Protein–Ligand-Based Pharmacophores: Generation and Utility Assessment in Computational Ligand Profiling

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

A pharmacophore-guided deep learning approach for bioactive molecular generation

The rational design of novel molecules with the desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets. We propose a Pharmacophore-Guided deep learning approach for bioactive Molecule Generation (PGMG). Through the guidance of pharmacophore, PGMG provides a flexible strategy for generating bioactive molecules. PGMG uses a graph neural network to encode spatially distributed chemical features and a transformer decoder to generate molecules. A latent variable is introduced to solve the many-to-many mapping between pharmacophores and molecules to improve the diversity of the generated molecules. Compared to existing methods, PGMG generates molecules with strong docking affinities and high scores of validity, uniqueness, and novelty. In the case studies, we use PGMG in a ligand-based and structure-based drug de novo design. Overall, the flexibility and effectiveness make PGMG a useful tool to accelerate the drug discovery process.

Identification of novel inhibitors of Neisseria gonorrhoeae MurI using homology modeling, structure-based pharmacophore, molecular docking, and molecular dynamics simulation-based approach

MurI is one of the most significant role players in the biosynthesis of the peptidoglycan layer in Neisseria gonorrhoeae (Ng). We attempted to highlight the structural and functional relationship between Ng-MurI and D-glutamate to design novel molecules targeting this interaction. The three-dimensional (3D) model of the protein was constructed by homology modeling and the quality and consistency of generated model were assessed. The binding site of the protein was identified by molecular docking studies and a pharmacophore was identified using the interactions of the control ligand. The structure-based pharmacophore model was validated and employed for high-throughput virtual screening and molecular docking to identify novel Ng-MurI inhibitors. Finally, the model was optimized by molecular dynamics (MD) simulations and the optimized model complex with the substrate glutamate and novel molecules facilitated us to confirm the stability of the protein-ligand docked complexes. The 100 ns MD simulations of the potential lead compounds with protein confirmed that the modeled complexes were stable. This study identifies novel potential compounds with good fitness and docking scores, which made the interactions of biological significance within the protein active site. Hence, the identified compounds may act as new leads to design and develop Ng-MurI inhibitors.Communicated by Ramaswamy H. Sarma.

Effects of Dietary Inclusion of Asiaticoside on Growth Performance, Lipid Metabolism, and Gut Microbiota in Yellow-Feathered Chickens

Excessive abdominal fat deposition in chickens is a major concern in the poultry industry. Nutritional interventions are a potential solution, but current options are limited. Asiaticoside (Asi), a herbal extract, has shown positive effects in animals, but its impact on poultry lipid metabolism is still unknown. In this study, the effects of dietary Asi on yellow-feathered chicken lipid metabolism and its potential mechanisms were investigated. A total of 120 chickens were randomly divided into three groups, with five replicates per group and 8 chickens per replicate. The chickens were fed a basal diet supplemented with 0, 0.01, or 0.05% Asi for 6 wk. The results showed that Asi down-regulated lipogenic gene expression and up-regulated lipid-breakdown-related genes in both the liver and fat tissues of the chickens, which resulted in a half reduction in abdominal fat while not affecting meat yield. Mechanistically, the hepatic and adipose PI3K/AKT pathway may be involved in Asi-induced fat loss in chickens as revealed by computer-aided reverse drug target prediction and gene expression analysis. Moreover, Asi ingestion also significantly modified the cecal microbiota of the chickens, resulting in a reduced Firmicutes to Bacteroidetes ratio and decreased abundance of bacteria positively correlated with abdominal fat deposition such as Ruminococcus, while increasing the abundance of bacteria inversely correlated with abdominal fat deposition such as Lactobacillus, Bacteroides, and Blautia. Collectively, these data suggest that Asi could ameliorate the abdominal fat deposition in yellow-feathered chickens, probably through modulating the PI3K/AKT pathway and gut microbiota function.

Developing new PI3Kγ inhibitors by combining pharmacophore modeling, molecular dynamic simulation, molecular docking, fragment-based drug design, and virtual screening

Since dysregulation of the phosphatidylinositol 3-kinase gamma (PI3Kγ) signaling pathway is associated with the pathogenesis of cancer, inflammation, and autoimmunity, PI3Kγ has emerged as an attractive target for drug development. IPI-549 is the only selective PI3Kγ inhibitor that has advanced to clinical trials, thus, IPI-549 could serve as a promising template for designing novel PI3Kγ inhibitors. In this present study, a modeling strategy consisting of common feature pharmacophore modeling, receptor-ligand pharmacophore modeling, and molecular dynamics simulation was utilized to identify the key pharmacodynamic characteristic elements of the target compound and the key residue information of the PI3Kγ interaction with the inhibitors. Then, 10 molecules were designed based on the structure-activity relationships, and some of them exhibited satisfactory predicted binding affinities to PI3Kγ. Finally, a hierarchical multistage virtual screening method, involving the developed common feature and receptor-ligand pharmacophore model and molecular docking, was constructed for screening the potential PI3Kγ inhibitors. Overall, we hope these findings would provide some guidance for the development of novel PI3Kγ inhibitors.

Dynamacophore model for breast cancer estrogen receptor alpha as an effective lead generation screening technique

Regardless to overwhelming quantum of cancer research worldwide, there are few drugs on the market to treat disease conditions. This is owing to multiple process inferences of drug targets in integrated pathways for invasion, growth, and metastasis. Over the past years, the death rate due to breast cancer has been increasing, that set the stage for improved better treatment. Therefore, there is a persistent and vital demand for innovative development of drugs to treat breast cancer. Many studies have reported that more than 60% of breast cancers are Estrogen receptor-α (ERα)-positive tumours and a key transcription factor, Estrogen receptor-α (ERα) was believed to promote proliferation of breast cancer cells. In this study, 150 ns of molecular dynamics was performed for protein-ligand complex to retrieve the potential stable conformations. The most populated dynamics cluster of 4-Hydroxytamoxifen intact with active site amino acid was selected to generate dynamacophore model (dynamic pharmacophore). Further, internal model validation with AU-ROC values ∼0.93 indicate the best model to screen library. The refined hits are funnelled in pharmacokinetics/dynamics, CDOCKER molecular docking, MM-GBSA and density functional theory to identify the promising ERα ligand candidates.Communicated by Ramaswamy H. Sarma.

Target identification by structure-based computational approaches: Recent advances and perspectives

The use of computational techniques in the early stages of drug discovery has recently experienced a boost, especially in the target identification step. Finding the biological partner(s) for new or existing synthetic and/or natural compounds by "wet" approaches may be challenging; therefore, preliminary in silico screening is even more recommended. After a brief overview of some of the most known target identification techniques, recent advances in structure-based computational approaches for target identification are reported in this digest, focusing on Inverse Virtual Screening and its recent applications. Moreover, future perspectives concerning the use of such methodologies, coupled or not with other approaches, are analyzed.

NRF2 in dermo-cosmetic: From scientific knowledge to skin care products

The skin is the organ that is most susceptible to the impact of the exposome. Located at the interface with the external environment, it protects internal organs through the barrier function of the epidermis. It must adapt to the consequences of the harmful effects of solar radiation, the various chemical constituents of atmospheric pollution, and wounds associated with mechanical damage: oxidation, cytotoxicity, inflammation, and so forth. In this biological context, a capacity to adapt to the various stresses caused by the exposome is essential; otherwise, more or less serious conditions may develop accelerated aging, pigmentation disorders, atopy, psoriasis, and skin cancers. Nrf2-controlled pathways play a key role at this level. Nrf2 is a transcription factor that controls genes involved in oxidative stress protection and detoxification of chemicals. Its involvement in UV protection, reduction of inflammation in processes associated with healing, epidermal differentiation for barrier function, and hair regrowth, has been demonstrated. The modulation of Nrf2 in the skin may therefore constitute a skin protection or care strategy for certain dermatological stresses and disorders initiated or aggravated by the exposome. Nrf2 inducers can act through different modes of action. Keap1-dependent mechanisms include modification of the cysteine residues of Keap1 by (pro)electrophiles or prooxidants, and disruption of the Keap1-Nrf2 complex. Indirect mechanisms are suggested for numerous phytochemicals, acting on upstream pathways, or via hormesis. While developing novel and safe Nrf2 modulators for skin care may be challenging, new avenues can arise from natural compounds-based molecular modeling and emerging concepts such as epigenetic regulation.

Drug Repurposing at the Interface of Melanoma Immunotherapy and Autoimmune Disease

Cancer cells have a remarkable ability to evade recognition and destruction by the immune system. At the same time, cancer has been associated with chronic inflammation, while certain autoimmune diseases predispose to the development of neoplasia. Although cancer immunotherapy has revolutionized antitumor treatment, immune-related toxicities and adverse events detract from the clinical utility of even the most advanced drugs, especially in patients with both, metastatic cancer and pre-existing autoimmune diseases. Here, the combination of multi-omics, data-driven computational approaches with the application of network concepts enables in-depth analyses of the dynamic links between cancer, autoimmune diseases, and drugs. In this review, we focus on molecular and epigenetic metastasis-related processes within cancer cells and the immune microenvironment. With melanoma as a model, we uncover vulnerabilities for drug development to control cancer progression and immune responses. Thereby, drug repurposing allows taking advantage of existing safety profiles and established pharmacokinetic properties of approved agents. These procedures promise faster access and optimal management for cancer treatment. Together, these approaches provide new disease-based and data-driven opportunities for the prediction and application of targeted and clinically used drugs at the interface of immune-mediated diseases and cancer towards next-generation immunotherapies.

In silico activity and ADMET profiling of phytochemicals from Ethiopian indigenous aloes using pharmacophore models

In silico profiling is used in identification of active compounds and guide rational use of traditional medicines. Previous studies on Ethiopian indigenous aloes focused on documentation of phytochemical compositions and traditional uses. In this study, ADMET and drug-likeness properties of phytochemicals from Ethiopian indigenous aloes were evaluated, and pharmacophore-based profiling was done using Discovery Studio to predict therapeutic targets. The targets were examined using KEGG pathway, gene ontology and network analysis. Using random-walk with restart algorithm, network propagation was performed in CODA network to find diseases associated with the targets. As a result, 82 human targets were predicted and found to be involved in several molecular functions and biological processes. The targets also were linked to various cancers and diseases of immune system, metabolism, neurological system, musculoskeletal system, digestive system, hematologic, infectious, mouth and dental, and congenital disorder of metabolism. 207 KEGG pathways were enriched with the targets, and the main pathways were metabolism of steroid hormone biosynthesis, lipid and atherosclerosis, chemical carcinogenesis, and pathways in cancer. In conclusion, in silico target fishing and network analysis revealed therapeutic activities of the phytochemicals, demonstrating that Ethiopian indigenous aloes exhibit polypharmacology effects on numerous genes and signaling pathways linked to many diseases.

Engineering and screening of novel β-1,3-xylanases with desired hydrolysate type by optimized ancestor sequence reconstruction and data mining

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A novel integrative strategy for engineering β-1,3-xylanases with desired products.

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AncXyl10 is the first successful example of ASR to shift the hydrolysate types.

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The hydrolysates of AncXyl10 was only β-1,3-xylobiose and β-1,3-xylotriose.

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The underlying mechanism laid a new groundwork towards hydrolase engineering.

Engineering of hydrolases to shift their hydrolysate types has not been attempted so far, though computer-assisted enzyme design has been successful. A novel integrative strategy for engineering and screening the β-1,3-xylanase with desired hydrolysate types was proposed, with the purpose to solve problems that the separation and preparation of β-1,3-xylo-oligosaccharides was in high cost yet in low yield as monosaccharides existed in the hydrolysates. By classifying the hydrolysate types and coding them into numerical values, two robust mathematical models with five selected attributes from molecular docking were established based on LogitBoost and partial least squares regression with overall accuracy of 83.3% and 100%, respectively. Then, they were adopted for efficient screening the potential mutagenesis library of β-1,3-xylanases that only product oligosaccharides. The virtually designed AncXyl10 was selected and experimentally verified to produce only β-1,3-xylobiose (60.38%) and β-1,3-xylotriose (39.62%), which facilitated the preparation of oligosaccharides with high purity. The underlying mechanism of AncXyl10 may associated with the gap processing and ancestral amino acid substitution in the process of ancestral sequence reconstruction. Since many carbohydrate-active enzymes have highly conserved active sites, the strategy and their biomolecular basis will shield a new light for engineering carbohydrates hydrolase to produce specific oligosaccharides.

Investigating natural compounds against oncogenic RET tyrosine kinase using pharmacoinformatic approaches for cancer therapeutics

Rearranged during transfection (RET) tyrosine kinase is a transmembrane receptor tyrosine kinase regulating vital aspects of cellular proliferation, differentiation, and survival. An outstanding challenge in designing protein kinase inhibitors is due to the development of drug resistance. The “gain of function” mutations in the RET gate-keeper residue, Val804, confers resistance to the majority of known RET inhibitors, including vandetanib. To curtail this resistance, researchers developed selpercatinib (LOXO-292) against the RET gate-keeper mutant forms – V804M and V804L. In the present in silico investigation, a receptor–ligand pharmacophore model was generated to identify small molecule inhibitors effective for wild-type (WT) as well as mutant RET kinase variants. The generated model was employed to screen 144 766 natural products (NPs) available in the ZINC database and the retrieved NPs were filtered for their drug-likeness. The resulting 2696 drug-like NPs were subjected to molecular docking with the RET WT kinase domain and a total of 27 molecules displayed better dock scores than the reference inhibitors – vandetanib and selpercatinib. From 27 NPs, an aggregate of 12 compounds demonstrated better binding free energy (BFE) scores than the reference inhibitors, towards RET. Thus, the 12 NPs were also subjected to docking, simulation, and BFE estimation towards the constructed gate-keeper RET mutant structures. The BFE calculations revealed 3 hits with better BFE scores than the reference inhibitors towards WT, V804M, and V804L RET variants. Thus, the scaffolds of hit compounds presented in this study could act as potent RET inhibitors and further provide insights for drug optimization targeting aberrant activation of RET signaling, specifically the mutation of gate-keeper residue – Val804.

Identification of natural product inhibitors against rearranged during transfection (RET) tyrosine kinase as cancer therapeutics using combination of in silico techniques.

Proteome-Scale Drug-Target Interaction Predictions: Approaches and Applications

Drug-Target interaction predictions are an important cornerstone of computer-aided drug discovery. While predictive methods around individual targets have a long history, the application of proteome-scale models is relatively recent. In this overview, we will provide the context required to understand advances in this emerging field within computational drug discovery, evaluate emerging technologies for suitability to given tasks, and provide guidelines for the design and implementation of new drug-target interaction prediction models. We will discuss the validation approaches used, and propose a set of key criteria that should be applied to evaluate their validity. We note that we find widespread deficiencies in the existing literature, making it difficult to judge the practical effectiveness of some of the techniques proposed from their publications alone. We hope that this review may help remedy this situation and increase awareness of several sources of bias that may enter into commonly used cross-validation methods. © 2021 Cyclica Inc. Current Protocols published by Wiley Periodicals LLC.

ePharmaLib: A Versatile Library of e-Pharmacophores to Address Small-Molecule (Poly-)Pharmacology

Bioactive compounds oftentimes bind to several target proteins, thereby exhibiting polypharmacology. Experimentally determining these interactions is however laborious, and structure-based virtual screening (SBVS) of bioactive compounds could expedite drug discovery by prioritizing hits for experimental validation. Here, we present ePharmaLib, a library of 15,148 e-pharmacophores modeled from solved structures of pharmaceutically relevant protein-ligand complexes of the screening Protein Data Bank (sc-PDB). ePharmaLib can be used for target fishing of phenotypic hits, side effect predictions, drug repurposing, and scaffold hopping. In retrospective SBVS, a good balance was obtained between computational efficiency and predictive accuracy. As a proof of concept, we carried out prospective SBVS in conjunction with a photometric assay, which inferred that the mechanism of action of neopterin (an endogenous immunomodulator) putatively stems from its inhibition (IC₅₀ = 18 μM) of the human purine nucleoside phosphorylase. This ready-to-use library is freely available at http://www.pharmbioinf.uni-freiburg.de/epharmalib.

Pharmacophore Modelling-Based Drug Repurposing Approaches for SARS-CoV-2 Therapeutics

The recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a devastating effect globally with no effective treatment. The swift strategy to find effective treatment against coronavirus disease 2019 (COVID-19) is to repurpose the approved drugs. In this pursuit, an exhaustive computational method has been used on the DrugBank compounds targeting nsp16/nsp10 complex (PDB code: 6W4H). A structure-based pharmacophore model was generated, and the selected model was escalated to screen DrugBank database, resulting in three compounds. These compounds were subjected to molecular docking studies at the protein-binding pocket employing the CDOCKER module available with the Discovery Studio v18. In order to discover potential candidate compounds, the co-crystallized compound S-adenosyl methionine (SAM) was used as the reference compound. Additionally, the compounds remdesivir and hydroxycholoroquine were employed for comparative docking. The results have shown that the three compounds have demonstrated a higher dock score than the reference compounds and were upgraded to molecular dynamics simulation (MDS) studies. The MDS results demonstrated that the three compounds, framycetin, kanamycin, and tobramycin, are promising candidate compounds. They have represented a stable binding mode at the targets binding pocket with an average protein backbone root mean square deviation below 0.3 nm. Additionally, they have prompted the hydrogen bonds during the entire simulations, inferring that the compounds have occupied the active site firmly. Taken together, our findings propose framycetin, kanamycin, and tobramycin as potent putative inhibitors for COVID-19 therapeutics.

Identification of Flavonoids as Putative ROS-1 Kinase Inhibitors Using Pharmacophore Modeling for NSCLC Therapeutics

Non-small cell lung cancer (NSCLC) is a lethal non-immunogenic malignancy and proto-oncogene ROS-1 tyrosine kinase is one of its clinically relevant oncogenic markers. The ROS-1 inhibitor, crizotinib, demonstrated resistance due to the Gly2032Arg mutation. To curtail this resistance, researchers developed lorlatinib against the mutated kinase. In the present study, a receptor-ligand pharmacophore model exploiting the key features of lorlatinib binding with ROS-1 was exploited to identify inhibitors against the wild-type (WT) and the mutant (MT) kinase domain. The developed model was utilized to virtually screen the TimTec flavonoids database and the retrieved drug-like hits were subjected for docking with the WT and MT ROS-1 kinase. A total of 10 flavonoids displayed higher docking scores than lorlatinib. Subsequent molecular dynamics simulations of the acquired flavonoids with WT and MT ROS-1 revealed no steric clashes with the Arg2032 (MT ROS-1). The binding free energy calculations computed via molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) demonstrated one flavonoid (Hit) with better energy than lorlatinib in binding with WT and MT ROS-1. The Hit compound was observed to bind in the ROS-1 selectivity pocket comprised of residues from the β-3 sheet and DFG-motif. The identified Hit from this investigation could act as a potent WT and MT ROS-1 inhibitor.

An Early Stage Researcher's Primer on Systems Medicine Terminology

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

The discovery of novel small molecule allosteric activators of aldehyde dehydrogenase 2

Aldehyde dehydrogenase 2 (ALDH2) plays important role in ethanol metabolism, and also serves as an important shield from the damage occurring under oxidative stress. A special inactive variant was found carried by 35-45% of East Asians. The variant carriers have recently been found at the higher risk for the diseases related to the damage occurring under oxidative stress, such as cardiovascular and cerebrovascular diseases. As a result, ALDH2 activators may potentially serve as a new class of therapeutics. Herein, N-benzylanilines were found as novel allosteric activators of ALDH2 by computational virtual screening using ligand-based and structure-based screening parallel screening strategy. Then a structural optimization was performed and has led to the discovery of the compound C6. It has good activity in vitro and in vivo, which could reduce infarct size by ∼70% in ischemic stroke rat models. This study provided good lead compounds for the further development of ALDH2 activators.

Computational Approaches to Identify Molecules Binding to Mycobacterium tuberculosis KasA

Tuberculosis is caused by Mycobacterium tuberculosis (Mtb) and is a deadly disease resulting in the deaths of approximately 1.5 million people with 10 million infections reported in 2018. Recently, a key condensation step in the synthesis of mycolic acids was shown to require β-ketoacyl-ACP synthase (KasA). A crystal structure of KasA with the small molecule DG167 was recently described, which provided a starting point for using computational structure-based approaches to identify additional molecules binding to this protein. We now describe structure-based pharmacophores, docking and machine learning studies with Assay Central as a computational tool for the identification of small molecules targeting KasA. We then tested these compounds using nanoscale differential scanning fluorimetry and microscale thermophoresis. Of note, we identified several molecules including the Food and Drug Administration (FDA)-approved drugs sildenafil and flubendazole with K _d values between 30-40 μM. This may provide additional starting points for further optimization.

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

Integrating Machine Learning-Based Virtual Screening With Multiple Protein Structures and Bio-Assay Evaluation for Discovery of Novel GSK3β Inhibitors

Glycogen synthase kinase-3β (GSK3β) is associated with various key biological processes, and it has been considered as a critical therapeutic target for the treatment of many diseases. However, it is a big challenge to develop ATP-competition GSK3β inhibitors because of the high sequence homology with other kinases. In this work, a novel parallel virtual screening strategy based on multiple GSK3β protein structures, integrating molecular docking, complex-based pharmacophore, and naive Bayesian classification, was developed to screen a large chemical database, the 50 compounds with top-scores then underwent a luminescent kinase assay, which led to the discovery of two GSK3β inhibitor hits. The high screening enrichment rate indicates the reliability and practicability of the integrated protocol. Finally, molecular docking and molecular dynamics simulation were employed to investigate the binding modes of the GSK3β inhibitors, and some “hot residues” critical to GSK3β affinity were highlighted. The present study may provide some valuable guidance for the development of novel GSK3β inhibitors.

Evolutionary chemical binding similarity approach integrated with 3D-QSAR method for effective virtual screening

BACKGROUND

Despite continued efforts using chemical similarity methods in virtual screening, currently developed approaches suffer from time-consuming multistep procedures and low success rates. We recently developed a machine learning-based chemical binding similarity model considering common structural features from molecules binding to the same, or evolutionarily related targets. The chemical binding similarity measures the resemblance of chemical compounds in terms of binding site similarity to better describe functional similarities that arise from target binding. In this study, we have shown how the chemical binding similarity could be used in virtual screening together with the conventional structure-based methods.

RESULTS

The chemical binding similarity, receptor-based pharmacophore, chemical structure similarity, and molecular docking methods were evaluated to identify an effective virtual screening procedure for desired target proteins. When we tested the chemical binding similarity method with test sets of 51 kinases, it outperformed the traditional structural similarity-based methods as well as structure-based methods, such as molecular docking and receptor-based pharmacophore modeling, in terms of finding active compounds. We further validated the results by performing virtual screening (using the chemical binding similarity and receptor-based pharmacophore methods) against a completely blind dataset for mitogen-activated protein kinase kinase 1 (MEK1), ephrin type-B receptor 4 (EPHB4) and wee1-like protein kinase (WEE1). The in vitro kinase binding assay confirmed that 6 out of 13 (46.2%) for MEK1 and 2 out of 12 (16.7%) for EPHB4 were newly identified only by the chemical binding similarity model.

CONCLUSIONS

We report that the virtual screening results could further be improved by combining the chemical binding similarity model with 3D-QSAR pharmacophore and molecular docking models. Not only the new inhibitors are identified in this study, but also many of the identified molecules have low structural similarity scores against already reported inhibitors and that show the revelation of novel scaffolds.

A Computational Approach with Biological Evaluation: Combinatorial Treatment of Curcumin and Exemestane Synergistically Regulates DDX3 Expression in Cancer Cell Lines

DDX3 belongs to RNA helicase family that demonstrates oncogenic properties and has gained wider attention due to its role in cancer progression, proliferation and transformation. Mounting reports have evidenced the role of DDX3 in cancers making it a promising target to abrogate DDX3 triggered cancers. Dual pharmacophore models were generated and were subsequently validated. They were used as 3D queries to screen the InterBioScreen database, resulting in the selection of curcumin that was escalated to molecular dynamics simulation studies. In vitro anti-cancer analysis was conducted on three cell lines such as MCF-7, MDA-MB-231 and HeLa, which were evaluated along with exemestane. Curcumin was docked into the active site of the protein target (PDB code 2I4I) to estimate the binding affinity. The compound has interacted with two key residues and has displayed stable molecular dynamics simulation results. In vitro analysis has demonstrated that both the candidate compounds have reduced the expression of DDX3 in three cell lines. However, upon combinatorial treatment of curcumin (10 and 20 μM) and exemestane (50 μM) a synergism was exhibited, strikingly downregulating the DDX3 expression and has enhanced apoptosis in three cell lines. The obtained results illuminate the use of curcumin as an alternative DDX3 inhibitor and can serve as a chemical scaffold to design new small molecules.

Assessing the Performance of Mixed Strategies To Combine Lipophilic Molecular Similarity and Docking in Virtual Screening

The accuracy of structure-based (SB) virtual screening (VS) is heavily affected by the scoring function used to rank a library of screened compounds. Even in cases where the docked pose agrees with the experimental binding mode of the ligand, the limitations of current scoring functions may lead to sensible inaccuracies in the ability to discriminate between actives and inactives. In this context, the combination of SB and ligand-based (LB) molecular similarity may be a promising strategy to increase the hit rates in VS. This study explores different strategies that aim to exploit the synergy between LB and SB methods in order to mitigate the limitations of these techniques, and to enhance the performance of VS studies by means of a balanced combination between docking scores and three-dimensional (3D) similarity. Particularly, attention is focused to the use of measurements of molecular similarity with PharmScreen, which exploits the 3D distribution of atomic lipophilicity determined from quantum mechanical-based continuum solvation calculations performed with the MST model, in conjunction with three docking programs: Glide, rDock, and GOLD. Different strategies have been explored to combine the information provided by docking and similarity measurements for re-ranking the screened ligands. For a benchmarking of 44 datasets, including 41 targets, the hybrid methods increase the identification of active compounds, according to the early (ROCe%) and total (AUC) enrichment metrics of VS, compared to pure LB and SB methods. Finally, the hybrid approaches are also more effective in enhancing the chemical diversity of active compounds. The datasets employed in this work are available in https://github.com/Pharmacelera/Molecular-Similarity-and-Docking.

Synthesis, Characterization, and Biological Evaluation of Novel 7-Oxo-7H-thiazolo[3,2-b]-1,2,4-triazine-2-carboxylic Acid Derivatives

A series of novel 7-oxo-7H-thiazolo[3,2-b]-1,2,4-triazine-2-carboxylic acid derivatives was synthesized in good yields by a multi-step procedure that included the generation of the S-alkylated derivatives from 6-substituted arylmethyl-3-mercapto-1,2,4-triazin-5-ones with ethyl 2-chloroacetoacetate, intramolecular cyclization with microwave irradiation, hydrolysis and amidation. All of the target compounds were fully characterized through ¹H-NMR, ¹³C-NMR and HRMS spectra. The intramolecular cyclization occurred regioselectively at the N2-position of 1,2,4-triazine ring, which was confirmed by compound 3e using single-crystal X-ray diffraction analysis. The antibacterial and antitubercular activities of the target compounds were evaluated. Compared with Ciprofloxacin and Rifampicin, compounds 5d, 5f and 5g containing the terminal amide fragment exhibited broad spectrum antibacterial activity, and carboxylic acid derivatives or its corresponding ethyl esters had less effect on antibacterial properties. The most potent compound 5f also displayed excellent in vitro antitubercular activity against Mycobacterium smegmatis (minimum inhibitory concentration (MIC) = 50 μg/mL) and better growth inhibition activity of leucyl-tRNA synthetase (78.24 ± 4.05% at 15 μg/mL).

Short communication for targeting natural compounds against HER2 kinase domain as potential anticancer drugs applying pharmacophore based molecular modelling approaches- part 2

Breast cancer is one of the common causes of death noticed in women globally. In order to find effective therapeutics, the current investigation has focussed on identifying candidate compounds for EGFR and HER2. Accordingly, the pharmacophore modelling approaches were adapted to identify two prospective compounds and were docked against the target 3RCD that is complexed with TAK-285 a known dual inhibitor. Focussing on the target 3RCD, our results have showed that the compounds have demonstrated a good binding affinity towards the target occupying the binding pocket. They have established key residue interactions with stable molecular dynamics simulation results. The Hit compounds have demonstrated a potential to penetrate the blood brain barrier thereby enriching their therapeutics towards breast cancer brain metastasis. Taken together, our findings propose two candidate compounds as EGFR/HER2 inhibitors that might serve as novel chemical spaces for designing and developing new inhibitors.

Structure based designing of benzimidazole/benzoxazole derivatives as anti-leishmanial agents

Folates are essential biomolecules required to carry out many crucial processes in leishmania parasite. Dihydrofolate reductase-thymidylate synthase (DHFR-TS) and pteridine reductase 1 (PTR1) involved in folate biosynthesis in leishmania have been established as suitable targets for development of chemotherapy against leishmaniasis. In the present study, various computational tools such as homology modelling, pharmacophore modelling, docking, molecular dynamics and molecular mechanics have been employed to design dual DHFR-TS and PTR1 inhibitors. Two designed molecules, i.e. 2-(4-((4-nitrobenzyl)oxy)phenyl)-1H-benzo[d]imidazole and 2-(4-((2,4-dichlorobenzyl)oxy)phenyl)-1H-benzo[d]oxazolemolecules were synthesized. MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide) assay was performed to evaluate in vitro activity of molecules against promastigote form of Leishmania donovani using Miltefosine as standard. 2-(4-((4-nitrobenzyl)oxy)phenyl)-1H-benzo[d]imidazole and 2-(4-((2,4-dichlorobenzyl)oxy)phenyl)-1H-benzo[d]oxazolemolecules were found to be moderately active with showed IC₅₀ = 68 ± 2.8 µM and 57 ± 4.2 µM, respectively.

Current computational methods for predicting protein interactions of natural products

Natural products (NPs) are an indispensable source of drugs and they have a better coverage of the pharmacological space than synthetic compounds, owing to their high structural diversity. The prediction of their interaction profiles with druggable protein targets remains a major challenge in modern drug discovery. Experimental (off-)target predictions of NPs are cost- and time-consuming, whereas computational methods, on the other hand, are much faster and cheaper. As a result, computational predictions are preferentially used in the first instance for NP profiling, prior to experimental validations. This review covers recent advances in computational approaches which have been developed to aid the annotation of unknown drug-target interactions (DTIs), by focusing on three broad classes, namely: ligand-based, target-based, and target-ligand-based (hybrid) approaches. Computational DTI prediction methods have the potential to significantly advance the discovery and development of novel selective drugs exhibiting minimal side effects. We highlight some inherent caveats of these methods which must be overcome to enable them to realize their full potential, and a future outlook is given.

Design and Biological Evaluation of 3-Aryl-4-alkylpyrazol-5-amines Based on the Target Fishing

BACKGROUND

Pyrazol-5-amine derivatives are an important class of heterocyclic compounds. However, there are less 4-alkyl substituted pyrazoles reported.

OBJECTIVE

Here reported are the design, synthesis and biological evaluation of 3-aryl-4- alkylpyrazol-5-amines derivatives.

METHODS

A serials of 3-aryl-4-alkylpyrazol-5-amines were designed and the biological action targets were screened by target fishing function of Discovery Studio software. The synthesis route involved 3-oxo-3-arylpropanenitrile formation, alkylation, pyrazole formation, and amides formation. The antitumor activities of these compounds were carried out by thiazolyl blue tetrazolium bromide (MTT) method using U-2 OS (osteosarcoma) and A549 (lung cancer) tumor cells.

RESULTS

Eight 3-aryl-4-alkylpyrazol-5-amines were synthesized, and their structures were verified by 1H NMR, 13C NMR, and HRMS. Thirteen pharmacophores were mapped out by target fishing. Compound 5h showed anti-proliferation activities against U-2 OS and A549 tumor cell with IC50 value of 0.9 μM and 1.2 μM, respectively.

CONCLUSION

Compound 5h might represent a promising scaffold for the further development of novel antitumor drugs.

Acteoside From Ligustrum robustum (Roxb.) Blume Ameliorates Lipid Metabolism and Synthesis in a HepG2 Cell Model of Lipid Accumulation

We aimed to ascertain the mechanism underlying the effects of acteoside (ACT) from Ligustrum robustum (Roxb.) Blume (Oleaceae) on lipid metabolism and synthesis. ACT, a water-soluble phenylpropanoid glycoside, is the most abundant and major active component of L. robustum; the leaves of L. robustum, known as kudingcha (bitter tea), have long been used in China as an herbal tea for weight loss. Recently, based on previous studies, our team reached a preliminary conclusion that phenylpropanoid glycosides from L. robustum most likely contribute substantially to reducing lipid levels, but the mechanism remains unclear. Here, we conducted an in silico screen of currently known phenylethanoid glycosides from L. robustum and attempted to explore the hypolipidemic mechanism of ACT, the representative component of phenylethanoid glycosides in L. robustum, using RNA-seq technology, quantitative real-time PCR (qPCR) and Western blotting. First, the screening results for six compounds were docked with 15 human protein targets, and 3 of 15 protein targets were related to cardiovascular diseases. Based on previous experimental data and docking results, we selected ACT, which exerted positive effects, for further study. We generated a lipid accumulation model using HepG2 cells treated with a high concentration of oleic acid and then extracted RNA from cells treated for 24 h with 50 μmol/L ACT. Subsequently, we performed a transcriptomic analysis of the RNA-seq results, which revealed a large number of differentially expressed genes. Finally, we randomly selected some genes and proteins for further validation using qPCR and Western blotting; the results agreed with the RNA-seq data and confirmed their reliability. In conclusion, our experiments proved that ACT from L. robustum alters lipid metabolism and synthesis by regulating the expression of multiple genes, including Scarb1, Scarb2, Srebf1, Dhcr7, Acat2, Hmgcr, Fdft1, and Lss, which are involved several pathways, such as the glycolytic, AMPK, and fatty acid degradation pathways.

Discovery of New Phosphoinositide 3-kinase Delta (PI3Kδ) Inhibitors via Virtual Screening using Crystallography-derived Pharmacophore Modelling and QSAR Analysis

BACKGROUND

PI3Kδ is predominantly expressed in hematopoietic cells and participates in the activation of leukocytes. PI3Kδ inhibition is a promising approach for treating inflammatory diseases and leukocyte malignancies. Accordingly, we decided to model PI3Kδ binding.

METHODS

Seventeen PI3Kδ crystallographic complexes were used to extract 94 pharmacophore models. QSAR modelling was subsequently used to select the superior pharmacophore(s) that best explain bioactivity variation within a list of 79 diverse inhibitors (i.e., upon combination with other physicochemical descriptors).

RESULTS

The best QSAR model (r2 = 0.71, r2 LOO = 0.70, r2 press against external testing list of 15 compounds = 0.80) included a single crystallographic pharmacophore of optimal explanatory qualities. The resulting pharmacophore and QSAR model were used to screen the National Cancer Institute (NCI) database for new PI3Kδ inhibitors. Two hits showed low micromolar IC50 values.

CONCLUSION

Crystallography-based pharmacophores were successfully combined with QSAR analysis for the identification of novel PI3Kδ inhibitors.

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.

Predicting protein targets for drug-like compounds using transcriptomics

An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation of set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions.

Bioactive compounds often disrupt cellular gene expression in ways that are difficult to predict. While the correlation between a cellular response after treatment with a small molecule and the knockdown of its target protein should be simple to establish, in practice this goal has been difficult to achieve. The main challenges are that data are noisy, drugs are not intended to be active in all cell types, and signals from a bona fide target(s) may be obscured by correlations with knockdowns of other proteins in the same pathway(s). Here, we find that a random forest classification model can detect meaningful correlational patterns when gene expression profiles after compound treatment and gene knockdowns in four or more cell lines are compared. When this approach is enriched by a structure-based screen, novel drug-target interactions can be predicted. We then validated new ligand-protein interactions for four difficult targets. Although the initial compounds are not especially potent in vitro, they are capable of disrupting their target pathway in the cell to an extent that generates a significant and characteristic gene expression profile. Collectively, our studies provide insight on cell-level transcriptomic responses to pharmaceutical intervention and the use of these patterns for target identification. In addition, the method provides a novel drug discovery pipeline to test chemistries without a priori knowledge of their target(s).

Novel Transforming Growth Factor-Beta Receptor 1 Antagonists through a Pharmacophore-Based Virtual Screening Approach

As new drugs for the treatment of malignant tumors, transforming growth factor-beta receptor 1 (TGFβR1) antagonists have attracted wide attention. Based on the crystal structure of TGFβR1-BMS22 complex, the pharmacophore model A02 with two hydrogen bond acceptors (HBAs) and four hydrophobic (HYD) properties was constructed. From the common features of active ligands reported in the literature, pharmacophore model B10 was also generated, which has two aromatic ring centers (RAs) and two HYD properties. The two models have high sensitivity and specificity to the training set, and they are highly consistent in spatial structure. Combining the two pharmacophore models, two novel skeleton structures with potential activity were selected by virtual screening from the DruglikeDiverse, MiniMaybridge, and ZINC Drug-Like databases. Four compounds (YXY01⁻YXY04) with potential anti-TGFβR1 activity were designed based on the new skeleton structures. In combination with Lipinski's rules; absorption, distribution, metabolism, excretion, and toxicity (ADMET); and, toxicological properties predicted in the study, YXY01-03 with the novel skeleton, good drug-like properties, and potential activity were finally discovered and may have higher safety relative to BMS22, which may be valuable for further research.

Pharmacophore and molecular dynamics based activity profiling of natural products for kinases involved in lung cancer

To determine the kinase inhibitory potential of natural products that could be utilized in lung cancer therapy in the near future, a pharmacophore-based activity profiling protocol using parallel pharmacophore-based virtual screening of ZINC-a natural product database-was employed. The work presented here is based on the previously explored fact that pharmacophore-based parallel screening is a reliable in silico protocol to predict the possible biological activities of any compound, or any compound library, by screening it with a number of pharmacophore models. The present study involves ligand-based pharmacophore modeling of various kinases, including EGFR (T790 M), cMET, ErbB2, FGFR and ALK, which are well established targets of normal as well resistant lung cancer. The generated pharmacophore models were then utilized for parallel and cross screening. The profiled molecules for each target were then validated using molecular docking and molecular dynamic simulations. The results show that kinase inhibitory activity profiling of some natural product molecules was successfully achieved. Graphical abstract Pharmacophore and activity profiling of natural products for kinases involved in lung cancer.

Receptor pharmacophore ensemble (REPHARMBLE): a probabilistic pharmacophore modeling approach using multiple protein-ligand complexes

Ensemble methods are gaining more importance in structure-based approaches as single protein-ligand complexes strongly influence the outcomes of virtual screening. Structure-based pharmacophore modeling based on a single protein-ligand complex with complex feature combinations is often limited to certain chemical classes. The REPHARMBLE (receptor pharmacophore ensemble) approach presented here examines the ability of an ensemble of selected protein-ligand complexes to populate pharmacophore space in the ligand binding site, rigorously assesses the importance of pharmacophore features using Poisson statistic and information theory-based entropy calculations, and generates pharmacophore models with high probabilities. In addition, an ensemble scoring function that combines all the resultant high-scoring pharmacophore models to score molecules is derived. The REPHARMBLE approach was evaluated on ten DUD-E benchmark datasets and afforded good screening performance, as measured by receiver operating characteristic, enrichment factor and Güner-Henry score. Although one of the high-scoring models achieved superior statistical results in each dataset, the ensemble scoring function balanced the shortcomings of each model and passed with close performance measures. This approach offers a reliable way of choosing the best-scoring features to build four-feature pharmacophore queries and customize a target-biased 'pharmacophore ensemble' scoring function for subsequent virtual screening.

Predictive classification models and targets identification for betulin derivatives as Leishmania donovani inhibitors

Betulin derivatives have been proven effective in vitro against Leishmania donovani amastigotes, which cause visceral leishmaniasis. Identifying the molecular targets and molecular mechanisms underlying their action is a currently an unmet challenge. In the present study, we tackle this problem using computational methods to establish properties essential for activity as well as to screen betulin derivatives against potential targets. Recursive partitioning classification methods were explored to develop predictive models for 58 diverse betulin derivatives inhibitors of L. donovani amastigotes. The established models were validated on a testing set, showing excellent performance. Molecular fingerprints FCFP_6 and ALogP were extracted as the physicochemical properties most extensively involved in separating inhibitors from non-inhibitors. The potential targets of betulin derivatives inhibitors were predicted by in silico target fishing using structure-based pharmacophore searching and compound-pharmacophore-target-pathway network analysis, first on PDB and then among L. donovani homologs using a PSI-BLAST search. The essential identified proteins are all related to protein kinase family. Previous research already suggested members of the cyclin-dependent kinase family and MAP kinases as Leishmania potential drug targets. The PSI-BLAST search suggests two L. donovani proteins to be especially attractive as putative betulin target, heat shock protein 83 and membrane transporter D1.

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.

Discovery of FIXa inhibitors by combination of pharmacophore modeling, molecular docking, and 3D-QSAR modeling

Human Coagulation Factor IXa (FIXa), specifically inhibited at the initiation stage of the blood coagulation cascade, is an excellent target for developing selective and safe anticoagulants. To explore this inhibitory mechanism, 86 FIXa inhibitors were selected to generate pharmacophore models and subsequently SAR models. Both best pharmacophore model and ROC curve were built through the Receptor-Ligand Pharmacophore Generation module. CoMFA model based on molecular docking and PLS factor analysis methods were developed. Model propagations values are q² = 0.709, r² = 0.949, and r²_pred = 0.905. The satisfactory q² value of 0.609, r² value of 0.962, and r²_pred value of 0.819 for CoMSIA indicated that the CoMFA and CoMSIA models are both available to predict the inhibitory activity on FIXa. On the basis of pharmacophore modeling, molecular docking, and 3D-QSAR modeling screening, six molecules are screened as potential FIXa inhibitors.

Ranking Enzyme Structures in the PDB by Bound Ligand Similarity to Biological Substrates

There are numerous applications that use the structures of protein-ligand complexes from the PDB, such as 3D pharmacophore identification, virtual screening, and fragment-based drug design. The structures underlying these applications are potentially much more informative if they contain biologically relevant bound ligands, with high similarity to the cognate ligands. We present a study of ligand-enzyme complexes that compares the similarity of bound and cognate ligands, enabling the best matches to be identified. We calculate the molecular similarity scores using a method called PARITY (proportion of atoms residing in identical topology), which can conveniently be combined to give a similarity score for all cognate reactants or products in the reaction. Thus, we generate a rank-ordered list of related PDB structures, according to the biological similarity of the ligands bound in the structures.

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We present PARITY, matching atoms in identical topology to gauge ligand similarity

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Bound-cognate ligand similarity is a useful metric for ranking PDB structures

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Only 26% of enzyme structures in the PDB have bound-cognate ligand similarity ≥0.7

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We provide rank-ordered lists of PDBs with the most biologically relevant ligands

Inhibition of neuronal FLT3 receptor tyrosine kinase alleviates peripheral neuropathic pain in mice

Peripheral neuropathic pain (PNP) is a debilitating and intractable chronic disease, for which sensitization of somatosensory neurons present in dorsal root ganglia that project to the dorsal spinal cord is a key physiopathological process. Here, we show that hematopoietic cells present at the nerve injury site express the cytokine FL, the ligand of fms-like tyrosine kinase 3 receptor (FLT3). FLT3 activation by intra-sciatic nerve injection of FL is sufficient to produce pain hypersensitivity, activate PNP-associated gene expression and generate short-term and long-term sensitization of sensory neurons. Nerve injury-induced PNP symptoms and associated-molecular changes were strongly altered in Flt3-deficient mice or reversed after neuronal FLT3 downregulation in wild-type mice. A first-in-class FLT3 negative allosteric modulator, discovered by structure-based in silico screening, strongly reduced nerve injury-induced sensory hypersensitivity, but had no effect on nociception in non-injured animals. Collectively, our data suggest a new and specific therapeutic approach for PNP.

Sensitisation of dorsal root ganglia neurons contributes to neuropathic pain. Here the authors demonstrate the cytokine FL contributes to sensitisation of DRGs via its receptor FLT3 expressed on neurons, and identify a novel FLT3 inhibitor that attenuates neuropathic pain in mice.

Structure-Based Kinase Profiling To Understand the Polypharmacological Behavior of Therapeutic Molecules

Several drugs elicit their therapeutic efficacy by modulating multiple cellular targets and possess varied polypharmacological actions. The identification of the molecular targets of a potent bioactive molecule is essential in determining its overall polypharmacological profile. Experimental procedures are expensive and time-consuming. Therefore, computational approaches are actively implemented in rational drug discovery. Here, we demonstrate a computational pipeline, based on reverse virtual screening technique using several consensus scoring strategies, and perform structure-based kinase profiling of 12 FDA-approved drugs. This target prediction showed an overall good performance, with an average AU-ROC greater than 0.85 for most drugs, and identified the true targets even at the top 2% cutoff. In contrast, 10 non-kinase binder drugs exhibited lower binding efficiency and appeared in the bottom of ranking list. Subsequently, we validated this pipeline on a potent therapeutic molecule, mahanine, whose polypharmacological profile related to targeting kinases is unknown. Our target-prediction method identified different kinases. Furthermore, we have experimentally validated that mahanine is able to modulate multiple kinases that are involved in cross-talk with different signaling molecules, which thereby exhibits its polypharmacological action. More importantly, in vitro kinase assay exhibited the inhibitory effect of mahanine on two such predicted kinases' (mTOR and VEGFR2) activity, with IC₅₀ values being ∼12 and ∼22 μM, respectively. Next, we generated a comprehensive drug-protein interaction fingerprint that explained the basis of their target selectivity. We observed that it is controlled by variations in kinase conformations followed by significant differences in crucial hydrogen-bond and van der Waals interactions. Such structure-based kinase profiling could provide useful information in revealing the unknown targets of therapeutic molecules from their polypharmacological behavior and would assist in drug discovery.

Pharmacophore-based virtual screening, molecular docking, molecular dynamics simulation, and biological evaluation for the discovery of novel BRD4 inhibitors

Bromodomain is a recognition module in the signal transduction of acetylated histone. BRD4, one of the bromodomain members, is emerging as an attractive therapeutic target for several types of cancer. Therefore, in this study, an attempt has been made to screen compounds from an integrated database containing 5.5 million compounds for BRD4 inhibitors using pharmacophore-based virtual screening, molecular docking, and molecular dynamics simulations. As a result, two molecules of twelve hits were found to be active in bioactivity tests. Among the molecules, compound 5 exhibited potent anticancer activity, and the IC₅₀ values against human cancer cell lines MV4-11, A375, and HeLa were 4.2, 7.1, and 11.6 μm, respectively. After that, colony formation assay, cell cycle, apoptosis analysis, wound-healing migration assay, and Western blotting were carried out to learn the bioactivity of compound 5.