Highly Flexible Ligand Docking: Benchmarking of the DockThor Program on the LEADS-PEP Protein-Peptide Data Set

Exploring the potential of Andrographis paniculata for developing novel HDAC inhibitors: an in silico approach

Cancer is one of the dreaded diseases of the twentieth century, emerging the major global causes of human morbidity. Cancer research in the last 15 years has provided unprecedented information on the role of epigenetics in cancer initiation and progression. Histone deacetylases (HDACs) are recognized as important epigenetic markers in cancer, whose overexpression leads to increased metastasis and angiogenesis. In the current study, thirty-four (34) compounds from Andrographis paniculata were screened for the identification of potential candidate drugs, targeting three Class I HDACs (Histone deacetylases), namely HDAC1 (PDB id 5ICN), HDAC3 (PDB id 4A69) and HDAC8 (PDB id 5FCW) through computer-assisted drug discovery study. Results showed that some of the phytochemicals chosen for this study exhibited significant drug-like properties. In silico molecular docking study further revealed that out of 34 compounds, the flavonoid Andrographidine E had the highest binding affinities towards HDAC1 (-9.261 Kcal mol-1) and 3 (-9.554 Kcal mol-1) when compared with the control drug Givinostat (-8.789 and -9.448 Kcal mol-1). The diterpenoid Andrographiside displayed the highest binding affinity (-9.588 Kcal mol-1) to HDAC8 compared to Givinostat (-8.947 Kcal mol-1). Statistical analysis using Principal Component Analysis tool revealed that all 34 phytocompounds could be clustered in four statistical groups. Most of them showed high or comparable inhibitory potentials towards HDAC target protein. Finally, the stability of top-ranked complexes (Andrographidine E-HDAC1 and HDAC3; Andrographiside-HDAC8) at the physiological condition was validated by Molecular Dynamic Simulation and MM-PBSA study.Communicated by Ramaswamy H. Sarma.

Treating chronic pulmonary heart disease with traditional Chinese medicine: Systematic evaluation and mechanistic insights into the resolving phlegm and activating blood approach

BACKGROUND

Chronic Pulmonary Heart Disease (CPHD) significantly impacts global health, especially among middle-aged and older adults. In China, the Traditional Chinese Medicine (TCM) technique of Resolving Phlegm and Activating Blood (RPAB) is widely used to treat CPHD, although high-quality evidence supporting its efficacy remains limited.

OBJECTIVES

The purpose of this study was to rigorously assess the clinical efficacy of RPAB for CPHD and elucidate the mechanisms underlying its primary herbal components.

METHODS

Through a detailed search of literature in both Chinese and English and strict inclusion and exclusion criteria, 18 randomized controlled trials (RCTs) were selected for meta-analysis. We identified RPAB's core herbal combinations using association rule analysis. This method statistically analyzes the frequency and correlation of herbal medicine usage. We then analyzed the chemical components of these combinations and investigated their potential intervention mechanisms on CPHD through network pharmacology.

RESULTS

The combination of RPAB with Western medicine was superior to Western medicine alone in improving blood gas analysis and pulmonary function and reducing plasma viscosity in CPHD patients. The core herbal combination identified was Astragalus membranaceus (Fisch.) Bunge, Ligusticum chuanxiong Hort. ex S. H. Qiu & al., and Stellaria alsine Grimm (ALS). This combination targeted 588 therapeutic and 27 core targets. It influenced ten core compounds across 34 pathways, primarily through the chemokine signaling pathway and the JAK-STAT signaling pathway.

CONCLUSION

RPAB with Western medicine significantly improves CPHD treatment outcomes. The study highlights the therapeutic potential of the ALS combination, which operates through multiple pathways to remodel pulmonary arteries, decrease inflammation, and lessen oxidative stress. These insights support the clinical application of RPAB in CPHD treatment and open new avenues for research and therapeutic development.

Three Dihydroquinolin-4-one Derivatives as Potential Biodiesel Additives: From the Molecular Structure to Machine Learning Approach

Biodiesel offers an alternative to fossil fuels, primarily because it is derived from renewable sources, with the potential to mitigate issues such as pollutant and greenhouse gas emissions, resource scarcity, and the market instability of petroleum derivatives. However, lower durability and stability pose challenges. To address this, researchers worldwide are exploring technologies that employ specific molecules to slow down biodiesel's oxidation process, thereby preserving its key physicochemical properties. This study investigates heterocyclic dihydroquinolinone derivatives as potential additives to enhance the oxidative stability of diesel-biodiesel blends. Comprehensive structural and computational analyses were carried out by density functional theory to investigate the reactivity aspects of these compounds as potential additive candidates. The supramolecular arrangements were predominantly stabilized by weak molecular interactions, such as C-H···O and C-H···π, which are associated with antioxidant and antibacterial properties. We demonstrate that these groups can act as electron-donating or electron-withdrawing substituents. We explored frontier molecular orbitals, which provide insights into chemical reactivity, acidity, basicity, and the best oxidizing and reducing agents. Finally, the molecular chemical potential maps indicate the nucleophilic and electrophilic regions and the Fukui indices show the sites of nucleophilic, electrophilic, and radical attacks. This comprehensive study paves the way to understanding how dihydroquinolinone-based compounds serve as alternatives for fuel additives.

Design and investigation of interactions of novel peptide conjugates of purine and pyrimidine derivatives with EGFR and its mutant T790M/L858R: an in silico and laboratory study

Peptide-based therapeutics have been gaining attention due to their ability to actively target tumor cells. Additionally, several varieties of nucleotide derivatives have been developed to reduce cell proliferation and induce apoptosis of tumor cells. In this work, we have developed novel peptide conjugates with newly designed purine analogs and pyrimidine derivatives and explored the binding interactions with the kinase domain of wild-type EGFR and its mutant EGFR [L858R/ T790M] which are known to be over-expressed in tumor cells. The peptides explored included WNWKV (derived from sea cucumber) and LARFFS, which in previous work was predicted to bind to Domain I of EGFR. Computational studies conducted to explore binding interactions include molecular docking studies, molecular dynamics simulations and MMGBSA to investigate the binding abilities and stability of the complexes. The results indicate that conjugation enhanced binding capabilities, particularly for the WNWKV conjugates. MMGBSA analysis revealed nearly twofold higher binding toward the T790M/L858R double mutant receptor. Several conjugates were shown to have strong and stable binding with both wild-type and mutant EGFR. As a proof of concept, we synthesized pyrimidine conjugates with both peptides and determined the KD values using SPR analysis. The results corroborated with the computational analyses. Additionally, cell viability and apoptosis studies with lung cancer cells expressing the wild-type and double mutant proteins revealed that the WNWKV conjugate showed greater potency than the LARFFS conjugate, while LARFFS peptide alone showed poor binding to the kinase domain. Thus, we have designed peptide conjugates that show potential for further laboratory studies for developing therapeutics for targeting the EGFR receptor and its mutant T790M/L858R.

Identification of lipid senolytics targeting senescent cells through ferroptosis induction

Cellular senescence is a key driver of the aging process and contributes to tissue dysfunction and age-related pathologies. Senolytics have emerged as a promising therapeutic intervention to extend healthspan and treat age-related diseases. Through a senescent cell-based phenotypic drug screen, we identified a class of conjugated polyunsaturated fatty acids, specifically α-eleostearic acid and its methyl ester derivative, as novel senolytics that effectively killed a broad range of senescent cells, reduced tissue senescence, and extended healthspan in mice. Importantly, these novel lipids induced senolysis through ferroptosis, rather than apoptosis or necrosis, by exploiting elevated iron, cytosolic PUFAs and ROS levels in senescent cells. Mechanistic studies and computational analyses further revealed their key targets in the ferroptosis pathway, ACSL4, LPCAT3, and ALOX15, important for lipid-induced senolysis. This new class of ferroptosis-inducing lipid senolytics provides a novel approach to slow aging and treat age-related disease, targeting senescent cells that are primed for ferroptosis.

Recent advances from computer-aided drug design to artificial intelligence drug design

Computer-aided drug design (CADD), a cornerstone of modern drug discovery, can predict how a molecular structure relates to its activity and interacts with its target using structure-based and ligand-based methods. Fueled by ever-increasing data availability and continuous model optimization, artificial intelligence drug design (AIDD), as an enhanced iteration of CADD, has thrived in the past decade. AIDD demonstrates unprecedented opportunities in protein folding, property prediction, and molecular generation. It can also facilitate target identification, high-throughput screening (HTS), and synthetic route prediction. With AIDD involved, the process of drug discovery is greatly accelerated. Notably, AIDD offers the potential to explore uncharted territories of chemical space beyond current knowledge. In this perspective, we began by briefly outlining the main workflows and components of CADD. Then through showcasing exemplary cases driven by AIDD in recent years, we describe the evolving role of artificial intelligence (AI) in drug discovery from three distinct stages, that is, chemical library screening, linker generation, and de novo molecular generation. In this process, we attempted to draw comparisons between the features of CADD and AIDD.

Translational Perspectives on the Therapeutic Potential of Hyptis Crenata Essential Oil Terpenes in Smooth Muscle Function

Monoterpenes and sesquiterpenes are classes of organic compounds found in various natural products, such as the essential oil of Hyptis crenata (EOHc). The therapeutic potential of these terpenes present in EOHc is evidenced by their effect on smooth muscle and potential clinical applications. Among the highlighted monoterpenes, such as sabinene, α-pinene, and β-pinene, a relaxing effect on rat intestinal smooth muscles is observed, attributed to interaction with calcium channels. Furthermore, monoterpenoids like borneol, cineole, and linalool also demonstrate vasorelaxant properties, suggesting potential in the treatment of cardiovascular conditions. Sesquiterpenes, such as caryophyllene and aromadendrenes, exhibit relaxing effects in various smooth muscle tissues, such as rat uterus and guinea pig ileum, indicating pharmacological potential in these areas. The translational exploration of targets, such as calcium channels and G protein-coupled receptors, highlights the importance of these compounds in discovering new therapies based on natural products for treating various medical conditions.

Potent Biological Activity of Fluorinated Derivatives of 2-Deoxy-d-Glucose in a Glioblastoma Model

BACKGROUND

One defining feature of various aggressive cancers, including glioblastoma multiforme (GBM), is glycolysis upregulation, making its inhibition a promising therapeutic approach. One promising compound is 2-deoxy-d-glucose (2-DG), a d-glucose analog with high clinical potential due to its ability to inhibit glycolysis. Upon uptake, 2-DG is phosphorylated by hexokinase to 2-DG-6-phosphate, which inhibits hexokinase and downstream glycolytic enzymes. Unfortunately, therapeutic use of 2-DG is limited by poor pharmacokinetics, suppressing its efficacy.

METHODS

To address these issues, we synthesized novel halogenated 2-DG analogs (2-FG, 2,2-diFG, 2-CG, and 2-BG) and evaluated their glycolytic inhibition in GBM cells. Our in vitro and computational studies suggest that these derivatives modulate hexokinase activity differently.

RESULTS

Fluorinated compounds show the most potent cytotoxic effects, indicated by the lowest IC50 values. These effects were more pronounced in hypoxic conditions. 19F NMR experiments and molecular docking confirmed that fluorinated derivatives bind hexokinase comparably to glucose. Enzymatic assays demonstrated that all halogenated derivatives are more effective HKII inhibitors than 2-DG, particularly through their 6-phosphates. By modifying the C-2 position with halogens, these compounds may overcome the poor pharmacokinetics of 2-DG. The modifications seem to enhance the stability and uptake of the compounds, making them effective at lower doses and over prolonged periods.

CONCLUSIONS

This research has the potential to reshape the treatment landscape for GBM and possibly other cancers by offering a more targeted, effective, and metabolically focused therapeutic approach. The application of halogenated 2-DG analogs represents a promising advancement in cancer metabolism-targeted therapies, with the potential to overcome current treatment limitations.

The protective effects of Myrtus communis subsp. on ovariectomized diabetic rats' renal and intestinal tissues: in vivo and in silico approaches

INTRODUCTION

Postmenopausal diabetes is a condition that affects millions of women and their quality of life. Also, kidney and small intestine tissues are damaged due to diabetes. The present study aimed to examine the protective effects of an extract prepared from Myrtus communis leaves on kidney and small intestine tissues against experimentally created postmenopausal diabetes.

METHODS

For this purpose, experimental rats were randomly divided into six groups (Control; ovariectomy:OVX, diabetic:D, ovariectomy + diabetic:OVX + D, ovariectomy + diabetic + oestrogen:OVX + D+E2, ovariectomy + diabetic + MC: OVX + D+MC) and kidney and small intestine tissues were taken after the experimental procedure.

RESULTS

Evaluations of biochemical parameters (glutathione and glutathione-related enzymes, antioxidant enzymes, etc.) showed that MC had a protective effect on kidney and small intestine tissues in diabetes and ovariectomy groups.

CONCLUSION

It can be suggested that MC extract has a protective effect on small intestine and kidney tissues in postmenopausal diabetes and may be a good herbal source for this purpose.

Ergostanes from the mushroom Trametes versicolor and their cancer cell inhibition: In vitro and in silico evaluation

In this study, five steroid compounds were isolated from the fruiting bodies mushroom Trametes versicolor. The compounds, 9,19-cyclolanostane-3,29-diol (3), ergosta-7,22-dien-3-acetate (4), and ergosta-8(14),22-dien-3β,5α,6β,7α-tetrol (5), were identified from T. versicolor for the first time. The five compounds were evaluated for their activity against cancer cell lines. Compound 5α,8α-epidioxyergosta-6,22-dien-3β-ol (1) was found to be the most effective against most of the cancer cell lines tested. In silico studies showed that compound 1 has good binding affinities to different cancer targets, namely cyclin-dependent kinase 2 (cdk2), human cyclin-dependent kinase 6 (cdk6), Human Topo IIa ATPase/AMP-PNP, anti-apoptotic protein Bcl-2, and Vegfr-2. It's also druglike based on Lipinski's rule of five and it's ADME/Tox properties. Therefore, compound 1 is a good candidate in the management of cancer. These results further show that T. versicolor is a potential source of drugs or drug leads for cancer treatment.

Peptides Evaluated In Silico, In Vitro, and In Vivo as Therapeutic Tools for Obesity: A Systematic Review

Bioinformatics has emerged as a valuable tool for screening drugs and understanding their effects. This systematic review aimed to evaluate whether in silico studies using anti-obesity peptides targeting therapeutic pathways for obesity, when subsequently evaluated in vitro and in vivo, demonstrated effects consistent with those predicted in the computational analysis. The review was framed by the question: "What peptides or proteins have been used to treat obesity in in silico studies?" and structured according to the acronym PECo. The systematic review protocol was developed and registered in PROSPERO (CRD42022355540) in accordance with the PRISMA-P, and all stages of the review adhered to these guidelines. Studies were sourced from the following databases: PubMed, ScienceDirect, Scopus, Web of Science, Virtual Heath Library, and EMBASE. The search strategies resulted in 1015 articles, of which, based on the exclusion and inclusion criteria, 7 were included in this systematic review. The anti-obesity peptides identified originated from various sources including bovine alpha-lactalbumin from cocoa seed (Theobroma cacao L.), chia seed (Salvia hispanica L.), rice bran (Oryza sativa), sesame (Sesamum indicum L.), sea buckthorn seed flour (Hippophae rhamnoides), and adzuki beans (Vigna angularis). All articles underwent in vitro and in vivo reassessment and used molecular docking methodology in their in silico studies. Among the studies included in the review, 46.15% were classified as having an "uncertain risk of bias" in six of the thirteen criteria evaluated. The primary target investigated was pancreatic lipase (n = 5), with all peptides targeting this enzyme demonstrating inhibition, a finding supported both in vitro and in vivo. Additionally, other peptides were identified as PPARγ and PPARα agonists (n = 2). Notably, all peptides exhibited different mechanisms of action in lipid metabolism and adipogenesis. The findings of this systematic review underscore the effectiveness of computational simulation as a screening tool, providing crucial insights and guiding in vitro and in vivo investigations for the discovery of novel anti-obesity peptides.

Construction of a 5-Gene super-enhancer-related signature for osteosarcoma prognosis and the regulatory role of TNFRSF11B in osteosarcoma

Osteosarcoma, one of the most common primary malignancies in children and adolescents, has the primary characteristics of a poor prognosis and high rate of metastasis. This study used super-enhancer-related genes derived from two different cell lines to construct five novel super-enhancer-related gene prognostic models for patients with osteosarcoma. The training and testing datasets were used to confirm the prognostic models of the five super-enhancer-related genes, which resulted in an impartial predictive element for osteosarcoma. The immunotherapy and prediction of the response to anticancer drugs have shown that the risk signature of the five super-enhancer-related genes positively correlate with chemosensitivity. Furthermore, functional analysis of the risk signature genes revealed a significant relationship between gene groups and the malignant characteristics of tumours. TNF Receptor Superfamily Member 11b (TNFRSF11B) was selected for functional verification. Silencing of TNFRSF11B suppressed the proliferation, migration, and invasion of osteosarcoma cells in vitro and suppressed osteosarcoma growth in vivo. Moreover, transcriptome sequencing was performed on MG-63 cells to study the regulatory mechanism of TNFRSF11B in osteosarcoma cells, and it was discovered that TNFRSF11B is involved in the development of osteosarcoma via the phosphoinositide 3-kinase signalling pathway. Following the identification of TNFRSF11B as a key gene, we selected an inhibitor that specifically targeted this gene and performed molecular docking simulations. In addition, risedronic acid inhibited osteosarcoma growth at both cellular and molecular levels. In conclusion, the super-enhancer-related gene signature is a viable therapeutic tool for osteosarcoma prognosis and treatment.

Rapid and sensitive detection of domoic acid in shellfish using a magnetic bead-based competitive ELISA with a high-affinity peptide as a molecular binder

Addressing the critical health concerns posed by domoic acid (DA), a neurotoxic compound produced by toxic marine algae and bioaccumulated in shellfish, necessitates the development of a rapid, precise, and robust detection system. Traditional DA detection methods have stability and sensitivity issues, which hinder effective toxin detection. To overcome these limitations, we developed a novel direct competitive enzyme-linked immunosorbent assay (dc-ELISA) platform that utilizes peptide-immobilized magnetic beads (MGBs/peptide). The affinity peptides identified through phage display and chemically synthesized with biotin labels present an innovative alternative to conventional antibodies for ELISA applications. Streptavidin-modified MGBs were used as the bioreceptor carriers to facilitate magnetic separation and simplify sample preparation, making the MGB/peptide-based dc-ELISA platform an ideal tool for comprehensive monitoring efforts. The developed platform exhibits a detection range of 0.5-10 ng mL-1 and a low limit of detection of 0.29 ng mL-1, offering enhanced sensitivity and cost-effectiveness. Moreover, our developed dc-ELISA demonstrated a high recovery rate when validated with DA-spiked CRM-mussel samples. This method overcomes the limitations of traditional detection techniques and offers a scalable and efficient approach to marine toxin surveillance with improved marine environmental monitoring and public health management.

Male autism spectrum disorder is linked to brain aromatase disruption by prenatal BPA in multimodal investigations and 10HDA ameliorates the related mouse phenotype

Male sex, early life chemical exposure and the brain aromatase enzyme have been implicated in autism spectrum disorder (ASD). In the Barwon Infant Study birth cohort (n = 1074), higher prenatal maternal bisphenol A (BPA) levels are associated with higher ASD symptoms at age 2 and diagnosis at age 9 only in males with low aromatase genetic pathway activity scores. Higher prenatal BPA levels are predictive of higher cord blood methylation across the CYP19A1 brain promoter I.f region (P = 0.009) and aromatase gene methylation mediates (P = 0.01) the link between higher prenatal BPA and brain-derived neurotrophic factor methylation, with independent cohort replication. BPA suppressed aromatase expression in vitro and in vivo. Male mice exposed to mid-gestation BPA or with aromatase knockout have ASD-like behaviors with structural and functional brain changes. 10-hydroxy-2-decenoic acid (10HDA), an estrogenic fatty acid alleviated these features and reversed detrimental neurodevelopmental gene expression. Here we demonstrate that prenatal BPA exposure is associated with impaired brain aromatase function and ASD-related behaviors and brain abnormalities in males that may be reversible through postnatal 10HDA intervention.

Synthesis of new non-natural L-glycosidic flavonoid derivatives and their evaluation as inhibitors of Trypanosoma cruzi ecto-nucleoside triphosphate diphosphohydrolase 1 (TcNTPDase1)

Trypanosoma cruzi is the pathogen of Chagas disease, a neglected tropical disease that affects more than 6 million people worldwide. There are no vaccines to prevent infection, and the therapeutic arsenal is very minimal and toxic. The unique E-NTPDase of T. cruzi (TcNTPDase1) plays essential roles in adhesion and infection and is a virulence factor. Quercetin is a flavonoid with antimicrobial, antiviral, and antitumor activities. Its potential as a partial inhibitor of NTPDases has also been demonstrated. In this work, we synthesized the non-natural L-glycoside derivatives of quercetin and evaluated them as inhibitors of recombinant TcNTPDase1 (rTcNTPDase1). These compounds, and quercetin and miquelianin, a natural quercetin derivative, were also tested. Compound 16 showed the most significant inhibitory effect (94%). Quercetin, miquelianin, and compound 14 showed inhibition close to 50%. We thoroughly investigated the inhibitory effect of 16. Our data suggested a competitive inhibition with a Ki of 8.39 μM (± 0.90). To better understand the interaction of compound 16 and rTcNTPDase1, we performed molecular dynamics simulations of the enzyme and docking analyses with the compounds. Our predictions show that compound 16 binds to the enzyme's catalytic site and interacts with important residues for NTPDase activity. As an inhibitor of a critical T. cruzi enzyme, (16) could be helpful as a starting point in the developing of a future treatment for Chagas disease. Furthermore, the discovery of (16) as an inhibitor of TcNTPDase1 may open new avenues in the study and development of new inhibitors of E-NTPDases.

Structural and Functional Characterization of New Lipid Transfer Proteins with Chitin-Binding Properties: Insights from Protein Structure Prediction, Molecular Docking, and Antifungal Activity

Faced with the emergence of multiresistant microorganisms that affect human health, microbial agents have become a serious global threat, affecting human health and plant crops. Antimicrobial peptides have attracted significant attention in research for the development of new microbial control agents. This work's goal was the structural characterization and analysis of antifungal activity of chitin-binding peptides from Capsicum baccatum and Capsicum frutescens seeds on the growth of Candida and Fusarium species. Proteins were initially submitted to extraction in phosphate buffer pH 5.4 and subjected to chitin column chromatography. Posteriorly, two fractions were obtained for each species, Cb-F1 and Cf-F1 and Cb-F2 and Cf-F2, respectively. The Cb-F1 (C. baccatum) and Cf-F1 (C. frutescens) fractions did not bind to the chitin column. The electrophoresis results obtained after chromatography showed two major protein bands between 3.4 and 14.2 kDa for Cb-F2. For Cf-F2, three major bands were identified between 6.5 and 14.2 kDa. One band from each species was subjected to mass spectrometry, and both bands showed similarity to nonspecific lipid transfer protein. Candida albicans and Candida tropicalis had their growth inhibited by Cb-F2. Cf-F2 inhibited the development of C. albicans but did not inhibit the growth of C. tropicalis. Both fractions were unable to inhibit the growth of Fusarium species. The toxicity of the fractions was tested in vivo on Galleria mellonella larvae, and both showed a low toxicity rate at high concentrations. As a result, the fractions have enormous promise for the creation of novel antifungal compounds.

Geometry Optimization Algorithms in Conjunction with the Machine Learning Potential ANI-2x Facilitate the Structure-Based Virtual Screening and Binding Mode Prediction

Structure-based virtual screening utilizes molecular docking to explore and analyze ligand-macromolecule interactions, crucial for identifying and developing potential drug candidates. Although there is availability of several widely used docking programs, the accurate prediction of binding affinity and binding mode still presents challenges. In this study, we introduced a novel protocol that combines our in-house geometry optimization algorithm, the conjugate gradient with backtracking line search (CG-BS), which is capable of restraining and constraining rotatable torsional angles and other geometric parameters with a highly accurate machine learning potential, ANI-2x, renowned for its precise molecular energy predictions reassembling the wB97X/6-31G(d) model. By integrating this protocol with binding pose prediction using the Glide, we conducted additional structural optimization and potential energy prediction on 11 small molecule-macromolecule and 12 peptide-macromolecule systems. We observed that ANI-2x/CG-BS greatly improved the docking power, not only optimizing binding poses more effectively, particularly when the RMSD of the predicted binding pose by Glide exceeded around 5 Å, but also achieving a 26% higher success rate in identifying those native-like binding poses at the top rank compared to Glide docking. As for the scoring and ranking powers, ANI-2x/CG-BS demonstrated an enhanced performance in predicting and ranking hundreds or thousands of ligands over Glide docking. For example, Pearson's and Spearman's correlation coefficients remarkedly increased from 0.24 and 0.14 with Glide docking to 0.85 and 0.69, respectively, with the addition of ANI-2x/CG-BS for optimizing and ranking small molecules binding to the bacterial ribosomal aminoacyl-tRNA receptor. These results suggest that ANI-2x/CG-BS holds considerable potential for being integrated into virtual screening pipelines due to its enhanced docking performance.

Targeting Leishmania infantum Mannosyl-oligosaccharide glucosidase with natural products: potential pH-dependent inhibition explored through computer-aided drug design

Visceral Leishmaniasis (VL) is a serious public health issue, documented in more than ninety countries, where an estimated 500,000 new cases emerge each year. Regardless of novel methodologies, advancements, and experimental interventions, therapeutic limitations, and drug resistance are still challenging. For this reason, based on previous research, we screened natural products (NP) from Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBEDB), Mexican Compound Database of Natural Products (BIOFACQUIM), and Peruvian Natural Products Database (PeruNPDB) databases, in addition to structural analogs of Miglitol and Acarbose, which have been suggested as treatments for VL and have shown encouraging action against parasite's N-glycan biosynthesis. Using computer-aided drug design (CADD) approaches, the potential inhibitory effect of these NP candidates was evaluated by inhibiting the Mannosyl-oligosaccharide Glucosidase Protein (MOGS) from Leishmania infantum, an enzyme essential for the protein glycosylation process, at various pH to mimic the parasite's changing environment. Also, computational analysis was used to evaluate the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profile, while molecular dynamic simulations were used to gather information on the interactions between these ligands and the protein target. Our findings indicated that Ocotillone and Subsessiline have potential antileishmanial effects at pH 5 and 7, respectively, due to their high binding affinity to MOGS and interactions in the active center. Furthermore, these compounds were non-toxic and had the potential to be administered orally. This research indicates the promising anti-leishmanial activity of Ocotillone and Subsessiline, suggesting further validation through in vitro and in vivo experiments.

Targeting the PANoptosis signaling pathway for myocardial protection: therapeutic potential of Xian Ling Gu Bao capsule

Introduction: Myocardial infarction (MI), the most prevalent ischemic heart disease, constitutes a primary cause of global cardiovascular disease with incidence and mortality. The pathogenesis of MI is exceedingly intricate, with PANoptosis playing a pivotal role in its pathological process. Xian Ling Gu Bao capsule (XLGB) contains various active components, including flavonoids, terpenes, and phenylpropanoids, and exhibits a wide range of pharmacological activities. However, it remains unclear whether XLGB can protect the myocardium from damage after MI. This study aimed to investigate the impact of XLGB on isoprenaline (ISO)-induced MI in mice and its potential mechanisms. Methods: This study assessed the protective effects of XLGB against ISO-induced MI through techniques such as echocardiography, HE staining, Masson staining, and enzyme-linked immunosorbent assay (ELISA). Furthermore, the potential mechanisms of XLGB's protective effects on MI were explored using bioinformatics, molecular docking, and molecular dynamics simulations. These mechanisms were further validated through immunofluorescence staining and Western blotting. Results: The results demonstrated that various doses of XLGB exhibited a significant reduction in myocardial injury induced by myocardial infarction. Intriguingly, higher dosages of XLGB displayed superior therapeutic efficacy compared to the positive control metoprolol. This protective effect is primarily achieved through the inhibition of oxidative stress and the inflammatory processes. Furthermore, we have elucidated that XLGB protected the myocardium from MI-induced damage by suppressing PANoptosis, with a critical role played by the NLRP3/Caspase3/RIP1 signaling pathway. Of particular note, the primary compounds of XLGB were found to directly interact with NLRP3/Caspase3/RIP1, a discovery further validated through molecular docking and molecular dynamics simulations. This suggests that NLRP3/Caspase3/RIP1 may be a therapeutic target for XLGB-induced myocardial protection. Conclusion: In summary, our findings reveal a novel property of XLGB: reverses myocardial damage following MI by inhibiting the NLRP3/Caspase3/RIP1-mediated PANoptosis pathway.

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability

Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors for a wide range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed "DrugMap," an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NF-κB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NF-κB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription-factor activity.

Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors

Nowadays, an efficient and robust virtual screening procedure is crucial in the drug discovery process, especially when performed on large and chemically diverse databases. Virtual screening methods, like molecular docking and classic QSAR models, are limited in their ability to handle vast numbers of compounds and to learn from scarce data, respectively. In this study, we introduce a universal methodology that uses a machine learning-based approach to predict docking scores without the need for time-consuming molecular docking procedures. The developed protocol yielded 1000 times faster binding energy predictions than classical docking-based screening. The proposed predictive model learns from docking results, allowing users to choose their preferred docking software without relying on insufficient and incoherent experimental activity data. The methodology described employs multiple types of molecular fingerprints and descriptors to construct an ensemble model that further reduces prediction errors and is capable of delivering highly precise docking score values for monoamine oxidase ligands, enabling faster identification of promising compounds. An extensive pharmacophore-constrained screening of the ZINC database resulted in a selection of 24 compounds that were synthesized and evaluated for their biological activity. A preliminary screen discovered weak inhibitors of MAO-A with a percentage efficiency index close to a known drug at the lowest tested concentration. The approach presented here can be successfully applied to other biological targets as target-specific knowledge is not incorporated at the screening phase.

Can Duvelisib and Eganelisib work for both cancer and COVID-19? Molecular-level insights from MD simulations and enhanced samplings

SARS-CoV-2 has caused severe illness and anxiety worldwide, evolving into more dreadful variants capable of evading the host's immunity. Cytokine storms, led by PI3Kγ, are common in cancer and SARS-CoV-2. Naturally, there is a yearning to see whether any drugs could alleviate cytokine storms for both. Upon investigation, we identified two anticancer drugs, Duvelisib and Eganelisib, that could also work against SARS-CoV-2. This report is the first to decipher their synergic therapeutic effectiveness against COVID-19 and cancer with molecular insights from atomistic simulations. In addition to PI3Kγ, these drugs exhibit specificity for the main protease among all SARS-CoV-2 targets, with significant negative binding free energies and small time-dependent conformational changes of the complexes. Complexation makes active sites and secondary structures highly mechanically stiff, with barely any deformation. Replica simulations estimated large pulling forces in enhanced sampling to dissociate the drugs from Mpro's active site. Furthermore, the radial distribution function (RDF) demonstrated that the therapeutic molecules were closest to the His41 and Cys145 catalytic dyad residues. Finally, analyses implied Duvelisib and Eganelisib as promising dual-purposed anti-COVID and anticancer drugs, potentially targeting Mpro and PI3Kγ to stop virus replication and cytokine storms concomitantly. We also distinguished hotspot residues imparting significant interactions.

An investigation of binding interactions of tumor-targeted peptide conjugated polyphenols with the kinase domain of ephrin B4 and B2 receptors

Recent studies have shown that Ephrin receptors may be upregulated in several types of cancers including breast, ovarian and endometrial cancers, making them a target for drug design. In this work, we have utilized a target-hopping approach to design new natural product-peptide conjugates and examined their interactions with the kinase-binding domain of EphB4 and EphB2 receptors. The peptide sequences were generated through point mutations of the known EphB4 antagonist peptide TNYLFSPNGPIA. Their anticancer properties and secondary structures were analyzed computationally. Conjugates of most optimum of peptides were then designed by binding the N-terminal of the peptides with the free carboxyl group of the polyphenols sinapate, gallate and coumarate, which are known for their inherent anticancer properties. To investigate if these conjugates have a potential to bind to the kinase domain, we carried out docking studies and MMGBSA free energy calculations of the trajectories based on the molecular dynamics simulations, with both the apo and the ATP bound kinase domains of both receptors. In most cases binding interactions occurred within the catalytic loop region, while in some cases the conjugates were found to spread out across the N-lobe and the DFG motif region. The conjugates were further tested for prediction of pharmacokinetic properties using ADME studies. Our results indicated that the conjugates were lipophilic and MDCK permeable with no CYP interactions. These findings provide an insight into the molecular interactions of these peptides and conjugates with the kinase domain of the EphB4 and EphB2 receptor. As a proof of concept, we synthesized and carried out SPR analysis with two of the conjugates (gallate-TNYLFSPNGPIA and sinapate-TNYLFSPNGPIA). Results indicated that the conjugates showed higher binding with the EphB4 receptor and minimal binding to EphB2 receptor. Sinapate-TNYLFSPNGPIA showed inhibitory activity against EphB4. These studies reveal that some of the conjugates may be developed for further investigation into in vitro and in vivo studies and potential development as therapeutics.

Simple ammonium salt and sigma-1 receptor ligand dipentylammonium provides neuroprotective effects in cell culture and Caenorhabditis elegans models of Alzheimer's disease

The sigma-1 receptor (σ-1R), a chaperone protein located at the mitochondria-associated membrane (MAM) of the endoplasmic reticulum, can interact with and modify the signaling pathways of various proteins, thereby modulating many disease pathologies, including Alzheimer's disease (AD). The σ-1R ligand dipentylammonium (DPA) was analyzed for its anti-AD properties using PC12 cells (in vitro) and Caenorhabditis elegans (in vivo) models along with molecular docking (in silico) analysis. DPA at 1 and 10 µM concentrations was able to significantly potentiate NGF-induced neurite growth length by 137.7 ± 12.0 and 187.8 ± 16.4, respectively, when compared to the control 76.9 ± 7.4. DPA also regulated neurite damage caused by Aβ(25-35) treatment in differentiated PC12 cells by improving cell viability and neurite length. In C. elegans, DPA could significantly extend the median and maximum lifespan of Aβ transgenic strain CL2006 without impacting wild-type nematodes. Additionally, it could significantly reduce the paralysis phenotype of another Aβ transgenic strain, CL4176, thereby improving the overall health in AD pathogenesis. This effect depended on σ-1R, as DPA could not modulate the lifespan of σ-1R mutant TM3443. This was further confirmed using agonist PRE084 and antagonist BD1047, wherein the agonist alone could extend the lifespan of CL2006, while the antagonist suppressed the effect of DPA in CL2006. Interestingly, neither had an TM3443. Further, molecular docking analysis showed that DPA had a similar binding affinity as that of PRE084, BD1047 and pentazocine against the σ-1R receptor in humans and C. elegans, which collectively suggests the anti-AD properties of DPA.

Enhancing Generalizability in Protein-Ligand Binding Affinity Prediction with Multimodal Contrastive Learning

Improving the generalization ability of scoring functions remains a major challenge in protein-ligand binding affinity prediction. Many machine learning methods are limited by their reliance on single-modal representations, hindering a comprehensive understanding of protein-ligand interactions. We introduce a graph-neural-network-based scoring function that utilizes a triplet contrastive learning loss to improve protein-ligand representations. In this model, three-dimensional complex representations and the fusion of two-dimensional ligand and coarse-grained pocket representations converge while distancing from decoy representations in latent space. After rigorous validation on multiple external data sets, our model exhibits commendable generalization capabilities compared to those of other deep learning-based scoring functions, marking it as a promising tool in the realm of drug discovery. In the future, our training framework can be extended to other biophysical- and biochemical-related problems such as protein-protein interaction and protein mutation prediction.

Rattus norvegicus Glutathione Transferase Omega 1 Localization in Oral Tissues and Interactions with Food Phytochemicals

Glutathione transferases are xenobiotic-metabolizing enzymes with both glutathione-conjugation and ligandin roles. GSTs are present in chemosensory tissues and fluids of the nasal/oral cavities where they protect tissues from exogenous compounds, including food molecules. In the present study, we explored the presence of the omega-class glutathione transferase (GSTO1) in the rat oral cavity. Using immunohistochemistry, GSTO1 expression was found in taste bud cells of the tongue epithelium and buccal cells of the oral epithelium. Buccal and lingual extracts exhibited thiol-transferase activity (4.9 ± 0.1 and 1.8 ± 0.1 μM/s/mg, respectively). A slight reduction from 4.9 ± 0.1 to 4.2 ± 0.1 μM/s/mg (p < 0.05; Student's t test) was observed in the buccal extract with 100 μM GSTO1-IN-1, a specific inhibitor of GSTO1. RnGSTO1 exhibited the usual activities of omega GSTs, i.e., thiol-transferase (catalytic efficiency of 8.9 × 104 M-1·s-1), and phenacyl-glutathione reductase (catalytic efficiency of 8.9 × 105 M-1·s-1) activities, similar to human GSTO1. RnGSTO1 interacts with food phytochemicals, including bitter compounds such as luteolin (Ki = 3.3 ± 1.9 μM). Crystal structure analysis suggests that luteolin most probably binds to RnGSTO1 ligandin site. Our results suggest that GSTO1 could interact with food phytochemicals in the oral cavity.

Seeds of nonhost species as sources of toxic compounds for the cowpea weevil Callosobruchus maculatus (F.)

Cowpea weevil, Callosobruchus maculatus, is the primary pest of stored cowpea seeds. The management of this infestation currently relies on insecticides, resulting in environmental pollution and selection of insecticide-resistant pests. Consequently, research efforts are being devoted to identify natural insecticides as sustainable and environment friendly alternatives for the control of C. maculatus. In this study, we explore the toxic effects of the nonhost seeds Parkia multijuga, Copaifera langsdorffii, Ormosia arborea, Amburana cearensis, Lonchocarpus guilleminianus, Sapindus saponaria, and Myroxylon peruiferum, on the cowpea weevil C. maculatus. Notably, all nonhost seeds led to reductions between 60 and 100% in oviposition by C. maculatus females. Additionally, the larvae were unable to penetrate the nonhost seeds. Artificial seeds containing 0.05% to 10% of cotyledon flour were toxic to C. maculatus larvae. Approximately 40% of larvae that consumed seeds containing 0.05% of O. arborea failed to develop, in contrast to control larvae. Proteomic analysis of A. cearensis and O. arborea seeds identify revealed a total of 371 proteins. From those, 237 are present in both seeds, 91 were exclusive to O. arborea seeds, and 43 were specific to A. cearensis seeds. Some of these proteins are related to defense, such as proteins containing the cupin domain and 11S seed storage protein. The in silico docking of cupin domain-containing proteins and 11S storage protein with N-acetylglucosamine (NAG)4 showed negative values of affinity energy, indicating spontaneous binding. These results showed that nonhost seeds have natural insecticide compounds with potential to control C. maculatus infestation.

Comparative Analysis of Leishmania major Nucleoside Hydrolases Toward Selecting Multi-target Strategy

PURPOSE

Leishmania causes multiple types of leishmaniasis in different parts of the world. It has a lack of metabolic machine to produce purine bases. Therefore, the parasite produces purine bases through the breakdown of nutritional nucleotides and it makes the nucleoside hydrolases (NHs) good drug targets. They have different substrate-preferring (SP) types. Our objectives were modeling and comparative analysis of these protein structures for Leishmania major.

METHOD

In this work, available sequences for all SP types of L. major NH enzymes including inosine-uridine preferring NH (IUNH), inosine-guanosine preferring NH (IGNH), and inosine-adenosine-guanosine preferring NH (IAGNH) were used to make 24 structural models via SWISS-MODEL and LOMETS. After evaluating the structural models, three enzyme structures were finalized and used to analyze substrate-binding pockets.

RESULTS

The three SP types of L. major NH enzymes that can breakdown purine nucleosides were highly different in terms of sequence, structure, and profile of interacting residues within the substrate-binding pockets. In this study, new enzyme structures have been presented for three SP types and they have been compared in different aspects and it indicated that they were very different from each other.

CONCLUSION

Although, previously indicated that from these three SP types in genera other than Leishmania, the role of IGNH and IAGNH was greater than IUNH in supplying purine bases, till this work, just IUNH has been structurally studied and used in drug-design investigations for Leishmania. Therefore, we are offering to use all three SP types of NHs as multi-target strategy in anti-leishmaniosis drug-design studies.

Exploring the therapeutic mechanisms and prognostic targets of Biochanin A in glioblastoma via integrated computational analysis and in vitro experiments

Glioblastoma (GBM) is the most aggressive brain tumor and is characterized by a poor prognosis and high recurrence and mortality rates. Biochanin A (BCA) exhibits promising clinical anti-tumor effects. In this study, we aimed to explore the pharmacological mechanisms by which BCA acts against GBM. Network pharmacology was employed to identify overlapping target genes between BCA and GBM. Differentially expressed genes from the Gene Expression Profiling Interactive Analysis 2 (GEPIA2) database were visualized using VolcaNose. Interactions among these overlapping genes were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins database. Protein-protein interaction networks were constructed using Cytoscape 3.8.1. The Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology enrichment analyses were conducted using the Database for Annotation, Visualization, and Integrated Discovery. Survival analyses for these genes were performed using the GEPIA2 database. The Chinese Glioma Genome Atlas database was used to study the correlations between key prognostic genes. Molecular docking was confirmed using the DockThor database and visualized with PyMol software. Cell viability was assessed via the CCK-8 assay, apoptosis and the cell cycle stages were examined using flow cytometry, and protein expression was detected using western blotting. In all, 63 genes were initially identified as potential targets for BCA in treating GBM. Enrichment analysis suggested that the pharmacological mechanisms of BCA primarily involved cell cycle inhibition, induction of cell apoptosis, and immune regulation. Based on these findings, AKT1, EGFR, CASP3, and MMP9 were preliminarily predicted as key prognostic target genes for BCA in GBM treatment. Furthermore, molecular docking analysis suggested stable binding of BCA to the target protein. In vitro experiments revealed the efficacy of BCA in inhibiting GBM, with an IC50 value of 98.37 ± 2.21 μM. BCA inhibited cell proliferation, induced cell apoptosis, and arrested the cell cycle of GBM cells. Furthermore, the anti-tumor effects of BCA on U251 cells were linked to the regulation of the target protein. We utilized integrated bioinformatics analyses to predict targets and confirmed through experiments that BCA possesses remarkable anti-tumor activities. We present a novel approach for multi-target treatment of GBM using BCA.

Targeting with Structural Analogs of Natural Products the Purine Salvage Pathway in Leishmania (Leishmania) infantum by Computer-Aided Drug-Design Approaches

Visceral Leishmaniasis (VL) has a high death rate, with 500,000 new cases and 50,000 deaths occurring annually. Despite the development of novel strategies and technologies, there is no adequate treatment for the disease. Therefore, the purpose of this study is to find structural analogs of natural products as potential novel drugs to treat VL. We selected structural analogs from natural products that have shown antileishmanial activities, and that may impede the purine salvage pathway using computer-aided drug-design (CADD) approaches. For these, we started with the vastly studied target in the pathway, the adenine phosphoribosyl transferase (APRT) protein, which alone is non-essential for the survival of the parasite. Keeping this in mind, we search for a substance that can bind to multiple targets throughout the pathway. Computational techniques were used to study the purine salvage pathway from Leishmania infantum, and molecular dynamic simulations were used to gather information on the interactions between ligands and proteins. Because of its low homology to human proteins and its essential role in the purine salvage pathway proteins network interaction, the findings further highlight the significance of adenylosuccinate lyase protein (ADL) as a therapeutic target. An analog of the alkaloid Skimmianine, N,N-diethyl-4-methoxy-1-benzofuran-6-carboxamide, demonstrated a good binding affinity to APRT and ADL targets, no expected toxicity, and potential for oral route administration. This study indicates that the compound may have antileishmanial activity, which was granted in vitro and in vivo experiments to settle this finding in the future.

The evaluations of the inhibition of orlistat on Clostridium perfringens sialidase (NanI) activity by in vitro and in silico approaches

Clostridium perfringens (C. perfringens) is a bacterium that causes serious problems in humans and animals such as food poisoning, gas gangrene and infections. C. perfringens has three sialidases (NanH, NanI, NanJ) and inhibition of NanI constitutes an approach in the treatment of C. perfringens since NanI provides the carbohydrate source necessary for the growth of bacteria. In our study, the inhibition effect of some drugs belonging to different drug groups on NanI activity was investigated. Among these drugs, orlistat (0.21±0.05 μM) was determined to have a lower IC50 value than the positive control quercetin (15.58±1.59 μM). It was determined in vitro by spectrofluorometric method. Additionally, NanI molecular docking studies with orlistatand quercetin were performed using iGemdock, DockThor and SwissDock. Orlistat (-93.93, -8.649 and -10.03 kcal/mol, respectively) was found to have a higher binding affinity than quercetin (-92.68, -7.491 and -8.70 kcal/mol, respectively), and the results were in line with in vitro studies. The results may suggest that orlistat is a molecule with drug potential for C. perfringens because it inhibits the drug target NanI, and that the inhibition efficiency can be increased by studies with orlistat derivatives.

Information Transmission in G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) are the largest class of receptors in the human genome and constitute about 30% of all drug targets. In this article, intended for a non-mathematical audience, both experimental observations and new theoretical results are compared in the context of information transmission across the cell membrane. The amount of information actually currently used or projected to be used in clinical settings is a small fraction of the information transmission capacity of the GPCR. This indicates that the number of yet undiscovered drug targets within GPCRs is much larger than what is currently known. Theoretical studies with some experimental validation indicate that localized heat deposition and dissipation are key to the identification of sites and mechanisms for drug action.

Recent developments in molecular modeling tools and applications related to pharmaceutical and biomedical research

In modern pharmaceutical and biomedical research, molecular modeling represents a useful tool to explore processes and their mechanistic bases at the molecular level. Integrating experimental and virtual analysis is a fruitful approach to study ligand-receptor interaction in chemical, biochemical and biological environments. In these fields, molecular docking and molecular dynamics are considered privileged techniques for modeling (bio)macromolecules and related complexes. This review aims to present the current landscape of molecular modeling in pharmaceutical and biomedical research by examining selected representative applications published in the last years and highlighting current topics and trends of this field. Thus, a systematic compilation of all published literature has not been attempted herein. After a brief overview of the main theoretical and computational tools used to investigate mechanisms at molecular level, recent applications of molecular modeling in drug discovery, ligand binding and for studying protein conformation and function will be discussed. Furthermore, specific sections will be devoted to the application of molecular modeling for unravelling enantioselective mechanisms underlying the enantioseparation of chiral compounds of pharmaceutical and biomedical interest as well as for studying new forms of noncovalent interactivity identified in biochemical and biological environments. The general aim of this review is to provide the reader with a modern overview of the topic, highlighting advancements and outlooks as well as drawbacks and pitfalls still affecting the applicability of theoretical and computational methods in the field of pharmaceutical and biomedical research.

Spectroscopic characterization of eupalitin-3-O-β-D-galactopyranoside from Boerhavia procumbens: In vivo hepato-protective potential in rat model

The liver is one of the most important organs responsible for detoxifying biomolecules and xenobiotics. Herein, we report the isolation, characterization, and hepatoprotective effect of the Boerhavia procumbens-derived eupalitin-3-O-β-D-galactopyranoside (EGP) compound. The structure of the EGP compound was deduced by using NMR spectroscopic techniques and mass spectrometry. The EGP hepatoprotective activities were evaluated with HepG2 cell viability and LDH assays in vitro, and CCl4-induced toxicity was investigated in vivo in the rat model. Compared to the CCl4-treated group, cells exposed to the EGP compound at 200 µg/ml showed increased cell viability (60.52 ± 1.22 %) and decreased LDH levels (23.81 ± 1.89 U/ml). The serum levels of SGPT, SGOT, ALP, and total bilirubin in the CCl4-treated group were substantially higher than those in the control group (64 ± 1.89 U/ml, 86 ± 1.47 U/ml, 252.6 ± 2.96 U/ml, and 5.45 ± 0.32 mg/dl, respectively). When compared to animals treated with CCl4 alone, the EGP compound's in vivo hepatoprotective effect at 60 mg/kg with CCl4 significantly (p < 0.01) decreased the levels of SGPT and SGOT (26 ± 1.34 U/ml and 42.92 ± 1.6 U/ml) respectively. Furthermore, our histological study showed a significant response in restoring and maintaining the normal morphological appearance of the liver. Thus, our results show that the EGP compound is a promising and novel lead molecule for better hepatotoxicity control and therapy.

Induced Circular Dichroism Analysis of Thermally Induced Conformational Changes on Protein Binding Sites Under a Crowding Environment

Protein-ligand interactions in crowded cellular environments play a crucial role in biological functions. The crowded environment can perturb the overall protein structure and local conformation, thereby influencing the binding pathway of protein-ligand reactions within the cellular milieu. Therefore, a detailed understanding of the local conformation is crucial for elucidating the intricacies of protein-ligand interactions in crowded cellular environments. In this study, we investigated the feasibility of induced circular dichroism (ICD) using 8-anilinonaphthalene-1-sulfonic acid (ANS) for local conformational analysis at the binding site in a crowding environment. Bovine serum albumin (BSA) concentration-dependent measurements were performed to assess the feasibility of ANS-ICD for analyzing protein interior binding sites. The results showed distinct changes in the ANS-ICD spectra of BSA solutions, indicating their potential for analyzing the internal conformation of proteins. Moreover, temperature-dependent measurements were performed in dilute and crowding environments, revealing distinct denaturation pathways of BSA binding sites. Principal component analysis of ANS-ICD spectral changes revealed lower temperature pre-denaturation in the crowded solution than that in the diluted solution, suggesting destabilization of binding sites owing to self-crowding repulsive interactions. The established ANS-ICD method can provide valuable conformational insights into protein-ligand interactions in crowded cellular environments.

Synthesis and In vitro and In silico Anti-inflammatory Activity of New Thiazolidinedione-quinoline Derivatives

BACKGROUND

Inflammation is a series of complex defense-related reactions. The inflammation cascade produces various pro-inflammatory mediators. Unregulated production of these pro-inflammatory mediators can lead to a wide range of diseases, including rheumatoid arthritis, sepsis, and inflammatory bowel disease. In the literature, the anti-inflammatory action of quinoline and thiazolidinedione nuclei are well established, alone, and associated with other nuclei. The synthesis of hybrid molecules is a strategy for obtaining more efficient molecules due to the union of pharmacophoric nuclei known to be related to pharmacological activity.

OBJECTIVES

Based on this, this work presents the synthesis of thiazolidinedione-quinoline molecular hybrids and their involvement in the modulation of cytokines involved in the inflammatory reaction cascade.

METHODS

After synthesis and characterization, the compounds were submitted to cell viability test (MTT), ELISA IFN-γ and TNF-α, adipogenic differentiation, and molecular docking assay with PPARy and COX-2 targets.

RESULTS

LPSF/ZKD2 and LPSF/ZKD7 showed a significant decrease in the concentration of IFN- γ and TNF-α, with a dose-dependent behavior. LPSF/ZKD4 at a concentration of 50 μM significantly reduced IL-6 expression. LPSF/ZKD4 demonstrates lipid accumulation with significant differences between the untreated and negative control groups, indicating a relevant agonist action on the PPARγ receptor. Molecular docking showed that all synthesized compounds have good affinity with PPARγ e COX-2, with binding energy close to -10,000 Kcal/mol.

CONCLUSION

These results demonstrate that the synthesis of quinoline-thiazolidinedione hybrids may be a useful strategy for obtaining promising candidates for new anti-inflammatory agents.

Multi-and many-objective optimization: present and future in de novo drug design

de novo Drug Design (dnDD) aims to create new molecules that satisfy multiple conflicting objectives. Since several desired properties can be considered in the optimization process, dnDD is naturally categorized as a many-objective optimization problem (ManyOOP), where more than three objectives must be simultaneously optimized. However, a large number of objectives typically pose several challenges that affect the choice and the design of optimization methodologies. Herein, we cover the application of multi- and many-objective optimization methods, particularly those based on Evolutionary Computation and Machine Learning techniques, to enlighten their potential application in dnDD. Additionally, we comprehensively analyze how molecular properties used in the optimization process are applied as either objectives or constraints to the problem. Finally, we discuss future research in many-objective optimization for dnDD, highlighting two important possible impacts: i) its integration with the development of multi-target approaches to accelerate the discovery of innovative and more efficacious drug therapies and ii) its role as a catalyst for new developments in more fundamental and general methodological frameworks in the field.

Toxicological evaluation of TBBPA by common carp (Cyprinus carpio) about the in vivo/vitro disturbance of the AHR pathway

Tetrabromobisphenol A (TBBPA) is a widely used plastic additive with high bioaccumulation potential and toxicity on both humans and wildlife. Currently, research on its ecotoxicity and the underlying mechanism is limited. Using common carp (Cyprinus carpio), we evaluated the toxicity of TBBPA, especially focusing on its alteration of a key metabolism-related pathway aryl hydrocarbon receptor (AHR), using in vivo/vitro assays and in silico simulation. The 96 h LC50 of TBBPA of common carp was 4.2 mg/L and belonged to the acute toxic level II. The bioaccumulation potential of TBBPA follows the role of liver > gill > brain and varies between 3- and 14-day exposure. On the AHR pathway respect, as expected, the metabolism-related cyp1a1 and cyp1b1 were upregulated in the liver and brain. Ahr2, the receptor, was also upregulated in the brain under TBBPA exposure. The alteration of gene expression was tissue-specific while the difference between 3- or 14-day exposure was minor. AHR inhibition assay indicated the 2, 3, 7, 8-tetrachlorodibenzo-p-dioxin (TCDD)-induced AHR transactivation can be inhibited by TBBPA suggesting it is not a potent agonist but a competitive antagonist. In silico analysis indicated TBBPA can be successfully docked into the binding cavity with similar poses but still have AHR-form-specific interactions. Molecular dynamics simulation proved TBBPA can be more flexible than the coplanar ligand TCDD, especially in ccaAHR1b with greater root-mean-square deviation (RMSD), of which TCDD-induced transactivation seemed not to be blocked by TBBPA. This research increased the understanding of TBBPA toxicity and alteration of the AHR pathway, and pointed out the need to perform additional toxicology evaluation of emerging contaminants, especially on non-model species.

In the quest for histone deacetylase inhibitors: current trends in the application of multilayered computational methods

Histone deacetylase (HDAC) inhibitors have gained attention over the past three decades because of their potential in the treatment of different diseases including various forms of cancers, neurodegenerative disorders, autoimmune, inflammatory diseases, and other metabolic disorders. To date, 5 HDAC inhibitor drugs are marketed for the treatment of hematological malignancies and several drug-candidate HDAC inhibitors are at different stages of clinical trials. However, due to the toxic side effects of these drugs resulting from the lack of target selectivity, active studies are ongoing to design and develop either class-selective or isoform-selective inhibitors. Computational methods have aided the discovery of HDAC inhibitors with the desired potency and/or selectivity. These methods include ligand-based approaches such as scaffold hopping, pharmacophore modeling, three-dimensional quantitative structure-activity relationships (3D-QSAR); and structure-based virtual screening (molecular docking). The current trends involve the application of the combination of these methods and incorporating molecular dynamics simulations coupled with Poisson-Boltzmann/molecular mechanics generalized Born surface area (MM-PBSA/MM-GBSA) to improve the prediction of ligand binding affinity. This review aimed at understanding the current trends in applying these multilayered strategies and their contribution to the design/identification of HDAC inhibitors.

In silico studies of anti-oxidative and hot temperament-based phytochemicals as natural inhibitors of SARS-CoV-2 Mpro

Main protease (Mpro) of SARS-CoV-2 is considered one of the key targets due to its role in viral replication. The use of traditional phytochemicals is an important part of complementary/alternative medicine, which also accompany the concept of temperament, where it has been shown that hot medicines cure cold and cold medicines cure hot, with cold and hot pattern being associated with oxidative and anti-oxidative properties in medicine, respectively. Molecular docking in this study has demonstrated that a number of anti-oxidative and hot temperament-based phytochemicals have high binding affinities to SARS-CoV-2 Mpro, both in the monomeric and dimeric deposited states of the protein. The highest ranking phytochemicals identified in this study included savinin, betulinic acid and curcumin. Complexes of savinin, betulinic acid, curcumin as well as Nirmatrelvir (the only approved inhibitor, used for comparison) bound to SARS-CoV-2 Mpro were further subjected to molecular dynamics simulations. Subsequently, RMSD, RMSF, Rg, number of hydrogen bonds, binding free energies and residue contributions (using MM-PBSA) and buried surface area (BSA), were analysed. The computational results suggested high binding affinities of savinin, betulinic acid and curcumin to both the monomeric and dimeric deposited states of Mpro, while highlighting the lower binding energy of betulinic acid in comparison with savinin and curcumin and even Nirmatrelvir, leading to a greater stability of the betulinic acid-SARS-CoV-2 Mpro complex. Overall, based on the increasing mutation rate in the spike protein and the fact that the SARS-CoV-2 Mpro remains highly conserved, this study provides an insight into the use of phytochemicals against COVID-19 and other coronavirus diseases.

Hierarchical Virtual Screening of Potential New Antibiotics from Polyoxygenated Dibenzofurans against Staphylococcus aureus Strains

Staphylococcus aureus is a microorganism with high morbidity and mortality due to antibiotic-resistant strains, making the search for new therapeutic options urgent. In this context, computational drug design can facilitate the drug discovery process, optimizing time and resources. In this work, computational methods involving ligand- and structure-based virtual screening were employed to identify potential antibacterial agents against the S. aureus MRSA and VRSA strains. To achieve this goal, tetrahydroxybenzofuran, a promising antibacterial agent according to in vitro tests described in the literature, was adopted as the pivotal molecule and derivative molecules were considered to generate a pharmacophore model, which was used to perform virtual screening on the Pharmit platform. Through this result, twenty-four molecules were selected from the MolPort® database. Using the Tanimoto Index on the BindingDB web server, it was possible to select eighteen molecules with greater structural similarity in relation to commercial antibiotics (methicillin and oxacillin). Predictions of toxicological and pharmacokinetic properties (ADME/Tox) using the eighteen most similar molecules, showed that only three exhibited desired properties (LB255, LB320 and LB415). In the molecular docking study, the promising molecules LB255, LB320 and LB415 showed significant values in both molecular targets. LB320 presented better binding affinity to MRSA (-8.18 kcal/mol) and VRSA (-8.01 kcal/mol) targets. Through PASS web server, the three molecules, specially LB320, showed potential for antibacterial activity. Synthetic accessibility (SA) analysis performed on AMBIT and SwissADME web servers showed that LB255 and LB415 can be considered difficult to synthesize and LB320 is considered easy. In conclusion, the results suggest that these ligands, particularly LB320, may bind strongly to the studied targets and may have appropriate ADME/Tox properties in experimental studies.

Screening of potential antiplasmodial agents targeting cysteine protease-Falcipain 2: a computational pipeline

The spread of antimalarial drug resistance is a substantial challenge in achieving global malaria elimination. Consequently, the identification of novel therapeutic candidates is a global health priority. Malaria parasite necessitates hemoglobin degradation for its survival, which is mediated by Falcipain 2 (FP2), a promising antimalarial target. In particular, FP2 is a key enzyme in the erythrocytic stage of the parasite's life cycle. Here, we report the screening of approved drugs listed in DrugBank using a computational pipeline that includes drug-likeness, toxicity assessments, oral toxicity evaluation, oral bioavailability, docking analysis, maximum common substructure (MCS) and molecular dynamics (MD) Simulations analysis to identify capable FP2 inhibitors, which are hence potential antiplasmodial agents. A total of 45 drugs were identified, which have positive drug-likeness, no toxic features and good bioavailability. Among these, six drugs showed good binding affinity towards FP2 compared to E64, an epoxide known to inhibit FP2. Notably, two of them, Cefalotin and Cefoxitin, shared the highest MCS with E64, which suggests that they possess similar biological activity as E64. In an investigation using MD for 100 ns, Cefalotin and Cefoxitin showed adequate protein compactness as well as satisfactory complex stability. Overall, these computational approach findings can be applied for designing and developing specific inhibitors or new antimalarial agents for the treatment of malaria infections.Communicated by Ramaswamy H. Sarma.

Molecular Docking and Molecular Dynamics Simulations in Related to Leishmania donovani: An Update and Literature Review

Leishmaniasis, a disease caused by Leishmania parasites and transmitted via sandflies, presents in two main forms: cutaneous and visceral, the latter being more severe. With 0.7 to 1 million new cases each year, primarily in Brazil, diagnosing remains challenging due to diverse disease manifestations. Traditionally, the identification of Leishmania species is inferred from clinical and epidemiological data. Advances in disease management depend on technological progress and the improvement of parasite identification programs. Current treatments, despite the high incidence, show limited efficacy due to factors like cost, toxicity, and lengthy regimens causing poor adherence and resistance development. Diagnostic techniques have improved but a significant gap remains between scientific progress and application in endemic areas. Complete genomic sequence knowledge of Leishmania allows for the identification of therapeutic targets. With the aid of computational tools, testing, searching, and detecting affinity in molecular docking are optimized, and strategies that assess advantages among different options are developed. The review focuses on the use of molecular docking and molecular dynamics (MD) simulation for drug development. It also discusses the limitations and advancements of current treatments, emphasizing the importance of new techniques in improving disease management.

Omics data analysis reveals common molecular basis of small cell lung cancer and COVID-19

The impact of COVID-19 infection on individuals with small cell lung cancer (SCLC) poses a serious threat. Unfortunately, the molecular basis of this severe comorbidity has yet to be elucidated. The present study addresses this gap utilizing publicly available omics data of COVID-19 and SCLC to explore the key molecules and associated pathways involved in the convergence of these diseases. Findings revealed 402 genes, that exhibited differential expression patterns in SCLC patients and also play a pivotal role in COVID-19 pathogenesis. Subsequent functional enrichment analyses identified relevant ontologies and pathways that are significantly associated with these genes, revealing important insights into their potential biological, molecular and cellular functions. The protein-protein interaction network, constructed under four combinatorial topological assessments, highlighted SMAD3, CAV1, PIK3R1, and FN1 as the primary components to this comorbidity. Our results suggest that these components significantly regulate this cross-talk triggering the PI3K-AKT and TGF-β signaling pathways. Lastly, this study made a multi-step computational attempt and identified corylifol A and ginkgetin from natural sources that can potentially inhibit these components. Therefore, the outcomes of this study offer novel perspectives on the common molecular mechanisms underlying SCLC and COVID-19 and present future opportunities for drug development.Communicated by Ramaswamy H. Sarma.

Interactions of the male contraceptive target EPPIN with semenogelin-1 and small organic ligands

Novel male contraceptives will promote gender equality in sharing contraceptive responsibility. The sperm-associated protein epididymal protease inhibitor (EPPIN) is a promising target for non-hormonal male contraception. EPPIN interacts with the semen coagulum protein semenogelin-1 (SEMG1) on the sperm surface, leading to transient inhibition of sperm motility after ejaculation. Small organic molecules targeting EPPIN's SEMG1-binding are under development as male contraceptives. Here, we combined computational approaches to uncover key aspects underlying EPPIN binding to SEMG1 and small organic ligands. We generated a human EPPIN model showing a typical arrangement of the WFDC (Whey-acid four disulfide core)-type and Kunitz-type domains, connected by a hinge region. Determining the EPPIN model's intrinsic motion by molecular dynamics simulations and normal mode analysis revealed a conformation, presenting a binding pocket that accommodates SEMG1Glu229-Gln247, EP055, and EP012. EPPIN's residues Phe63 and Lys68 (WFDC domain), Asp71 (hinge region), and Asn113, Asn114, and Asn115 (Kunitz domain) were identified as hot spots for SEMG1, EP055, and EP012 binding. Moreover, hydrophobic and hydrophilic residues in the WFDC and Kunitz domains allow plasma membrane anchoring, orienting the EPPIN binding pocket to the solvent. Targeting EPPIN's essential residues for its biomolecular interactions may improve the rational design of EPPIN ligands as spermiostatic compounds.

Immunoinformatics applications in the development of therapeutic vaccines against human papillomavirus-related infections and cervical cancer

The human papillomavirus (HPV) represents the most prevalent sexually transmitted infectious agent worldwide. HPV penetrates the epithelium through microlesions and establishes an infectious focus that can lead to the development of cervical cancer. Prophylactic HPV vaccines are available, but do not affect already-established infections. Using in silico prediction tools is a promising strategy for identifying and selecting vaccine candidate T cell epitopes. An advantage of this strategy is that epitopes can be selected according to the degree of conservation within a group of antigenic proteins. This makes achieving comprehensive genotypic coverage possible with a small set of epitopes. Therefore, this paper revises the general characteristics of HPV biology and the current knowledge on developing therapeutic peptide vaccines against HPV-related infections and cervical cancer.

Effect of preprocessing and simulation parameters on the performance of molecular docking studies

CONTEXT

Molecular docking is an important and rapid tool that provides a comprehensive view of different molecular mechanisms. It is often used to verify the binding interactions of many pairs of molecules and is much faster than more rigorous approaches. However, its application requires carefully preprocessing each molecule and selecting a series of simulation parameters, which is not always done correctly. We show how preprocessing and simulation parameters can positively or negatively impact molecular docking performance. For example, the inclusion of hydrogen atoms leads to better redocking scores, but molecular dynamics simulations must be performed under certain constraints; otherwise, it may worsen performance rather than improve it. This study clarifies the importance and influence of these different parameters in the simulation results.

METHODS

We analyzed the influence of different parameters on the predictive ability of molecular docking techniques using two software packages: AutoDock Vina and AutoDock-GPU. Thus, 90 receptor-ligand complexes were redocked, evaluating the root mean square deviation (RMSD) between the original position of the ligand (receptor-ligand complex obtained experimentally) and that obtained by the software for every analysis. We investigated the influence of hydrogen atoms (on the receptor and on the receptor-ligand complex), partial charges (QEq, QTPIE, EEM, EEM2015ha, MMFF94, Gasteiger-Marsili, and no charge), search boxes (size and exhaustiveness), ligand characteristics (size and number of torsions), and the use of molecular dynamics (of the receptor or the receptor-ligand complex) before docking analyses.

Protein structural insights into a rare PCSK9 gain-of-function variant (R496W) causing familial hypercholesterolemia in a Saudi family: whole exome sequencing and computational analysis

Familial hypercholesterolemia (FH) is a globally underdiagnosed genetic condition associated with premature cardiovascular death. The genetic etiology data on Arab FH patients is scarce. Therefore, this study aimed to identify the genetic basis of FH in a Saudi family using whole exome sequencing (WES) and multidimensional bioinformatic analysis. Our WES findings revealed a rare heterozygous gain-of-function variant (R496W) in the exon 9 of the PCSK9 gene as a causal factor for FH in this family. This variant was absent in healthy relatives of the proband and 200 healthy normolipidemic controls from Saudi Arabia. Furthermore, this variant has not been previously reported in various regional and global population genomic variant databases. Interestingly, this variant is classified as "likely pathogenic" (PP5) based on the variant interpretation guidelines of the American College of Medical Genetics (ACMG). Computational functional characterization suggested that this variant could destabilize the native PCSK9 protein and alter its secondary and tertiary structural features. In addition, this variant was predicted to negatively influence its ligand-binding ability with LDLR and Alirocumab antibody molecules. This rare PCSK9 (R496W) variant is likely to expand our understanding of the genetic basis of FH in Saudi Arabia. This study also provides computational structural insights into the genotype-protein phenotype relationship of PCSK9 pathogenic variants and contributes to the development of personalized medicine for FH patients in the future.

Machine Learning Advances in Predicting Peptide/Protein-Protein Interactions Based on Sequence Information for Lead Peptides Discovery

Peptides have shown increasing advantages and significant clinical value in drug discovery and development. With the development of high-throughput technologies and artificial intelligence (AI), machine learning (ML) methods for discovering new lead peptides have been expanded and incorporated into rational drug design. Predictions of peptide-protein interactions (PepPIs) and protein-protein interactions (PPIs) are both opportunities and challenges in computational biology, which will help to better understand the mechanisms of disease and provide the impetus for the discovery of lead peptides. This paper comprehensively reviews computational models for PepPI and PPI predictions. It begins with an introduction of various databases of peptide ligands and target proteins. Then it discusses data formats and feature representations for proteins and peptides. Furthermore, classical ML methods and emerging deep learning (DL) methods that can be used to train prediction models of PepPI and PPI are classified into four categories, and their advantages and disadvantages are analyzed. To assess the relative performance of different models, different validation protocols and evaluation indexes are discussed. The goal of this review is to help researchers quickly get started to develop computational frameworks using these integrated resources and eventually promote the discovery of lead peptides.

Evaluation of therapeutic potentials of selected phytochemicals against Nipah virus, a multi-dimensional in silico study

The current study attempted to evaluate the potential of fifty-three (53) natural compounds as Nipah virus attachment glycoprotein (NiV G) inhibitors through in silico molecular docking study. Pharmacophore alignment of the four (4) selected compounds (Naringin, Mulberrofuran B, Rutin and Quercetin 3-galactoside) through Principal Component Analysis (PCA) revealed that common pharmacophores, namely four H bond acceptors, one H bond donor and two aromatic groups were responsible for the residual interaction with the target protein. Out of these four compounds, Naringin was found to have the highest inhibitory potential ( - 9.19 kcal mol^-1) against the target protein NiV G, when compared to the control drug, Ribavirin ( - 6.95 kcal mol^-1). The molecular dynamic simulation revealed that Naringin could make a stable complex with the target protein in the near-native physiological condition. Finally, MM-PBSA (Molecular Mechanics-Poisson-Boltzmann Solvent-Accessible Surface Area) analysis in agreement with our molecular docking result, showed that Naringin ( - 218.664 kJ mol^-1) could strongly bind with the target protein NiV G than the control drug Ribavirin ( - 83.812 kJ mol^-1).

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03595-y.