Prevalence and drug susceptibility of clinical Candida species in nasopharyngeal cancer patients in Vietnam

In the nature, Candida species are normal inhabitants and can be observed in a wide variety of vertebrates. In humans, especially for cancer patients who fall prey to opportunistic pathogens, this group of susceptible multi-drug resistant and biofilm-forming yeasts, are among the commonest ones. In this study, Candida species in 76 oral lesion samples from Vietnamese nasopharyngeal-cancer patients were isolated, morphologically identified using CHROMagar™, germ tube formation, and chlamydospore formation tests, and molecularly confirmed by PCR-RFLP. The drug susceptibility of these isolates was then tested, and the gene ERG11 was DNA sequenced to investigate the mechanism of resistance. The results showed that Candida albicans remained the most prevalent species (63.16% of the cases), followed by Candida glabrata, Candida tropicalis, and Candida krusei. The rates of resistance of non-albicans Candida for tested drugs were 85.71%, 53.57%, and 57.14% to fluconazole, clotrimazole, and miconazole, respectively. Although the drug-resistance rate of Candida albicans was lower than that of non-albicans Candida, it was higher than expected, suggesting an emerging drug-resistance phenomenon. Furthermore, ERG11 DNA sequencing revealed different mutations (especially K128T), implying the presence of multiple resistance mechanisms. Altogether, the results indicate an alarming drug-resistance situation in Candida species in Vietnamese cancer patients and emphasize the importance of species identification and their drug susceptibility prior to treatment.

Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors

Poly ADP-ribose polymerase 1 (PARP1) is an attractive therapeutic target for cancer treatment. Machine-learning scoring functions constitute a promising approach to discovering novel PARP1 inhibitors. Cutting-edge PARP1-specific machine-learning scoring functions were investigated using semi-synthetic training data from docking activity-labelled molecules: known PARP1 inhibitors, hard-to-discriminate decoys property-matched to them with generative graph neural networks and confirmed inactives. We further made test sets harder by including only molecules dissimilar to those in the training set. Comprehensive analysis of these datasets using five supervised learning algorithms, and protein-ligand fingerprints extracted from docking poses and ligand only features revealed one highly predictive scoring function. This is the PARP1-specific support vector machine-based regressor, when employing PLEC fingerprints, which achieved a high Normalized Enrichment Factor at the top 1% on the hardest test set (NEF1% = 0.588, median of 10 repetitions), and was more predictive than any other investigated scoring function, especially the classical scoring function employed as baseline.

One-step synthesis of diaryloxadiazoles as potent inhibitors of BCRP

Aim: BCRP plays a major role in the efflux of cytotoxic molecules, limiting their antiproliferative activity. We aimed to design and synthesize new BCRP inhibitors to render cancerous tumors more sensitive toward anticancer agents. Materials & methods: Based on our previous work, we conceived potential BCRP inhibitors derived from 1,3,4-oxadiazoles bearing two substituted phenyl rings. Results: Evaluating 19 derivatives, we found that 2,5-diaryl-1,3,4-oxadiazoles possessing methoxy groups were the most active. The highest activity was recorded with derivatives bearing three methoxy groups. The most active compound (3j) was selective in inhibiting BCRP and nontoxic as evidenced by cellular tests. Conclusion: 3j is a promising BCRP inhibitor thanks to its synthetic accessibility and biological profile.

Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers

INTRODUCTION

Small-molecule Programmable Cell Death Protein 1/Programmable Death-Ligand 1 (PD1/PDL1) inhibition via PDL1 dimerization has the potential to lead to inexpensive drugs with better cancer patient outcomes and milder side effects. However, this therapeutic approach has proven challenging, with only one PDL1 dimerizer reaching early clinical trials so far. There is hence a need for fast and accurate methods to develop alternative PDL1 dimerizers.

OBJECTIVES

We aim to show that structure-based virtual screening (SBVS) based on PDL1-specific machine-learning (ML) scoring functions (SFs) is a powerful drug design tool for detecting PD1/PDL1 inhibitors via PDL1 dimerization.

METHODS

By incorporating the latest MLSF advances, we generated and evaluated PDL1-specific MLSFs (classifiers and inactive-enriched regressors) on two demanding test sets.

RESULTS

60 PDL1-specific MLSFs (30 classifiers and 30 regressors) were generated. Our large-scale analysis provides highly predictive PDL1-specific MLSFs that benefitted from training with large volumes of docked inactives and enabling inactive-enriched regression.

CONCLUSION

PDL1-specific MLSFs strongly outperformed generic SFs of various types on this target and are released here without restrictions.

Discovery of potent chromone-based autotaxin inhibitors inspired by cannabinoids

Autotaxin (ATX) is an enzyme primarily known for the production of lysophosphatidic acid. Being involved in the development of major human diseases, such as cancer and neurodegenerative diseases, the enzyme has been featured in multiple studies as a pharmacological target. We previously found that the cannabinoid tetrahydrocannabinol (THC) could bind and act as an excellent inhibitor of ATX. This study aims to use the cannabinoid scaffold as a starting point to find cannabinoid-unrelated ATX inhibitors, following a funnel down approach in which large chemical libraries sharing chemical similarities with THC were screened to identify lead scaffold types for optimization. This approach allowed us to identify compounds bearing chromone and indole scaffolds as promising ATX inhibitors. Further optimization led to MEY-003, which is characterized by the direct linkage of an N-pentyl indole to the 5,7-dihydroxychromone moiety. This molecule has potent inhibitory activity towards ATX-β and ATX-ɣ as evidenced by enzymatic studies and its mode of action was rationalized by structural biology studies using macromolecular X-ray crystallography.

A practical guide to machine-learning scoring for structure-based virtual screening

Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in size. Artificial intelligence and, more concretely, its machine-learning (ML) branch, including deep learning, have effectively exploited these data sets to build scoring functions (SFs) for SBVS against targets with an atomic-resolution 3D model (e.g., generated by X-ray crystallography or predicted by AlphaFold2). Often outperforming their generic and non-ML counterparts, target-specific ML-based SFs represent the state of the art for SBVS. Here, we present a comprehensive and user-friendly protocol to build and rigorously evaluate these new SFs for SBVS. This protocol is organized into four sections: (i) using a public benchmark of a given target to evaluate an existing generic SF; (ii) preparing experimental data for a target from public repositories; (iii) partitioning data into a training set and a test set for subsequent target-specific ML modeling; and (iv) generating and evaluating target-specific ML SFs by using the prepared training-test partitions. All necessary code and input/output data related to three example targets (acetylcholinesterase, HMG-CoA reductase, and peroxisome proliferator-activated receptor-α) are available at https://github.com/vktrannguyen/MLSF-protocol , can be run by using a single computer within 1 week and make use of easily accessible software/programs (e.g., Smina, CNN-Score, RF-Score-VS and DeepCoy) and web resources. Our aim is to provide practical guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules within a given compound library.

Beware of Simple Methods for Structure-Based Virtual Screening: The Critical Importance of Broader Comparisons

We discuss how data unbiasing and simple methods such as protein-ligand Interaction FingerPrint (IFP) can overestimate virtual screening performance. We also show that IFP is strongly outperformed by target-specific machine-learning scoring functions, which were not considered in a recent report concluding that simple methods were better than machine-learning scoring functions at virtual screening.

Structure-based virtual screening for PDL1 dimerizers: Evaluating generic scoring functions

The interaction between PD1 and its ligand PDL1 has been shown to render tumor cells resistant to apoptosis and promote tumor progression. An innovative mechanism to inhibit the PD1/PDL1 interaction is PDL1 dimerization induced by small-molecule PDL1 binders. Structure-based virtual screening is a promising approach to discovering such small-molecule PD1/PDL1 inhibitors. Here we investigate which type of generic scoring functions is most suitable to tackle this problem. We consider CNN-Score, an ensemble of convolutional neural networks, as the representative of machine-learning scoring functions. We also evaluate Smina, a commonly used classical scoring function, and IFP, a top structural fingerprint similarity scoring function. These three types of scoring functions were evaluated on two test sets sharing the same set of small-molecule PD1/PDL1 inhibitors, but using different types of inactives: either true inactives (molecules with no in vitro PD1/PDL1 inhibition activity) or assumed inactives (property-matched decoy molecules generated from each active). On both test sets, CNN-Score performed much better than Smina, which in turn strongly outperformed IFP. The fact that the latter was the case, despite precluding any possibility of exploiting decoy bias, demonstrates the predictive value of CNN-Score for PDL1. These results suggest that re-scoring Smina-docked molecules with CNN-Score is a promising structure-based virtual screening method to discover new small-molecule inhibitors of this therapeutic target.

Diversity-Oriented Synthesis: Amino Acetophenones as Building Blocks for the Synthesis of Natural Product Analogs

Diversity-Oriented Synthesis (DOS) represents a strategy to obtain molecule libraries with diverse structural features starting from one common compound in limited steps of synthesis. During the last two decades, DOS has become an unmissable strategy in organic synthesis and is fully integrated in various drug discovery processes. On the other hand, natural products with multiple relevant pharmacological properties have been extensively investigated as scaffolds for ligand-based drug design. In this article, we report the amino dimethoxyacetophenones that can be easily synthesized and scaled up from the commercially available 3,5-dimethoxyaniline as valuable starting blocks for the DOS of natural product analogs. More focus is placed on the synthesis of analogs of flavones, coumarins, azocanes, chalcones, and aurones, which are frequently studied as lead compounds in drug discovery.

True Accuracy of Fast Scoring Functions to Predict High-Throughput Screening Data from Docking Poses: The Simpler the Better

Hundreds of fast scoring functions have been developed over the last 20 years to predict binding free energies from three-dimensional structures of protein-ligand complexes. Despite numerous statistical promises, we believe that none of them has been properly validated for daily prospective high-throughput virtual screening studies, mostly because in silico screening challenges usually employ artificially built and biased datasets. We here carry out a fully unbiased evaluation of four scoring functions (Pafnucy, Δ_vinaRF₂₀, IFP, and GRIM) on an in-house developed data collection of experimental high-confidence screening data (LIT-PCBA) covering about 3 million data points on 15 diverse pharmaceutical targets. All four scoring functions were applied to rescore the docking poses of LIT-PCBA compounds in conditions mimicking exactly standard drug discovery scenarios and were compared in terms of propensity to enrich true binders in the top 1%-ranked hit lists. Interestingly, rescoring based on simple interaction fingerprints or interaction graphs outperforms state-of-the-art machine learning and deep learning scoring functions in most of the cases. The current study notably highlights the strong tendency of deep learning methods to predict affinity values within a very narrow range centered on the mean value of samples used for training. Moreover, it suggests that knowledge of pre-existing binding modes is the key to detecting the most potent binders.

Peramivir binding affinity with influenza A neuraminidase and research on its mutations using an induced-fit docking approach

Influenza A virus (IAV) has caused epidemic infections worldwide, with many strains resistant to inhibitors of a surface protein, neuraminidase (NA), due to point mutations on its structure. A novel NA inhibitor named peramivir was recently approved, but no exhaustive computational research regarding its binding affinity with wild-type and mutant NA has been conducted. In this study, a thorough investigation of IAV-NA PDB entries of 9 subtypes is described, providing a list of residues constituting the protein-ligand binding sites. The results of induced-fit docking approach point out key residues of wild-type NA participating in hydrogen bonds and/or ionic interactions with peramivir, among which Arg 368 is responsible for a peramivir-NA ionic interaction. Mutations on this residue greatly reduced the binding affinity of peramivir with NA, with 3 mutations R378Q, R378K and R378L (NA6) capable of deteriorating the docking performance of peramivir by over 50%. 200 compounds from 6-scaffolds were docked into these 3 mutant versions, revealing 18 compounds giving the most promising results. Among them, CMC-2012-7-1527-56 (benzoic acid scaffold, IC50 = 32 nM in inhibitory assays with IAV) is deemed the most potential inhibitor of mutant NA resisting both peramivir and zanamivir, and should be further investigated.

Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses

Ligand docking at a protein site can be improved by prioritizing poses by similarity to validated binding modes found in the crystal structures of ligand/protein complexes. The interactions formed in the predicted model are searched in each of the reference 3D structures, taken individually. We propose to merge the information provided by all references, creating a single representation of all known binding modes. The method is called LID, an acronym for Local Interaction Density. LID was benchmarked in a pose prediction exercise on 19 proteins and 1382 ligands using PLANTS as docking software. It was also tested in a virtual screening challenge on eight proteins, with a dataset of 140,000 compounds from DUD-E and PubChem. LID significantly improved the performance of the docking program in both pose prediction and virtual screening. The gain is comparable to that obtained with a rescoring approach based on the individual comparison of reference binding modes (the GRIM method). Importantly, LID is effective with a small number of references. LID calculation time is negligible compared to the docking time.

All in One: Cavity Detection, Druggability Estimate, Cavity-Based Pharmacophore Perception, and Virtual Screening

Discovering the very first ligands of pharmacologically important targets in a fast and cost-efficient manner is an important issue in drug discovery. In the absence of structural information on either endogenous or synthetic ligands, computational chemists classically identify the very first hits by docking compound libraries to a binding site of interest, with well-known biases arising from the usage of scoring functions. We herewith propose a novel computational method tailored to ligand-free protein structures and consisting in the generation of simple cavity-based pharmacophores to which potential ligands could be aligned by the use of a smooth Gaussian function. The method, embedded in the IChem toolkit, automatically detects ligand-binding cavities, then predicts their structural druggability, and last creates a structure-based pharmacophore for predicted druggable binding sites. A companion tool (Shaper2) was designed to align ligands to cavity-derived pharmacophoric features. The proposed method is as efficient as state-of-the-art virtual screening methods (ROCS, Surflex-Dock) in both posing and virtual screening challenges. Interestingly, IChem-Shaper2 is clearly orthogonal to these latter methods in retrieving unique chemotypes from high-throughput virtual screening data.

Modulators of the Efflux Pump Cdr1p of Candida albicans: Mechanisms of Action and Chemical Features

BACKGROUND

The multicomponent primary active ATP-binding cassette transporter Cdr1p in the structure of the pathogenic yeast Candida albicans is among the culprits of antifungal agent resistance reported in recent decades. So far, various potential novel inhibitors/ modulators of this protein have been purified, synthesized, and biologically tested, with results showing their ability to effectively reverse CaCdr1p-mediated drug resistance phenomenon. These compounds are of diverse origins, possess non-identical structural features and adopt different mechanisms of action.

METHOD

A structured search of chemical features and mechanisms of studied modulators of CaCdr1p was carried out using both original research publications and review articles. The nature of possible inhibitory mechanisms against the pump was analyzed in relation to the structure and the activity of the transporter. A process of summarizing modulatory spectra of the listed compounds against 2 other efflux pumps of Candida albicans namely Cdr2p and Mdr1p was also conducted, during which selective inhibitors of Cdr1p were revealed.

RESULTS

In this article, 6 possible mechanisms with which a molecule can manifest their activity against the efflux pump are described, and a list of nearly 50 CaCdr1p modulators found in literatures along with their respective mechanism(s) (if already identified) is provided, summarizing the results obtained so far in the search of new inhibitors of the drug extrusion transporter that can enhance the potency of commonly used antifungal agents. A table of inhibitory spectra of the listed compounds against Cdr1p, Cdr2p and Mdr1p is also given, with several selective modulators of Cdr1p finally indicated.

CONCLUSION

The findings of this review contribute to future studies regarding CaCdr1p and its modulators by summarizing the results obtained so far on this emerging issue of health sciences. Further research concerning novel compounds capable of inhibiting Cdr1p needs to be carried out in hopes of completing the lists provided in this article.