Computational approaches for modeling and structural design of biological systems: A comprehensive review

Unveiling the tyrosinase inhibitory potential of phenolics from Centaurium spicatum: Bridging in silico and in vitro perspectives

Phenolics, abundant in plants, constitute a significant portion of phytoconstituents consumed in the human diet. The phytochemical screening of the aerial parts of Centaurium spicatum led to the isolation of five phenolics. The anti-tyrosinase activities of the isolated compounds were assessed through a combination of in vitro experiments and multiple in silico approaches. Docking and molecular dynamics (MD) simulation techniques were utilized to figure out the binding interactions of the isolated phytochemicals with tyrosinase. The findings from molecular docking analysis revealed that the isolated phenolics were able to bind effectively to tyrosinase and potentially inhibit substrate binding, consequently diminishing the catalytic activity of tyrosinase. Among isolated compounds, cichoric acid displayed the lowest binding energy and the highest extent of polar interactions with the target enzyme. Analysis of MD simulation trajectories indicated that equilibrium was reached within 30 ns for all complexes of tyrosinase with the isolated phenolics. Among the five ligands studied, cichoric acid exhibited the lowest interaction energies, rendering its complex with tyrosinase the most stable. Considering these collective findings, cichoric acid emerges as a promising candidate for the design and development of a potential tyrosinase inhibitor. Furthermore, the in vitro anti-tyrosinase activity assay unveiled significant variations among the isolated compounds. Notably, cichoric acid exhibited the most potent inhibitory effect, as evidenced by the lowest IC50 value (7.92 ± 1.32 µg/ml), followed by isorhamnetin and gentiopicrin. In contrast, sinapic acid demonstrated the least inhibitory activity against tyrosinase, with the highest IC50 value. Moreover, cichoric acid exhibited a mixed inhibition mode against the hydrolysis of l-DOPA catalyzed by tyrosinase, with Ki value of 1.64. Remarkably, these experimental findings align well with the outcomes of docking and MD simulations, underscoring the consistency and reliability of our computational predictions with the actual inhibitory potential observed in vitro.

Identification of cholinesterases inhibitors from flavonoids derivatives for possible treatment of Alzheimer's disease: In silico and in vitro approaches

Nowadays, one of the methods to prevent the progress of Alzheimer's disease (AD) is to prescribe compounds that inhibit the acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzymes. Researchers are actively pursuing compounds, particularly of natural origin, that exhibit enhanced efficacy and reduced side effects. The inhibition of AChE and BChE using natural flavonoids represents a promising avenue for regulating AD. This study aims to identify alternative flavonoids capable of modulating AD by down-regulating AChE and BChE activity through a molecular docking approach. Molecular docking analysis identified Ginkgetin and Kolaflavanone as potent inhibitors of AChE and BChE, respectively, among the selected flavonoids. Asn87 and Ala127 involved in the interactions of AChE-Ginkgetin complex through conventional hydrogen bonds. While in the BChE-Kolaflavanone complex, Asn83, Ser79, Gln 47, and Ser287 are involved. In vitro analysis further corroborated the inhibitory potential, with Ginkgetin exhibiting an IC50 of 3.2 mM against AChE, and Kolaflavanone displaying an IC50 of 3.6 mM against BChE. These findings underscore the potential of Ginkgetin and Kolaflavanone as candidate inhibitors for the treatment of AD through the inhibition of AChE and BChE enzymes. Nevertheless, additional in vitro and in vivo studies are imperative to validate the efficacy of these compounds.

Chemical Analysis and Investigation of Antimicrobial and Antibiofilm Activities of Prangos trifida (Apiaceae)

Plants of the genus Prangos are intensively investigated as potential new sources of bioactive isolated products. In this work, the chemical composition of volatile constituents (essential oils and headspace volatiles) and dichloromethane extracts, as well as antimicrobial and antibiofilm activities of essential oils and MFDEs (methanol fractions of dichloromethane extracts) of Prangos trifida from Serbia, were investigated. Volatiles of roots, leaves, stems and fruits, and fatty acids and phytosterols in dichloromethane extracts of roots and fruits were analyzed by GC-FID-MS, whereas coumarins in MFDEs by LC-MS and some isolated coumarins by 1H-NMR. Minimum inhibitory concentrations (MICs) and minimum bactericidal concentrations/minimum fungicidal concentrations (MBCs/MFCs) of essential oils and MFDEs were determined against 13 microorganisms. Antibiofilm activity was assessed against four microorganisms. Additionally, congo red and ergosterol binding assays were conducted to elucidate selected mechanisms of antibiofilm action in the case of Candida albicans. Total of 52 volatile constituents, 16 fatty acids, eight phytosterols and 10 coumarins were identified. Essential oils demonstrated significant activity, surpassing that of commercial food preservatives, against six tested molds from the Aspergillus, Penicillium and Trichoderma genera, as well as against bacteria Staphylococcus aureus and Bacillus cereus. Most of the oils strongly inhibited the formation of biofilms by S. aureus, Listeria monocytogenes and Escherichia coli. MFDEs exhibited noteworthy effects against B. cereus and the tested Aspergillus species, particularly A. niger, and significantly inhibited C. albicans biofilm formation. This inhibition was linked to a marked reduction in exopolysaccharide production, while antifungal mechanisms associated with ergosterol remained unaffected.

Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.