SHAFTS: A Hybrid Approach for 3D Molecular Similarity Calculation. 1. Method and Assessment of Virtual Screening

MAI-TargetFisher: A proteome-wide drug target prediction method synergetically enhanced by artificial intelligence and physical modeling

Computational target identification plays a pivotal role in the drug development process. With the significant advancements of deep learning methods for protein structure prediction, the structural coverage of human proteome has increased substantially. This progress inspired the development of the first genome-wide small molecule targets scanning method. Our method aims to localize drug targets and detect potential off-target effects early in the drug discovery process, thereby improving the success rate of drug development. We have constructed a high-quality database of protein structures with annotated potential binding sites, covering 82% of the protein-coding genome. On the basis of this database, to enhance our search capabilities, we have integrated computational techniques, including both artificial intelligence-based and biophysical model-based methods. This integration led to the development of a target identification method called Multi-Algorithm Integrated Target Fisher (MAI-TargetFisher). MAI-TargetFisher leverages the complementary strengths of various methods while minimizing their weaknesses, enabling precise database navigation to generate a reliably ranked set of candidate targets for an active query molecule. Importantly, our work is the first comprehensive scan of protein surfaces across the entire human genome, aimed at evaluating potential small molecule binding sites on each protein. Through a series of evaluations on benchmark and a target identification task, the results demonstrate the high hit rates and good reliability of our method under the validation of wet experiments. We have also made available a freely accessible web server at https://bailab.siais.shanghaitech.edu.cn/mai-targetfisher for non-commercial use.

Tamsulosin ameliorates bone loss by inhibiting the release of Cl- through wedging into an allosteric site of TMEM16A

TMEM16A, a key calcium-activated chloride channel, is crucial for many physiological and pathological processes such as cancer, hypertension, and osteoporosis, etc. However, the regulatory mechanism of TMEM16A is poorly understood, limiting the discovery of effective modulators. Here, we unveil an allosteric gating mechanism by presenting a high-resolution cryo-EM structure of TMEM16A in complex with a channel inhibitor that we identified, Tamsulosin, which is resolved at 2.93 Å. Tamsulosin wedges itself into a pocket within the extracellular domain of TMEM16A, surrounded by α1-α2, α5-α6, and α9-α10 loops. This binding stabilizes a transient preopen conformation of TMEM16A, which is activated by Ca2+ ions while still preserving a closed pore to prevent Cl- permeation. Validation of this binding site through computational, electrophysiological, and functional experiments, along with site-directed mutagenesis, confirmed the pivotal roles of the pocket-lining residues R605 and E624 on α5-α6 loop in modulating Tamsulosin binding and pore activity. Tamsulosin induces significant positional shifts in extracellular loops, particularly the α5-α6 loop, which moves toward the extracellular exit of the pore, leading to noticeable structural rearrangements in pore-lining helices. The hinges induced by P595 in α5 and G711 in α7 introduce flexibility to the transmembrane helices, orienting Y593 to collaborate with I641 in effectively gating the preopening pore. Notably, Tamsulosin demonstrates significant antiosteoporotic effects by inhibiting TMEM16A, suggesting potential for its repurposing in new therapeutic indications. Our study not only enhances our understanding of the gating mechanism of TMEM16A inhibition but also facilitates structure-based drug design targeting TMEM16A.

3DSTarPred: A Web Server for Target Prediction of Bioactive Small Molecules Based on 3D Shape Similarity

Target identification plays a critical role in preclinical drug development. The in silico approach has been developed and widely applied to assist medicinal chemists and pharmacologists in drug target identification. There are many target prediction web servers available today that have revealed both advantages and shortcomings in practical applications. Here, we present 3DSTarPred, a web server for three-dimensional (3D) shape similarity-based target prediction of small molecules. A benchmark study showed that 3DSTarPred achieved a target prediction success rate of 76.27%, which was higher than that of existing target prediction web servers. In addition, the performance of 3DSTarPred in the target prediction of diverse substructures/superstructures was also better than that of the existing target prediction web servers. In case studies, 3DSTarPred was used to identify the potential targets of two small molecules, one being kaempferol, a natural lead compound for the treatment of Alzheimer's disease (AD), and the other being sildenafil, a candidate for drug repurposing in AD. The case studies further demonstrated the reliability and success of 3DSTarPred in practice. The 3DSTarPred server is freely available at http://3dstarpred.pumc.wecomput.com.

ROSHAMBO: Open-Source Molecular Alignment and 3D Similarity Scoring

Efficient virtual screening techniques are critical in drug discovery for identifying potential drug candidates. We present an open-source package for molecular alignment and 3D similarity calculations optimized for large-scale virtual screening of small molecules. This work parallels widely used proprietary tools and offers an approach complementary to structure-based virtual screening. Our package employs the PAPER software for optimizing molecular alignments based on Gaussian volume overlaps. GPU acceleration is utilized to significantly reduce computational time and resource requirements. After obtaining the optimal alignments between the target and the query molecules, both shape and color (based on pharmacophore features) scores are computed to assess molecular similarity, with aligned molecules optionally being output in sdf format. The package was benchmarked using the DUDE-Z public data sets. Results demonstrated the package's near-state-of-the-art performance and robustness across multiple target classes, with speed that enables many routine ligand-based drug discovery workflows. As an open-source and freely available resource (github.com/molecularinformatics/roshambo) with both a convenient Python API and command line interface, our package also addresses the need for accessible and efficient virtual screening tools in drug discovery.

Conformational Space Profiling Enhances Generic Molecular Representation for AI-Powered Ligand-Based Drug Discovery

The molecular representation model is a neural network that converts molecular representations (SMILES, Graph) into feature vectors, and is an essential module applied across a wide range of artificial intelligence-driven drug discovery scenarios. However, current molecular representation models rarely consider the three-dimensional conformational space of molecules, losing sight of the dynamic nature of small molecules as well as the essence of molecular conformational space that covers the heterogeneity of molecule properties, such as the multi-target mechanism of action, recognition of different biomolecules, dynamics in cytoplasm and membrane. In this study, a new model named GeminiMol is proposed to incorporate conformational space profiles into molecular representation learning, which extracts the feature of capturing the complicated interplay between the molecular structure and the conformational space. Although GeminiMol is pre-trained on a relatively small-scale molecular dataset (39290 molecules), it shows balanced and superior performance not only on 67 molecular properties predictions but also on 73 cellular activity predictions and 171 zero-shot tasks (including virtual screening and target identification). By capturing the molecular conformational space profile, the strategy paves the way for rapid exploration of chemical space and facilitates changing paradigms for drug design.

Development of Degraders of Cyclin-Dependent Kinases and Based on Rational Drug Design

Degradation of target proteins has been considered to be a promising therapeutic approach, but the rational design of compounds for degradation remains a challenge. In this study, we reasonably designed and synthesized only 10 compounds to discover effective CDK4/6 protein degraders. Among the newly synthesized compounds, 7f achieved dual degradation of CDK4/6 protein, with DC50 values of 10.5 and 2.5 nM, respectively. Compound 7f also exhibited inhibitory proliferative activity against Jurkat cells with an IC50 value of 0.18 μM. Furthermore, 7f induced cell apoptosis and G1 phase cell cycle arrest in a dose-dependent manner in Jurkat cells. In conclusion, these findings demonstrate the potential of 7f as a CDK4/6 degrader and a potential therapeutic strategy against cancer, thereby expanding the potential of CDK4/6 dual PROTACs.

Insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment: A systematic review

BACKGROUND

In the search for better anticancer drugs, computer-aided drug design (CADD) techniques play an indispensable role in facilitating the lengthy and costly drug discovery process especially when natural products are involved. Anthraquinone is one of the most widely-recognized natural products with anticancer properties. This review aimed to systematically assess and synthesize evidence on the utilization of CADD techniques centered on the anthraquinone scaffold for cancer treatment.

METHODS

The conduct and reporting of this review were done in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2020 guideline. The protocol was registered in the "International prospective register of systematic reviews" database (PROSPERO: CRD42023432904) and also published recently. The search strategy was designed based on the combination of concept 1 "CADD or virtual screening", concept 2 "anthraquinone" and concept 3 "cancer". The search was executed in PubMed, Scopus, Web of Science and MedRxiv on 30 June 2023.

RESULTS

Databases searching retrieved a total of 317 records. After deduplication and applying the eligibility criteria, the final review ended up with 32 articles in which 3 articles were found by citation searching. The CADD methods used in the studies were either structure-based alone (69%) or combined with ligand-based methods via parallel (9%) or sequential (22%) approaches. Molecular docking was performed in all studies, with Glide and AutoDock being the most popular commercial and public software used respectively. Protein data bank was used in most studies to retrieve the crystal structure of the targets of interest while the main ligand databases were PubChem and Zinc. The utilization of in-silico techniques has enabled a deeper dive into the structural, biological and pharmacological properties of anthraquinone derivatives, revealing their remarkable anticancer properties in an all-rounded fashion.

CONCLUSION

By harnessing the power of computational tools and leveraging the natural diversity of anthraquinone compounds, researchers can expedite the development of better drugs to address the unmet medical needs in cancer treatment by improving the treatment outcome for cancer patients.

AutoDock-SS: AutoDock for Multiconformational Ligand-Based Virtual Screening

Ligand-based virtual screening (LBVS) can be pivotal for identifying potential drug leads, especially when the target protein's structure is unknown. However, current LBVS methods are limited in their ability to consider the ligand conformational flexibility. This study presents AutoDock-SS (Similarity Searching), which adapts protein-ligand docking for use in LBVS. AutoDock-SS integrates novel ligand-based grid maps and AutoDock-GPU into a novel three-dimensional LBVS workflow. Unlike other approaches based on pregenerated conformer libraries, AutoDock-SS's built-in conformational search optimizes conformations dynamically based on the reference ligand, thus providing a more accurate representation of relevant ligand conformations. AutoDock-SS supports two modes: single and multiple ligand queries, allowing for the seamless consideration of multiple reference ligands. When tested on the Directory of Useful Decoys─Enhanced (DUD-E) data set, AutoDock-SS surpassed alternative 3D LBVS methods, achieving a mean AUROC of 0.775 and an EF1% of 25.72 in single-reference mode. The multireference mode, evaluated on the augmented DUD-E+ data set, demonstrated superior accuracy with a mean AUROC of 0.843 and an EF1% of 34.59. This enhanced performance underscores AutoDock-SS's ability to treat compounds as conformationally flexible while considering the ligand's shape, pharmacophore, and electrostatic potential, expanding the potential of LBVS methods.

A High-Quality Data Set of Protein-Ligand Binding Interactions Via Comparative Complex Structure Modeling

High-quality protein-ligand complex structures provide the basis for understanding the nature of noncovalent binding interactions at the atomic level and enable structure-based drug design. However, experimentally determined complex structures are scarce compared with the vast chemical space. In this study, we addressed this issue by constructing the BindingNet data set via comparative complex structure modeling, which contains 69,816 modeled high-quality protein-ligand complex structures with experimental binding affinity data. BindingNet provides valuable insights into investigating protein-ligand interactions, allowing visual inspection and interpretation of structural analogues' structure-activity relationships. It can also be used for evaluating machine-learning-based scoring functions. Our results indicate that machine learning models trained on BindingNet could reduce the bias caused by buried solvent-accessible surface area, as we previously found for models trained on the PDBbind data set. We also discussed strategies to improve BindingNet and its potential utilization for benchmarking the molecular docking methods and ligand binding free energy calculation approaches. The BindingNet complements PDBbind in constructing a sufficient and unbiased protein-ligand binding data set and is freely available at http://bindingnet.huanglab.org.cn.

SENSAAS-Flex: a joint optimization approach for aligning 3D shapes and exploring the molecular conformation space

MOTIVATION

Popular shape-based alignment methods handle molecular flexibility by utilizing conformational ensembles to select the most fitted conformer. However, the initial conformer library generation step is computationally intensive and limiting to the overall alignment process. In this work, we describe a method to perform flexible alignment of two molecular shapes by optimizing the 3D conformation. SENSAAS-Flex, an add-on to the SENSAAS tool, is able to proceed from a limited set of initial conformers through an iterative process where additional conformational optimizations are made at the substructure level and constrained by the target shape.

RESULTS

In self- and cross-alignment experiments, SENSAAS-Flex is able to reproduce the crystal structure geometry of ligands of the AstraZeneca Molecule Overlay Test set and PDBbind refined dataset. Our study shows that the point-based representation of molecular surfaces is appropriate in terms of shape constraint to sample the conformational space and perform flexible molecular alignments.

AVAILABILITY AND IMPLEMENTATION

The documentation and source code are available at https://chemoinfo.ipmc.cnrs.fr/Sensaas-flex/sensaas-flex-main.tar.gz.

Recent Progress of Protein Tertiary Structure Prediction

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.

Template-guided method for protein-ligand complex structure prediction: Application to CASP15 protein-ligand studies

Critical Assessment of Structure Prediction 15 (CASP15) added a new category of ligand prediction to promote the development of protein/RNA-ligand modeling methods, which have become important tools in modern drug discovery. A total of 22 targets were released, including 18 protein-ligand targets and 4 RNA-ligand targets. We applied our recently developed template-guided method to the protein-ligand complex structure predictions. The method combined a physicochemical, molecular docking method, and a bioinformatics-based ligand similarity method. The Protein Data Bank was scanned for template structures containing the target protein, homologous proteins, or proteins sharing a similar fold with the target protein. The binding modes of the co-bound ligands in the template structures were used to guide the complex structure prediction for the target. The CASP assessment results show that the overall performance of our method was ranked second when the top predicted model was considered for each target. Here, we analyzed our predictions in detail, and discussed the challenges including protein conformational changes, large and flexible ligands, and multiple diverse ligands in a binding pocket.

Discovery and Mechanism Study of SARS-CoV-2 3C-like Protease Inhibitors with a New Reactive Group

3CLpro is an attractive target for the treatment of COVID-19. Using the scaffold hopping strategy, we identified a potent inhibitor of 3CLpro (3a) that contains a thiocyanate moiety as a novel warhead that can form a covalent bond with Cys145 of the protein. Tandem mass spectrometry (MS/MS) and X-ray crystallography confirmed the mechanism of covalent formation between 3a and the protein in its catalytic pocket. Moreover, several analogues of compound 3a were designed and synthesized. Among them, compound 3h shows the best inhibition of 3CLpro with an IC50 of 0.322 μM and a kinact/Ki value of 1669.34 M-1 s-1, and it exhibits good target selectivity for 3CLpro against host proteases. Compound 3c inhibits SARS-CoV-2 in Vero E6 cells (EC50 = 2.499 μM) with low cytotoxicity (CC50 > 200 μM). These studies provide ideas and insights to explore and develop new 3CLpro inhibitors in the future.

Comprehensive survey of target prediction web servers for Traditional Chinese Medicine

Traditional Chinese medicine (TCM) is characterized by multi-components, multiple targets, and complex mechanisms of action and therefore has significant advantages in treating diseases. However, the clinical application of TCM prescriptions is limited due to the difficulty in elucidating the effective substances and the lack of current scientific evidence on the mechanisms of action. In recent years, the development of network pharmacology based on drug systems research has provided a new approach for understanding the complex systems represented by TCM. The determination of drug targets is the core of TCM network pharmacology research. Over the past years, many web tools for drug targets with various features have been developed to facilitate target prediction, significantly promoting drug discovery. Therefore, this review introduces the widely used web tools for compound-target interaction prediction databases and web resources in TCM pharmacology research, and it compares and analyzes each web tool based on their basic properties, including the underlying theory, algorithms, datasets, and search results. Finally, we present the remaining challenges for the promising future of compound-target interaction prediction in TCM pharmacology research. This work may guide researchers in choosing web tools for target prediction and may also help develop more TCM tools based on these existing resources.

Synthesis, Nematicidal Evaluation, and the Structure-Activity Relationship Study of Aurone Derivatives

Plant-parasitic nematodes (PPNs) are one of the major threats to modern agriculture. Chemical nematicides are still required for the management of PPNs. Based on our previous work, the structure of aurone analogues was obtained using a hybrid 3D similarity calculation method (SHAFTS, SHApe-FeaTure Similarity). Thirty-seven compounds were synthesized. The nematicidal activity of target compounds against Meloidogyne incognita (root-knot nematode, M. incognita) was evaluated, and the structure-activity relationship of synthesized compounds was analyzed. The results showed that compound 6 and some of its derivatives exhibited impressive nematicidal activity. Among these compounds, compound 32 bearing 6-F showed the best in vitro and in vivo nematicidal activity. Its lethal concentration 50% after exposure to 72 h (LC_50/72 h) value was 1.75 mg/L, and the inhibition rate reached 97.93% in the sand at 40 mg/L. At the same time, compound 32 also exhibited excellent inhibition on egg hatching and moderate inhibition on the motility of Caenorhabditis elegans (C. elegans).

Virtual Screening-Based Study of Novel Anti-Cancer Drugs Targeting G-Quadruplex

In order to develop new anti-cancer drugs more efficiently and reduce side effects based on active drug targets, the virtual drug screening was carried out through the target of G-quadruplexes and 23 hit compounds were, thus, screened out as potential anticancer drugs. Six classical G-quadruplex complexes were introduced as query molecules, and the three-dimensional similarity of molecules was calculated by shape feature similarity (SHAFTS) method so as to reduce the range of potential compounds. Afterwards, the molecular docking technology was utilized to perform the final screening followed by the exploration of the binding between each compound and four different structures of G-quadruplex. In order to verify the anticancer activity of the selected compounds, compounds 1, 6 and 7 were chosen to treat A549 cells in vitro, the lung cancer epithelial cells, for further exploring their anticancer activity. These three compounds were found to be of good characteristics in the treatment of cancer, which revealed the great application prospect of the virtual screening method in developing new drugs.

Discovery, evaluation and mechanism study of WDR5-targeted small molecular inhibitors for neuroblastoma

Neuroblastoma is the most common and deadliest tumor in infancy. WDR5 (WD Repeat Domain 5), a critical factor supporting an N-myc transcriptional complex via its WBM site and interacting with chromosome via its WIN site, promotes the progression of neuroblastoma, thus making it a potential anti-neuroblastoma drug target. So far, a few WIN site inhibitors have been reported, and the WBM site disruptors are rare to see. In this study we conducted virtual screening to identify candidate hit compounds targeting the WBM site of WDR5. As a result, 60 compounds were selected as candidate WBM site inhibitors. Cell proliferation assay demonstrated 6 structurally distinct WBM site inhibitors, numbering as compounds 4, 7, 11, 13, 19 and 22, which potently suppressed 3 neuroblastoma cell lines (MYCN-amplified IMR32 and LAN5 cell lines, and MYCN-unamplified SK-N-AS cell line). Among them, compound 19 suppressed the proliferation of IMR32 and LAN5 cells with EC50 values of 12.34 and 14.89 μM, respectively, and exerted a moderate inhibition on SK-N-AS cells, without affecting HEK293T cells at 20 μM. Analysis of high-resolution crystal complex structure of compound 19 against WDR5 revealed that it competitively occupied the hydrophobic pocket where V264 was located, which might disrupt the interaction of MYC with WDR5 and further MYC-medicated gene transcription. By performing RNA-seq analysis we demonstrated the differences in molecular action mechanisms of the compound 19 and a WIN site inhibitor OICR-9429. Most interestingly, we established the particularly high synergy rate by combining WBM site inhibitor 19 and the WIN site inhibitor OICR-9429, providing a novel therapeutic avenue for neuroblastoma.

Ovatodiolide inhibits SARS-CoV-2 replication and ameliorates pulmonary fibrosis through suppression of the TGF-β/TβRs signaling pathway

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection continues to pose threats to public health. The clinical manifestations of lung pathology in COVID-19 patients include sustained inflammation and pulmonary fibrosis. The macrocyclic diterpenoid ovatodiolide (OVA) has been reported to have anti-inflammatory, anti-cancer, anti-allergic, and analgesic activities. Here, we investigated the pharmacological mechanism of OVA in suppressing SARS-CoV-2 infection and pulmonary fibrosis in vitro and in vivo. Our results revealed that OVA was an effective SARS-CoV-2 3CLpro inhibitor and showed remarkable inhibitory activity against SARS-CoV-2 infection. On the other hand, OVA ameliorated pulmonary fibrosis in bleomycin (BLM)-induced mice, reducing inflammatory cell infiltration and collagen deposition in the lung. OVA decreased the levels of pulmonary hydroxyproline and myeloperoxidase, as well as lung and serum TNF-ɑ, IL-1β, IL-6, and TGF-β in BLM-induced pulmonary fibrotic mice. Meanwhile, OVA reduced the migration and fibroblast-to-myofibroblast conversion of TGF-β1-induced fibrotic human lung fibroblasts. Consistently, OVA downregulated TGF-β/TβRs signaling. In computational analysis, OVA resembles the chemical structures of the kinase inhibitors TβRI and TβRII and was shown to interact with the key pharmacophores and putative ATP-binding domains of TβRI and TβRII, showing the potential of OVA as an inhibitor of TβRI and TβRII kinase. In conclusion, the dual function of OVA highlights its potential for not only fighting SARS-CoV-2 infection but also managing injury-induced pulmonary fibrosis.

CSAlign and CSAlign-Dock: Structure alignment of ligands considering full flexibility and application to protein-ligand docking

Structure prediction of protein-ligand complexes, called protein-ligand docking, is a critical computational technique that can be used to understand the underlying principle behind the protein functions at the atomic level and to design new molecules regulating the functions. Protein-ligand docking methods have been employed in structure-based drug discovery for hit discovery and lead optimization. One of the important technical challenges in protein-ligand docking is to account for protein conformational changes induced by ligand binding. A small change such as a single side-chain rotation upon ligand binding can hinder accurate docking. Here we report an increase in docking performance achieved by structure alignment to known complex structures. First, a fully flexible compound-to-compound alignment method CSAlign is developed by global optimization of a shape score. Next, the alignment method is combined with a docking algorithm to dock a new ligand to a target protein when a reference protein-ligand complex structure is available. This alignment-based docking method, called CSAlign-Dock, showed superior performance to ab initio docking methods in cross-docking benchmark tests. Both CSAlign and CSAlign-Dock are freely available as a web server at https://galaxy.seoklab.org/csalign.

ANTIAGE-DB: A Database and Server for the Prediction of Anti-Aging Compounds Targeting Elastase, Hyaluronidase, and Tyrosinase

Natural products bear a multivariate biochemical profile with antioxidant, anti-inflammatory, antibacterial, and antitumoral properties. Along with their natural sources, they have been widely used both as anti-aging and anti-melanogenic agents due to their effective contribution in the elimination of reactive oxygen species (ROS) caused by oxidative stress. Their anti-aging activity is mainly related to their capacity of inhibiting enzymes like Human Neutrophil Elastase (HNE), Hyaluronidase (Hyal) and Tyrosinase (Tyr). Herein, we accumulated literature information (covering the period 1965–2020) on the inhibitory activity of natural products and their natural sources towards these enzymes. To navigate this information, we developed a database and server termed ANTIAGE-DB that allows the prediction of the anti-aging potential of target compounds. The server operates in two axes. First a comparison of compounds by shape similarity can be performed against our curated database of natural products whose inhibitory potential has been established in the literature. In addition, inverse virtual screening can be performed for a chosen molecule against the three targeted enzymes. The server is open access, and a detailed report with the prediction results is emailed to the user. ANTIAGE-DB could enable researchers to explore the chemical space of natural based products, but is not limited to, as anti-aging compounds and can predict their anti-aging potential. ANTIAGE-DB is accessed online.

Inhibition of the dopamine transporter promotes lysosome biogenesis and ameliorates Alzheimer's disease-like symptoms in mice

INTRODUCTION

Lysosomes are degradative organelles that maintain cellular homeostasis and protein quality control. Transcription factor EB (TFEB)-mediated lysosome biogenesis enhances lysosome-dependent degradation and alleviates neurodegenerative diseases, but the mechanisms underlying TFEB regulation and modification are still poorly understood.

METHODS

By screening novel small-molecule compounds, we identified a group of lysosome-enhancing compounds (LYECs) that promote TFEB activation and lysosome biogenesis.

RESULTS

One of these compounds, LH2-051, significantly inhibited the function of the dopamine transporter (DAT) and subsequently promoted lysosome biogenesis. We uncovered cyclin-dependent kinase 9 (CDK9) as a novel regulator of DAT-mediated lysosome biogenesis and identified six novel CDK9-phosphorylated sites on TFEB. We observed that signal transduction by the DAT-CDK9-TFEB axis occurs on lysosomes. Finally, we found that LH2-051 enhanced the degradation of amyloid beta plaques and improved the memory of amyloid precursor protein (APP)/Presenilin 1 (PS1) mice.

DISCUSSION

We identified the DAT-CDK9-TFEB signaling axis as a novel regulator of lysosome biogenesis. Our study sheds light on the mechanisms of protein quality control under pathophysiological conditions.

AMTDB: A comprehensive database of autophagic modulators for anti-tumor drug discovery

Autophagy, originally described as a mechanism for intracellular waste disposal and recovery, has been becoming a crucial biological process closely related to many types of human tumors, including breast cancer, osteosarcoma, glioma, etc., suggesting that intervention of autophagy is a promising therapeutic strategy for cancer drug development. Therefore, a high-quality database is crucial for unraveling the complicated relationship between autophagy and human cancers, elucidating the crosstalk between the key autophagic pathways, and autophagic modulators with their remarkable antitumor activities. To achieve this goal, a comprehensive database of autophagic modulators (AMTDB) was developed. AMTDB focuses on 153 cancer types, 1,153 autophagic regulators, 860 targets, and 2,046 mechanisms/signaling pathways. In addition, a variety of classification methods, advanced retrieval, and target prediction functions are provided exclusively to cater to the different demands of users. Collectively, AMTDB is expected to serve as a powerful online resource to provide a new clue for the discovery of more candidate cancer drugs.

Chemical Diversity and Potential Target Network of Woody Peony Flower Essential Oil from Eleven Representative Cultivars (Paeonia × suffruticosa Andr.)

Woody peony (Paeonia × suffruticosa Andr.) has many cultivars with genetic variances. The flower essential oil is valued in cosmetics and fragrances. This study was to investigate the chemical diversity of essential oils of eleven representative cultivars and their potential target network. Hydro-distillation afforded yields of 0.11–0.25%. Essential oils were analyzed by GC-MS and GC-FID which identified 105 compounds. Three clusters emerged from multivariate analysis, representative of phloroglucinol trimethyl ether (‘Caihui’), citronellol (‘Jingyu’, ‘Zhaofen’ and ‘Baiyuan Zhenghui’) and mixed (the rest of the cultivars) chemotypes. ‘Zhaofen’ and ‘Jingyu’ also exhibited low levels of other rose-related compounds. The main components were subjected to a target network approach. Drug-likeness screening gave 20 compounds with predictive blood–brain barrier permeation. Compound target network identified six key compounds, namely nerol, citronellol, geraniol, geranic acid, cis-3-hexen-1-ol and 1-hexanol. Top enriched terms in GO, KEGG and DisGeNET were mostly related to the central nervous system (CNS). Protein—protein interactions revealed a core network of 14 targets, 11 of which were CNS-related (targets for antidepressants, analgesics, antipsychotics, anti-Alzheimer’s and anti-Parkinson’s agents). This work provides useful information on the production of woody peony essential oils with specific chemotypes and reveals their potential importance in aromatherapy for alternative treatment of CNS disorders.

FitDock: protein-ligand docking by template fitting

Protein-ligand docking is an essential method in computer-aided drug design and structural bioinformatics. It can be used to identify active compounds and reveal molecular mechanisms of biological processes. A successful docking usually requires thorough conformation sampling and scoring, which are computationally expensive and difficult. Recent studies demonstrated that it can be beneficial to docking with the guidance of existing similar co-crystal structures. In this work, we developed a protein-ligand docking method, named FitDock, which fits initial conformation to the given template using a hierarchical multi-feature alignment approach, subsequently explores the possible conformations and finally outputs refined docking poses. In our comprehensive benchmark tests, FitDock showed 40%-60% improvement in terms of docking success rate and an order of magnitude faster over popular docking methods, if template structures exist (> 0.5 ligand similarity). FitDock has been implemented in a user-friendly program, which could serve as a convenient tool for drug design and molecular mechanism exploration. It is now freely available for academic users at http://cao.labshare.cn/fitdock/.

ePharmer: An integrated and graphical software for pharmacophore-based virtual screening

Pharmacophore-based virtual screening (VS) has emerged as an efficient computer-aided drug design technique when appraising multiple ligands with similar structures or targets with unknown crystal structures. Current pharmacophore modeling and analysis software suffers from inadequate integration of mainstream methods and insufficient user-friendly program interface. In this study, we propose a stand-alone, integrated, graphical software for pharmacophore-based VS, termed ePharmer. Both ligand-based and structure-based pharmacophore generation methods were integrated into a compact architecture. Fine-grained modules were carefully organized into the computing, integration, and visualization layers. Graphical design covered the global user interface and specific user operations including editing, evaluation, and task management. Metabolites prediction analysis with the chosen VS result is provided for preselection of wet experiments. Moreover, the underlying computing units largely adopted the preliminary work of our research team. The presented software is currently in client use and will be released for both professional and nonexpert users. Experimental results verified the favorable computing capability, user convenience, and case performance of the proposed software.

Dissimilar Ligands Bind in a Similar Fashion: A Guide to Ligand Binding-Mode Prediction with Application to CELPP Studies

The molecular similarity principle has achieved great successes in the field of drug design/discovery. Existing studies have focused on similar ligands, while the behaviors of dissimilar ligands remain unknown. In this study, we developed an intercomparison strategy in order to compare the binding modes of ligands with different molecular structures. A systematic analysis of a newly constructed protein–ligand complex structure dataset showed that ligands with similar structures tended to share a similar binding mode, which is consistent with the Molecular Similarity Principle. More importantly, the results revealed that dissimilar ligands can also bind in a similar fashion. This finding may open another avenue for drug discovery. Furthermore, a template-guiding method was introduced for predicting protein–ligand complex structures. With the use of dissimilar ligands as templates, our method significantly outperformed the traditional molecular docking methods. The newly developed template-guiding method was further applied to recent CELPP studies.

MCDB: A comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and target prediction

Mitotic catastrophe (MC) is a form of programmed cell death induced by mitotic process disorders, which is very important in tumor prevention, development, and drug resistance. Because rapidly increased data for MC is vigorously promoting the tumor-related biomedical and clinical study, it is urgent for us to develop a professional and comprehensive database to curate MC-related data. Mitotic Catastrophe Database (MCDB) consists of 1214 genes/proteins and 5014 compounds collected and organized from more than 8000 research articles. Also, MCDB defines the confidence level, classification criteria, and uniform naming rules for MC-related data, which greatly improves data reliability and retrieval convenience. Moreover, MCDB develops protein sequence alignment and target prediction functions. The former can be used to predict new potential MC-related genes and proteins, and the latter can facilitate the identification of potential target proteins of unknown MC-related compounds. In short, MCDB is such a proprietary, standard, and comprehensive database for MC-relate data that will facilitate the exploration of MC from chemists to biologists in the fields of medicinal chemistry, molecular biology, bioinformatics, oncology and so on. The MCDB is distributed on http://www.combio-lezhang.online/MCDB/index_html/.

A comprehensive comparative assessment of 3D molecular similarity tools in ligand-based virtual screening

Three-dimensional (3D) molecular similarity, one major ligand-based virtual screening (VS) method, has been widely used in the drug discovery process. A variety of 3D molecular similarity tools have been developed in recent decades. In this study, we assessed a panel of 15 3D molecular similarity programs against the DUD-E and LIT-PCBA datasets, including commercial ROCS and Phase, in terms of screening power and scaffold-hopping power. The results revealed that (1) SHAFTS, LS-align, Phase Shape_Pharm and LIGSIFT showed the best VS capability in terms of screening power. Some 3D similarity tools available to academia can yield relatively better VS performance than commercial ROCS and Phase software. (2) Current 3D similarity VS tools exhibit a considerable ability to capture actives with new chemotypes in terms of scaffold hopping. (3) Multiple conformers relative to single conformations will generally improve VS performance for most 3D similarity tools, with marginal improvement observed in area under the receiving operator characteristic curve values, enrichment factor in the top 1% and hit rate in the top 1% values showed larger improvement. Moreover, redundancy and complementarity analyses of hit lists from different query seeds and different 3D similarity VS tools showed that the combination of different query seeds and/or different 3D similarity tools in VS campaigns retrieved more (and more diverse) active molecules. These findings provide useful information for guiding choices of the optimal 3D molecular similarity tools for VS practices and designing possible combination strategies to discover more diverse active compounds.

Potential Biological Targets Prediction, ADME Profiling, & Molecular Docking studies of Novel Steroidal Products from Cunninghamella Blakesleana

BACKGROUND

New potential biological targets prediction through inverse molecular docking technique is an another smart strategy to forecast the possibility of compounds being biologically active against various target receptors.

OBJECTIVES

In this case of designed study, we screened our recently obtained novel acetylinic steroidal biotransformed products [(1) 8-β-methyl-14-α-hydroxy∆4tibolone (2) 9-α-Hydroxy∆4 tibolone (3) 8-β-methyl-11-β-hydroxy∆4tibolone (4) 6-β-hydroxy∆4tibolone, (5) 6-β-9-α-dihydroxy∆4tibolone (6) 7-β-hydroxy∆4tibolone) ] from fungi Cunninghemella Blakesleana to predict their possible biological targets and profiling of ADME properties.

METHOD

The prediction of pharmacokinetics properties membrane permeability as well as bioavailability radar properties were carried out by using Swiss target prediction, and Swiss ADME tools, respectively these metabolites were also subjected to predict the possible mechanism of action along with associated biological network pathways by using Reactome data-base.

RESULTS

All the six screened compounds possess excellent drug ability criteria, and exhibited exceptionally excellent non inhibitory potential against all five isozymes of CYP450 enzyme complex, including (CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4) respectively. All the screened compounds are lying within the acceptable pink zone of bioavailability radar and showing excellent descriptive properties. Compounds [1-4 & 6] are showing high BBB (Blood Brain Barrier) permeation, while compound 5 is exhibiting high HIA (Human Intestinal Absorption) property of (Egan Egg).

CONCLUSION

In conclusion, the results of this study smartly reveals that in-silico based studies are considered to provide robustness towards a rational drug designing and development approach, therefore in this way it helps to avoid the possibility of failure of drug candidates in the later experimental stages of drug development phases.

Anti-Ageing Potential of S. euboea Heldr. Phenolics

In recent years, the use of Sideritis species as bioactive agents is increasing exponentially. The present study aimed to investigate the chemical constituents, as well as the anti-ageing potential of the cultivated Sideritis euboea Heldr. The chemical fingerprinting of the ethyl acetate residue of this plant was studied using 1D and 2D-NMR spectra. Isomeric compounds belonging to acylated flavone derivatives and phenylethanoid glycosides were detected in the early stage of the experimental process through 2D-NMR techniques. Overall, thirty-three known compounds were isolated and identified. Some of them are reported for the first time not only in S. euboea, but also in genus Sideritis L. The anti-ageing effect of the ethyl acetate residue and the isolated specialized products was assessed as anti-hyaluronidase activity. In silico docking simulation revealed the interactions of the isolated compounds with hyaluronidase. Furthermore, the in vitro study on the inhibition of hyaluronidase unveiled the potent inhibitory properties of ethyl acetate residue and apigenin 7-O-β-d-glucopyranoside. Though, the isomers of apigenin 7-O-p-coumaroyl-glucosides and also the 4′-methyl-hypolaetin 7-O-[6′′′-O-acetyl-β-d-allopyranosyl]-(1→2)-β-d-glucopyranoside exerted moderate hyaluronidase inhibition. This research represents the first study to report on the anti-hyaluronidase activity of Sideritis species, confirming its anti-inflammatory, cytotoxic and anti-ageing effects and its importance as an agent for cosmetic formulations as also anticancer potential.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

A Comprehensive Review of Cholinesterase Modeling and Simulation

The present article reviews published efforts to study acetylcholinesterase and butyrylcholinesterase structure and function using computer-based modeling and simulation techniques. Structures and models of both enzymes from various organisms, including rays, mice, and humans, are discussed to highlight key structural similarities in the active site gorges of the two enzymes, such as flexibility, binding site location, and function, as well as differences, such as gorge volume and binding site residue composition. Catalytic studies are also described, with an emphasis on the mechanism of acetylcholine hydrolysis by each enzyme and novel mutants that increase catalytic efficiency. The inhibitory activities of myriad compounds have been computationally assessed, primarily through Monte Carlo-based docking calculations and molecular dynamics simulations. Pharmaceutical compounds examined herein include FDA-approved therapeutics and their derivatives, as well as several other prescription drug derivatives. Cholinesterase interactions with both narcotics and organophosphate compounds are discussed, with the latter focusing primarily on molecular recognition studies of potential therapeutic value and on improving our understanding of the reactivation of cholinesterases that are bound to toxins. This review also explores the inhibitory properties of several other organic and biological moieties, as well as advancements in virtual screening methodologies with respect to these enzymes.

Diosgenin Derivatives as Potential Antitumor Agents: Synthesis, Cytotoxicity, and Mechanism of Action

Thirty-two new diosgenin derivatives were designed, synthesized, and evaluated for their cytotoxic activities in three human cancer cell lines (A549, MCF-7, and HepG2) and normal human liver cells (L02) using an MTT assay in vitro. Most compounds, especially 8, 18, 26, and 30, were more potent when compared with diosgenin. The structure-activity relationship results suggested that the presence of a succinic acid or glutaric acid linker, a piperazinyl amide terminus, and lipophilic cations are all beneficial for promoting cytotoxic activity. Notably, compound 8 displayed excellent cytotoxic activity against HepG2 cells (IC₅₀ = 1.9 μM) and showed relatively low toxicity against L02 cells (IC₅₀ = 18.6 μM), showing some selectivity between normal and tumor cells. Studies on its cellular mechanism of action showed that compound 8 induces G0/G1 cell cycle arrest and apoptosis in HepG2 cells. Predictive studies indicated that p38α mitogen-activated protein kinase (MAPK) is the optimum target of 8 based on its 3D molecular similarity, and docking studies showed that compound 8 fits well into the active site of p38α-MAPK and forms relatively strong interactions with the surrounding amino acid residues. Accordingly, compound 8 may be used as a promising lead compound for the development of new antitumor agents.

Mapping Ligand-Shape Space for Protein-Ligand Systems: Distinguishing Key-in-Lock and Hand-in-Glove Proteins

Many of the recently developed methods to study the shape of molecules permit one conformation of one molecule to be compared to another conformation of the same or a different molecule: a relative shape. Other methods provide an absolute description of the shape of a conformation that does not rely on comparisons or overlays. Any absolute description of shape can be used to generate a self-organizing map (shape map) that places all molecular shapes relative to one another; in the studies reported here, the shape fingerprint and ultrafast shape recognition methods are employed to create such maps. In the shape maps, molecules that are near one another have similar shapes, and the maps for the 102 targets in the DUD-E set have been generated. By examining the distribution of actives in comparison with their physical-property-matched decoys, we show that the proteins of key-in-lock type (relatively rigid receptor and ligand) can be distinguished from those that are more of a hand-in-glove type (more flexible receptor and ligand). These are linked to known differences in protein flexibility and binding-site size.

Molecular Scaffold Hopping via Holistic Molecular Representation

Molecular descriptors encode a variety of molecular representations for computer-assisted drug discovery. Here, we focus on the Weighted Holistic Atom Localization and Entity Shape (WHALES) descriptors, which were originally designed for scaffold hopping from natural products to synthetic molecules. WHALES descriptors capture molecular shape and partial charges simultaneously. We introduce the key aspects of the WHALES concept and provide a step-by-step guide on how to use these descriptors for virtual compound screening and scaffold hopping. The results presented can be reproduced by using the code freely available from URL: github.com/ETHmodlab/scaffold_hopping_whales .

Maximum common property: a new approach for molecular similarity

The maximum common property similarity (MCPhd) method is presented using descriptors as a new approach to determine the similarity between two chemical compounds or molecular graphs. This method uses the concept of maximum common property arising from the concept of maximum common substructure and is based on the electrotopographic state index for atoms. A new algorithm to quantify the similarity values of chemical structures based on the presented maximum common property concept is also developed in this paper. To verify the validity of this approach, the similarity of a sample of compounds with antimalarial activity is calculated and compared with the results obtained by four different similarity methods: the small molecule subgraph detector (SMSD), molecular fingerprint based (OBabel_FP2), ISIDA descriptors and shape-feature similarity (SHAFTS). The results obtained by the MCPhd method differ significantly from those obtained by the compared methods, improving the quantification of the similarity. A major advantage of the proposed method is that it helps to understand the analogy or proximity between physicochemical properties of the molecular fragments or subgraphs compared with the biological response or biological activity. In this new approach, more than one property can be potentially used. The method can be considered a hybrid procedure because it combines descriptor and the fragment approaches.

Lei-gong-gen formula granule attenuates hyperlipidemia in rats via cGMP-PKG signaling pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Lei-gong-gen formula granule (LFG) is a folk prescription derived from Zhuang nationality, the largest ethnic minority among the 56 nationalities in China. It is composed of three herbs, namely Centella asiatica (L.) Urb., Eclipta prostrata (L.) L., Smilax glabra Roxb. It has been widely used as health protection tea for many years to prevent cardiovascular and cerebrovascular diseases such as hyperlipidemia and hypertension.

AIM OF THE STUDY

This study validated the lipid-lowering effect of LFG in a hyperlipidemia rat model. Then we employed network pharmacology and molecular biological approach to identify the active ingredients of LFG, corresponding targets, and its anti-hyperlipidemia mechanisms.

MATERIALS AND METHODS

Hyperlipidemia rat model was established by feeding male Sprague-Dawley rats with high-fat diet for two weeks. LFG (two doses of 10 and 20 g/kg) was administered orally to hyperlipidemia rat model for 4 weeks, twice per day. Serum levels of total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) were monitored in rats pre and post-treatment. Hematoxylin-eosin staining was applied to observe the pathology and lipid accumulation of liver. We then performed network pharmacology analysis to predict the ingredients, their associated targets, and hyperlipidemia associated targets. Pathway analysis with significant genes was carried out using KEGG pathway. These genes and proteins intersectioned between compound targets and hyperlipidemia targets were further verified with samples from hyperlipidemia rats treated with LFG using Real-time RT-PCR and Western Blot.

RESULTS

LFG attenuated hyperlipidemia in rat model, and this was characterized with decreased serum levels of TC, LDL-C, liver wet weight, and liver index. LFG alleviated the hepatic steatosis in hyperlipidemia rats. Network pharmacology analysis identified 53 bioactive ingredients from LFG formula (three herbs), which link to 765 potential targets. 53 hyperlipidemia associated genes were retrieved from public databases. There were 10 common genes between ingredients-targets and hyperlipidemia associated genes, which linked to 20 bioactive ingredients. Among these 10 genes, 3 of them were validated to be involved in LFG's anti-hyperlipidemia effect using Real-time RT-PCR, namely ADRB2 encoding beta-2 adrenergic receptor, NOS3 encoding nitric oxide synthase 3, LDLR encoding low-density lipoprotein receptor. The cGMP-PKG signaling pathway was enriched for hyperlipidemia after pharmacology network analysis with ADRB2, NOS3, and LDLR. Interestingly, expression of cGMP-dependent protein kinase (PKG) was downregulated in hyperlipidemia rat after LFG treatment. Molecular docking study further supported that ferulic acid, histidine, p-hydroxybenzoic acid, and linalool were potential active ingredients for LFG's anti-hyperlipidemia effect. LC-MS/MS analysis confirmed that ferulic acid and p-hydroxybenzoic acid were active ingredients of LFG.

CONCLUSION

LFG exhibited the lipid-lowering effect, which might be attributed to downregulating ADRB2 and NOS3, and upregulating LDLR through the cGMP-PKG signaling pathway in hyperlipidemia rat. Ferulic acid and p-hydroxybenzoic acid might be the underlying active ingredients which affect the potential targets for their anti-hyperlipidemia effect.

DIA-DB: A Database and Web Server for the Prediction of Diabetes Drugs

The DIA-DB is a web server for the prediction of diabetes drugs that uses two different and complementary approaches: (a) comparison by shape similarity against a curated database of approved antidiabetic drugs and experimental small molecules and (b) inverse virtual screening of the input molecules chosen by the users against a set of therapeutic protein targets identified as key elements in diabetes. As a proof of concept DIA-DB was successfully applied in an integral workflow for the identification of the antidiabetic chemical profile in a complex crude plant extract. To this end, we conducted the extraction and LC-MS based chemical profile analysis of Sclerocarya birrea and subsequently utilized this data as input for our server. The server is open to all users, registration is not necessary, and a detailed report with the results of the prediction is sent to the user by email once calculations are completed. This is a novel public domain database and web server specific for diabetes drugs and can be accessed online through http://bio-hpc.eu/software/dia-db/.

sensaas: Shape-based Alignment by Registration of Colored Point-based Surfaces

sensaas is a tool developed for aligning and comparing molecular shapes and sub-shapes. Alignment is obtained by registration of 3D point-based representations of the van der Waals surface. The method uses local properties of the shape to identify the correspondence relationships between two point clouds containing up to several thousand colored (labeled) points. Our rigid-body superimposition method follows a two-stage approach. An initial alignment is obtained by matching pose-invariant local 3D descriptors, called FPFH, of the input point clouds. This stage provides a global superimposition of the molecular surfaces, without any knowledge of their initial pose in 3D space. This alignment is then refined by optimizing the matching of colored points. In our study, each point is colored according to its closest atom, which itself belongs to a user defined physico-chemical class. Finally, sensaas provides an alignment and evaluates the molecular similarity by using Tversky coefficients. To assess the efficiency of this approach, we tested its ability to reproduce the superimposition of X-ray structures of the benchmarking AstraZeneca (AZ) data set and, compared its results with those generated by the two shape-alignment approaches shaep and shafts. We also illustrated submatching properties of our method with respect to few substructures and bioisosteric fragments. The code is available upon request from the authors (demo version at https://chemoinfo.ipmc.cnrs.fr/SENSAAS).

Valorisation of stachysetin from cultivated Stachys iva Griseb. as anti-diabetic agent: a multi-spectroscopic and molecular docking approach

Stachys species are considered as important medicinal plants with numerous health benefit effects. In continuation of our research on the Greek Stachys species, the chemical profile of the aerial parts of cultivated S. iva Griseb. has been explored. The NMR profiles of the plant extract/infusion were used to guide the isolation process, leading to the targeted isolation of seventeen known compounds. The rare acylated flavonoid, stachysetin, was isolated for the third time from plant species in the international literature. Identification of the characteristic signals of stachysetin in the 1D ¹H-NMR spectrum of the crude extract was presented. In order to evaluate the potential of the identified chemical space in Stachys to bear possible bioactivity against diabetes, we performed an in silico screening against 17 proteins implicated in diabetes, as also ligand based similarity metrics against established anti-diabetic drugs. The results capitalized the anti-diabetic potency of stachysetin. Its binding profile to the major drug carrier plasma protein serum albumin was also explored along with its photophysical properties suggesting that stachysetin could be recognized and delivered in plasma through serum albumin and also could be tracked through near-infrared imaging. Communicated by Ramaswamy H. Sarma.

Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors

A new coronavirus, known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the aetiological agent responsible for the 2019-2020 viral pneumonia outbreak of coronavirus disease 2019 (COVID-19)^1-4. Currently, there are no targeted therapeutic agents for the treatment of this disease, and effective treatment options remain very limited. Here we describe the results of a programme that aimed to rapidly discover lead compounds for clinical use, by combining structure-assisted drug design, virtual drug screening and high-throughput screening. This programme focused on identifying drug leads that target main protease (M^pro) of SARS-CoV-2: M^pro is a key enzyme of coronaviruses and has a pivotal role in mediating viral replication and transcription, making it an attractive drug target for SARS-CoV-2^5,6. We identified a mechanism-based inhibitor (N3) by computer-aided drug design, and then determined the crystal structure of M^pro of SARS-CoV-2 in complex with this compound. Through a combination of structure-based virtual and high-throughput screening, we assayed more than 10,000 compounds-including approved drugs, drug candidates in clinical trials and other pharmacologically active compounds-as inhibitors of M^pro. Six of these compounds inhibited M^pro, showing half-maximal inhibitory concentration values that ranged from 0.67 to 21.4 μM. One of these compounds (ebselen) also exhibited promising antiviral activity in cell-based assays. Our results demonstrate the efficacy of our screening strategy, which can lead to the rapid discovery of drug leads with clinical potential in response to new infectious diseases for which no specific drugs or vaccines are available.

Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors

A new coronavirus, known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the aetiological agent responsible for the 2019-2020 viral pneumonia outbreak of coronavirus disease 2019 (COVID-19)^1-4. Currently, there are no targeted therapeutic agents for the treatment of this disease, and effective treatment options remain very limited. Here we describe the results of a programme that aimed to rapidly discover lead compounds for clinical use, by combining structure-assisted drug design, virtual drug screening and high-throughput screening. This programme focused on identifying drug leads that target main protease (M^pro) of SARS-CoV-2: M^pro is a key enzyme of coronaviruses and has a pivotal role in mediating viral replication and transcription, making it an attractive drug target for SARS-CoV-2^5,6. We identified a mechanism-based inhibitor (N3) by computer-aided drug design, and then determined the crystal structure of M^pro of SARS-CoV-2 in complex with this compound. Through a combination of structure-based virtual and high-throughput screening, we assayed more than 10,000 compounds-including approved drugs, drug candidates in clinical trials and other pharmacologically active compounds-as inhibitors of M^pro. Six of these compounds inhibited M^pro, showing half-maximal inhibitory concentration values that ranged from 0.67 to 21.4 μM. One of these compounds (ebselen) also exhibited promising antiviral activity in cell-based assays. Our results demonstrate the efficacy of our screening strategy, which can lead to the rapid discovery of drug leads with clinical potential in response to new infectious diseases for which no specific drugs or vaccines are available.

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

Applying Machine Learning to Ultrafast Shape Recognition in Ligand-Based Virtual Screening

Ultrafast Shape Recognition (USR), along with its derivatives, are Ligand-Based Virtual Screening (LBVS) methods that condense 3-dimensional information about molecular shape, as well as other properties, into a small set of numeric descriptors. These can be used to efficiently compute a measure of similarity between pairs of molecules using a simple inverse Manhattan Distance metric. In this study we explore the use of suitable Machine Learning techniques that can be trained using USR descriptors, so as to improve the similarity detection of potential new leads. We use molecules from the Directory for Useful Decoys-Enhanced to construct machine learning models based on three different algorithms: Gaussian Mixture Models (GMMs), Isolation Forests and Artificial Neural Networks (ANNs). We train models based on full molecule conformer models, as well as the Lowest Energy Conformations (LECs) only. We also investigate the performance of our models when trained on smaller datasets so as to model virtual screening scenarios when only a small number of actives are known a priori. Our results indicate significant performance gains over a state of the art USR-derived method, ElectroShape 5D, with GMMs obtaining a mean performance up to 430% better than that of ElectroShape 5D in terms of Enrichment Factor with a maximum improvement of up to 940%. Additionally, we demonstrate that our models are capable of maintaining their performance, in terms of enrichment factor, within 10% of the mean as the size of the training dataset is successively reduced. Furthermore, we also demonstrate that running times for retrospective screening using the machine learning models we selected are faster than standard USR, on average by a factor of 10, including the time required for training. Our results show that machine learning techniques can significantly improve the virtual screening performance and efficiency of the USR family of methods.