Relevance of Molecular Docking Studies in Drug Designing

MDFit: automated molecular simulations workflow enables high throughput assessment of ligands-protein dynamics

Molecular dynamics (MD) simulation is a powerful tool for characterizing ligand-protein conformational dynamics and offers significant advantages over docking and other rigid structure-based computational methods. However, setting up, running, and analyzing MD simulations continues to be a multi-step process making it cumbersome to assess a library of ligands in a protein binding pocket using MD. We present an automated workflow that streamlines setting up, running, and analyzing Desmond MD simulations for protein-ligand complexes using machine learning (ML) models. The workflow takes a library of pre-docked ligands and a prepared protein structure as input, sets up and runs MD with each protein-ligand complex, and generates simulation fingerprints for each ligand. Simulation fingerprints (SimFP) capture protein-ligand compatibility, including stability of different ligand-pocket interactions and other useful metrics that enable easy rank-ordering of the ligand library for pocket optimization. SimFPs from a ligand library are used to build & deploy ML models that predict binding assay outcomes and automatically infer important interactions. Unlike relative free-energy methods that are constrained to assess ligands with high chemical similarity, ML models based on SimFPs can accommodate diverse ligand sets. We present two case studies on how SimFP helps delineate structure-activity relationship (SAR) trends and explain potency differences across matched-molecular pairs of (1) cyclic peptides targeting PD-L1 and (2) small molecule inhibitors targeting CDK9.

Novel antiarthritic mechanisms of Azelaic acid against CFA-induced arthritis in rats by modulating pro- and anti-inflammatory cytokines network

An immunologic system attacking the body's own tissues is a hallmark of autoimmune disorders, which encompass a wide range of unique conditions. Numerous essential biologic functions, including the regulation of the immune system, inflammation, cell division, and tissue repair, are carried out by cytokines. Natural compounds are an effective treatment for autoimmune illnesses by modulation of inflammatory cytokines and infiltration of leukocytes into the inflamed tissue. Here, anti-arthritic study was carried out using oral administration of Azelaic acid (AzA) for 28 days with doses (20, 40, and 80 mg/kg) in Complete Freund's Adjuvant (CFA) induced arthritis model. AzA ameliorated the adjuvant-induced arthritis by decreasing arthritic score, paw volume, improved body-weight alterations and serum levels of PGE2, 5-LOX and anti-ccp. AzA showed significant down regulation of NF-κB, COX-II, TNF-α, IL-17, IL-1β, IL-6, and up regulation of IL4 and IL10. Hemoglobin and RBCs count remarkably increased and ESR, CRP, platelets, WBCs levels markedly reduced in post treatment. In addition, the weakened SOD (superoxide dismutase), Catalase (CAT), Glutathione (GSH) activity and the increased levels of malondialdehyde (MDA) were all reversed by AzA treatment. And showed improved radiographical and histologic alterations in the structure of the joints. Molecular docking studies targeting COX-II, iNOS, TNF-α, 5-LOX, IL4, IL10, IL-6, and IL-17 establish a correlation between theoretical and experimental results. Results showed that AzA inhibit pro-inflammatory cytokines (COX-II, TNF-α, 5-LOX, IL-17, NF-κB, IL-1β, and IL-6) and increase anti-inflammatory cytokines, which supported the anti-arthritic and immunomodulatory potential of AzA.

Silk Gland Factor 1 Plays a Pivotal Role in Larval Settlement of the Fouling Mussel Mytilopsis sallei

Most fouling organisms have planktonic larval and benthic adult stages. Larval settlement, the planktonic-benthic transition, is the critical point when biofouling begins. However, our understanding of the molecular mechanisms of larval settlement is limited. In our previous studies, we identified that the AMP-activated protein kinase-silk gland factor 1 (AMPK-SGF1) pathway was involved in triggering the larval settlement in the fouling mussel M. sallei. In this study, to further confirm the pivotal role of SGF1, multiple targeted binding compounds of SGF1 were obtained using high-throughput virtual screening. It was found that the targeted binding compounds, such as NAD+ and atorvastatin, could significantly induce and inhibit the larval settlement, respectively. Furthermore, the qRT-PCR showed that the expression of the foot proteins' genes was significantly increased after the exposure to 10 μM NAD+, while the gene expression was significantly suppressed after the exposure to 10 μM atorvastatin. Additionally, the production of the byssus threads of the adults was significantly increased after the exposure to 10-20 μM of NAD+, while the production of the byssus threads was significantly decreased after the exposure to 10-50 μM of atorvastatin. This work will deepen our understanding of SGF1 in triggering the larval settlement in mussels and will provide insights into the potential targets for developing novel antifouling agents.

Molecular Structure, Spectroscopic, Frontier Molecular Orbital Analysis, Molecular Docking Studies, and In Vitro DNA-Binding Studies of Osmium(II)-Cymene Complexes with Aryl Phosphine and Aryl Phosphonium Assemblies

X-ray crystallography, spectroscopy, computational methods, molecular docking studies, and in vitro DNA-binding studies have been useful in the investigations of intermolecular and intramolecular interactions of osmium-cymene oxalato complexes with aryl phosphine and aryl phosphonium groups in both primary and secondary coordination spheres, respectively. Molecular structures of the novel complexes PPh4[Os(η6-p-cymene)Br(κ2-O,O'-C2O4)] (1) and [Os(η6-p-cymene) (κ2-O,O'-C2O4)PPh3] (2) were resolved by single-crystal X-ray diffraction (XRD). Primary and secondary coordination sphere contacts were investigated using Hirshfeld surface analysis which was supported by molecular docking (MD) studies. The MD data obtained predicted significant differences in binding energy across three receptors for the two osmium complexes. An in vitro DNA-binding study was accomplished using UV-Vis spectroscopy which showed that both 1 and 2 bond with DNA through an intercalation approach. The optimized molecular geometry, frontier molecular orbital (EHOMO and ELUMO) energies, global electrophilicity index (ω), chemical hardness (η), chemical potential (µ), and the energy band gap (EHOMO-ELUMO) were calculated utilizing density functional theory (DFT) methods. Computed structural parameters (bond lengths and angles) support the experimental single-crystal XRD data.

Biomarker triterpenoids of Centella asiatica as potential antidepressant agents: Combining in vivo and in silico studies

Although there are various treatments available for depression, some patients may experience resistance to treatment or encounter adverse effects. Centella asiatica (C. asiatica) is an ancient medicinal herb used in Ayurvedic medicine for its rejuvenating, neuroprotective and psychoactive properties. This study aims to explore the antidepressant-like effects of the major constituents found in C. asiatica, i.e., asiatic acid, asiaticoside, madecassic acid, and madecassoside at three doses (1.25, 2.5, and 5 mg/kg, i.p), on the behavioural and cortisol level of unpredictable chronic stress (UCS) zebrafish model. Based on the findings from the behavioural study, the cortisol levels in the zebrafish body after treatment with the two most effective compounds were measured using enzyme-linked immunosorbent assay (ELISA). Furthermore, a molecular docking study was conducted to predict the inhibitory impact of the triterpenoid compounds on serotonin reuptake. The in vivo results indicate that madecassoside (1.25, 2.5, and 5 mg/kg), asiaticoside and asiatic acid (5 mg/kg) activated locomotor behaviour. Madecassoside at all tested doses and asiaticoside at 2.5 and 5 mg/kg significantly decreased cortisol levels compared to the stressed group, indicating the potential regulation effect of madecassoside and asiaticoside on the hypothalamic-pituitary-adrenal axis overactivity. This study highlights the potential benefits of madecassoside and asiaticoside in alleviating depressive symptoms through their positive effects on behaviour and the hypothalamic-pituitary-adrenal (HPA)- axis in a chronic unpredictable stress zebrafish model. Furthermore, the in silico study provided additional evidence to support these findings. These promising results suggest that C. asiatica may be a valuable and cost-effective therapeutic option for depression, and further research should be conducted to explore its potential benefits.

Structure-based virtual screening of mangiferin derivatives with antidiabetic action: a molecular docking and dynamics study and MPO-based drug-likeness approach

Extracts from Mangifera indica leaves and its main component, mangiferin, have proven antidiabetic activity. In this study, mangiferin and its natural derivatives Homomangiferin (HMF), Isomangiferin (IMF), Neomangiferin (NMF), Glucomangiferin (GMF), Mangiferin 6'-gallate (MFG), and Norathyriol (NRT) were compared regarding their action on Diabetes mellitus (DM), employing docking and molecular dynamics (MD) simulations to analyze interactions with the aldose reductase enzyme, the precursor to the conversion of glucose into sorbitol. Notably, HMF showed significant affinity to residues in the active site of the enzyme, including Trp 79, His 110, Trp 111, Phe 122, and Phe 300, with an energy of - 7.2 kcal/mol, observed in the molecular docking simulations. MD reinforced the formation of stable complexes for HMF and MFG with the aldose reductase, with interaction potential energies (IPE) in the order of - 300.812 ± 52 kJ/mol and - 304.812 ± 52 kJ/mol, respectively. The drug-likeness assessment, by multiparameter optimization (MPO), highlighted that HMF and IMF have similarities with polyphenols and glycosidic flavonoids recently patented as antidiabetics, revealing that high polarity (TPSA > 180 Å2) is a favorable property for subcutaneous administration, especially because of the gradual passive cell permeability values in biological tissues, with Papp values estimated at < 10 × 10-6 cm/s. These compounds are metabolically stable against metabolic enzymes, resulting in a low toxic incidence by metabolic activation, corroborating with a lethal dose (LD50) greater than 2000 mg/kg. In this way, HMF showed a systematic alignment between predicted pharmacokinetics and pharmacodynamics, characterizing it as the most favorable substance for inhibiting aldose reductase.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-03978-9.

Gigantol, a promising natural drug for inflammation: a literature review and computational based study

Gigantol, a bibenzyl compound extracted from various medicinal plants, has shown a number of biological activities, making it an attractive candidate for potential medical applications. This systematic review aims to shed light on gigantol's promising role in inflammation treatment and its underlying mechanisms. Gigantol exhibits potential anti-inflammatory properties in pre-clinical pharmacological test systems. It effectively reduced the levels of pro-inflammatory markers and arachidonic acid metabolites through various pathways, such as NF-κB, AKT, PI3K, and JNK/cPLA2/12-LOX. The in-silico investigations demonstrated that the MMP-13 enzyme served as the most promising target for gigantol with highest binding affinity (docking score = -8.8 kcal/mol). Encouragingly, the absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis of gigantol confirmed its compatibility with the necessary physiochemical, pharmacokinetic, and toxicity properties, bolstering its potential as a drug candidate. Gigantol, with its well-documented anti-inflammatory properties, could be a promising agent for treating inflammation in the near future.

In silico drug design strategies for discovering novel tuberculosis therapeutics

INTRODUCTION

Tuberculosis remains a significant concern in global public health due to its intricate biology and propensity for developing antibiotic resistance. Discovering new drugs is a protracted and expensive endeavor, often spanning over a decade and incurring costs in the billions. However, computer-aided drug design (CADD) has surfaced as a nimbler and more cost-effective alternative. CADD tools enable us to decipher the interactions between therapeutic targets and novel drugs, making them invaluable in the quest for new tuberculosis treatments.

AREAS COVERED

In this review, the authors explore recent advancements in tuberculosis drug discovery enabled by in silico tools. The main objectives of this review article are to highlight emerging drug candidates identified through in silico methods and to provide an update on the therapeutic targets associated with Mycobacterium tuberculosis.

EXPERT OPINION

These in silico methods have not only streamlined the drug discovery process but also opened up new horizons for finding novel drug candidates and repositioning existing ones. The continued advancements in these fields hold great promise for more efficient, ethical, and successful drug development in the future.

A Comprehensive Study to Unleash the Putative Inhibitors of Serotype2 of Dengue Virus: Insights from an In Silico Structure-Based Drug Discovery

Dengue fever is a mosquito-borne disease that claims the lives of millions of people around the world. A number of factors like disease's non-specific symptoms, increased viral mutation, growing antiviral drug resistance due to reduced susceptibility, unavailability of an effective vaccine for dengue, weak immunity against the virus, and many more are involved. Dengue belongs to the Flaviviridae family of viruses. The two species of the vector transmitting dengue are Aedes aegypti and Aedes albopictus, with the former one being dominant. Serotypes 2 of dengue fever are spread to the human body and cause severe illness. Recently, dengue has imposed an aggressive effect synergistically with the COVID-19 pandemic. As a result, we concentrated our efforts on finding a potential therapeutic. For this, we chose natural compounds to fight dengue fever, which is currently regarded as successful among many drug therapies. Following this, we started the in silico experiment with 922 plant extracts as lead compounds to fight serotype 2. In this study, we used SwissADME for analyzing ligand drug-likeness, pkCSM for designing an ADMET profile, Autodock vina 4.2 and Swissdock tools for molecular docking, and finally Desmond for molecular dynamics simulation. Ultimately 45 were found effective against the 2'O methyltransferase protein of serotype 2. CHEMBL376820 was found as possible therapeutic candidates for inhibiting methyltransferase protein in this thorough analysis. Nevertheless, more in vitro and in vivo research are required to substantiate their potential therapeutic efficacy.

An integrated docking and molecular dynamics simulation approach to discover potential inhibitors of activin receptor-like kinase 1

Activin receptor-like kinase 1 (ALK1) is a transmembrane receptor involved in crucial signaling pathways associated with angiogenesis and vascular development. Inhibition of ALK1 signaling has emerged as a promising therapeutic strategy for various angiogenesis-related diseases, including cancer and hereditary hemorrhagic telangiectasia. This study aimed to investigate the potential of phytoconstituents as inhibitors of ALK1 using a combined approach of virtual screening and molecular dynamics (MDs) simulations. Phytoconstituents from the IMPPAT 2.0 database underwent virtual screening to identify potential inhibitors of ALK1. The compounds were initially filtered based on physicochemical parameters, following Lipinski's rules and the PAINS filter. Subsequently, compounds demonstrating high binding affinities in docking analysis were further analyzed. Additional assessments, including ADMET, PAINS, and PASS evaluations, were conducted to identify more potent hits. Through interaction analysis, a phytoconstituent, Candidine, exhibited appreciable affinity and specific interactions with the ALK1 active site. To validate the results, MD simulations and principal components analysis were performed. The MD simulations demonstrated that Candidine stabilized the ALK1 structure and reduced conformational fluctuations. In conclusion, Candidine shows promising potential as binding partners of ALK1. These findings provide a foundation for further exploration and development of Candidine as a lead molecule for therapeutic interventions targeting ALK1-associated diseases.

Identification of coumarin derivatives targeting acetylcholinesterase for Alzheimer's disease by field-based 3D-QSAR, pharmacophore model-based virtual screening, molecular docking, MM/GBSA, ADME and MD Simulation study

Alzheimer's disease (AD) leads to gradual memory loss including other compromised cognitive abilities. Acetylcholinesterase (AChE), an important biochemical enzyme from the cholinesterase (ChE) family, is recognized as primary pharmacological target for treating AD. Currently marketed drugs for AD treatment are primarily AChE inhibitors and coumarin derivatives comprising a wide variety of pharmacological activities have proved their efficacy towards AChE inhibition. Ensaculin (KA-672 HCl), a compound that belong to the coumarin family, is a clinical trial candidate for AD treatment. Therefore, a ligand library was prepared with 60 reported coumarin derivatives for field-based 3D-QSAR and pharmacophore modelling. The field-based 3D-QSAR model obtained at partial least square (PLS) factor 7, was the best validated model that predicted activity closer to original activity for each ligand introduced. The contour maps demonstrated spatial distribution of favourable and unfavorable steric, hydrophobic, electrostatic and H-bond donor and acceptor contours around coumarin nucleus. The best pharmacophore model, ADHRR_1 exhibited five essential pharmacophoric features of four different traits for optimum AChE inhibition. Virtual screening through ADHRR_1 accompanied with molecular docking and MM/GBSA identified 10 HITs from a 4,00,000 coumarin derivatives from PubChem database. HITs comprised docking scores ranging from -12.096 kcal/mol to -8.271 kcal/mol and compared with the reference drug Donepezil (-8.271 kcal/mol). ADME properties analysis led into detecting two leads (HIT 1 and HIT 2) among these 10 HITs. Molecular Dynamics Simulation indicated thermodynamic stability of the complex of lead compounds with AChE protein. Finally, thorough survey of the experimental results from 3D-QSAR modelling, pharmacophore modelling and molecular docking interactions led us to develop the lead formula I for future advancements in treating AD through AChE inhibitors.

New Perspectives about Drug Candidates Targeting HTLV-1 and Related Diseases

Among the human T-lymphotropic virus (HTLV) types, HTLV-1 is the most prevalent, and it has been linked to a spectrum of diseases, including HAM/TSP, ATLL, and hyperinfection syndrome or disseminated strongyloidiasis. There is currently no globally standard first-line treatment for HTLV-1 infection and its related diseases. To address this, a comprehensive review was conducted, analyzing 30 recent papers from databases PubMed, CAPES journals, and the Virtual Health Library (VHL). The studies encompassed a wide range of therapeutic approaches, including antiretrovirals, immunomodulators, antineoplastics, amino acids, antiparasitics, and even natural products and plant extracts. Notably, the category with the highest number of articles was related to drugs for the treatment of ATLL. Studies employing mogamulizumab as a new perspective for ATLL received greater attention in the last 5 years, demonstrating efficacy, safe use in the elderly, significant antitumor activity, and increased survival time for refractory patients. Concerning HAM/TSP, despite corticosteroid being recommended, a more randomized clinical trial is needed to support treatment other than corticoids. The study also included a comprehensive review of the drugs used to treat disseminated strongyloidiasis in co-infection with HTLV-1, including their administration form, in order to emphasize gaps and facilitate the development of other studies aiming at better-directed methodologies. Additionally, docking molecules and computer simulations show promise in identifying novel therapeutic targets and repurposing existing drugs. These advances are crucial in developing more effective and targeted treatments against HTLV-1 and its related diseases.

Antiemetic effects of sclareol, possibly through 5-HT3 and D2 receptor interaction pathways: In-vivo and in-silico studies

BACKGROUND

Emesis is a complex physiological phenomenon that serves as a defense against numerous toxins, stressful situations, adverse medication responses, chemotherapy, and movement. Nevertheless, preventing emesis during chemotherapy or other situations is a significant issue for researchers. Hence, the majority view contends that successfully combining therapy is the best course of action. In-vivo analysis offers a more comprehensive grasp of how compounds behave within a complex biological environment, whereas in-silico evaluation refers to the use of computational models to forecast biological interactions.

OBJECTIVES

The objectives of the present study were to evaluate the effects of Sclareol (SCL) on copper sulphate-induced emetic chicks and to investigate the combined effects of these compounds using a conventional co-treatment approach and in-silico study.

METHODS

SCL (5, 10, and 15 mg/kg) administered orally with or without pre-treatment with anti-emetic drugs (Ondansetron (ODN): 24 mg/kg, Domperidone (DOM): 80 mg/kg, Hyoscine butylbromide (HYS): 100 mg/kg, and Promethazine hydrochloride (PRO): 100 mg/kg) to illustrate the effects and the potential involvement with 5HT3, D2, M3/AChM, H1, or NK1 receptors by SCL. Furthermore, an in-silico analysis was conducted to forecast the role of these receptors in the emetic process.

RESULTS

The results suggest that SCL exerted a dose-dependent anti-emetic effect on the chicks. Pretreatment with SCL-10 significantly minimized the number of retches and lengthened the emesis tendency of the experimental animals. SCL-10 significantly increased the anti-emetic effects of ODN and DOM. However, compared to the ODN-treated group, (SCL-10 + ODN) group considerably (p < 0.0001) extended the latency duration (109.40 ± 1.03 s) and significantly (p < 0.01) decreased the number of retches (20.00 ± 0.70), indicating an anti-emetic effect on the test animals. In in-silico analysis, SCL exhibited promising binding affinities with suggesting receptors.

CONCLUSION

SCL-10 exerted an inhibitory-like effect on emetic chicks, probably through the interaction of the 5HT3 and D2 receptors. Further studies are highly appreciated to validate this study and determine the precise mechanism(s) behind the anti-emetic effects of SCL. We expect that SCL-10 may be utilized as an antiemetic treatment in a single dosage form or that it may function as a synergist with other traditional medicines.

Identification of defactinib derivatives targeting focal adhesion kinase using ensemble docking, molecular dynamics simulations and binding free energy calculations

Focal adhesion kinase (FAK) belongs to the nonreceptor tyrosine kinases, which selectively phosphorylate tyrosine residues on substrate proteins. FAK is associated with bladder, esophageal, gastric, neck, breast, ovarian and lung cancers. Thus, FAK has been considered as a potential target for tumor treatment. Currently, there are six adenosine triphosphate (ATP)-competitive FAK inhibitors tested in clinical trials but no approved inhibitors targeting FAK. Defactinib (VS-6063) is a second-generation FAK inhibitor with an IC50 of 0.6 nM. The binding model of VS-6063 with FAK may provide a reference model for developing new antitumor FAK-targeting drugs. In this study, the VS-6063/FAK binding model was constructed using ensemble docking and molecular dynamics simulations. Furthermore, the molecular mechanics/generalized Born (GB) surface area (MM/GBSA) method was employed to estimate the binding free energy between VS-6063 and FAK. The key residues involved in VS-6063/FAK binding were also determined using per-residue energy decomposition analysis. Based on the binding model, VS-6063 could be separated into seven regions to enhance its binding affinity with FAK. Meanwhile, 60 novel defactinib-based compounds were designed and verified using ensemble docking. Overall, the present study improves our understanding of the binding mechanism of human FAK with VS-6063 and provides new insights into future drug designs targeting FAK.Communicated by Ramaswamy H. Sarma.

Development of a new drug candidate for the inhibition of Lassa virus glycoprotein and nucleoprotein by modification of evodiamine as promising therapeutic agents

The Lassa virus (LASV), an RNA virus prevalent in West and Central Africa, causes severe hemorrhagic fever with a high fatality rate. However, no FDA-approved treatments or vaccines exist. Two crucial proteins, LASV glycoprotein and nucleoprotein, play vital roles in pathogenesis and are potential therapeutic targets. As effective treatments for many emerging infections remain elusive, cutting-edge drug development approaches are essential, such as identifying molecular targets, screening lead molecules, and repurposing existing drugs. Bioinformatics and computational biology expedite drug discovery pipelines, using data science to identify targets, predict structures, and model interactions. These techniques also facilitate screening leads with optimal drug-like properties, reducing time, cost, and complexities associated with traditional drug development. Researchers have employed advanced computational drug design methods such as molecular docking, pharmacokinetics, drug-likeness, and molecular dynamics simulation to investigate evodiamine derivatives as potential LASV inhibitors. The results revealed remarkable binding affinities, with many outperforming standard compounds. Additionally, molecular active simulation data suggest stability when bound to target receptors. These promising findings indicate that evodiamine derivatives may offer superior pharmacokinetics and drug-likeness properties, serving as a valuable resource for professionals developing synthetic drugs to combat the Lassa virus.

Catalytic resilience of multicomponent aromatic ring-hydroxylating dioxygenases in Pseudomonas for degradation of polycyclic aromatic hydrocarbons

Hydrophobic organic compounds, either natural or introduced through anthropogenic activities, pose a serious threat to all spheres of life, including humankind. These hydrophobic compounds are recalcitrant and difficult to degrade by the microbial system; however, microbes have also evolved their metabolic and degradative potential. Pseudomonas species have been reported to have a multipotential role in the biodegradation of aromatic hydrocarbons through aromatic ring-hydroxylating dioxygenases (ARHDs). The structural complexity of different hydrophobic substrates and their chemically inert nature demands the explicit role of evolutionary conserved multicomponent enzyme ARHDs. These enzymes catalyze ring activation and subsequent oxidation by adding two molecular oxygen atoms onto the vicinal carbon of the aromatic nucleus. This critical metabolic step in the aerobic mode of degradation of polycyclic aromatic hydrocarbons (PAHs) catalyzed by ARHDs can also be explored through protein molecular docking studies. Protein data analysis enables an understanding of molecular processes and monitoring complex biodegradation reactions. This review summarizes the molecular characterization of five ARHDs from Pseudomonas species already reported for PAH degradation. Homology modeling for the amino acid sequences encoding the catalytic α-subunit of ARHDs and their docking analyses with PAHs suggested that the enzyme active sites show flexibility around the catalytic pocket for binding of low molecular weight (LMW) and high molecular weight (HMW) PAH substrates (naphthalene, phenanthrene, pyrene, benzo[α]pyrene). The alpha subunit harbours variable catalytic pockets and broader channels, allowing relaxed enzyme specificity toward PAHs. ARHD's ability to accommodate different LMW and HMW PAHs demonstrates its 'plasticity', meeting the catabolic demand of the PAH degraders.

Bruceine a exerts antitumor effect against colon cancer by accumulating ROS and suppressing PI3K/Akt pathway

Bruceine A (BA), a quassic ester from bruceine javanica, regulates diverse intracellular signal transduction pathways and manifests a variety of biological activities, however, its pharmacological mechanism in treating colon cancer (CC) is unclear. In this study, we investigated the anticancer effects of BA on CC cells and the underlying mechanisms. The network pharmacology research indicated that Akt1 and Jun and PI3K/Akt pathways are the predominant targets and critical signaling pathways, respectively, for BA treatment of CC. Meanwhile, molecular docking results implied that BA could conjugate to pivotal proteins in the PI3K/Akt pathway. BA remarkably suppressed the proliferation of CC cells HCT116 and CT26 with 48-h IC50 of 26.12 and 229.26 nM, respectively, and the expression of p-PI3K/p-Akt was restrained by BA at the molecular level as verified by Western blot assay. Further mechanistic studies revealed BA impacted cell cycle-related proteins by regulating the expression of P27 (a protein bridging the PI3K/Akt signaling pathway with cycle-related proteins), arresting the cell cycle in the G2 phase, inhibiting the proliferation of HCT116 and CT26, and facilitated the apoptosis in CC cells by activating the mitochondria-associated apoptosis protein Bax and accumulating reactive oxygen species, in addition to BA apparently inhibited the migration of CC cells. Taken together, our results demonstrated that BA might be a promising chemotherapy drug in the treatment of CC.

Assessing the potential of NS2B/NS3 protease inhibitors biomarker in curbing dengue virus infections: In silico vs. In vitro approach

An increase in the occurrence of viral infectious diseases is a global concern for human health. According to a WHO report, dengue virus (DENV) is one of the most common viral diseases affecting approximately 400 million people annually, with worsening symptoms in nearly 1% of cases. Both academic and industrial researchers have conducted numerous studies on viral epidemiology, virus structure and function, source and route of infection, treatment targets, vaccines, and drugs. The development of CYD-TDV or Dengvaxia^® vaccine has been a major milestone in dengue treatment. However, evidence has shown that vaccines have some drawbacks and limitations. Therefore, researchers are developing dengue antivirals to curb infections. DENV NS2B/NS3 protease is a DENV enzyme essential for replication and virus assembly, making it an interesting antiviral target. For faster hit and lead recognition of DENV targets, methods to screen large number of molecules at lower costs are essential. Similarly, an integrated and multidisciplinary approach involving in silico screening and confirmation of biological activity is required. In this review, we discuss recent strategies for searching for novel DENV NS2B/NS3 protease inhibitors from the in silico and in vitro perspectives, either by applying one of the approaches or by integrating both. Therefore, we hope that our review will encourage researchers to integrate the best strategies and encourage further developments in this area.

Interactions between curcumin and human salt-induced kinase 3 elucidated from computational tools and experimental methods

Natural products are widely used for treating mitochondrial dysfunction-related diseases and cancers. Curcumin, a well-known natural product, can be potentially used to treat cancer. Human salt-induced kinase 3 (SIK3) is one of the target proteins for curcumin. However, the interactions between curcumin and human SIK3 have not yet been investigated in detail. In this study, we studied the binding models for the interactions between curcumin and human SIK3 using computational tools such as homology modeling, molecular docking, molecular dynamics simulations, and binding free energy calculations. The open activity loop conformation of SIK3 with the ketoenol form of curcumin was the optimal binding model. The I72, V80, A93, Y144, A145, and L195 residues played a key role for curcumin binding with human SIK3. The interactions between curcumin and human SIK3 were also investigated using the kinase assay. Moreover, curcumin exhibited an IC₅₀ (half-maximal inhibitory concentration) value of 131 nM, and it showed significant antiproliferative activities of 9.62 ± 0.33 µM and 72.37 ± 0.37 µM against the MCF-7 and MDA-MB-23 cell lines, respectively. This study provides detailed information on the binding of curcumin with human SIK3 and may facilitate the design of novel salt-inducible kinases inhibitors.

Identification of a potential structure-based GPCR drug for interstitial cystitis/bladder pain syndrome: in silico protein structure analysis and molecular docking

INTRODUCTION AND HYPOTHESIS

There is currently no effective treatment for interstitial cystitis / bladder pain syndrome (IC/BPS) and thus seriously reduces the quality of life of patients. The purpose of this study is to analyze the structure and function of G protein coupled receptors related to IC/BPS by integrating bioinformatics and provide basis for the development of new drugs for IC/BPS.

METHODS

We used ProtParam and DNAMAN to analyze the physical and chemical properties of GPR18 and GPR183 proteins. The secondary and tertiary structure, conservative domain, phosphorylation site of both proteins were predicted by ProtScale, PredictProtein, SWISS-MODEL and GPS5.0 respectively. Multiple sequence alignment of the proteins were carried out by DNAMAN and the phylogenetic tree was constructed by MEGA. Further, the molecular docking verification of cannabidiol and both proteins were carried out by using AutoDock Vin.

RESULTS

GPR18 and GPR183 proteins were composed of 331 and 361 amino acids respectively. α-helix is the highest in the secondary structure of the two proteins. Both proteins contain seven transmembrane domains specific to G protein coupled receptors. And homology analysis showed that the two proteins had high homology. In terms of molecular docking, cannabidiol, a non psychoactive component extracted from the cannabis, can form effective molecular binding with GPR18 and GPR183 proteins.

CONCLUSIONS

We identified the structures of GPR18 and GPR183 proteins and their highly homologous evolutionary properties. Furthermore, both proteins can form effective binding with cannabidiol which provides new insights for the development of IC/BPS drugs by targeting G protein coupled receptors.

Design of short peptides and peptide amphiphiles as collagen mimics and an investigation of their interactions with collagen using molecular dynamics simulations and docking studies

Short peptide sequences and bolaamphiphiles derived from natural proteins are gaining importance due to their ability to form unique nanoscale architectures for a variety of biological applications. In this work, we have designed six short peptides (triplet or monomeric forms) and two peptide bolaamphiphiles that either incorporate the bioactive collagen motif (Gly-X-Y) or sequences where Gly, Pro, or hydroxyproline (Hyp) are replaced by Ala or His. For the bolaamphiphiles, a malate moiety was used as the aliphatic linker for connecting His with Hyp to create collagen mimics. Stability of the assemblies was assessed through molecular dynamics simulations and results indicated that (Pro-Ala-His)₃ and (Ala-His-Hyp)₃ formed the most stable structures, while the amphiphiles and the monomers showed some disintegration over the course of the 200 ns simulation, though most regained structural integrity and formed fibrillar structures, and micelles by the end of the simulation, likely due to the formation of more thermodynamically stable conformations. Multiple replica simulations (REMD) were also conducted where the sequences were simulated at different temperatures. Our results showed excellent convergence in most cases compared to constant temperature molecular dynamics simulation. Furthermore, molecular docking and MD simulations of the sequences bound to collagen triple helix structure revealed that several of the sequences had a high binding affinity and formed stable complexes, particularly (Pro-Ala-His)₃ and (Ala-His-Hyp)₃. Thus, we have designed new hybrid-peptide-based sequences which may be developed for potential applications as biomaterials for tissue engineering or drug delivery.

Lipophilicity and ADMET Analysis of Quinoline-1,4-quinone Hybrids

Lipophilicity is one of the basic properties of a potential drug determining its solubility in non-polar solvents and, consequently, its ability to passively penetrate the cell membrane, as well as the occurrence of various pharmacokinetic processes, including adsorption, distribution, metabolism, excretion, and toxicity (ADMET). Heterocyclic compounds containing a nitrogen atom play a significant role in the search for new drugs. In this study, lipophilicity as well as other physicochemical, pharmacokinetic and toxicity properties affecting the bioavailability of the quinolone-1,4-quinone hybrids are presented. Lipophilicity was determined experimentally as well as theoretically using various computer programs. The tested compounds showed low values of experimental lipophilicity and its relationship with the type of 1,4-quinone moiety. Introduction of the nitrogen atom reduced the lipophilicity depending on the position at the 5,8-quinolinedione moiety. The bioavailability of the tested compounds was determined in silico using the ADMET parameters. The obtained parameters showed that most of the hybrids can be used orally and do not exhibit neurotoxic effects. Similarity analysis was used to examine the relationship between the ADMET parameters and experimental lipophilicity. The ability of hybrids to interact with biological targets was characterized by global reactivity descriptors. The molecular docking study showed that the hybrids can inhibit the BCL-2 protein.

Design of novel quinoline derivatives as antibreast cancer using 3D-QSAR, molecular docking and pharmacokinetic investigation

Breast cancer has been one of the most challenging women's cancers and leading cause of mortality for decades. There are several studies being conducted all the time to find a cure for breast cancer. Quinoline derivatives have shown their potential as antitumor agents in breast cancer therapy. In this work, three-dimensional quantitative structure-activity relationships (3D-QSAR) and molecular docking with aromatase enzyme (Protein Data Bank: 3S7S) studies were performed to suggest the current scenario of quinoline derivatives as antitumor agents and to refine the path of these derivatives to discover and develop new drugs against breast cancer. For developing the 3D-QSAR model, comparative molecular similarity indices analysis (CoMSIA) and comparative molecular field analysis (CoMFA) were included. To attain the high level of predictability, the best CoMSIA model was applied. External validation utilizing a test set has been used in order to validate the predictive capabilities of the built model. According to the findings, electrostatic, hydrophobic and hydrogen bond donor, and acceptor fields had a significant impact on antibreast cancer activity. Thus, we generated a variety of novel effective aromatase inhibitors based on prior findings and we predicted their inhibitory activity using the built model. In addition, absorption, distribution, metabolism, elimination and toxicity properties were employed to explore the effectiveness of new drug candidates.

Evodiamine and Rutaecarpine as Potential Anticancer Compounds: A Combined Computational Study

Evodiamine (EVO) and rutaecarpine (RUT) are the main active compounds of the traditional Chinese medicinal herb Evodia rutaecarpa. Here, we fully optimized the molecular geometries of EVO and RUT at the B3LYP/6-311++G (d, p) level of density functional theory. The natural population analysis (NPA) charges, frontier molecular orbitals, molecular electrostatic potentials, and the chemical reactivity descriptors for EVO and RUT were also investigated. Furthermore, molecular docking, molecular dynamics simulations, and the analysis of the binding free energies of EVO and RUT were carried out against the anticancer target topoisomerase 1 (TOP1) to clarify their anticancer mechanisms. The docking results indicated that they could inhibit TOP1 by intercalating into the cleaved DNA-binding site to form a TOP1−DNA−ligand ternary complex, suggesting that they may be potential TOP1 inhibitors. Molecular dynamics (MD) simulations evaluated the binding stability of the TOP1−DNA−ligand ternary complex. The calculation of binding free energy showed that the binding ability of EVO with TOP1 was stronger than that of RUT. These results elucidated the structure−activity relationship and the antitumor mechanism of EVO and RUT at the molecular level. It is suggested that EVO and RUT may be potential compounds for the development of new anticancer drugs.

Designer Benzodiazepines' Activity on Opioid Receptors: A Docking Study

BACKGROUND

Previous studies have reported that benzodiazepines (BZDs) seem to enhance euphoric and reinforcing properties of opioids in opioid users so that a direct effect on opioid receptors has been postulated, together with a possible synergistic induction of severe side effects due to co use of BDZs and opioids. This is particularly worrisome given the appearance on the market of designer benzodiazepines (DBZDs), whose activity/toxicity profiles are scarcely known.

OBJECTIVES

This study aimed to evaluate, through computational studies, the binding affinity (or lack thereof) of 101 DBZDs identified online on the kappa, mu, and delta opioid receptors (K, M, DOR); and to assess whether their mechanism of action could include activation of the latter.

METHODS

MOE® was used for the computational studies. Pharmacophore mapping based on strong opioids agonist binders' 3D chemical features was used to filter the DBZDs. Resultant DBZDs were docked into the crystallised 3D active conformation of KOR (PDB6B73), DOR (PDB6PT3) and MOR (PDB5C1M). Co-crystallised ligands and four strong agonists were used as reference compounds. A score (S, Kcal/mol) representative of the predicted binding affinity, and a description of ligand interactions were obtained from MOE®.

RESULTS

The docking results, filtered for S < -8.0 and the interaction with the Asp residue, identified five DBZDs as putative binders of the three ORs : ciclotizolam, fluloprazolam, JQ1, Ro 48-6791, and Ro 48-8684.

CONCLUSION

It may be inferred that at least some DBZDs may have the potential to activate opioid receptors. This could mediate/increase their anxiolytic, analgesic, and addiction potentials, as well as worsen the side effects associated with opioid co-use.

Pharmacological investigation of brucine anti-ulcer potential

Gastric ulcer is one of the most common chronic gastrointestinal diseases characterized by a significant defect in the mucosal barrier. The current study has been conducted to evaluate the brucine anti-ulcer effect. Brucine has binding energy values ranging from −2.99 to −8.11 kcal/mol against chosen targets, according to in silico research. Brucine exhibits an inhibitory effect against Helicobacter pylori. In vivo findings revealed that brucine (3 mg/kg) showed effective results in healing ethanol-induced ulcer lesions of the gastric region in rats. Brucine showed an inhibitory effect against H⁺/K⁺-ATPase. Levels of glutathione, glutathione-s-transferase, and catalase were enhanced in the gastric rat tissue with the use of brucine, while a significant decrease in lipid peroxide levels was seen. Histopathological evaluation showed improvement in cellular architecture and a decrease in inflammatory indicators like cyclooxygenase, tumor necrosis factor, and nuclear factor kappa B expression, validated through immunohistochemistry, enzyme-linked immunosorbent assay, and Western blot techniques. In the reverse transcription–polymerase chain reaction, brucine decreased H⁺/K⁺-ATPase mRNA levels. This study reveals that brucine possesses stable binding affinities against selected targets. Brucine exhibits an anti-ulcer effect, mediated via anti-H. pylori, H⁺/K⁺-ATPase inhibition, and antioxidant and anti-inflammatory pathways.

Anti-inflammatory activity and cytotoxicity against ovarian cancer cell lines by amide alkaloids and piperic esters isolated from Piper longum fruits: In vitro assessments and molecular docking simulation

Three new amide alkaloids, piperlongumamides D-F (14, 19, and 32); a new piperic ester, piperlongumester A (45); and two new natural compounds, methyl (2E,4Z)-5-(1,3-benzodioxol-5-yl)penta-2,4-dienoate (46) and trans-piperolein B ester (47), along with 41 known compounds were isolated from the fruits of Piper longum L. Their structures were identified by analyzing spectroscopic data, including mass spectrometry, 1D, and 2D NMR data. The anti-inflammatory and cytotoxic activities of all isolated compounds (1-47) were evaluated. Compounds 3, 6, and 19 inhibited nitric oxide production with IC₅₀ values of 16.1 ± 0.94, 14.5 ± 0.57, and 27.3 ± 1.11 μM, respectively, whereas compound 1 exhibited strong cytotoxic activity toward three ovarian cancer cell lines A2780, TOV-112D, and SK-OV3, with IC₅₀ values of 6.7 ± 0.77, 5.8 ± 0.29, and 48.3 ± 0.40 μM, respectively. Molecular docking simulations were performed to identify the interaction and binding mechanisms of these active metabolites with proteins related to inflammation and cancer.

MutCov: A pipeline for evaluating the effect of mutations in spike protein on infectivity and antigenicity of SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing an outbreak of coronavirus disease 2019 (COVID-19), is a major threat to public health worldwide. Previous studies have shown that the spike protein of SARS-CoV-2 determines viral infectivity and major antigenicity. However, the spike protein has been undergoing various mutations, which bring a great challenge to the prevention and treatment of COVID-19. Here we present the MutCov, a pipeline for evaluating the effect of mutations in spike protein on infectivity and antigenicity of SARS-CoV-2 by calculating the binding free energy between spike protein and angiotensin-converting enzyme 2 (ACE2) or neutralizing monoclonal antibody (mAb). The predicted infectivity and antigenicity were highly consistent with biologically experimental results, and demonstrated that the MutCov achieved good prediction performance. In conclusion, the MutCov is of high importance for systematically evaluating the effect of novel mutations and improving the prevention and treatment of COVID-19. The source code and installation instruction of MutCov are freely available at http://jianglab.org.cn/MutCov.

A lesson for the maestro of the replication fork: Targeting the protein-binding interface of proliferating cell nuclear antigen for anticancer therapy

The proliferating cell nuclear antigen (PCNA) has emerged as a promising candidate for the development of novel cancer therapeutics. PCNA is a nononcogenic mediator of DNA replication that regulates a diverse range of cellular functions and pathways through a comprehensive list of protein-protein interactions. The hydrophobic binding pocket on PCNA offers an opportunity for the development of inhibitors to target various types of cancers and modulate protein-protein interactions. In the present study, we explored the binding modes and affinity of molecule I1 (standard molecule) with the previously suggested dimer interface pocket and the hydrophobic pocket present on the frontal side of the PCNA monomer. We also identified potential lead molecules from the library of in-house synthesized 3-methylenisoindolin-1-one based molecules to inhibit the protein-protein interactions of PCNA. Our results were based on robust computational methods, including molecular docking, conventional, steered, and umbrella sampling molecular dynamics simulations. Our results suggested that the standard inhibitor I1 interacts with the hydrophobic pocket of PCNA with a higher affinity than the previously suggested binding site. Also, the proposed molecules showed better or comparable binding free energies as calculated by the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach and further validated by enhanced umbrella sampling simulations. In vitro and in vivo methods could test the computationally suggested molecules for advancement in the drug discovery pipeline.

Investigation of Unprecedented Sites and Proposition of New Ligands for Programmed Cell Death Protein I through Molecular Dynamics with Probes and Virtual Screening

Cancer immunotherapy has attracted increasing attention over the last few years. Programmed cell death protein 1 (PD-1) promotes self-tolerance and inhibits immune responses by modulating the T-cell function. The interaction between PD-1 and programmed cell death ligand-1 (PD-L1) leads to immune exhaustion, protecting cancer cells from destruction. Here, we computationally designed a novel ligand named 1508 that binds to an unprecedented PD-1 cavity identified by MixMD and defined by amino acid residues Lys78 to Val97. We showed through a set of MD simulations totaling 12.5 μs that ligand 1508 establishes frequent cation-π and hydrogen bonding interactions with amino acid residues Lys78 and Arg86, respectively, and stabilizes the PD-1 C'D loop in a conformation that does not favor PD-1-PD-L1 complex formation. This study highlights the power of MixMD in exposing new cavities prone to protein-protein complex inhibition and establishes the basis for the design of new molecules that target the PD-1 C'D cavity as an alternative for exploring the modulation of the PD-1-PD-L1 complex in cancer therapy.

Molecular Mechanism for the Allosteric Inhibition of the Human Serotonin Transporter by Antidepressant Escitalopram

Human serotine transporter (hSERT) is one of the most influential drug targets, and its allosteric modulators (e.g., escitalopram) have emerged to be the next-generation medication for psychiatric disorders. However, the molecular mechanism underlying the allosteric modulation of hSERT is still elusive. Here, the simulation strategies of conventional (cMD) and steered (SMD) molecular dynamics were applied to investigate this molecular mechanism from distinct perspectives. First, cMD simulations revealed that escitalopram's binding to hSERT's allosteric site simultaneously enhanced its binding to the orthosteric site. Then, SMD simulation identified that the occupation of hSERT's allosteric site by escitalopram could also block its dissociation from the orthosteric site. Finally, by comparing the simulated structures of two hSERT-escitalopram complexes with and without allosteric modulation, a new conformational coupling between an extracellular (Arg104-Glu494) and an intracellular (Lys490-Glu494) salt bridge was identified. In summary, this study explored the mechanism underlying the allosteric modulation of hSERT by collectively applying two MD simulation strategies, which could facilitate our understanding of the allosteric modulations of not only hSERT but also other clinically important therapeutic targets.

AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs

Antioxidant proteins can not only balance the oxidative stress in the body, but are also an important component of antioxidant drugs. Accurate identification of antioxidant proteins is essential to help humans fight diseases and develop new drugs. In this paper, we developed a friendly method AOPM to identify antioxidant proteins. 188D and the Composition of k-spaced Amino Acid Pairs were adopted as the feature extraction method. In addition, the Max-Relevance-Max-Distance algorithm (MRMD) and random forest were the feature selection and classifier, respectively. We used 5-folds cross-validation and independent test dataset to evaluate our model. On the test dataset, AOPM presented a higher performance compared with the state-of-the-art methods. The sensitivity, specificity, accuracy, Matthew’s Correlation Coefficient and an Area Under the Curve reached 87.3, 94.2, 92.0%, 0.815 and 0.972, respectively. In addition, AOPM still has excellent performance in predicting the catalytic enzymes of antioxidant drugs. This work proved the feasibility of virtual drug screening based on sequence information and provided new ideas and solutions for drug development.

SARS-CoV-2 main protease and papain-like protease inhibition by abietane-type diterpenes isolated from the branches of Glyptostrobus pensilis using molecular docking studies

Using various chromatographic methods, five abietane-type diterpenes were isolated from the branches of Glyptostrobus pensilis for the first time. The chemical structures of the isolates were identified by modern spectroscopic techniques, including ¹H and ¹³C nuclear magnetic resonance spectroscopy and by comparison with the literature. In addition, the binding potential of the isolated compounds to replicase protein, SARS-CoV-2 main protease and papain-like protease, were examined using molecular docking studies. In silico results suggested that G. pensilis as well as abietane-types diterpenes are potential candidates for the prevention and treatment of SARS-CoV-2.

SYNBIP: synthetic binding proteins for research, diagnosis and therapy

The success of protein engineering and design has extensively expanded the protein space, which presents a promising strategy for creating next-generation proteins of diverse functions. Among these proteins, the synthetic binding proteins (SBPs) are smaller, more stable, less immunogenic, and better of tissue penetration than others, which make the SBP-related data attracting extensive interest from worldwide scientists. However, no database has been developed to systematically provide the valuable information of SBPs yet. In this study, a database named ‘Synthetic Binding Proteins for Research, Diagnosis, and Therapy (SYNBIP)’ was thus introduced. This database is unique in (a) comprehensively describing thousands of SBPs from the perspectives of scaffolds, biophysical & functional properties, etc.; (b) panoramically illustrating the binding targets & the broad application of each SBP and (c) enabling a similarity search against the sequences of all SBPs and their binding targets. Since SBP is a human-made protein that has not been found in nature, the discovery of novel SBPs relied heavily on experimental protein engineering and could be greatly facilitated by in-silico studies (such as AI and computational modeling). Thus, the data provided in SYNBIP could lay a solid foundation for the future development of novel SBPs. The SYNBIP is accessible without login requirement at both official (https://idrblab.org/synbip/) and mirror (http://synbip.idrblab.net/) sites.

A Survey for Predicting ATP Binding Residues of Proteins Using Machine Learning Methods

Protein-ligand interactions are necessary for majority protein functions. Adenosine-5'-triphosphate (ATP) is one such ligand that plays vital role as a coenzyme in providing energy for cellular activities, catalyzing biological reaction and signaling. Knowing ATP binding residues of proteins is helpful for annotation of protein function and drug design. However, due to the huge amounts of protein sequences influx into databases in the post-genome era, experimentally identifying ATP binding residues is cost-ineffective and time-consuming. To address this problem, computational methods have been developed to predict ATP binding residues. In this review, we briefly summarized the application of machine learning methods in detecting ATP binding residues of proteins. We expect this review will be helpful for further research.

Formulation of pH responsive multilamellar vesicles for targeted delivery of hydrophilic antibiotics

Fight against antimicrobial resistance calls for innovative strategies that can target infection sites and enhance activity of antibiotics. Herein is a pH responsive multilamellar vesicles (MLVs) for targeting bacterial infection sites. The vancomycin (VCM) loaded MLVs had 62.25 ± 8.7 nm, 0.15 ± 0.01 and -5.55 ± 2.76 mV size, PDI and zeta potential, respectively at pH 7.4. The MLVs had a negative ZP at pH 7.4 that switched to a positive charge and faster release of the drug at acidic pH. The encapsulation efficiency was found to be 46.34 ± 3.88 %. In silico studies of the lipids, interaction suggested an energetically stable system. Studies to determine the minimum inhibitory concentration studies (MIC) showed the MLVs had 2-times and 8-times MIC against Staphylococcus aureus (SA) and Methicillin resistance SA respectively at physiological pH. While at pH 6.0 there was 8 times reduction in MICs for the formulation against SA and MRSA in comparison to the bare drug. Fluorescence-activated Cell Sorting (FACS) studies demonstrated that even with 8-times lower MIC, MLVs had a similar elimination ability of MRSA cells when compared to the bare drug. Fluorescence microscopy showed MLVs had the ability to clear biofilms while the bare drug could not. Mice skin infection models studies showed that the colony finding units (CFUs) of MRSA recovered from groups treated with MLVs was 4,050 and 525-fold lower than the untreated and bare VCM treated groups, respectively. This study demonstrated pH-responsive multilamellar vesicles as effective system for targeting and enhancing antibacterial agents.

A heterogeneous network embedding framework for predicting similarity-based drug-target interactions

Accurate prediction of drug-target interactions (DTIs) through biological data can reduce the time and economic cost of drug development. The prediction method of DTIs based on a similarity network is attracting increasing attention. Currently, many studies have focused on predicting DTIs. However, such approaches do not consider the features of drugs and targets in multiple networks or how to extract and merge them. In this study, we proposed a Network EmbeDding framework in mulTiPlex networks (NEDTP) to predict DTIs. NEDTP builds a similarity network of nodes based on 15 heterogeneous information networks. Next, we applied a random walk to extract the topology information of each node in the network and learn it as a low-dimensional vector. Finally, the Gradient Boosting Decision Tree model was constructed to complete the classification task. NEDTP achieved accurate results in DTI prediction, showing clear advantages over several state-of-the-art algorithms. The prediction of new DTIs was also verified from multiple perspectives. In addition, this study also proposes a reasonable model for the widespread negative sampling problem of DTI prediction, contributing new ideas to future research. Code and data are available at https://github.com/LiangYu-Xidian/NEDTP.

Review of unsupervised pretraining strategies for molecules representation

In recent years, the computer-assisted techniques make a great progress in the field of drug discovery. And, yet, the problem of limited labeled data problem is still challenging and also restricts the performance of these techniques in specific tasks, such as molecular property prediction, compound-protein interaction and de novo molecular generation. One effective solution is to utilize the experience and knowledge gained from other tasks to cope with related pursuits. Unsupervised pretraining is promising, due to its capability of leveraging a vast number of unlabeled molecules and acquiring a more informative molecular representation for the downstream tasks. In particular, models trained on large-scale unlabeled molecules can capture generalizable features, and this ability can be employed to improve the performance of specific downstream tasks. Many relevant pretraining works have been recently proposed. Here, we provide an overview of molecular unsupervised pretraining and related applications in drug discovery. Challenges and possible solutions are also summarized.

Molecular Engineering of Curcumin, an Active Constituent of Curcuma longa L. (Turmeric) of the Family Zingiberaceae with Improved Antiproliferative Activity

Cancer is the world’s second leading cause of death, accounting for nearly 10 million deaths and 19.3 million new cases in 2020. Curcumin analogs are gaining popularity as anticancer agents currently. We reported herein the isolation, molecular engineering, molecular docking, antiproliferative, and anti-epidermal growth factor receptor (anti-EGFR) activities of curcumin analogs. Three curcumin analogs were prepared and docked against the epidermal growth factor receptor (EGFR), revealing efficient binding. Antiproliferative activity against 60 NCI cancer cell lines was assessed using National Cancer Institute (NCI US) protocols. The compound 3b,c demonstrated promising antiproliferative activity in single dose (at 10 µM) as well as five dose (0.01, 0.10, 1.00, 10, and 100 µM). Compound 3c inhibited leukemia cancer panel better than other cancer panels with growth inhibition of 50% (GI₅₀) values ranging from 1.48 to 2.73 µM, and the most promising inhibition with GI₅₀ of 1.25 µM was observed against leukemia cell line SR, while the least inhibition was found against non-small lung cancer cell line NCI-H226 with GI₅₀ value of 7.29 µM. Compounds 3b,c demonstrated superior antiproliferative activity than curcumin and gefitinib. In molecular docking, compound 3c had the most significant interaction with four H-bonds and three π–π stacking, and compound 3c was found to moderately inhibit EGFR. The curcumin analogs discovered in this study have the potential to accelerate the anticancer drug discovery program.

Current status and future prospects of drug-target interaction prediction

Drug-target interaction prediction is important for drug development and drug repurposing. Many computational methods have been proposed for drug-target interaction prediction due to their potential to the time and cost reduction. In this review, we introduce the molecular docking and machine learning-based methods, which have been widely applied to drug-target interaction prediction. Particularly, machine learning-based methods are divided into different types according to the data processing form and task type. For each type of method, we provide a specific description and propose some solutions to improve its capability. The knowledge of heterogeneous network and learning to rank are also summarized in this review. As far as we know, this is the first comprehensive review that summarizes the knowledge of heterogeneous network and learning to rank in the drug-target interaction prediction. Moreover, we propose three aspects that can be explored in depth for future research.

Dasatinib-SIK2 Binding Elucidated by Homology Modeling, Molecular Docking, and Dynamics Simulations

Salt-inducible kinases (SIKs) are calcium/calmodulin-dependent protein kinase (CAMK)-like (CAMKL) family members implicated in insulin signal transduction, metabolic regulation, inflammatory response, and other processes. Here, we focused on SIK2, which is a target of the Food and Drug Administration (FDA)-approved pan inhibitor N-(2-chloro-6-methylphenyl)-2-(6-(4-(2-hydroxyethyl)piperazin-1-yl)-2-methylpyrimidin-4-ylamino)thiazole-5-carboxamide (dasatinib), and constructed four representative SIK2 structures by homology modeling. We investigated the interactions between dasatinib and SIK2 via molecular docking, molecular dynamics simulation, and binding free energy calculation and found that dasatinib showed strong binding affinity for SIK2. Binding free energy calculations suggested that the modification of various dasatinib regions may provide useful information for drug design and to guide the discovery of novel dasatinib-based SIK2 inhibitors.

Identification of the key target profiles underlying the drugs of narrow therapeutic index for treating cancer and cardiovascular disease

An appropriate therapeutic index is crucial for drug discovery and development since narrow therapeutic index (NTI) drugs with slight dosage variation may induce severe adverse drug reactions or potential treatment failure. To date, the shared characteristics underlying the targets of NTI drugs have been explored by several studies, which have been applied to identify potential drug targets. However, the association between the drug therapeutic index and the related disease has not been dissected, which is important for revealing the NTI drug mechanism and optimizing drug design. Therefore, in this study, two classes of disease (cancers and cardiovascular disorders) with the largest number of NTI drugs were selected, and the target property of the corresponding NTI drugs was analyzed. By calculating the biological system profiles and human protein–protein interaction (PPI) network properties of drug targets and adopting an AI-based algorithm, differentiated features between two diseases were discovered to reveal the distinct underlying mechanisms of NTI drugs in different diseases. Consequently, ten shared features and four unique features were identified for both diseases to distinguish NTI from NNTI drug targets. These computational discoveries, as well as the newly found features, suggest that in the clinical study of avoiding narrow therapeutic index in those diseases, the ability of target to be a hub and the efficiency of target signaling in the human PPI network should be considered, and it could thus provide novel guidance in the drug discovery and clinical research process and help to estimate the drug safety of cancer and cardiovascular disease.

Molecular Docking Studies of Naringenin and its Protective Efficacy against Methotrexate Induced Oxidative Tissue Injury

BACKGROUND

Although Methotrexate (MTX) possesses a wide clinical spectrum of activity, its toxic side effects on normal cells and drug resistance often hamper its successful outcome. Naringenin (NG) one of the promising bioactive flavonoids that are extensively found in grapes, citrus fruits, and fruit arils of Pithecellobium dulce.

OBJECTIVE

Only a few experimental in vivo studies on the efficacy of NG against chemotherapeutic drugs have been carried out. Aiming to fill this gap, the present study was carried out to characterize and identify its possible therapeutic targets and also to explore its protective efficacy against MTX induced tissue damage.

METHODS

Oxidative stress was induced in mice with MTX (20 mg/kg B.wt) and animals were orally administered with 10 mg/kg B.wt NG for 10 consecutive days. On day 11, all animals were sacrificed, and hematological and serum biochemical parameters were analyzed. The antioxidant efficacy of NG against MTX was evaluated by quantifying tissue superoxide dismutase (SOD), glutatione peroxidase (GPx), reduced glutathione (GSH) and catalase along with oxidative stress markers [malondialdehyde (MDA) and nitric oxide (NO)]. Further, the histopathological analysis was performed to confirm the protective efficacy of FPD. In silico docking studies were also performed to exploring antioxidant enzyme-based targets.

RESULTS

Our results showed that concurrent administration of NG counteracted oxidative stress induced by MTX, as evidenced by increased expression of antioxidant markers, decreased expression of renal and hepatotoxicity serum marker enzymes (p <0.05). Molecular docking study was performed using Auto dock vina to understand the mechanism of ligand binding (S-NG and R-NG) with antioxidant enzymes. The binding affinity of S-NG with catalase, GPx, ALP, and SGPT was -10.1, -7.1, -7.1, and -7.3 kcal/mol respectively, whereas for R-NG was -10.8, -7.1, -7.6, and -7.4 kcal/mol respectively. Further, histopathological analysis affirmed the protective efficacy of NG against MTX induced hepatic and renal toxicities.

CONCLUSION

Treatment with NG significantly reduced MTX induced pancytopenia, renal, and hepatic toxicity.

LC-MS/MS-Based Quantitative Proteomics Analysis of Different Stages of Non-Small-Cell Lung Cancer

Lung cancer has a higher incidence rate and mortality rate than all other cancers. Early diagnosis and treatment of lung cancer remain a major challenge, and the 5-year survival rate of its patients is only 15%. Basic and clinical research, especially the discovery of biomarkers, is crucial for improving the diagnosis and treatment of lung cancer patients. To identify novel biomarkers for lung cancer, we used the iTRAQ8-plex labeling technology combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to analyze the serum and urine of patients with different stages of lung adenocarcinoma and healthy individuals. A total of 441 proteins were identified in the serum, and 1,161 proteins were identified in the urine. The levels of elongation factor 1-alpha 2, proteasome subunit alpha type, and spermatogenesis-associated protein increased significantly in the serum of patients with lung cancer compared with those in healthy controls. The levels of transmembrane protein 143, cadherin 5, fibronectin 1, and collectin-11 decreased significantly in the serum of patients with metastases compared with those of nonmetastatic lung cancer patients. In the urine of stage III and IV lung cancer patients, the prostate-specific antigen and prostatic acid phosphatase decreased significantly, whereas neutrophil defensin 1 increased significantly. The results of LC-MS/MS were confirmed by enzyme-linked immunosorbent assay (ELISA) for transmembrane protein 143, cadherin 5, fibronectin 1, and collectin-11 in the serum. These proteins may be a potential early diagnosis and metastasis biomarkers for lung adenocarcinoma. Furthermore, the relative content of these markers in the serum and urine could be used to determine the progression of lung adenocarcinoma and achieve accurate staging and diagnosis.

Recent Advances in Predicting Protein S-Nitrosylation Sites

Protein S-nitrosylation (SNO) is a process of covalent modification of nitric oxide (NO) and its derivatives and cysteine residues. SNO plays an essential role in reversible posttranslational modifications of proteins. The accurate prediction of SNO sites is crucial in revealing a certain biological mechanism of NO regulation and related drug development. Identification of the sites of SNO in proteins is currently a very hot topic. In this review, we briefly summarize recent advances in computationally identifying SNO sites. The challenges and future perspectives for identifying SNO sites are also discussed. We anticipate that this review will provide insights into research on SNO site prediction.