Zanimljivo, in silico analizom procijenjeno je da je najvjerojatnija interakcija testiranih oksima s porodicom G-protein-spregnutih membranskih receptora (GPCR). K tomu, eksperimentalno je potvrđeno da testirani oksimi djeluju kao mogući antagonisti acetilkolina za vezanje na membranske AChR, potvrđujući tako i računalnu in silico procjenu. Iako interakcije ispitanih oksima s membranskim receptorima treba dodatno potvrditi, takve bi ih interakcije učinile kandidatima za razvoj specifičnih terapija u drugim područjima istraživanja, osim u istraživanjima povezanima s kolinesterazama, npr. kao moguće protutumorske lijekove, putem utjecaja na fluks iona Ca2+ uključenoga u progresiju tumora

Recent advances in in silico target fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies

The chemical interactome space between the human host and the genetically defined gut metabotypes

The bacteria that colonize the gastrointestinal tracts of mammals represent a highly selected microbiome that has a profound influence on human physiology by shaping the host's metabolic and immune system activity. Despite the recent advances on the biological principles that underlie microbial symbiosis in the gut of mammals, mechanistic understanding of the contributions of the gut microbiome and how variations in the metabotypes are linked to the host health are obscure. Here, we mapped the entire metabolic potential of the gut microbiome based solely on metagenomics sequencing data derived from fecal samples of 124 Europeans (healthy, obese and with inflammatory bowel disease). Interestingly, three distinct clusters of individuals with high, medium and low metabolic potential were observed. By illustrating these results in the context of bacterial population, we concluded that the abundance of the Prevotella genera is a key factor indicating a low metabolic potential. These metagenome-based metabolic signatures were used to study the interaction networks between bacteria-specific metabolites and human proteins. We found that thirty-three such metabolites interact with disease-relevant protein complexes several of which are highly expressed in cells and tissues involved in the signaling and shaping of the adaptive immune system and associated with squamous cell carcinoma and bladder cancer. From this set of metabolites, eighteen are present in DrugBank providing evidence that we carry a natural pharmacy in our guts. Furthermore, we established connections between the systemic effects of non-antibiotic drugs and the gut microbiome of relevance to drug side effects and health-care solutions.link_to_subscribed_fulltex