Search Results (6)

Deep-sea mollusks represent untapped resources for searching novel biologically active peptides effectual against many chronic diseases. Here we presented the identification of four novel angiotensin I-converting enzyme (ACE) inhibitory peptides from the deep-sea mollusk Gigantidas platifrons by using a combined approach of peptidomics and virtual screening. Fifteen protein hydrolysates from five deep-sea macroorganisms were prepared using three different proteases and were determined for their ACE inhibitory activities. Pepsin hydrolysate of G. platifrons protein (GPp) demonstrated the highest inhibition rate against ACE at 400 μg/mL. Then, targeted investigation was conducted on the GPp with peptidomic profiling; more than 3000 peptides were de novo identified, which were then subject to virtual screening using the docking software Smina. Subsequently, 29 peptides were selected and synthesized based on the affinity threshold and the interactions with ACE active sites. More than 58% peptides were biologically active, showing more than 50% inhibition to ACE at 400 μM. Four peptides, LAAHFAR, YAAPYR, NGAGPYGRP, and FTTFGK, exhibited low micromolar inhibition. The most potent peptide, LAAHFAR with an IC₅₀ of 6.01 ± 1.06 μM, was subject to molecular dynamics simulations for revealing atomistic interaction analysis. LAAHFAR forms comprehensively stable hydrogen bonds with the classic active site of ACE, and its N terminal arginine residue is anchored by additional hydrogen bonding to Cys370, Asp377, and Thr372. This study highlights deep-sea mollusks as an important source of novel ACE inhibitory peptides, contributing to the development of new therapeutic ingredients or functional food agents against hypertension. Full article

Smart supply chains, enabled by Industry 4.0 technologies, are increasingly recognized as key drivers of competitiveness, leveraging data across the value chain to enhance visibility, responsiveness, and resilience, while supporting better planning, optimized resource utilization, and agile customer service. Effective data management has thus become a strategic capability, fostering operational performance, innovation, and long-term value creation. However, existing research and practice remain fragmented, often focusing on isolated functions such as production, logistics, or quality, the most data-intensive and critical domains in smart manufacturing, without comprehensively addressing data acquisition, storage, integration, analysis, and visualization across all supply chain phases. This article addresses these gaps through a systematic literature review of 55 peer-reviewed studies published between 2020 and 2025, conducted following PRISMA guidelines using Scopus and Web of Science. Contributions are categorized into reviews, frameworks/models, and empirical studies, and the analysis examines how data is collected, integrated, and leveraged across the entire supply chain. By adopting a holistic perspective, this study provides a comprehensive understanding of data management in smart manufacturing supply chains, highlights current practices and persistent challenges, and identifies key avenues for future research. Full article

The primary cause of atherosclerotic cardiovascular disease (ASCVD) is elevated levels of low-density lipoprotein cholesterol (LDL-C). Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays a crucial role in this process by binding to the LDL receptor (LDL-R) domain, leading to reduced influx of LDL-C and decreased LDL-R cell surface presentation on hepatocytes, resulting higher circulating levels of LDL-C. As a consequence, PCSK9 has been identified as a crucial target for drug development against dyslipidemia and hypercholesterolemia, aiming to lower plasma LDL-C levels. This research endeavors to identify promising inhibitory candidates that target the allosteric site of PCSK9 through an in silico approach. To start with, the FDA-approved Drug Library from Selleckchem was selected and virtually screened by docking studies using Glide extra-precision (XP) docking mode and Smina software (Version 1.1.2). Subsequently, rescoring of 100 drug compounds showing good average docking scores were performed using Gnina software (Version 1.0) to generate CNN Score and CNN binding affinity. Among the drug compounds, amikacin, bestatin, and natamycin were found to exhibit higher docking scores and CNN affinities against the PCSK9 enzyme. Molecular dynamics simulations further confirmed that these drug molecules established the stable protein–ligand complexes when compared to the apo structure of PCSK9 and the complex with the co-crystallized ligand structure. Moreover, the MM-GBSA calculations revealed binding free energy values ranging from −84.22 to −76.39 kcal/mol, which were found comparable to those obtained for the co-crystallized ligand structure. In conclusion, these identified drug molecules have the potential to serve as inhibitors PCSK9 enzyme and these finding could pave the way for the development of new PCSK9 inhibitory drugs in future in vitro research. Full article

Leveraging machine learning has been shown to improve the accuracy of structure-based virtual screening. Furthermore, a tremendous amount of empirical data is publicly available, which further enhances the performance of the machine learning approach. In this proof-of-concept study, the 3CL^pro enzyme of SARS-CoV-2 was used. Structure-based virtual screening relies heavily on scoring functions. It is widely accepted that target-specific scoring functions may perform more effectively than universal scoring functions in real-world drug research and development processes. It would be beneficial to drug discovery to develop a method that can effectively build target-specific scoring functions. In the current study, the bindingDB database was used to retrieve experimental data. Smina was utilized to generate protein-ligand complexes for the extraction of InteractionFingerPrint (IFP) and SimpleInteractionFingerPrint SIFP fingerprints via the open drug discovery tool (oddt). The present study found that randomforestClassifier and randomforestRegressor performed well when used with the above fingerprints along the Molecular ACCess System (MACCS), Extended Connectivity Fingerprint (ECFP4), and ECFP6. It was found that the area under the precision-recall curve was 0.80, which is considered a satisfactory level of accuracy. In addition, our enrichment factor analysis indicated that our trained scoring function ranked molecules correctly compared to smina’s generic scoring function. Further molecular dynamics simulations indicated that the top-ranked molecules identified by our developed scoring function were highly stable in the active site, supporting the validity of our developed process. This research may provide a template for developing target-specific scoring functions against specific enzyme targets. Full article

Animal venoms are considered as a promising source of new drugs. Sea anemones release polypeptides that affect electrical activity of neurons of their prey. Voltage dependent sodium (Nav) channels are the common targets of Av1, Av2, and Av3 toxins from Anemonia viridis and CgNa from Condylactis gigantea. The toxins bind to the extracellular side of a channel and slow its fast inactivation, but molecular details of the binding modes are not known. Electrophysiological measurements on Periplaneta americana neuronal preparation revealed differences in potency of these toxins to increase nerve activity. Av1 and CgNa exhibit the strongest effects, while Av2 the weakest effect. Extensive molecular docking using a modern SMINA computer method revealed only partial overlap among the sets of toxins’ and channel’s amino acid residues responsible for the selectivity and binding modes. Docking positions support earlier supposition that the higher neuronal activity observed in electrophysiology should be attributed to hampering the fast inactivation gate by interactions of an anemone toxin with the voltage driven S4 helix from domain IV of cockroach Nav channel (NavPaS). Our modelling provides new data linking activity of toxins with their mode of binding in site 3 of NavPaS channel. Full article

Antioxidative and anti-inflammatory activities of Ganoderma lucidum (Curt.: Fr.) P. Karst. (Aphyllophoromycetideae) from tropical South India were investigated. The chloroform extract of the mushroom showed marked free radical scavenging activities. The anti-inflammatory activity of the extract at concentrations of 100 and 50 mg/kg was evaluated in carrageenan induced acute and formalin induced chronic inflammatory models in mice. The extract showed remarkable antiinflammatory activity in both models, comparable to the standard reference drug diclofenac. The results suggest that anti-inflammatory activity of the chloroform extract of G. lucidum is possibly attributed to its free radical scavenging properties. This study also reveals the potent therapeutic uses of G. lucidum from South India. Full article