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

Liver Sinusoidal Endothelial Cells in the Regulation of Immune Responses and Fibrosis in Metabolic Dysfunction-Associated Fatty Liver Disease

Liver Sinusoidal Endothelial Cells (LSECs) play a crucial role in maintaining liver homeostasis, regulating immune responses, and fibrosis in liver diseases. This review explores the unique functions of LSECs in liver pathology, particularly their roles in immune tolerance, antigen presentation, and the modulation of hepatic stellate cells (HSCs) during fibrosis. LSECs act as key regulators of immune balance in the liver by preventing excessive immune activation while also filtering antigens and interacting with immune cells, including Kupffer cells and T cells. Metabolic Dysfunction-Associated Fatty Liver Disease(MAFLD) is significant because it can lead to advanced liver dysfunction, such as cirrhosis and liver cancer. The prevalence of Metabolic Associated Steatohepatitis (MASH) is increasing globally, particularly in the United States, and is closely linked to rising rates of obesity and type 2 diabetes. Early diagnosis and intervention are vital to prevent severe outcomes, highlighting the importance of studying LSECs in liver disease. However, during chronic liver diseases, LSECs undergo dysfunction, leading to their capillarization, loss of fenestrations, and promotion of pro-fibrotic signaling pathways such as Transforming growth factor-beta (TGF-β), which subsequently activates HSCs and contributes to the progression of liver fibrosis. The review also discusses the dynamic interaction between LSECs, HSCs, and other hepatic cells during the progression of liver diseases, emphasizing how changes in LSEC phenotype contribute to liver scarring and fibrosis. Furthermore, it highlights the potential of LSECs as therapeutic targets for modulating immune responses and preventing fibrosis in liver diseases. By restoring LSECs' function and targeting pathways associated with their dysfunction, novel therapies could be developed to halt or reverse liver disease progression. The findings of this review reinforce the importance of LSECs in liver pathology and suggest that they hold significant promises as targets for future treatment strategies aimed at addressing chronic liver diseases.

Discovery, design, and engineering of enzymes based on molecular retrobiosynthesis

Biosynthesis-a process utilizing biological systems to synthesize chemical compounds-has emerged as a revolutionary solution to 21st-century challenges due to its environmental sustainability, scalability, and high stereoselectivity and regioselectivity. Recent advancements in artificial intelligence (AI) are accelerating biosynthesis by enabling intelligent design, construction, and optimization of enzymatic reactions and biological systems. We first introduce the molecular retrosynthesis route planning in biochemical pathway design, including single-step retrosynthesis algorithms and AI-based chemical retrosynthesis route design tools. We highlight the advantages and challenges of large language models in addressing the sparsity of chemical data. Furthermore, we review enzyme discovery methods based on sequence and structure alignment techniques. Breakthroughs in AI-based structural prediction methods are expected to significantly improve the accuracy of enzyme discovery. We also summarize methods for de novo enzyme generation for nonnatural or orphan reactions, focusing on AI-based enzyme functional annotation and enzyme discovery techniques based on reaction or small molecule similarity. Turning to enzyme engineering, we discuss strategies to improve enzyme thermostability, solubility, and activity, as well as the applications of AI in these fields. The shift from traditional experiment-driven models to data-driven and computationally driven intelligent models is already underway. Finally, we present potential challenges and provide a perspective on future research directions. We envision expanded applications of biocatalysis in drug development, green chemistry, and complex molecule synthesis.

Alzheimer's Disease: Exploring Pathophysiological Hypotheses and the Role of Machine Learning in Drug Discovery

Alzheimer's disease (AD) is a major neurodegenerative dementia, with its complex pathophysiology challenging current treatments. Recent advancements have shifted the focus from the traditionally dominant amyloid hypothesis toward a multifactorial understanding of the disease. Emerging evidence suggests that while amyloid-beta (Aβ) accumulation is central to AD, it may not be the primary driver but rather part of a broader pathogenic process. Novel hypotheses have been proposed, including the role of tau protein abnormalities, mitochondrial dysfunction, and chronic neuroinflammation. Additionally, the gut-brain axis and epigenetic modifications have gained attention as potential contributors to AD progression. The limitations of existing therapies underscore the need for innovative strategies. This study explores the integration of machine learning (ML) in drug discovery to accelerate the identification of novel targets and drug candidates. ML offers the ability to navigate AD's complexity, enabling rapid analysis of extensive datasets and optimizing clinical trial design. The synergy between these themes presents a promising future for more effective AD treatments.

Prospects for synthetic biology in 21st Century agriculture

Plant synthetic biology has emerged as a transformative field in agriculture, offering innovative solutions to enhance food security, provide resilience to climate change, and transition to sustainable farming practices. By integrating advanced genetic tools, computational modeling, and systems biology, researchers can precisely modify plant genomes to enhance traits such as yield, stress tolerance, and nutrient use efficiency. The ability to design plants with specific characteristics tailored to diverse environmental conditions and agricultural needs holds great potential to address global food security challenges. Here, we highlight recent advancements and applications of plant synthetic biology in agriculture, focusing on key areas such as photosynthetic efficiency, nitrogen fixation, drought tolerance, pathogen resistance, nutrient use efficiency, biofortification, climate resilience, microbiology engineering, synthetic plant genomes, and the integration of artificial intelligence (AI) with synthetic biology. These innovations aim to maximize resource use efficiency, reduce reliance on external inputs, and mitigate environmental impacts associated with conventional agricultural practices. Despite challenges related to regulatory approval and public acceptance, the integration of synthetic biology in agriculture holds immense promise for creating more resilient and sustainable agricultural systems, contributing to global food security and environmental sustainability. Rigorous multi-field testing of these approaches will undoubtedly be required to ensure reproducibility.

DFT, Molecular Docking, ADME, and Cardiotoxicity Studies of Persuasive Thiazoles as Potential Inhibitors of the Main Protease of SARS-CoV-2

This study explores the capability of thiazoles as potent inhibitors of SARS-CoV-2 Mpro. Seventeen thiazoles (1-17) were screened for their linking affinity with the active site of SARS-CoV-2 Mpro and compared with the FDA-recommended antiviral drugs, Remdesivir and Baricitinib. Density Functional Theory (DFT) calculations provided electronic and energetic properties of these ligands, shedding light on their stability and reactivity. Molecular docking analysis revealed that thiazole derivatives exhibited favorable linking affinities with various functional sites of SARS-CoV-2 proteins, including spike receptor-linking zone, nucleocapsid protein N-terminal RNA linking zone, and Mpro. Notably, compounds 3, 10, and 12 displayed the best interaction with 6LZG as compared to FDA-approved antiviral drugs Remdesivir and Baricitinib, while compounds 1, 10, and 8 exhibited strong linking with 6 M3 M and also better than Remdesivir and Baricitinib. Additionally, compounds 3, 1, and 6 showed promising interactions with 6LU7 but only compound 3 performed better than Baricitinib. An ADME (Absorption, Distribution, Metabolism, and Excretion) study provided insights into the pharmacokinetics and drug-likeness of these compounds, with all ligands demonstrating good physicochemical characteristics, lipophilicity, water solubility, pharmacokinetics, drug-likeness, and medicinal chemistry attributes. The results suggest that these selected thiazole derivatives hold promise as potential candidates for further drug development.

Identifying key genes against rutin on human colorectal cancer cells via ROS pathway by integrated bioinformatic analysis and experimental validation

Colorectal cancer (CRC) poses a significant global health challenge, characterized by substantial prevalence variations across regions. This study delves into the therapeutic potential of rutin, a polyphenol abundant in fruits, for treating CRC. The primary objectives encompass identifying molecular targets and pathways influenced by rutin through an integrated approach combining bioinformatic analysis and experimental validation. Employing Gene Set Enrichment Analysis (GSEA), the study focused on identifying potential differentially expressed genes (DEGs) associated with CRC, specifically those involved in regulating reactive oxygen species, metabolic reprogramming, cell cycle regulation, and apoptosis. Utilizing diverse databases such as GEO2R, CTD, and Gene Cards, the investigation revealed a set of 16 targets. A pharmacological network analysis was subsequently conducted using STITCH and Cytoscape, pinpointing six highly upregulated genes within the rutin network, including TP53, PCNA, CDK4, CCNEB1, CDKN1A, and LDHA. Gene Ontology (GO) analysis predicted functional categories, shedding light on rutin's potential impact on antioxidant properties. KEGG pathway analysis enriched crucial pathways like metabolic and ROS signaling pathways, HIF1a, and mTOR signaling. Diagnostic assessments were performed using UALCAN and GEPIA databases, evaluating mRNA expression levels and overall survival for the identified targets. Molecular docking studies confirmed robust binding associations between rutin and biomolecules such as TP53, PCNA, CDK4, CCNEB1, CDKN1A, and LDHA. Experimental validation included inhibiting colorectal cell HT-29 growth and promoting cell growth with NAC through MTT assay. Flow cytometric analysis also observed rutin-induced G1 phase arrest and cell death in HT-29 cells. RT-PCR demonstrated reduced expression levels of target biomolecules in HT-29 cells treated with rutin. This comprehensive study underscores rutin's potential as a promising therapeutic avenue for CRC, combining computational insights with robust experimental evidence to provide a holistic understanding of its efficacy.

Aphthous stomatitis - computational biology suggests external biotic stimulus and immunogenic cell death involved

BACKGROUND

The exact cause of recurrent aphthous stomatitis is still unknown, making it a challenge to develop effective treatments. This study employs computational biology to investigate the molecular basis of recurrent aphthous stomatitis, aiming to identify the nature of the stimuli triggering these ulcers and the type of cell death involved.

METHODS

To understand the molecular underpinnings of recurrent aphthous stomatitis, we used the Génie tool for gene identification, targeting those associated with cell death in recurrent aphthous stomatitis. The ToppGene Suite was employed for functional enrichment analysis. We also used Reactome and InteractiVenn for protein integration and prioritization against a PANoptosis gene list, enabling the construction of a protein-protein interaction network to pinpoint key proteins in recurrent aphthous stomatitis pathogenesis.

RESULTS

The study's computational approach identified 1,375 protein-coding genes linked to recurrent aphthous stomatitis. Critical among these were proteins responsive to bacterial stimuli, especially high mobility group protein B1 (HMGB1), toll-like receptor 2 (TLR2), and toll-like receptor 4 (TLR4). The enrichment analysis suggested an external biotic factor, likely bacterial, as a triggering agent in recurrent aphthous stomatitis. The protein interaction network highlighted the roles of tumor necrosis factor (TNF), NF-kappa-B essential modulator (IKBKG), and tumor necrosis factor receptor superfamily member 1A (TNFRSF1A), indicating an immunogenic cell death mechanism, potentially PANoptosis, in recurrent aphthous stomatitis.

CONCLUSION

The findings propose that bacterial stimuli could trigger recurrent aphthous stomatitis through a PANoptosis-related cell death pathway. This new understanding of recurrent aphthous stomatitis pathogenesis underscores the significance of oral microbiota in the condition. Future experimental validation and therapeutic strategy development based on these findings are necessary.

Recent progress in artificial intelligence and machine learning for novel diabetes mellitus medications development

Diabetes mellitus, stemming from either insulin resistance or inadequate insulin secretion, represents a complex ailment that results in prolonged hyperglycemia and severe complications. Patients endure severe ramifications such as kidney disease, vision impairment, cardiovascular disorders, and susceptibility to infections, leading to significant physical suffering and imposing substantial socio-economic burdens. This condition has evolved into an increasingly severe health crisis. There is an urgent need to develop new treatments with improved efficacy and fewer adverse effects to meet clinical demands. However, novel drug development is costly, time-consuming, and often associated with side effects and suboptimal efficacy, making it a major challenge. Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized drug development across its comprehensive lifecycle, spanning drug discovery, preclinical studies, clinical trials, and post-market surveillance. These technologies have significantly accelerated the identification of promising therapeutic candidates, optimized trial designs, and enhanced post-approval safety monitoring. Recent advances in AI, including data augmentation, interpretable AI, and integration of AI with traditional experimental methods, offer promising strategies for overcoming the challenges inherent in AI-based drug discovery. Despite these advancements, there exists a notable gap in comprehensive reviews detailing AI and ML applications throughout the entirety of developing medications for diabetes mellitus. This review aims to fill this gap by evaluating the impact and potential of AI and ML technologies at various stages of diabetes mellitus drug development. It does that by synthesizing current research findings and technological advances so as to effectively control diabetes mellitus and mitigate its far-reaching social and economic impacts. The integration of AI and ML promises to revolutionize diabetes mellitus treatment strategies, offering hope for improved patient outcomes and reduced healthcare burdens worldwide.

Quantitative structure-activity relationship and ADME prediction studies on series of spirooxindoles derivatives for anti-cancer activity against colon cancer cell line HCT-116

Forty-one derivatives of spirooxindoles, active against HCT-116 colon cancer cells, underwent pharmacophore-based 3D-QSAR analysis to understand their correlation with anti-cancer activity. The study identified a seven-point pharmacophore model (ADHHRRR1) and QSAR models, offering insights for lead optimization and novel analogue design, thus advancing anti-cancer drug discovery. This research underscores the value of molecular modeling in elucidating structure-activity relationships and enhancing drug development efforts.

Genome-wide expression analysis in a Fabry disease human podocyte cell line

Fabry disease (FD) is an X-linked lysosomal disease caused by an enzyme deficiency of alpha-galactosidase A (α-gal A). This deficiency leads to the accumulation of glycosphingolipids in lysosomes, resulting in a range of clinical symptoms. The complex pathogenesis of FD involves lysosomal dysfunction, altered autophagy, and mitochondrial abnormalities. Omics sciences, particularly transcriptomic analysis, comprehensively understand molecular mechanisms underlying diseases. This study focuses on genome-wide expression analysis in an FD human podocyte model to gain insights into the underlying mechanisms of podocyte dysfunction. Human control and GLA-edited podocytes were used. Gene expression data was generated using RNA-seq analysis, and differentially expressed genes were identified using DESeq2. Principal component analysis and Spearman correlation have explored gene expression trends. Functional enrichment and Reporter metabolite analyses were conducted to identify significantly affected metabolites and metabolic pathways. Differential expression analysis revealed 247 genes with altered expression levels in GLA-edited podocytes compared to control podocytes. Among these genes, 136 were underexpressed, and 111 were overexpressed in GLA-edited cells. Functional analysis of differentially expressed genes showed their involvement in various pathways related to oxidative stress, inflammation, fatty acid metabolism, collagen and extracellular matrix homeostasis, kidney injury, apoptosis, autophagy, and cellular stress response. The study provides insights into molecular mechanisms underlying Fabry podocyte dysfunction. Integrating transcriptomics data with genome-scale metabolic modeling further unveiled metabolic alterations in GLA-edited podocytes. This comprehensive approach contributes to a better understanding of Fabry disease and may lead to identifying new biomarkers and therapeutic targets for this rare lysosomal disorder.

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.