Search Results (302)

Objectives: Thiocarbohydrazones represent an important class of Schiff base derivatives with versatile chemical and biological properties. Methods: Herein, we present a combined in silico spectroscopic and molecular docking investigation of N′-benzylidenehydrazinecarbothiohydrazide (1). Results: Conformational docking studies were conducted against cathepsin B, acetylcholinesterase, HER2, protein kinase C, and protein kinase A. The compound displayed favorable binding affinities and key interactions within the catalytic sites of all targets, with the strongest predicted binding observed for acetylcholinesterase. Notably, all conformers exhibited higher affinity for protein kinase C than the reference inhibitor balanol, and hydroxylation led to an approximately 10% enhancement in docking performance. Density functional theory (DFT) calculations were employed to analyze vibrational properties, and IR and Raman spectra were computed to elucidate structural features and conformational behavior. Conclusions: The integrated spectroscopic and docking analyses provide mechanistic insights into ligand–target interactions and support rational drug design. These findings identify thiocarbohydrazone derivatives as promising multi-target candidates for the development of enzyme inhibitors relevant to neurodegenerative, oncological, and inflammatory diseases. Full article

This review examines recent quantum chemical methodologies applied to investigating antibiotic structure and mechanisms of action. The discussion is organised into three sections: (1) enzymatic hydrolysis of the β-lactam ring, (2) interactions of antibiotics with ribosomal subunits and enzyme active sites, and (3) complex formation with metal ions. Each section evaluates how quantum chemical approaches, particularly density functional theory (DFT) and hybrid QM/MM techniques, model molecular processes relevant to antibiotic function, including transition states, electron density analyses, and metal coordination effects on antibacterial activity. Selected studies demonstrate the utility of these methodologies in interpreting experimental data and predicting physicochemical and biological properties of novel compounds. Distinct from previous literature, this review provides a comparative and up-to-date synthesis of quantum chemical methods related to enzymatic mechanisms and metal-based antibiotic systems, emphasising experimental validation strategies and practical guidelines for method selection in antibiotic research. It also identifies areas where quantum chemical modelling can integrate with experimental pharmacology and structural biology to support the rational design of next-generation antimicrobial agents. The review concludes by advocating an interdisciplinary framework combining quantum chemistry, biochemistry, and pharmacology to address antibiotic resistance. The review focuses primarily on antibiotics targeting bacterial cell wall and protein synthesis, particularly β-lactam antibiotics, ribosome-targeting agents, and their interactions with metal ions. Computational methods discussed are mainly limited to DFT, ab initio, and hybrid QM/MM approaches. It does not cover membrane-disrupting antibiotics, antiviral or antifungal agents, machine learning-based prediction methods, or purely molecular dynamics approaches outside a quantum mechanical context. Full article

Pyrazole derivatives have attracted considerable interest in medicinal chemistry as adaptable frameworks for developing cholinesterase inhibitors, owing to their advantageous physicochemical properties and structural flexibility. The heteroaromatic characteristics of the pyrazole core allow for various substitution patterns, promoting selective interactions with both the catalytically active site (CAS) and the peripheral anionic site (PAS) of cholinesterase enzymes. These attributes enable pyrazole-based drugs to be viable candidates for the therapy of cognitive disorders, especially Alzheimer’s disease. This study aims to systematically describe medicinal chemistry studies on pyrazole-based cholinesterase inhibitors conducted from 2020 to 2025. The focus is on structural alterations of the pyrazole core and their impact on the inhibitory action against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) using structure–activity relationship (SAR) analysis. Recent advancements in in vitro enzymatic inhibition studies, molecular docking, kinetic analysis, ADME predictions, and multi-target-directed ligand (MTDL) techniques are rigorously evaluated to elucidate trends in potency, selectivity, and drug-like characteristics based on information retrieved from three search engines: Scopus, PubMed, and Google Scholar. This review addresses significant challenges in pharmacokinetics, blood–brain barrier permeability, and safety while delineating prospects for integrating rational design, computational modeling, and biological validation to expedite the development of clinically relevant pyrazole-based cholinesterase inhibitors for Alzheimer’s disease. Full article

Animal venoms represent an extraordinary, yet largely untapped, biochemical reservoir for oncological drug discovery. This review provides a comprehensive analysis of venom-derived enzyme inhibitors as emerging anticancer agents, emphasizing their chemical diversity, structure–activity relationships (SAR), molecular targets, and mechanistic pathways. Venom-derived peptides and proteins exhibit exceptional binding affinity and structural rigidity, characteristics frequently enforced by conserved disulfide networks. This specific architecture allows them to selectively modulate critical cancer-associated enzymes, including matrix metalloproteinases, phospholipases A₂, serine proteases, and kinases. Inhibiting these highly specific targets successfully disrupts tumour angiogenesis, extracellular matrix remodelling, and metastatic dissemination, while simultaneously inducing apoptosis through unique pathways such as reactive oxygen species generation. Modern computational approaches, encompassing deep learning algorithms, molecular docking, and molecular dynamics simulations, are substantially accelerating and transforming the discovery pipeline by rapidly mapping intricate peptide–receptor interactions and guiding rational drug design. Translating these potent molecules into clinical therapeutics remains heavily challenged by pharmacokinetic instability, rapid proteolytic degradation, and systemic toxicity. The integration of computationally optimized scaffolds with advanced targeted delivery platforms, such as nanocarriers and liposomal encapsulation, offers a highly viable strategy to overcome these barriers, ultimately paving the way for next-generation, venom-inspired cancer therapies. Full article

The combination of computational and experimental methods has become indispensable for optimization and rational enzyme design. Recently, the development of artificial intelligence (AI)-based tools has further streamlined enzyme engineering pipelines, enabling more accurate designs, while reducing the number of variants required for experimental validation. However, due to the intricate complexity of enzymatic systems, significant challenges must be addressed before we take the next step to fully optimize the use of these AI-guided enzyme design methodologies. These challenges include un-curated datasets, the need to consider both the static and dynamic structure of enzymes, and the requirement for effective interdisciplinary collaborations to ensure the integration of computational and experimental approaches. Here, we present recent advances in AI-based computational enzyme design, discussing the main challenges in the field and how a combination with classical physics-based methods could help overcome them. We further explore novel trends that could completely modulate the future of protein design and provide our outlook on the key concepts and future opportunities that will shape the next steps of enzyme design. Full article

Seasonal epidemics and high variability of influenza viruses pose a severe threat to global public health security. Neuraminidase, a key functional enzyme in the life cycle of influenza viruses, represents an important target for anti-influenza drug development. Given the continuous emergence of drug-resistant strains against first-line clinical neuraminidase inhibitors (NAIs) such as oseltamivir, there is an urgent need to develop novel, broad-spectrum, and resistance-overcoming NAIs. Natural products, characterized by structural diversity and a wide range of biological activities, provide abundant resources for the discovery of new NAIs. Recent advances in computer-aided drug design, intelligent analytical platforms, and modern screening technologies have accelerated the identification of natural product-derived NAIs. In particular, biosensor-based strategies, including electrochemical, fluorescence, bioluminescence, and surface-enhanced Raman scattering biosensors, have demonstrated significant advantages in sensitivity, selectivity, rapid response, and high-throughput screening. In combination with computational methods and experimental approaches such as affinity ultrafiltration and activity-guided separation, these technologies have promoted the development of intelligent, precise, and multimodal screening platforms. Looking forward, the integration of biosensor-based high-throughput screening platforms with artificial intelligence algorithms is expected to drive the next generation of natural product screening platforms and facilitate the efficient discovery and clinical translation of novel NAIs. This paper systematically reviews the research progress of screening strategies for natural product-derived NAIs; introduces representative natural active NAIs, including phenols, terpenoids, and alkaloids; and prospects future development directions, aiming to provide a scientific reference for the efficient discovery of NAIs from natural products. Full article

Monoacylglycerol lipase (MAGL) is a key enzyme at the interface between the endocannabinoid system and lipid metabolism, playing a pivotal role in the hydrolysis of the endocannabinoid 2-arachidonoylglycerol and in the regulation of lipid mediators involved in inflammation, pain, neurodegeneration and cancer. Owing to its therapeutic relevance, MAGL has emerged as an attractive pharmacological target, stimulating extensive research efforts aimed at the development of potent and selective modulators of its activity. Advances in medicinal chemistry, together with the increasing application of innovative computational approaches and biochemical methods to assess MAGL activity, have significantly expanded the chemical space of compounds capable of modulating this enzyme. This review provides a comprehensive overview of selective MAGL modulators reported in the scientific literature from 2020 to the present, excluding compounds described exclusively in patent literature and MAGL probes, as this area has been recently reviewed elsewhere, ranging from classical enzyme inhibitors to modulators acting through alternative strategies, such as targeted protein degradation. Overall, this review highlights the structural diversity and the main strategies that have emerged in recent years in modulating MAGL and it aims to guide the rational design of next-generation MAGL-targeting agents. Full article

Histone deacetylases (HDACs) are important epigenetic regulatory enzymes contributing to cancer proliferation, which could be critical targets in cancer therapy. The structural similarities of the existing HDAC inhibitors have resulted in an increase in the drug resistance. In this study, coumarin was employed as the core scaffold for structural derivatisation to develop a novel class of HDAC inhibitors based on computer-aided design (CADD). Their anti-tumor activity was evaluated against esophageal squamous cell lines. The results showed that most compounds exhibited potent anti-proliferative activity against KYSE70 and KYSE150. Among them, compound 4s and 4p exhibited the most potent activity with IC₅₀ values of 3.44 μM and 3.39 μM against KYSE70. To validate the target of the synthesized compounds, transcriptome sequencing was performed and the results revealed that a total of 487 genes were differentially expressed, including 190 up-regulated and 297 down-regulated genes. Among these, 79 genes were associated with the HDAC regulatory network, accounting for 16.2% of the differentially expressed genes. Molecular docking demonstrated that compound 4s could effectively enter the active site of HDAC, engaging with the cap group, zinc-binding group, and linker region. This multiple interaction network provides a structural basis for the potent inhibitory activity of compound 4s. In conclusion, a series of novel HDAC inhibitors with a coumarin scaffold were discovered, and their mode of action was revealed. This provides a valuable guide for the development of novel HDAC-targeting therapeutics. Full article

Isofenphos-methyl (IFP), a highly toxic and persistent organophosphate pesticide (OP), is widely used for soil pest control in crops but poses severe risks to ecological safety and human health because of its environmental accumulation and bioaccumulation. Herein, a sensitive and specific indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) was developed for rapid IFP detection in vegetables. A novel IFP hapten was rationally designed and synthesized via computer-aided molecular simulation, and its structure was validated by liquid chromatography–tandem mass spectrometry (LC–MS/MS) and nuclear magnetic resonance (NMR). High-specificity anti-IFP monoclonal antibodies (mAbs) with strong anti-matrix interference were prepared for the first time using a matrix effect-enhanced screening strategy. The optimized ic-ELISA showed high sensitivity, with an IC₅₀ of 6.087 ng/mL and a detection range of 1.165–30.490 ng/mL, and no cross-reactivity with other common OPs. Spiked recovery experiments in celery and chili pepper matrices yielded recoveries of 81.87–97.95% (RSD < 5.44%), with highly consistent LC–MS/MS results. The method exhibited a weak positive matrix effect in vegetable matrices, eliminating complex pretreatment and enabling rapid onsite detection. Full article

The escalating crisis of herbicide-resistant weeds threatens global wheat production. While key mutations are well-documented in weeds, the principles governing their interactions in wheat remain largely unknown. Here, we first developed a novel wheat germplasm carrying the acetolactate synthase (TaALS) Ser-627-Asn (S627N) mutation via ethyl methanesulfonate (EMS) mutagenesis. We then employed a computational design strategy to explore its synergy with the prevalent Trp-548-Leu (W548L) mutation—a combination not yet reported in nature. Integrated molecular dynamics (MD) simulations and free energy landscape analysis revealed that the in silico W548L/S627N double mutant triggers synergistic global destabilization of the herbicide–enzyme complex. Binding affinity progressively weakened from wild-type (−25.54 ± 2.05 kcal/mol) to the double mutant (−18.13 ± 2.76 kcal/mol), driven by a polarity inversion at the Arg-347 anchor. Comparative transcriptomic profiling of the S627N germplasm confirmed the absence of deleterious metabolic feedback in the branched-chain amino acid biosynthesis pathway. This work exemplifies a paradigm shift from mimicking natural variation to predictive crop design via multiplex gene editing. Full article

Biocatalysis has emerged as a mainstay in the field of sustainable chemical synthesis owing to its high selectivity, mild reaction conditions, and reduced environmental impact. Traditional enzyme engineering approaches, such as rational design and directed evolution, are often associated with limited throughput and a limited understanding of sequence–structure–function relationships, despite high experimental costs. In recent years, the integration of machine learning (ML) into enzyme engineering has emerged as a transformative approach, enabling data-driven prediction, design, and optimization of biocatalysts, thereby enhancing performance and applications. This review provides a comprehensive overview of ML-guided strategies to improve key enzymatic parameters, including the turnover number (k_cat), substrate affinity (K_m), and catalytic efficiency (k_cat/K_m), with a focus on mechanistic insights and performance outcomes. The integration of ML models into design–build–test–learn (DBTL) cycles accelerated directed evolution, reduced screening efforts, and enabled targeted mutagenesis. Beyond applications, this review also discusses the current limitations of ML-guided approaches, including data scarcity, model interpretability, and challenges in predicting complex mutations and allosteric effects. The gap between computational predictions and experimental outcomes is identified, and the role of ML integration with enzyme kinetics, molecular dynamics, and high-throughput experimentation is emphasized. Future directions, such as generative AI, explainable ML, and autonomous laboratories, are discussed for next-generation biocatalytic applications. Full article

The efficacy of enzyme therapy is limited by their poor stability under physiological conditions. Thermostable enzymes, derived from extremophilic organisms or generated by advanced protein engineering, offer a revolutionary solution to this long-standing challenge. They are widely used in industrial biocatalysis. Their therapeutic applications are poorly investigated and spread across diverse disciplines. While most applications are in the preclinical stages, emerging evidence from animal models demonstrates proof-of-concept for thermostable antioxidant enzymes in cardiovascular, neurodegenerative, and inflammatory diseases. This review critically assesses the translational landscape, distinguishing between established therapeutic enzymes (e.g., asparaginase, PEGylated SOD) and emerging experimental candidates. This narrative review consolidates existing knowledge about thermostable enzyme engineering and their emerging functions as molecular therapies, particularly in oxidative stress-related diseases. This review synthesizes recent advances in structural biology, computational protein design, biomaterials engineering, and translational antioxidant strategies, highlighting how breaking down disciplinary barriers is accelerating the development of sustainable and self-regenerating antioxidant platforms. By integrating molecular precision with systems-level therapeutic design, engineered thermostable antioxidant enzymes exemplify the future of biological development, where multidisciplinary collaboration drives innovation against oxidative stress-driven pathologies. Engineered thermostable enzymes provide a versatile basis for next-generation therapeutics, with the potential to address medical needs through improved stability, targeted activity, and multifunctional design. Full article

Diabetes mellitus remains a major metabolic disorder requiring the development of new and effective α-glucosidase inhibitors. The present study aimed to identify, design, and optimize novel 3-amino-2,4-diarylbenzo[4,5]imidazo[1,2-α]pyrimidine derivatives with promising inhibitory activity against the α-glucosidase enzyme using a comprehensive in silico strategy. Approximately 300 molecular descriptors were calculated to characterize a dataset of 32 compounds (Peytam et al.) and to investigate the structural factors governing their biological activity. Based on these descriptors, a multiple linear regression model was developed to predict the inhibitory activities of the compounds against alpha-glucosidase. The developed model demonstrated satisfactory predictive performance and was internally and externally validated to ensure its accuracy, robustness, and reproducibility. In addition, the applicability domain analysis confirmed the reliability of the predictions. Using the validated QSAR model, seven new derivatives were designed with predicted pIC₅₀ values exceeding the maximum activity of the parent compounds. The leverage analysis demonstrated that all newly designed compounds were located within the applicability domain of the model, supporting the reliability of the predictions. To further evaluate their inhibitory potential, molecular docking studies were performed to investigate the interactions between the designed compounds and the α-glucosidase active site. The docking results revealed favorable binding interactions comparable to those reported for known α-glucosidase inhibitors. Furthermore, ADMET analysis indicated generally favorable pharmacokinetic properties, although potential CYP3A4 inhibition-related pharmacokinetic risks were identified and discussed. Molecular dynamics simulations, including replicated runs and MM/GBSA binding free energy calculations, confirmed the stability of the most promising protein–ligand complexes throughout the simulation period. In conclusion, this study proposes a robust and integrated computational workflow combining descriptor generation, QSAR modeling, applicability domain analysis, molecular docking, ADMET prediction, and molecular dynamics simulations for the rational design of potential α-glucosidase inhibitors. The findings highlight the therapeutic potential of the designed derivatives and provide a valuable in silico framework for the future development of antidiabetic agents. Full article

Consolidated bioprocessing (CBP) is difficult to monitor because enzyme production, lignocellulose degradation, sugar release, and fermentation occur simultaneously under sparse measurement, feedstock variability, and plant–model mismatch conditions. This study proposes a computational sensor-set design framework for digital-twin-assisted CBP monitoring. A five-state virtual plant, consisting of active biomass, cellulolytic enzyme activity, residual insoluble substrate, soluble sugar, and ethanol, was used to evaluate all 16 ethanol-mandatory measurement packages formed from ethanol, sugar, biomass, enzyme, and residual-substrate proxy channels. Candidate sensor sets were assessed using finite-difference output sensitivities, Fisher-information-based state-observability and parameter-identifiability analyses, eigenvalue and parameter-correlation diagnostics, and paired Monte Carlo unscented Kalman filter soft-sensing reconstruction. Within the tested five-state virtual-plant benchmark and with the specified excitation schedule, noise assumptions, burden indices, and scoring objective, ethanol-only sensing provided the weakest support for state-aware CBP digital-twin reconstruction. At a $6\mathrm{h}$ sampling interval, the state-observability log-pseudodeterminant increased from 4.18 with ethanol-only sensing to 8.56 after adding soluble sugar and to 16.42 with full-proxy monitoring. The ethanol–sugar–biomass–substrate package also gave strong reduced state-observability performance, with log-pseudodeterminants of 15.12, 13.76, and 12.51 at 6, 12, and $24\mathrm{h}$, respectively. Biomass and enzyme proxies contributed strongly to parameter learning, and the ethanol–sugar–biomass–enzyme package gave the strongest active parameter-identifiability performance, with log-pseudodeterminants of 10.82, 9.06, and 6.67 at 6, 12, and $24\mathrm{h}$, respectively. In the paired soft-sensing analysis, full-proxy monitoring reduced the mean latent-state RMSE from 1.1899 to 0.3756, followed by ethanol–biomass–enzyme–substrate with 0.3843 and ethanol–sugar–biomass–substrate with 0.4121. The primary aggregate ranking identified ethanol–sugar–biomass–substrate as the best overall package, with a sensor-value score of 0.8432 and a burden index of 7.0, followed by full-proxy monitoring with a score of 0.8173 and a burden index of 10.0. Robustness tests showed that ethanol–sugar–biomass–substrate remained top-ranked under uniform noise scaling, full UKF missingness, delay and bias stress test conditions, most scoring-weight scenarios, and all tested sensor-specific burden workflows. Full-proxy monitoring remained a close competitor under independent sensor-specific noise variation conditions and became top-ranked for some alternative operating trajectories. The proposed framework provides a simulation-based method for prioritizing informative measurement packages before implementing CBP digital twins in laboratory and pilot-plant settings. Full article

The extraordinary genetic diversity of human immunodeficiency virus type 1 (HIV-1), driven by high mutation and recombination rates, poses significant challenges for diagnostics, therapy, and vaccine development. While variable regions enable immune escape, hyperconserved regions are critical for viral function and represent promising targets for novel therapeutic interventions. This study aimed to develop and validate a bioinformatic algorithm for quantitative assessment of sequence conservation and automated identification of functionally significant conserved regions across all major HIV-1 proteins. A total of 1119 full-length HIV-1 genome sequences representing major subtypes (A1, A2, A6, B, C, D, F1, F2, G, H, J, K) were analyzed. Normalized Shannon entropy (S-index) was calculated for each alignment column. Statistical thresholds for conserved regions were established using 95% confidence intervals derived from bootstrap resampling. Two complementary algorithms, clustering and local maxima detection, were applied to identify conserved regions, which were subsequently mapped to known functional domains based on literature data. Protein conservation varied markedly, with S_m values ranging from 0.784 (Vpu) to 0.920 (Pol). Gag, Pol, and Vpr demonstrated the highest overall conservation, while Env, Rev, Tat, and Vpu exhibited pronounced variability interspersed with conserved domains. In total, 25 conserved regions in Gag, 49 in Pol, 28 in Env, and 6–4 regions in accessory proteins (Vif, Vpr, Rev, Tat, Nef, Vpu) were identified. These regions corresponded to critical functional elements including enzyme catalytic centers, zinc fingers, receptor-binding sites, protein interaction interfaces, and membrane-anchoring domains. The developed computational framework enables statistically grounded identification of evolutionarily constrained regions across analyzed HIV-1 subtypes. The identified conserved regions represent candidate sites for further investigation and may inform downstream studies focused on antiviral target prioritization, immunogen design, and diagnostic assay development. However, their translational applicability requires additional analytical, structural, and experimental validation. Full article