Matching single cells across modalities with contrastive learning and optimal transport.

Understanding the interactions between the biomolecules that govern cellular behaviors remains an emergent question in biology. Recent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecules in the same cell, opening new avenues for understanding cellular complexity and heterogeneity. Still, the resulting multimodal single-cell datasets present unique challenges arising from the high dimensionality and multiple sources of acquisition noise. Computational methods able to match cells across different modalities offer an appealing alternative towards this goal. In this work, we propose MatchCLOT, a novel method for modality matching inspired by recent promising developments in contrastive learning and optimal transport. MatchCLOT uses contrastive learning to learn a common representation between two modalities and applies entropic optimal transport as an approximate maximum weight bipartite matching algorithm. Our model obtains state-of-the-art performance on two curated benchmarking datasets and an independent test dataset, improving the top scoring method by 26.1% while preserving the underlying biological structure of the multimodal data. Importantly, MatchCLOT offers high gains in computational time and memory that, in contrast to existing methods, allows it to scale well with the number of cells. As single-cell datasets become increasingly large, MatchCLOT offers an accurate and efficient solution to the problem of modality matching.

scQUEST: Quantifying tumor ecosystem heterogeneity from mass or flow cytometry data.

With mass and flow cytometry, millions of single-cell profiles with dozens of parameters can be measured to comprehensively characterize complex tumor ecosystems. Here, we present scQUEST, an open-source Python library for cell type identification and quantification of tumor ecosystem heterogeneity in patient cohorts. We provide a step-by-step protocol on the application of scQUEST on our previously generated human breast cancer single-cell atlas using mass cytometry and discuss how it can be adapted and extended for other datasets and analyses. For complete details on the use and execution of this protocol, please refer to Wagner et al. (2019).

ATHENA: analysis of tumor heterogeneity from spatial omics measurements.

SUMMARY: Tumor heterogeneity has emerged as a fundamental property of most human cancers, with broad implications for diagnosis and treatment. Recently, spatial omics have enabled spatial tumor profiling, however computational resources that exploit the measurements to quantify tumor heterogeneity in a spatially aware manner are largely missing. We present ATHENA (Analysis of Tumor HEterogeNeity from spAtial omics measurements), a computational framework that facilitates the visualization, processing and analysis of tumor heterogeneity from spatial omics measurements. ATHENA uses graph representations of tumors and bundles together a large collection of established and novel heterogeneity scores that quantify different aspects of the complexity of tumor ecosystems. AVAILABILITY AND IMPLEMENTATION: ATHENA is available as a Python package under an open-source license at: https://github.com/AI4SCR/ATHENA. Detailed documentation and step-by-step tutorials with example datasets are also available at: https://ai4scr.github.io/ATHENA/. The data presented in this article are publicly available on Figshare at https://figshare.com/articles/dataset/zurich_pkl/19617642/2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Universal encoding of next generation DNA-encoded chemical libraries.

DNA-encoded chemical libraries (DELs) are useful tools for the discovery of small molecule ligands to protein targets of pharmaceutical interest. Compared with single-pharmacophore DELs, dual-pharmacophore DELs simultaneously display two chemical moieties on both DNA strands, and allow for the construction of highly diverse and pure libraries, with a potential for targeting larger protein surfaces. Although methods for the encoding of simple, fragment-like dual-display libraries have been established, more complex libraries require a different encoding strategy. Here, we present a robust and convenient "large encoding design" (LED), which facilitates the PCR-amplification of multiple codes distributed among two partially complementary DNA strands. We experimentally implemented multiple coding regions and we compared the new DNA encoding scheme with previously reported dual-display DEL modalities in terms of amplifiability and performance in test selections against two target proteins. With the LED methodology in place, we foresee the construction and screening of DELs of unprecedented sizes and designs.

VenomPred: A Machine Learning Based Platform for Molecular Toxicity Predictions.

The use of in silico toxicity prediction methods plays an important role in the selection of lead compounds and in ADMET studies since in vitro and in vivo methods are often limited by ethics, time, budget and other resources. In this context, we present our new web tool VenomPred, a user-friendly platform for evaluating the potential mutagenic, hepatotoxic, carcinogenic and estrogenic effects of small molecules. VenomPred platform employs several in-house Machine Learning (ML) models developed with datasets derived from VEGA QSAR, a software that includes a comprehensive collection of different toxicity models and has been used as a reference for building and evaluating our ML models. The results showed that our models achieved equal or better performance than those obtained with the reference models included in VEGA QSAR. In order to improve the predictive performance of our platform, we adopted a consensus approach combining the results of different ML models, which was able to predict chemical toxicity better than the single models. This improved method was thus implemented in the VenomPred platform, a freely accessible webserver that takes the SMILES (Simplified Molecular-Input Line-Entry System) strings of the compounds as input and sends the prediction results providing a probability score about their potential toxicity.

Quantification of tumor heterogeneity: from data acquisition to metric generation.

Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.

From Anti-infective Agents to Cancer Therapy: A Drug Repositioning Study Revealed a New Use for Nitrofuran Derivatives.

BACKGROUND: The progression of ovarian cancer seems to be related to HDAC1, HDAC3, and HDAC6 activity. A possible strategy for improving therapies for treating ovarian carcinoma, minimizing the preclinical screenings, is the repurposing of already approved pharmaceutical products as inhibitors of these enzymes. OBJECTIVE: This work was aimed to implement a computational strategy for identifying new HDAC inhibitors for ovarian carcinoma treatment among approved drugs. METHOD: The CHEMBL database was used to construct training, test, and decoys sets for performing and validating HDAC1, HDAC3 and HDAC6 3D-QSAR models obtained by using the FLAP program. Docking and MD simulations were used in combination with the generated models to identify novel potential HDAC inhibitors. Cell viability assays and Western blot analyses were performed on normal and cancer cells for a direct evaluation of the anti-proliferative activity and an in vitro estimation of HDAC inhibition of the compounds selected through in silico screening. RESULT: The best quantitative prediction was obtained for the HDAC6 3D-QSAR model. The screening of approved drugs highlighted a new potential use as HDAC inhibitors for some compounds, in particular nitrofuran derivatives, usually known for their antibacterial activity and frequently used as antimicrobial adjuvant therapy in cancer treatment. Experimental evaluation of these derivatives highlighted a significant antiproliferative activity against cancer cell lines overexpressing HDAC6, and an increase in acetylated alpha-tubulin levels. CONCLUSION: Experimental results support the hypothesis of potential direct interaction of nitrofuran derivatives with HDACs. In addition to the possible repurposing of already approved drugs, this work suggests the nitro group as a new zinc-binding group, able to interact with the catalytic zinc ion of HDACs.

A Single-Stranded DNA-Encoded Chemical Library Based on a Stereoisomeric Scaffold Enables Ligand Discovery by Modular Assembly of Building Blocks.

A versatile and Lipinski-compliant DNA-encoded library (DEL), comprising 366 600 glutamic acid derivatives coupled to oligonucleotides serving as amplifiable identification barcodes is designed, constructed, and characterized. The GB-DEL library, constructed in single-stranded DNA format, allows de novo identification of specific binders against several pharmaceutically relevant proteins. Moreover, hybridization of the single-stranded DEL with a set of known protein ligands of low to medium affinity coupled to a complementary DNA strand results in self-assembled selectable chemical structures, leading to the identification of affinity-matured compounds.

Comparative evaluation of DNA-encoded chemical selections performed using DNA in single-stranded or double-stranded format.

DNA-encoded chemical libraries (DEL) are increasingly being used for the discovery and optimization of small organic ligands to proteins of biological or pharmaceutical interest. The DNA fragments, that serve as amplifiable identification barcodes for individual compounds in the library, are typically used in double-stranded DNA format. To the best of our knowledge, a direct comparison of DEL selections featuring DNA in either single- or double-stranded DNA format has not yet been reported. In this article, we describe a comparative evaluation of selections with two DEL libraries (named GB-DEL and NF-DEL), based on different chemical designs and produced in both single- and double-stranded DNA format. The libraries were selected in identical conditions against multiple protein targets, revealing comparable and reproducible fingerprints for both types of DNA formats. Surprisingly, selections performed with single-stranded DNA barcodes exhibited improved enrichment factors compared to double-stranded DNA. Using high-affinity ligands to carbonic anhydrase IX as benchmarks for selection performance, we observed an improved selectivity for the NF-DEL library (on average 2-fold higher enrichment factors) in favor of single-stranded DNA. The enrichment factors were even higher for the GB-DEL selections (approximately 5-fold), compared to the same library in double-stranded DNA format. Collectively, these results indicate that DEL libraries can conveniently be synthesized and screened in both single- and double-stranded DNA format, but single-stranded DNA barcodes typically yield enhanced enrichment factors.

Development of a cheminformatics platform for selectivity analyses of carbonic anhydrase inhibitors.

The selectivity for a specific human Carbonic Anhydrase (hCA) isoform is an important property a hCA inhibitor (CAI) should be endowed with, in order to constitute a valuable therapeutic tool for the treatment of a desired pathology. In this context, we developed a chemoinformatic platform that allows the analysis of the structure and selectivity profile of known CAIs reported in literature, with the aim of identifying structural motifs connected to ligand selectivity, thus providing useful guidelines for the design of novel ligands selective for the desired hCA isoform. The platform is able to perform ultrafast structure and selectivity analyses through ligand fingerprint similarity, with no need of structural information about the target receptor and ligands' binding mode. It is easily accessible to the non-expert user through the implementation of a KNIME Analytic Platform workflow and could be extended to analyze the selectivity profile of known ligands of different target proteins.

Extensive Reliability Evaluation of Docking-Based Target-Fishing Strategies.

The development of target-fishing approaches, aimed at identifying the possible protein targets of a small molecule, represents a hot topic in medicinal chemistry. A successful target-fishing approach would allow for the elucidation of the mechanism of action of all therapeutically interesting compounds for which the actual target is still unknown. Moreover, target-fishing would be essential for preventing adverse effects of drug candidates, by predicting their potential off-targets, and it would speed up drug repurposing campaigns. However, due to the huge number of possible protein targets that a small-molecule might interact with, experimental target-fishing approaches are out of reach. In silico target-fishing represents a valuable alternative, and examples of receptor-based approaches, exploiting the large number of crystallographic protein structures determined to date, have been reported in the literature. To the best of our knowledge, no proper evaluation of such approaches is, however, reported yet. In the present work, we extensively assessed the reliability of docking-based target-fishing strategies. For this purpose, a set of X-ray structures belonging to different targets was selected, and a dataset of compounds, including 10 experimentally active ligands for each target, was created. A target-fishing benchmark database was then obtained, and used to assess the performance of 13 different docking procedures, in identifying the correct target of the dataset ligands. Moreover, a consensus docking-based target-fishing strategy was developed and evaluated. The analysis highlighted that specific features of the target proteins could affect the reliability of the protocol, which however, proved to represent a valuable tool in the proper applicability domain. Our study represents the first extensive performance assessment of docking-based target-fishing approaches, paving the way for the development of novel efficient receptor-based target fishing strategies.

Discovery of long-chain salicylketoxime derivatives as monoacylglycerol lipase (MAGL) inhibitors.

Monoacylglycerol lipase (MAGL) is the enzyme hydrolyzing the endocannabinoid 2-arachidonoylglycerol (2-AG) to free arachidonic acid and glycerol. Therefore, MAGL is implicated in many physiological processes involving the regulation of the endocannabinoid system and eicosanoid network. MAGL inhibition represents a potential therapeutic target for many diseases, including cancer. Nowadays, most MAGL inhibitors inhibit this enzyme by an irreversible mechanism of action, potentially leading to unwanted side effects from chronic treatment. Herein, we report the discovery of long-chain salicylketoxime derivatives as potent and reversible MAGL inhibitors. The compounds herein described are characterized by a good target selectivity for MAGL and by antiproliferative activities against a series of cancer cell lines. Finally, modeling studies suggest a reasonable hypothetical binding mode for this class of compounds.

Rational Development of MAGL Inhibitors.

Hit identification and hit-to-lead optimization are key steps of the early drug discovery program. Starting from the X-ray crystal structure of the human monoacylglycerol lipase (hMAGL), we herein describe the computational and experimental procedures that we applied for identifying and optimizing a new active inhibitor of this target enzyme. A receptor-based virtual screening method is reported in details, together with enzymatic assays and a first round of hit optimization.

Development of a Fingerprint-Based Scoring Function for the Prediction of the Binding Mode of Carbonic Anhydrase II Inhibitors.

Carbonic anhydrase II (CAII) is a zinc-containing metalloenzyme whose aberrant activity is associated with various diseases such as glaucoma, osteoporosis, and different types of tumors; therefore, the development of CAII inhibitors, which can represent promising therapeutic agents for the treatment of these pathologies, is a current topic in medicinal chemistry. Molecular docking is a commonly used tool in structure-based drug design of enzyme inhibitors. However, there is still a need for improving docking reliability, especially in terms of scoring functions, since the complex pattern of energetic contributions driving ligand⁻protein binding cannot be properly described by mathematical functions only including approximated energetic terms. Here we report a novel CAII-specific fingerprint-based (IFP) scoring function developed according to the ligand⁻protein interactions detected in the CAII-inhibitor co-crystal structures of the most potent CAII ligands. Our IFP scoring function outperformed the ability of Autodock4 scoring function to identify native-like docking poses of CAII inhibitors and thus allowed a considerable improvement of docking reliability. Moreover, the ligand⁻protein interaction fingerprints showed a useful application in the binding mode analysis of structurally diverse CAII ligands.

Discovery of 1,5-Diphenylpyrazole-3-Carboxamide Derivatives as Potent, Reversible, and Selective Monoacylglycerol Lipase (MAGL) Inhibitors.

Monoacylglycerol lipase (MAGL) is a serine hydrolase that plays an important role in the degradation of the endocannabinoid neurotransmitter 2-arachidonoylglycerol, which is implicated in many physiological processes. Beyond the possible utilization of MAGL inhibitors as anti-inflammatory, antinociceptive, and anticancer agents, their application has encountered obstacles due to the unwanted effects caused by the irreversible inhibition of this enzyme. The possible application of reversible MAGL inhibitors has only recently been explored, mainly due to the deficiency of known compounds possessing efficient reversible inhibitory activities. In this work, we report a new series of reversible MAGL inhibitors. Among them, compound 26 showed to be a potent MAGL inhibitor (IC50 = 0.51 µM, Ki = 412 nM) with a good selectivity versus fatty acid amide hydrolase (FAAH), α/ß-hydrolase domain-containing 6 (ABHD6), and 12 (ABHD12). Interestingly, this compound also possesses antiproliferative activities against two different cancer cell lines and relieves the neuropathic hypersensitivity induced in vivo by oxaliplatin.

Development of terphenyl-2-methyloxazol-5(4H)-one derivatives as selective reversible MAGL inhibitors.

Monoacylglycerol lipase is a serine hydrolase that plays a major role in the degradation of the endocannabinoid neurotransmitter 2-arachidonoylglycerol. A wide number of MAGL inhibitors are reported in literature; however, many of them are characterised by an irreversible mechanism of action and this behavior determines an unwanted chronic MAGL inactivation, which acquires a functional antagonism of the endocannabinoid system. The possible use of reversible MAGL inhibitors has only recently been explored, due to the lack of known compounds possessing efficient reversible inhibitory activities. In this work, we report a new series of terphenyl-2-methyloxazol-5(4H)-one derivatives characterised by a reversible MAGL-inhibition mechanism. Among them, compound 20b showed to be a potent MAGL reversible inhibitor (IC50 = 348 nM) with a good MAGL/FAAH selectivity. Furthermore, this compound showed antiproliferative activities against two different cancer cell lines that overexpress MAGL.

Identification of Transthyretin Fibril Formation Inhibitors Using Structure-Based Virtual Screening.

Transthyretin (TTR) is the primary carrier for thyroxine (T4 ) in cerebrospinal fluid and a secondary carrier in blood. TTR is a stable homotetramer, but certain factors, genetic or environmental, could promote its degradation to form amyloid fibrils. A docking study using crystal structures of wild-type TTR was planned; our aim was to design new ligands that are able to inhibit TTR fibril formation. The computational protocol was thought to overcome the multiple binding modes of the ligands induced by the peculiarity of the TTR binding site and by the pseudosymmetry of the site pockets, which generally weaken such structure-based studies. Two docking steps, one that is very fast and a subsequent step that is more accurate, were used to screen the Aldrich Market Select database. Five compounds were selected, and their activity toward inhibiting TTR fibril formation was assessed. Three compounds were observed to be actives, two of which have the same potency as the positive control, and the other was found to be a promising lead compound. These results validate a computational protocol that is able to archive information on the key interactions between database compounds and TTR, which is valuable for supporting further studies.

Structural Optimization of 4-Chlorobenzoylpiperidine Derivatives for the Development of Potent, Reversible, and Selective Monoacylglycerol Lipase (MAGL) Inhibitors.

Monoacylglycerol lipase (MAGL) inhibitors are considered potential therapeutic agents for a variety of pathological conditions, including several types of cancer. Many MAGL inhibitors are reported in literature; however, most of them showed an irreversible mechanism of action, which caused important side effects. The use of reversible MAGL inhibitors has been only partially investigated so far, mainly because of the lack of compounds with good MAGL reversible inhibition properties. In this study, starting from the (4-(4-chlorobenzoyl)piperidin-1-yl)(4-methoxyphenyl)methanone (CL6a) lead compound that showed a reversible mechanism of MAGL inhibition (Ki = 8.6 µM), we started its structural optimization and we developed a new potent and selective MAGL inhibitor (17b, Ki = 0.65 µM). Furthermore, modeling studies suggested that the binding interactions of this compound replace a structural water molecule reproducing its H-bonds in the MAGL binding site, thus identifying a new key anchoring point for the development of new MAGL inhibitors.

A Virtual Screening Study for Lactate Dehydrogenase 5 Inhibitors by Using a Pharmacophore-based Approach.

Inhibitors of human lactate dehydrogenase 5 (hLDH5) are promising therapeutic agents against cancer. This enzyme is generally found to be overexpressed in most invasive cancer cells and is linked to their vitality especially under hypoxic conditions. In this study, with the aim of identifying new hLDH5 inhibitors, a receptor-based pharmacophore modeling approach has been tested and, in order to verify the reliability of the reported approach, the Gold and Platinum database from Asinex were filtered. The top-ranked compounds were experimentally tested for their hLDH5 inhibition activity and enzymatic assays revealed that, among the ten selected compounds, two proved to inhibit the enzyme activity with Ki values in the micromolar range (Ki =33.1-76.7 µM).

Reliability analysis and optimization of the consensus docking approach for the development of virtual screening studies.

Ligand-protein docking is one of the most common techniques used in virtual screening campaigns. Despite the large number of docking software available, there is still the need of improving the efficacy of docking-based virtual screenings. To date, only very few studies evaluated the possibility of combining the results of different docking methods to achieve higher success rates in virtual screening studies (consensus docking). In order to better understand the range of applicability of this approach, we carried out an extensive enriched database analysis using the DUD dataset. The consensus docking protocol was then refined by applying modifications concerning the calculation of pose consensus and the combination of docking methods included in the procedure. The results obtained suggest that this approach performs as well as the best available methods found in literature, confirming the idea that this procedure can be profitably used for the identification of new hit compounds.