Machine-learning methods for ligand-protein molecular docking

Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities

Molecular docking is a widely used technique in drug discovery to predict the binding mode of a given ligand to its target. However, the identification of the near-native binding pose in docking experiments still represents a challenging task as the scoring functions currently employed by docking programs are parametrized to predict the binding affinity, and, therefore, they often fail to correctly identify the ligand native binding conformation. Selecting the correct binding mode is crucial to obtaining meaningful results and to conveniently optimizing new hit compounds. Deep learning (DL) algorithms have been an area of a growing interest in this sense for their capability to extract the relevant information directly from the protein-ligand structure. Our review aims to present the recent advances regarding the development of DL-based pose selection approaches, discussing limitations and possible future directions. Moreover, a comparison between the performances of some classical scoring functions and DL-based methods concerning their ability to select the correct binding mode is reported. In this regard, two novel DL-based pose selectors developed by us are presented.

Integration of Hydrogen-Deuterium Exchange Mass Spectrometry with Molecular Dynamics Simulations and Ensemble Reweighting Enables High Resolution Protein-Ligand Modeling

Hydrogen-Deuterium exchange mass spectrometry's (HDX-MS) utility in identifying and characterizing protein-small molecule interaction sites has been established. The regions that are seen to be protected from exchange upon ligand binding indicate regions that may be interacting with the ligand, giving a qualitative understanding of the ligand binding pocket. However, quantitatively deriving an accurate high-resolution structure of the protein-ligand complex from the HDX-MS data remains a challenge, often limiting its use in applications such as small molecule drug design. Recent efforts have focused on the development of methods to quantitatively model Hydrogen-Deuterium exchange (HDX) data from computationally modeled structures to garner atomic level insights from peptide-level resolution HDX-MS. One such method, HDX ensemble reweighting (HDXer), employs maximum entropy reweighting of simulated HDX data to experimental HDX-MS to model structural ensembles. In this study, we implement and validate a workflow which quantitatively leverages HDX-MS data to accurately model protein-small molecule ligand interactions. To that end, we employ a strategy combining computational protein-ligand docking, molecular dynamics simulations, HDXer, and dimensional reduction and clustering approaches to extract high-resolution drug binding poses that most accurately conform with HDX-MS data. We apply this workflow to model the interaction of ERK2 and FosA with small molecule compounds and inhibitors they are known to bind. In five out of six of the protein-ligand pairs tested, the HDX derived protein-ligand complexes result in a ligand root-mean-square deviation (RMSD) within 2.5 Å of the known crystal structure ligand.

A comprehensive review of new small molecule drugs approved by the FDA in 2022: Advance and prospect

In 2022, the U.S. Food and Drug Administration approved a total of 16 marketing applications for small molecule drugs, which not only provided dominant scaffolds but also introduced novel mechanisms of action and clinical indications. The successful cases provide valuable information for optimizing efficacy and enhancing pharmacokinetic properties through strategies like macrocyclization, bioequivalent group utilization, prodrug synthesis, and conformation restriction. Therefore, gaining an in-depth understanding of the design principles and strategies underlying these drugs will greatly facilitate the development of new therapeutic agents. This review focuses on the research and development process of these newly approved small molecule drugs including drug design, structural modification, and improvement of pharmacokinetic properties to inspire future research in this field.

Computational approach based on freely accessible tools for antimicrobial drug designR2

Antimicrobial drug development is crucial for public health, especially with the emergence of pandemics and drug resistance that prompts the search for new therapeutic resources. In this context, in silico assays consist of a valuable approach in the rational drug design because they enable a faster and more cost-effective identification of drug candidates compared to in vitro screening. However, once a potential drug is identified, in vitro and in vivo assays are essential to verify the expected activity of the compound and advance it through the subsequent stages of drug development. This work aims to outline an in silico protocol that utilizes only freely available computational tools for identifying new potential antimicrobial agents, which is also suitable in the broad spectrum of drug designR1;R2. Additionally, this paper reviews relevant computational methods in this context and provides a summary of information concerning the protein-ligand interaction.

Bioinformatics and network pharmacology discover the molecular mechanism of Liuwei Dihuang pills in treating cerebral palsy

A collection of chronic central motor, postural, and activity restriction symptoms are referred to as cerebral palsy (CP). Previous research suggests that a number of perinatal variables, including hypoxia, may be linked to CP. And the pathophysiological process that causes brain injury in growing fetuses is mostly caused by amniotic fluid infection and intra-amniotic inflammation. Still, there is still much to learn about the molecular mechanism of CP. The goal of this study was to identify the molecular mechanism of Liuwei Dihuang pill (LWDHP) in the treatment of CP using network pharmacology and bioinformatics. The Chinese medicine database provided the LWDHP components and targets, the CP illness gene data set was gathered from a disease, and the expression profile of children with CP was chosen from anther database. Using the Kyoto Encyclopedia of Genes and Genomes and gene ontology databases, a network of interactions between proteins was created, and functional enrichment analysis was carried out. Analysis of traditional Chinese medicine found that the key active ingredients of LWDHP are quercetin, Stigmasterol and kaempferol. Through enrichment analysis, it was found that the hub genes for LWDHP treatment of CP are CXCL8, MMP9, EGF, PTGS2, SPP1, BCL2L1, MMP1, and AR. K EGG analysis found that LWDHP treatment of CP mainly regulates PI3K-Akt signaling pathway, IL-17 signaling pathway, Jak-STAT signaling pathway, NF-kappa B signaling pathway, etc. To summarize, LWDHP regulates immunological and inflammatory variables through a variety of components, targets, and signaling pathways, which plays a significant role in the development and management of CP.

Recent strategies in target identification of natural products: Exploring applications in chronic inflammation and beyond

Natural products are a treasure trove for drug discovery, especially in the areas of infection, inflammation and cancer, due to their diverse bioactivities and complex, and varied structures. Chronic inflammation is closely related to many diseases, including complex diseases such as cancer and neurodegeneration. Improving target identification for natural products contributes to elucidating their mechanism of action and clinical progress. It also facilitates the discovery of novel druggable targets and the elimination of undesirable ones, thereby significantly enhancing the productivity of drug discovery and development. Moreover, the rise of polypharmacological strategies, considered promising for the treatment of complex diseases, will further increase the demand for target deconvolution. This review underscores strategies for identifying natural product targets (NPs) in the context of chronic inflammation over the past 5 years. These strategies encompass computational methodologies for early target discovery and the anticipation of compound binding sites, proteomics-driven approaches for target delineation and experimental biology techniques for target validation and comprehensive mechanistic exploration.

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.

Bioinformatics analysis based on extracted ingredients combined with network pharmacology, molecular docking and molecular dynamics simulation to explore the mechanism of Jinbei oral liquid in the therapy of idiopathic pulmonary fibrosis

Objective

Jinbei oral liquid (JBOL), which is derived from a traditional hospital preparation, is frequently utilized to treat idiopathic pulmonary fibrosis (IPF) and has shown efficacy in clinical therapy. However, there are now several obstacles facing the mechanism inquiry, including target proteins, active components, and the binding affinity between crucial compounds and target proteins. To gain additional insight into the mechanisms underlying JBOL in anti-IPF, this study used bioinformation technologies, including network pharmacology, molecular docking, and molecular dynamic simulation, with a substantial amount of data based on realistic constituents.

Methods

Using network pharmacology, we loaded 118 realistic compounds into the SwissTargetPrediction and SwissADME databases and screened the active compounds and target proteins. IPF-related targets were collected from the OMIM, DisGeNET, and GeneCards databases, and the network of IPF-active constituents was built with Cytoscape 3.10.1. The GO and KEGG pathway enrichment analyses were carried out using Metascape, and the protein-protein interaction (PPI) network was constructed to screen the key targets with the STRING database. Finally, the reciprocal affinity between the active molecules and the crucial targets was assessed through the use of molecular docking and molecular dynamics simulation.

Results

A total of 122 targets and 34 tested active compounds were summarized in this investigation. Among these, kaempferol, apigenin, baicalein were present in high degree. PPI networks topological analysis identified eight key target proteins. AGE-RAGE, EGFR, and PI3K-Akt signaling pathways were found to be regulated during the phases of cell senescence, inflammatory response, autophagy, and immunological response in anti-IPF of JBOL. It was verified by molecular docking and molecular dynamics simulation that the combining way and binding energy between active ingredients and selected targets.

Conclusions

This work forecasts the prospective core ingredients, targets, and signal pathways of JBOL in anti-IPF, which has confirmed the multiple targets and pathways of JBOL in anti-IPF and provided the first comprehensive assessment with bioinformatic approaches. With empirical backing and an innovative approach to the molecular mechanism, JBOL is being considered as a potential new medication.

Exploring the therapeutic potential of Xiangsha Liujunzi Wan in Crohn's disease: from network pharmacology approach to experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

Xiangsha Liujunzi Wan (LJZW) is a traditional Chinese medicine (TCM) formula containing a variety of traditional Chinese herb components. Its principal components are often used in the treatment of gastrointestinal diseases and contribute to the treatment of Crohn's disease (CD).

AIM OF THE STUDY

To explore the therapeutic potential of LJZW in CD through network pharmacology, bioinformatics, molecular docking, and experimental verification.

METHODS

The principal bioactive components and corresponding targets of LJZW were ascertained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Potential targets for CD were identified in GeneCards, OMIM, DrugBank, DisGeNET, CTD, and Gene Expression Omnibus (GEO) databases. Intersection targets of LJZW and CD were identified using a Venn diagram and visualized using Cytoscape 3.8.0 to construct a protein-protein interaction (PPI) network. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed to assess the function of intersection targets. AutoDockTools and PyMOL were used for molecular docking to recognize the association between the core ingredients of LJZW and the core targets of CD. Subsequently, a series of experiments were conducted for validation.

RESULTS

The network pharmacology results indicated that there were 156 bioactive components and 268 corresponding targets for LJZW, 3023 primary relevant targets for CD, and 169 intersection targets for LJZW and CD. The PPI network was employed to identify five hub genes and six clusters. The GO functional analysis indicated that intersection targets are primarily correlated with oxidative stress and inflammatory responses. KEGG pathway analysis revealed that these targets were primarily associated with the phosphotylinosital 3 kinase (PI3K)-protein kinase B (AKT) and mitogen-activated protein kinase (MAPK) signaling pathways. The molecular docking results showed that the core ingredients of LJZW had good binding ability with the core targets of CD. A series of experiments demonstrated that LJZW could effectively attenuate TNBS-induced colitis symptoms, inhibit the inflammatory response, and protect intestinal barrier function by inhibiting the PI3K-AKT and MAPK signaling pathways, thus preventing and treating CD.

CONCLUSION

LJZW has the characteristics of multi-component, multi-target, and multi-pathway treatment, which helps to improve the treatment of CD, protect the intestinal barrier, and exert the effect of anti-inflammatory therapy by inhibiting PI3K-AKT and MAPK signaling pathways.

Screening of specific binding peptide for β-lactoglobulin using phage display technology

β-lactoglobulin (β-Lg) is a major food allergen, there is an urgent need to develop a rapid method for detecting β-Lg in order to avoid contact or ingestion by allergic patients. Peptide aptamers have high affinity, specificity, and stability, and have broad prospects in the field of rapid detection. Using β-Lg as the target, this study screened 11 peptides (P1-11) from a phage display library. Using molecular docking technology to predict binding energy and binding mode of proteins and peptides. Select the peptides with the best binding ability to β-Lg (P5, P7, P8) through ELISA. Combining them with whey protein, casein, and bovine serum protein, it was found that P7 has the best specificity for β-Lg, with an inhibition rate of 87.99%. Verified by molecular dynamics that P7 binds well with β-Lg. Therefore, this peptide can be used for the recognition of β-Lg, becoming a new recognition element for detecting β-Lg.

The stripe rust effector Pst3180.3 inhibits the transcriptional activity of TaMYB4L to modulate wheat immunity and analyzes the key active sites of the interaction conformation

Puccinia striiformis f. sp. tritici (Pst) has a wide range and serious damage, which severely threatens global wheat production. In this study, we focused on an effector protein Pst3180.3, which was induced to be highly expressed during the Pst infection stage. The N-terminal 19 amino acid of Pst3180.3 was verified to function as a signal peptide and transferred to cytoplasm and nucleus of wheat following Pst infection. Transient overexpression of Pst3180.3 in Nicotiana benthamiana inhibited programmed cell death triggered via BAX. The instantaneous silencing of Pst3180.3 by BSMV- HIGS significantly reduced the number of uredinia and increased accumulation of reactive oxygen species. Those results indicated that Pst3180.3 is an important pathogenic factor of Pst. Interaction of Pst3180.3 with a transcription factor TaMYB4L in host was confirmed through yeast two-hybrid, luciferase complementation, and co-immunoprecipitation. Virus-induced gene silencing of TaMYB4L weakened the resistance to Pst, indicated that TaMYB4L may be involved in the positive regulation of plant immunity. Dual-luciferase assays revealed that Pst3180.3 inhibited the transcriptional activity of TaMYB4L. Meanwhile, molecular docking analysis identified the key residue sites for the interaction and binding between Pst3180.3 and MYB4L. Those results demonstrated that Pst3180.3 binds to TaMYB4L and interacts to inhibit wheat resistance to Pst infection.

Calculation of solvation force in molecular dynamics simulation by deep-learning method

Electrostatic calculations are generally used in studying the thermodynamics and kinetics of biomolecules in solvent. Generally, this is performed by solving the Poisson-Boltzmann equation on a large grid system, a process known to be time consuming. In this study, we developed a deep neural network to predict the decomposed solvation free energies and forces of all atoms in a molecule. To train the network, the internal coordinates of the molecule were used as the input data, and the solvation free energies along with transformed atomic forces from the Poisson-Boltzmann equation were used as labels. Both the training and prediction tasks were accelerated on GPU. Formal tests demonstrated that our method can provide reasonable predictions for small molecules when the network is well-trained with its simulation data. This method is suitable for processing lots of snapshots of molecules in a long trajectory. Moreover, we applied this method in the molecular dynamics simulation with enhanced sampling. The calculated free energy landscape closely resembled that obtained from explicit solvent simulations.

Regulation of optimized new Shengmai powder on cardiomyocyte apoptosis and ferroptosis in ischemic heart failure rats: The mediating role of phosphatidylinositol-3-kinase/protein kinase B/tumor protein 53 signaling pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Optimized New Shengmai Powder (ONSMP) is a sophisticated traditional Chinese medicinal formula renowned for bolstering vital energy, optimizing blood circulation, and mitigating fluid retention. After years of clinical application, ONSMP has shown a significant impact in improving myocardial injury and cardiac function and has a positive effect on treating heart failure. However, many unknowns exist about the molecular biological mechanisms of how ONSMP exerts its therapeutic effects, which require further research and exploration.

AIM OF THE STUDY

Exploring the potential molecular biological mechanisms by which ONSMP ameliorates cardiomyocyte apoptosis and ferroptosis in ischemic heart failure (IHF).

MATERIALS AND METHODS

First, we constructed a rat model of IHF by inducing acute myocardial infarction through surgery and using echocardiography, organ coefficients, markers of heart failure, antioxidant markers, and histopathological examination to assess the effects of ONSMP on cardiomyocyte apoptosis and ferroptosis in IHF rats. Next, we used bioinformatics analysis techniques to analyze the active components, signaling pathways, and core targets of ONSMP and calculated the interactions between core targets and corresponding elements. Finally, we detected the positive expression of apoptosis and ferroptosis markers and core indicators of signaling pathways by immunohistochemistry; detected the mean fluorescence intensity of core indicators of signaling pathways by immunofluorescence; detected the protein expression of signaling pathways and downstream effector molecules by western blotting; and detected the mRNA levels of p53 and downstream effector molecules by quantitative polymerase chain reaction.

RESULTS

ONSMP can activate the Ser83 site of ASK by promoting the phosphorylation of the PI3K/AKT axis, thereby inhibiting the MKK3/6-p38 axis and the MKK4/7-JNK axis signaling to reduce p53 expression, and can also directly target and inhibit the activity of p53, ultimately inhibiting p53-mediated mRNA and protein increases in PUMA, SAT1, PIG3, and TFR1, as well as mRNA and protein decreases in SLC7A11, thereby inhibiting cardiomyocyte apoptosis and ferroptosis, effectively improving cardiac function and ventricular remodeling in IHF rat models.

CONCLUSION

ONSMP can inhibit cardiomyocyte apoptosis and ferroptosis through the PI3K/AKT/p53 signaling pathway, delaying the development of IHF.

Leonurus japonicus Houtt. modulates neuronal apoptosis in intracerebral hemorrhage: Insights from network pharmacology and molecular docking

ETHNOPHARMACOLOGICAL RELEVANCE

Leonurus japonicus Houtt. (Labiatae), commonly known as Chinese motherwort, is a herbaceous flowering plant that is native to Asia. It is widely acknowledged in traditional medicine for its diuretic, hypoglycemic, antiepileptic properties and neuroprotection. Currently, Leonurus japonicus (Leo) is included in the Pharmacopoeia of the People's Republic of China. Traditional Chinese Medicine (TCM) recognizes Leo for its myriad pharmacological attributes, but its efficacy against ICH-induced neuronal apoptosis is unclear.

AIMS OF THE STUDY

This study aimed to identify the potential targets and regulatory mechanisms of Leo in alleviating neuronal apoptosis after ICH.

MATERIALS AND METHODS

The study employed network pharmacology, UPLC-Q-TOF-MS technique, molecular docking, pharmacodynamic studies, western blotting, and immunofluorescence techniques to explore its potential mechanisms.

RESULTS

Leo was found to assist hematoma absorption, thus improving the neurological outlook in an ICH mouse model. Importantly, molecular docking highlighted JAK as Leo's potential therapeutic target in ICH scenarios. Further experimental evidence demonstrated that Leo adjusts JAK1 and STAT1 phosphorylation, curbing Bax while augmenting Bcl-2 expression.

CONCLUSION

Leo showcases potential in mitigating neuronal apoptosis post-ICH, predominantly via the JAK/STAT mechanism.

Binding affinity screening of polyphenolic compounds in Stachys affinis extract (SAE) for their potential antioxidant and anti-inflammatory effects

Free radical is a marker in various inflammatory diseases. The antioxidant effect protects us from this damage, which also plays an essential role in preventing inflammation. Inflammation protects the body from biological stimuli, and pro-inflammatory mediators are negatively affected in the immune system. Inflammation caused by LPS is an endotoxin found in the outer membrane of Gram-negative bacteria, which induces immune cells to produce inflammatory cytokines such as cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase. Based on this, the antioxidant and anti-inflammatory effects of plant extracts were investigated. First, the main phenolic compounds for the five peaks obtained from Stachys affinis extract (SAE) were identified. The antioxidant effect of each phenolic compound was confirmed through HPLC analysis before and after the competitive binding reaction between DPPH and the extract. Afterward, the anti-inflammatory effect of each phenolic compound was confirmed through competitive binding between COX2 and the extract in HPLC analysis. Lastly, the anti-inflammatory effect of SAE was confirmed through in vitro experiments and also confirmed in terms of structural binding through molecular docking. This study confirmed that phenolic compounds in SAE extract have potential antioxidant and anti-inflammatory effects, and may provide information for primary screening of medicinal plants.

Analysis of environmental pollutant Bisphenol F elicited prostate injury targets and underlying mechanisms through network toxicology, molecular docking, and multi-level bioinformatics data integration

Bisphenol F (BPF) has gained prominence as an alternative to bisphenol A (BPA) in various manufacturing applications, yet being detected in diverse environments and posed potential public health risk. This research aims to elucidate the putative toxic targets and underlying molecular mechanisms of prostate injury induced by exposure to BPF through multi-level bioinformatics data, integrating network toxicology and molecular docking. Systematically leveraging multilevel databases, we determined 276 targets related to BPF and prostate injury. Subsequent screenings through STRING and Cytoscape tool highlighted 27 key targets, including BCL2, HSP90AA1, MAPK3, ESR1, and CASP3. GO and KEGG enrichment analyses demonstrated enrichment of targets involved in apoptosis, abnormal hormonal activities, as well as cancer-related signal transduction cascades, ligand-receptor interaction networks, and endocrine system signaling pathways. Molecular docking simulations conducted via Autodock corroborated high-affinity binding interaction between BPF and key targets. The results indicate that BPF exposure can contribute to the initiation and progression of prostate cancer and prostatic hyperplastic by modulating apoptosis and proliferation, altering nerve function in blood vessel endothelial cells, and disrupting androgen metabolism. This study offers theoretical underpinnings for comprehending the molecular mechanisms implicated in BPF-elicited prostatic toxicity, while concomitantly establishing foundational framework for the development of prophylactic and therapeutic strategies for prostatic injuries related to polycarbonate and epoxy resin plastics incorporated with BPF, as well as environments afflicted by elevated levels of these compounds.

Lysine lactylation in the regulation of tumor biology

Lysine lactylation (Kla), a newly discovered post-translational modification (PTM) of lysine residues, is progressively revealing its crucial role in tumor biology. A growing body of evidence supports its capacity of transcriptional regulation through histone modification and modulation of non-histone protein function. It intricately participates in a myriad of events in the tumor microenvironment (TME) by orchestrating the transitions of immune states and augmenting tumor malignancy. Its preferential modification of metabolic proteins underscores its specific regulatory influence on metabolism. This review focuses on the effect and the probable mechanisms of Kla-mediated regulation of tumor metabolism, the upstream factors that determine Kla intensity, and its potential implications for the clinical diagnosis and treatment of tumors.

The investigation of potential mechanism of Fuzhengkangfu Decoction against Diabetic myocardial injury based on a combined strategy of network pharmacology, transcriptomics, and experimental verification

BACKGROUND AND OBJECTIVES

Diabetic cardiomyopathy (DCM) is a cardiac condition resulting from myocardial damage caused by diabetes mellitus (DM), currently lacking specific therapeutic interventions. Fuzhengkangfu decoction (FZK) plays an important role in the prevention and treatment of various cardiovascular diseases. However, the efficacy and potential mechanisms of FZK are not fully understood. This study aims to investigate the protective effect and mechanisms of FZK against DCM.

METHODOLOGIES

Rats were given a high-calorie diet along with a low dosage of streptozotocin (STZ) to establish a rat model of DCM. The diabetic rats received FZK or normal saline subcutaneously for 12 weeks. Echocardiography was conducted to evaluate their heart function characteristics. Rat heart morphologies were assessed using Sirius Red staining and H&E staining. Transcriptome sequencing analysis and network pharmacology were used to reveal possible targets and mechanisms. Molecular docking was conducted to validate the association between the primary components of FZK and the essential target molecules. Finally, both in vitro and in vivo studies were conducted on the cardioprotective properties and mechanism of FZK.

RESULTS

According to the results of network pharmacology, FZK may prevent DCM by reducing oxidative stress and preventing apoptosis. Transcriptomics confirmed that FZK protected against DCM-induced myocardial fibrosis and remodelling, as predicted by network pharmacology, and suggested that FZK regulated the expression of oxidative stress and apoptosis-related proteins. Integrating network pharmacology and transcriptome analysis results revealed that the AGE-RAGE signalling pathway-associated MMP2, SLC2A1, NOX4, CCND1, and CYP1A1 might be key targets. Molecular docking showed that Poricoic acid A and 5-O-Methylvisammioside had the highest docking activities with these targets. We further conducted in vivo experiments, and the results showed that FZK significantly attenuated left ventricular remodelling, reduced myocardial fibrosis, and improved cardiac contractile function. And, our study demonstrated that FZK effectively reduced oxidative stress and apoptosis of cardiomyocytes. The data showed that Erk, NF-κB, and Caspase 3 phosphorylation was significantly inhibited, and Bcl-2/Bax was significantly increased after FZK treatment. In vitro, FZK significantly reduced AGEs-induced ROS increase and apoptosis in cardiomyocytes. Furthermore, FZK significantly inhibited the phosphorylation of Erk and NF-κB proteins and decreased the expression of MMP2. All the results confirmed that FZK inhibited the activation of the Erk/NF-κB pathway in AGE-RAGE signalling and alleviated oxidative stress and apoptosis of cardiomyocytes. In summary, we verified that FZK protects against DCM by inhibiting myocardial apoptotic remodelling through the suppression of the AGE-RAGE signalling pathway.

CONCLUSION

In conclusion, our research indicates that FZK demonstrates anti-cardiac dysfunction properties by reducing oxidative stress and cardiomyocyte apoptosis through the AGE-RAGE pathway in DCM, showing potential for therapeutic use.

Haloketones: A class of unregulated priority DBPs with high contribution to drinking water cytotoxicity

Although unregulated aliphatic disinfection byproducts (DBPs) had a much higher concentration and cytotoxicity than known aromatic DBPs, a recent study indicated that seven classes of regulated and unregulated priority DBPs (one and two-carbon-atom DBPs) just accounted for 16.2% of disinfected water cytotoxicity in the U.S., meaning some of the highly toxic aliphatic DBPs may be overlooked. Haloketones (HKs) are an essential class of priority DBPs with a 1-100 µg/L concentration in drinking water but lack cytotoxicity data. This study investigated the cytotoxicity of seven HKs using Chinese hamster ovary (CHO) cells. The order for cytotoxicity of HKs from most to least toxic was: 1,3-dichloroacetone (LC50: 1.0 ± 0.20 μM) ≈ 1,3-dibromoacetone (1.5 ± 0.19 μM) ≈ bromoacetone (1.9 ± 0.49 μM) > chloroacetone (4.3 ± 0.22 μM) > 1,1,3-trichloropropanone (6.6 ± 0.46 μM) > 1,1,1-trichloroacetone (222 ± 7.7 μM) > hexachloroacetone (3269 ± 344 μM). The cytotoxicity of HKs was higher than most regulated and priority aliphatic DBPs in mono-halogenated, di-halogenated, and tri-halogenated categories. A prediction model of HK cytotoxicity was developed based on the quantitative structure-activity relationship (QSAR), optimizing structures and computing descriptors with Gaussian 09 W. The average concentrations of HKs in representative drinking water samples from South Carolina (U.S.) and Suzhou (China) were 12.4 and 0.9 μg/L, respectively, accounting for 18.8% and 1.7% of their specific total DBPs measured (i.e. not TOX). For South Carolina drinking water, their contributions to total calculated additive cytotoxicity of aliphatic DBPs and overall drinking water cytotoxicity were 86.7% and 14.0%, respectively, demonstrating that HKs are an essential class of overlooked DBPs with a high contribution to drinking water cytotoxicity. Our study can help to explain the conflict that why regulated and priority DBPs (except HKs) just accounted for 16% of chlorinated drinking water cytotoxicity even enough they had much higher concentration and cytotoxicity than known aromatic DBPs.

Aristolochic acids-hijacked p53 promotes liver cancer cell growth by inhibiting ferroptosis

Aristolochic acids (AAs) have been identified as a significant risk factor for hepatocellular carcinoma (HCC). Ferroptosis is a type of regulated cell death involved in the tumor development. In this study, we investigated the molecular mechanisms by which AAs enhanced the growth of HCC. By conducting bioinformatics and RNA-Seq analyses, we found that AAs were closely correlated with ferroptosis. The physical interaction between p53 and AAs in HepG2 cells was validated by bioinformatics analysis and SPR assays with the binding pocket sites containing Pro92, Arg174, Asp207, Phe212, and His214 of p53. Based on the binding pocket that interacts with AAs, we designed a mutant and performed RNA-Seq profiling. Interestingly, we found that the binding pocket was responsible for ferroptosis, GADD45A, NRF2, and SLC7A11. Functionally, the interaction disturbed the binding of p53 to the promoter of GADD45A or NRF2, attenuating the role of p53 in enhancing GADD45A and suppressing NRF2; the mutant did not exhibit the same effects. Consequently, this event down-regulated GADD45A and up-regulated NRF2, ultimately inhibiting ferroptosis, suggesting that AAs hijacked p53 to down-regulate GADD45A and up-regulate NRF2 in HepG2 cells. Thus, AAs treatment resulted in the inhibition of ferroptosis via the p53/GADD45A/NRF2/SLC7A11 axis, which led to the enhancement of tumor growth. In conclusion, AAs-hijacked p53 restrains ferroptosis through the GADD45A/NRF2/SLC7A11 axis to enhance tumor growth. Our findings provide an underlying mechanism by which AAs enhance HCC and new insights into p53 in liver cancer. Therapeutically, the oncogene NRF2 is a promising target for liver cancer.

AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors

The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and accurately predicting their activity using vast datasets. The advancement of artificial intelligence (AI) technology has enabled machine learning and deep learning algorithms to excel in uncovering patterns in bio-element data and predicting their performance. This review explores the application of AI algorithms in the rational design of bio-elements, activity prediction, and the regulation of transcription-factor-based biosensor response performance using AI-designed elements. We discuss the advantages, adaptability, and biological challenges addressed by the AI algorithms in various applications, highlighting their powerful potential in analyzing biological data. Furthermore, we propose innovative solutions to the challenges faced by AI algorithms in the field and suggest future research directions. By consolidating current research and demonstrating the practical applications and future potential of AI in synthetic biology, this review provides valuable insights for advancing both academic research and practical applications in biotechnology.

Drug-Target Interaction Prediction Based on an Interactive Inference Network

Drug-target interactions underlie the actions of chemical substances in medicine. Moreover, drug repurposing can expand use profiles while reducing costs and development time by exploiting potential multi-functional pharmacological properties based upon additional target interactions. Nonetheless, drug repurposing relies on the accurate identification and validation of drug-target interactions (DTIs). In this study, a novel drug-target interaction prediction model was developed. The model, based on an interactive inference network, contains embedding, encoding, interaction, feature extraction, and output layers. In addition, this study used Morgan and PubChem molecular fingerprints as additional information for drug encoding. The interaction layer in our model simulates the drug-target interaction process, which assists in understanding the interaction by representing the interaction space. Our method achieves high levels of predictive performance, as well as interpretability of drug-target interactions. Additionally, we predicted and validated 22 Alzheimer's disease-related targets, suggesting our model is robust and effective and thus may be beneficial for drug repurposing.

Utility of the Morgan Fingerprint in Structure-Based Virtual Ligand Screening

In modern drug discovery, virtual ligand screening (VLS) is frequently applied to identify possible hits before experimental testing and refinement due to its cost-effective nature for large compound libraries. For decades, efforts have been devoted to developing VLS methods with high accuracy. These include the state-of-the-art FINDSITE suite of approaches FINDSITEcomb2.0, FRAGSITE, and FRAGSITE2 and the meta version FRAGSITEcomb that were developed in our lab. These methods combine ligand homology modeling (LHM), traditional ligand similarity methods, and more recently machine learning approaches to rank ligands and have proven to be superior to most recent deep learning and large language model-based approaches. Here, we describe further improvements to our previous best methods by combining the Morgan fingerprint (MF) with the originally used PubChem fingerprint and FP2 fingerprint. We then benchmarked FINDSITEcomb2.0M, FRAGSITEM, FRAGSITE2M, and the composite meta-approach FRAGSITEcombM. On the 102 target DUD-E set, the 1% enrichment factor (EF1%) and area under the precision-recall curve (AUPR) of FRAGSITEcomb increased from 42.0/0.59 to 47.6/0.72. This 0.72 AUPR is significantly better than that of the state-of-the-art deep learning-based method DenseFS's AUPR of 0.443. An independent test on the 81 targets DEKOIS2.0 set shows that EF1%/AUPR increases from 18.3/0.520 to 23.1/0.683. An ablation investigation shows that the MF contributes to most of the improvement of all four approaches. Thus, the MF is a useful addition to structure-based VLS.

Enhanced optical imaging and fluorescent labeling for visualizing drug molecules within living organisms

The visualization of drugs in living systems has become key techniques in modern therapeutics. Recent advancements in optical imaging technologies and molecular design strategies have revolutionized drug visualization. At the subcellular level, super-resolution microscopy has allowed exploration of the molecular landscape within individual cells and the cellular response to drugs. Moving beyond subcellular imaging, researchers have integrated multiple modes, like optical near-infrared II imaging, to study the complex spatiotemporal interactions between drugs and their surroundings. By combining these visualization approaches, researchers gain supplementary information on physiological parameters, metabolic activity, and tissue composition, leading to a comprehensive understanding of drug behavior. This review focuses on cutting-edge technologies in drug visualization, particularly fluorescence imaging, and the main types of fluorescent molecules used. Additionally, we discuss current challenges and prospects in targeted drug research, emphasizing the importance of multidisciplinary cooperation in advancing drug visualization. With the integration of advanced imaging technology and molecular design, drug visualization has the potential to redefine our understanding of pharmacology, enabling the analysis of drug micro-dynamics in subcellular environments from new perspectives and deepening pharmacological research to the levels of the cell and organelles.

Computational analysis of curcumin-mediated alleviation of inflammation in periodontitis patients with experimental validation in mice

AIM

Using network pharmacology and experimental validation to explore the therapeutic efficacy and mechanism of curcumin (Cur) in periodontitis treatment.

MATERIALS AND METHODS

Network pharmacology was utilized to predict target gene interactions of Cur-Periodontitis. Molecular docking was used to investigate the binding affinity of Cur for the predicted targets. A mouse model with ligature-induced periodontitis (LIP) was used to verify the therapeutic effect of Cur. Microcomputed tomography (micro-CT) was used to evaluate alveolar bone resorption, while western blotting, haematoxylin-eosin staining and immunohistochemistry were used to analyse the change in immunopathology. SYTOX Green staining was used to assess the in vitro effect of Cur in a mouse bone marrow-isolated neutrophil model exposed to lipopolysaccharide.

RESULTS

Network pharmacology identified 114 potential target genes. Enrichment analysis showed that Cur can modulate the production of neutrophil extracellular traps (NETs). Molecular docking experiments suggested that Cur effectively binds to neutrophil elastase (ELANE), peptidylarginine deiminase 4 (PAD4) and cathepsin G, three enzymes involved in NETs. In LIP mice, Cur alleviated alveolar bone resorption and reduced the expression of ELANE and PAD4 in a time-dependent but dose-independent manner. Cur can directly inhibit NET formation in the cell model.

CONCLUSIONS

Our research suggested that Cur may alleviate experimental periodontitis by inhibiting NET formation.

Advances in machine-learning approaches to RNA-targeted drug design

RNA molecules play multifaceted functional and regulatory roles within cells and have garnered significant attention in recent years as promising therapeutic targets. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI's potential in computer-aided drug design (CADD) to discover novel drug compounds that target RNA. Although machine-learning (ML) approaches have been widely adopted in the discovery of small molecules targeting proteins, the application of ML approaches to model interactions between RNA and small molecule is still in its infancy. Compared to protein-targeted drug discovery, the major challenges in ML-based RNA-targeted drug discovery stem from the scarcity of available data resources. With the growing interest and the development of curated databases focusing on interactions between RNA and small molecule, the field anticipates a rapid growth and the opening of a new avenue for disease treatment. In this review, we aim to provide an overview of recent advancements in computationally modeling RNA-small molecule interactions within the context of RNA-targeted drug discovery, with a particular emphasis on methodologies employing ML techniques.

Screening Antioxidant Components in Different Parts of Dandelion Using Online Gradient Pressure Liquid Extraction Coupled with High-Performance Liquid Chromatography Antioxidant Analysis System and Molecular Simulations

Utilizing online gradient pressure liquid extraction (OGPLE) coupled with a high-performance liquid chromatography antioxidant analysis system, we examined the antioxidative active components present in both the aerial parts and roots of dandelion. By optimizing the chromatographic conditions, we identified the ferric reducing-antioxidant power system as the most suitable for online antioxidant reactions in dandelion. Compared to offline ultrasonic extraction, the OGPLE method demonstrated superior efficiency in extracting chemical components with varying polarities from the samples. Liquid chromatography-mass spectrometry revealed twelve compounds within the dandelion samples, with nine demonstrating considerable antioxidant efficacy. Of these, the aerial parts and roots of dandelion contained nine and four antioxidant constituents, respectively. Additionally, molecular docking studies were carried out to investigate the interaction between these nine antioxidants and four proteins associated with oxidative stress (glutathione peroxidase, inducible nitric oxide synthase, superoxide dismutase, and xanthine oxidase). The nine antioxidant compounds displayed notable binding affinities below -5.0 kcal/mol with the selected proteins, suggesting potential receptor-ligand interactions. These findings contribute to enhancing our understanding of dandelion and provide a comprehensive methodology for screening the natural antioxidant components from herbs.

Protective effect of esculentoside A against myocardial infarction via targeting C-X-C motif chemokine receptor 2

Myocardial infarction (MI) is the primary cause of cardiac mortality. Esculentoside A (EsA), a triterpenoid saponin, has anti-inflammatory and antioxidant activities. However, its effect on MI remains unknown. In this study, the protective effect and mechanisms of EsA against MI were investigated. EsA significantly alleviated hypoxia-induced HL-1 cell injury, including increasing cell viability, inhibiting reactive oxygen species (ROS) production, mitochondrial membrane potential (MMP) and lactate dehydrogenase (LDH) leakage. In mouse MI model by left coronary artery (LAD) ligating, EsA obviously restored serum levels of creatine kinase isoenzymes (CK-MB), cardiac troponin I (cTnI), superoxide dismutase (SOD) and malondialdehyde (MDA). In addition, the cardioprotective effect of EsA was further confirmed by infarct size, electrocardiogram and echocardiography. Mechanistically, the targeted binding relationship between EsA and C-X-C motif chemokine receptor 2 (CXCR2) was predicted by molecular docking and dynamics, and validated by small molecule pull-down and surface plasmon resonance tests. EsA inhibited CXCR2 level both in vitro and in vivo, correspondingly alleviated oxidative stress by suppressing NOX1 and NOX2 and relieved inflammation through inhibiting p65 and p-p65. It demonstrated that EsA could play a cardioprotective role by targeting CXCR2. However, the effect of EsA against MI was abolished in combination with CXCR2 overexpression both in vitro and in vivo. This study revealed that EsA showed excellent cardioprotective activities by targeting CXCR2 to alleviate oxidative stress and inflammation in MI. EsA may function as a novel CXCR2 inhibitor and a potent candidate for the prevention and intervention of MI in the future.

The Development of Aptamer-Based Gold Nanoparticle Lateral Flow Test Strips for the Detection of SARS-CoV-2 S Proteins on the Surface of Cold-Chain Food Packaging

The COVID-19 pandemic over recent years has shown a great need for the rapid, low-cost, and on-site detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, an aptamer-based colloidal gold nanoparticle lateral flow test strip was well developed to realize the visual detection of wild-type SARS-CoV-2 spike proteins (SPs) and multiple variants. Under the optimal reaction conditions, a low detection limit of SARS-CoV-2 S proteins of 0.68 nM was acquired, and the actual detection recovery was 83.3% to 108.8% for real-world samples. This suggests a potential tool for the prompt detection of SARS-CoV-2 with good sensitivity and accuracy, and a new method for the development of alternative antibody test strips for the detection of other viral targets.

In silico drug design strategies for discovering novel tuberculosis therapeutics

INTRODUCTION

Tuberculosis remains a significant concern in global public health due to its intricate biology and propensity for developing antibiotic resistance. Discovering new drugs is a protracted and expensive endeavor, often spanning over a decade and incurring costs in the billions. However, computer-aided drug design (CADD) has surfaced as a nimbler and more cost-effective alternative. CADD tools enable us to decipher the interactions between therapeutic targets and novel drugs, making them invaluable in the quest for new tuberculosis treatments.

AREAS COVERED

In this review, the authors explore recent advancements in tuberculosis drug discovery enabled by in silico tools. The main objectives of this review article are to highlight emerging drug candidates identified through in silico methods and to provide an update on the therapeutic targets associated with Mycobacterium tuberculosis.

EXPERT OPINION

These in silico methods have not only streamlined the drug discovery process but also opened up new horizons for finding novel drug candidates and repositioning existing ones. The continued advancements in these fields hold great promise for more efficient, ethical, and successful drug development in the future.

GSScore: a novel Graphormer-based shell-like scoring method for protein-ligand docking

Protein-ligand interactions (PLIs) are essential for cellular activities and drug discovery. But due to the complexity and high cost of experimental methods, there is a great demand for computational approaches to recognize PLI patterns, such as protein-ligand docking. In recent years, more and more models based on machine learning have been developed to directly predict the root mean square deviation (RMSD) of a ligand docking pose with reference to its native binding pose. However, new scoring methods are pressingly needed in methodology for more accurate RMSD prediction. We present a new deep learning-based scoring method for RMSD prediction of protein-ligand docking poses based on a Graphormer method and Shell-like graph architecture, named GSScore. To recognize near-native conformations from a set of poses, GSScore takes atoms as nodes and then establishes the docking interface of protein-ligand into multiple bipartite graphs within different shell ranges. Benefiting from the Graphormer and Shell-like graph architecture, GSScore can effectively capture the subtle differences between energetically favorable near-native conformations and unfavorable non-native poses without extra information. GSScore was extensively evaluated on diverse test sets including a subset of PDBBind version 2019, CASF2016 as well as DUD-E, and obtained significant improvements over existing methods in terms of RMSE, $R$ (Pearson correlation coefficient), Spearman correlation coefficient and Docking power.

RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method

Small molecule drugs can be used to target nucleic acids (NA) to regulate biological processes. Computational modeling methods, such as molecular docking or scoring functions, are commonly employed to facilitate drug design. However, the accuracy of the scoring function in predicting the closest-to-native docking pose is often suboptimal. To overcome this problem, a machine learning model, RmsdXNA, was developed to predict the root-mean-square-deviation (RMSD) of ligand docking poses in NA complexes. The versatility of RmsdXNA has been demonstrated by its successful application to various complexes involving different types of NA receptors and ligands, including metal complexes and short peptides. The predicted RMSD by RmsdXNA was strongly correlated with the actual RMSD of the docked poses. RmsdXNA also outperformed the rDock scoring function in ranking and identifying closest-to-native docking poses across different structural groups and on the testing dataset. Using experimental validated results conducted on polyadenylated nuclear element for nuclear expression triplex, RmsdXNA demonstrated better screening power for the RNA-small molecule complex compared to rDock. Molecular dynamics simulations were subsequently employed to validate the binding of top-scoring ligand candidates selected by RmsdXNA and rDock on MALAT1. The results showed that RmsdXNA has a higher success rate in identifying promising ligands that can bind well to the receptor. The development of an accurate docking score for a NA-ligand complex can aid in drug discovery and development advancements. The code to use RmsdXNA is available at the GitHub repository https://github.com/laiheng001/RmsdXNA.

Prediction of potential targets and toxicological insights of Astragalus in liver cancer based on network pharmacology: Integrating systems biology, drug interaction networks, and toxicological perspectives

This study investigates Astragalus's efficacy as a novel therapeutic option for primary liver cancer (PLC), capitalizing on its anti-inflammatory and antiviral effects. We utilized network pharmacology to unveil Astragalus's potential targets against PLC, revealing significant gene expression alterations in treated samples-20 genes were up-regulated, and 20 were down-regulated compared to controls. Our analysis extended to single-cell resolution, where we processed scRNA-seq data to discern 15 unique cell clusters within the immune, malignant, and stromal compartments through advanced algorithms like UMAP and tSNE. To delve deeper into the functional implications of these gene expression changes, we conducted comprehensive gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, alongside Gene Set Variation Analysis, to elucidate the biological processes and pathways involved. Further, we constructed protein-protein interaction networks to visualize the intricate molecular interplay, highlighting the down-regulation of MT1E in PLC cells, a finding corroborated by quantitative polymerase chain reaction. Molecular docking studies affirmed the potent interaction between Astragalus's active compounds and MT proteins, underscoring a targeted therapeutic mechanism. Our investigation also encompassed a detailed cellular landscape analysis, identifying nine cell subgroups related to MT1 expression and specifying five cell subsets through the SingleR package. Advanced trajectory and cell-cell interaction analyses offered deeper insights into the dynamics of MT1-associated cellular subpopulations. This comprehensive methodology not only underpins Astragalus's promising role in PLC treatment but also advances our understanding of its molecular and cellular mechanisms, paving the way for targeted therapeutic strategies.

Naodesheng decoction regulating vascular function via G-protein-coupled receptors: network analysis and experimental investigations

Introduction: Ischemic stroke (IS) is a detrimental neurological disease with limited treatment options. Recanalization of blocked blood vessels and restoring blood supply to ischemic brain tissue are crucial for post-stroke rehabilitation. The decoction Naodesheng (NDS) composed of five Chinese botanical drugs, including Panax notoginseng (Burk.) F. H. Chen, Ligusticum chuanxiong Hort., Carthamus tinctorius L., Pueraria lobata (Willd.) Ohwi, and Crataegus pinnatifida Bge., is a blood-activating and stasis-removing herbal medicine commonly used for the clinical treatment of cerebrovascular diseases in China. However, the material basis of NDS on the effects of blood circulation improvement and vascular tone regulation remains unclear. Methods: A database comprising 777 chemical metabolites of NDS was constructed. Then, the interactions between various herbal metabolites of NDS and five vascular tone modulation G-protein-coupled receptors (GPCRs), including 5-HT1AR, 5-HT1BR, β2-AR, AT1R, and ETBR, were assessed by molecular docking. Using network analysis and vasomotor experiment of the cerebral basilar artery, the potential material basis underlying the vascular regulatory effects of NDS was further explored. Results: The Naodesheng Effective Component Group (NECG) was found to induce relaxation of rat basilar artery rings precontracted using Endothelin-1 (ET-1) and KCl in vitro in a dose-dependent manner. Several metabolites of NDS, including C. tinctorius, C. pinnatifida, and P. notoginseng, were found to be the main plant resources of metabolites with high docking scores. Furthermore, several metabolites in NDS, including formononetin-7-glucoside, hydroxybenzoyl-coumaric anhydride, methoxymecambridine, puerarol, and pyrethrin II, were found to target multiple vascular GPCRs. Metabolites with moderate-to-high binding energy were verified to have good rat basilar artery-relaxing effects, and the maximum artery relaxation effects of all three metabolites, namely, isorhamnetin, kaempferol, and daidzein, were found to exceed 90%. Moreover, metabolites of NDS were found to exert a synergistic effect by interacting with vascular GPCR targets, and these metabolites may contribute to the cerebrovascular regulatory function of NDS. Discussion: The study reports that various metabolites of NDS contribute to its vascular tone regulating effects and demonstrates the multi-component and multi-target characteristics of NDS. Among them, metabolites with moderate-to-high binding scores in NDS may play an important role in regulating vascular function.

Application of Machine Learning in the Quantitative Analysis of the Surface Characteristics of Highly Abundant Cytoplasmic Proteins: Toward AI-Based Biomimetics

Proteins in the crowded environment of human cells have often been studied regarding nonspecific interactions, misfolding, and aggregation, which may cause cellular malfunction and disease. Specifically, proteins with high abundance are more susceptible to these issues due to the law of mass action. Therefore, the surfaces of highly abundant cytoplasmic (HAC) proteins directly exposed to the environment can exhibit specific physicochemical, structural, and geometrical characteristics that reduce nonspecific interactions and adapt to the environment. However, the quantitative relationships between the overall surface descriptors still need clarification. Here, we used machine learning to identify HAC proteins using hydrophobicity, charge, roughness, secondary structures, and B-factor from the protein surfaces and quantified the contribution of each descriptor. First, several supervised learning algorithms were compared to solve binary classification problems for the surfaces of HAC and extracellular proteins. Then, logistic regression was used for the feature importance analysis of descriptors considering model performance (80.2% accuracy and 87.6% AUC) and interpretability. The HAC proteins showed positive correlations with negatively and positively charged areas but negative correlations with hydrophobicity, the B-factor, the proportion of beta structures, roughness, and the proportion of disordered regions. Finally, the details of each descriptor could be explained concerning adaptative surface strategies of HAC proteins to regulate nonspecific interactions, protein folding, flexibility, stability, and adsorption. This study presented a novel approach using various surface descriptors to identify HAC proteins and provided quantitative design rules for the surfaces well-suited to human cellular crowded environments.

Exploration of the in vitro Antiviral Effects and the Active Components of Changyanning Tablets Against Enterovirus 71

Purpose

This study aims to investigate the in vitro antiviral effects of the aqueous solution of Changyanning (CYN) tablets on Enterovirus 71 (EV71), and to analyze its active components.

Methods

The in vitro anti-EV71 effects of CYN solution and its herbal ingredients were assessed by testing the relative viral RNA (vRNA) expression level and the cell viability rates. Material basis analysis was performed using HPLC-Q-TOF-MS/MS detection. Potential targets and active components were identified by network pharmacology and molecular docking. The screened components were verified by in vitro antiviral experiments.

Results

CYN solution exerted anti-EV71 activities as the vRNA is markedly reduced after treatment, with a half maximal inhibitory concentration (IC50) of 996.85 μg/mL. Of its five herbal ingredients, aqueous extract of Mosla chinensis (AEMC) and leaves of Liquidambar formosana Hance (AELLF) significantly inhibited the intracellular replication of EV71, and the IC50 was tested as 202.57 μg/mL and 174.77 μg/mL, respectively. Based on HPLC-Q-TOF-MS/MS results, as well as the comparison with the material basis of CYN solution, a total of 44 components were identified from AEMC and AELLF. Through network pharmacology, AKT1, ALB, and SRC were identified as core targets. Molecular docking performed between core targets and the components indicated that 21 components may have anti-EV71 effects. Of these, nine were selected for in vitro pharmacodynamic verification, and only rosmarinic acid manifested in vitro anti-EV71 activity, with an IC50 of 11.90 μg/mL. Moreover, rosmarinic acid can stably bind with three core targets by forming hydrogen bonds.

Conclusion

CYN solution has inhibitory effects on EV71 replication in vitro, and its active component was identified as rosmarinic acid. Our study provides a new approach for screening and confirmation of the effective components in Chinese herbal preparation.

Optimized New Shengmai Powder modulation of cAMP/Rap1A signaling pathway attenuates myocardial fibrosis in heart failure

BACKGROUND

Optimized New Shengmai Powder (ONSMP) is a traditional Chinese medicine formula with significant anti-heart failure and myocardial fibrosis effects, but the specific molecular biological mechanisms are not fully understood.

METHODS

In this study, we first used network pharmacology to analyze the ONSMP's active ingredients, core signaling pathways, and core targets. Second, calculate the affinity and binding modes of the ONSMP components to the core targets using molecular docking. Finally, the heart failure rat model was established by ligating the left anterior descending branch of the coronary artery and assessing the effect of ONSMP on myocardial fibrosis in heart failure using echocardiography, cardiac organ coefficients, heart failure markers, and pathological sections after 4 weeks of drug intervention. The cAMP level in rat myocardium was determined using Elisa, the α-SMA and FSP-1 positive expression determined by immunohistochemistry, and the protein and mRNA levels of the cAMP/Rap1A signaling pathway were detected by Western Blotting and quantitative real-time PCR, respectively.

RESULTS

The result shows that the possible mechanism of ONSMP in reducing myocardial fibrosis also includes the use of 12 active ingredients such as baicalin, vitamin D, resveratrol, tanshinone IIA, emodin, 15,16-dihydrotanshinone-i to regulate β1-AR, AC6, EPAC1, Rap1 A, STAT3, and CCND1 on the cAMP/Rap1A signaling pathway, thereby inhibiting the proliferation of cardiac fibroblasts and reduce the excessive secretion of collagen, effectively improve cardiac function and ventricular remodeling in heart failure rats.

CONCLUSION

This research shows that ONSMP can inhibit myocardial fibrosis and delay heart failure through the cAMP/Rap1A signaling pathway.

Elucidation of protein-ligand interactions by multiple trajectory analysis methods

The identification of interaction between protein and ligand including binding positions and strength plays a critical role in drug discovery. Molecular docking and molecular dynamics (MD) techniques have been widely applied to predict binding positions and binding affinity. However, there are few works that describe the systematic exploration of the MD trajectory evolution in this context, potentially leaving out important information. To address the problem, we build a framework, Moira (molecular dynamics trajectory analysis), which enables automating the whole process ranging from docking, MD simulations and various analyses as well as visualizations. We utilized Moira to analyze 400 MD simulations in terms of their geometric features (root mean square deviation and protein-ligand interaction profiler) and energetics (molecular mechanics Poisson-Boltzmann surface area) for these trajectories. Finally, we demonstrate the performance of different analysis techniques in distinguishing native poses among four poses.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

Molecular Mechanism of Salvia miltiorrhiza in the Treatment of Colorectal Cancer Based on Network Pharmacology and Molecular Docking Technology

Purpose

This study aimed to investigate the effect of Salvia miltiorrhiza on colorectal cancer, as well as the mechanisms involved.

Methods

The active compounds of Salvia miltiorrhiza and the associated genes in colorectal cancer were sourced from publicly available databases. Targets associated with colorectal cancer were identified by searching the GeneCards and OMIM databases. Subsequently, the Cytoscape 3.6.0 software was employed to create a regulatory network that illustrates the relationships among active ingredients, colorectal cancer, and their corresponding targets. The String database was utilized to generate a PPI network. Molecular docking studies, conducted with AutoDock Vina, verified the binding capabilities of these active components to core targets. The findings from network pharmacology analysis were corroborated through in vitro experiments.

Results

In this study, we identified 39 active components derived from Salvia miltiorrhiza that are predicted to target 544 genes associated with colorectal cancer through network pharmacology. Through a combined analysis of network pharmacology, we isolated three key targets: SRC, IL6, and INS. Molecular docking results convincingly demonstrated Salvia miltiorrhiza's strong binding affinity to these targets. Additionally, in vitro experiments confirmed that Salvia miltiorrhiza effectively inhibited the progression of colorectal cancer via regulating the INS/SRC/IL6 pathway.

Conclusion

Salvia miltiorrhiza emerges as a compelling herbal intervention for colorectal cancer. This study lays the foundation for potential future clinical trials assessing the efficacy of Salvia miltiorrhiza in the management of colorectal cancer.

Huangqi Jiuni decoction prevents acute kidney injury induced by severe burns by inhibiting activation of the TNF/NF-κB pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Huangqi Jiuni decoction (HQJND) is a prescription for the treatment of severe burns provided based on traditional Chinese and Western medicine, which is created by the First Affiliated Hospital of Anhui Medical University. It consists of 12 herbs and has been used clinically for decades. It has greatly shortened the course of the disease, but the mechanism by which HQJND treats the disease still remains unclear.

AIM OF THE STUDY

Hence, the objective of this investigation was to utilize modern pharmacological tools to demonstrate the efficacy and mechanism of HQJND in the treatment of acute kidney injury (AKI) caused by severe burns.

MATERIALS AND METHODS

In this study, the chemical constituents in HQJND were first examined using liquid chromatography tandem mass spectrometry (LC-MS/MS). Then, by using network pharmacology, we screened the targets of drug and disease action, and predicted the signaling pathways acting in the course of drug treatment of disease. Finally, we attempted to verify the efficacy of the drug and explored its therapeutic mechanism after the establishment of an animal model, herbal gavage treatment, collection of rat kidneys and serum for renal function, quantitative real-time Polymerase Chain Reaction (RT-qPCR), Western Blotting (WB), Hematoxylin and eosin (HE) staining and Immunohistochemistry (IHC).

RESULTS

The 14 important active ingredients in HQJND was analyzed by liquid chromatography tandem mass spectrometry, while network pharmacology screening was performed to identify 353 disease-associated marker genes and 286 drug targets, finally identifying the TNF/NF-κB (tumor necrosis factor/nuclear factor kappa-B) signaling site: the key pathway of burn-induced acute kidney injury when HQJND intervened. The serum renal function and histopathology of rats demonstrated that the use of HQJND significantly improved the renal function in severe burns. RT-qPCR and WB confirmed that the TNF/NF-κB signaling pathway was activated in the Model group of rats, and HQJND could curb the signaling pathway because it moderated the expressions of key proteins in the process.

CONCLUSION

Based on modern pharmacology, we explored an effective herbal preparation to ameliorate the impairment of renal function after severe burns, which is most likely to function through the TNF/NF-κB signaling pathway.

Tripterygium wilfordii Hook.f induced kidney injury through mediating inflammation via PI3K-Akt/HIF-1/TNF signaling pathway: A study of network toxicology and molecular docking

We intend to explore potential mechanisms of Tripterygium wilfordii Hook.f (TwHF) induced kidney injury (KI) using the methods of network toxicology and molecular docking. We determined TwHF potential compounds with its targets and KI targets, obtained the TwHF induced KI targets after intersecting targets of TwHF and KI. Then we conducted protein-protein interaction (PPI) network, gene expression analysis, gene ontology (GO) function and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis to explore the mechanism of TwHF-induced KI. Finally we conducted molecular docking to verify the core toxic compounds and the targets. We obtained 12 TwHF toxic compounds and 62 TwHF-induced KI targets. PPI network, gene expression analysis and GO function enrichment analysis unveiled the key biological process and suggested the mechanism of TwHF-induced KI might be associated with inflammation, immune response, hypoxia as well as oxidative stress. KEGG pathway enrichment analysis indicated PI3K-Akt signaling pathway, HIF-1 signaling pathway and TNF signaling pathway were key signaling pathways of TwHF induced KI. Molecular docking showed that the binding energy of core targets and toxic compounds was all less than -6.5 kcal/mol that verified the screening ability of network pharmacology and provided evidence for modifying TwHF toxic compounds structure. Through the study, we unveiled the mechanism of TwHF induce KI that TwHF might activate PI3K-Akt signaling pathway as well as TNF signaling pathway to progress renal inflammation, mediate hypoxia via HIF-1 signaling pathway to accelerate inflammatory processes, and also provided a theoretical basis for modifying TwHF toxic compounds structure as well as supported the follow-up research.

Adelmidrol ameliorates liver ischemia-reperfusion injury through activating Nrf2 signaling pathway

Liver ischemia/reperfusion (I/R) injury commonly occurs after various liver surgeries. Adelmidrol, an N- palmitoylethanolamide analog, has anti-inflammatory, anti-oxidant, and anti-injury properties. To investigate whether adelmidrol could reduce liver I/R injury, we established a mouse of liver I/R injury and an AML12 cell hypoxia-reoxygenation model to perform experiments using multiple indicators. Serum ALT and AST levels, and H&E staining were used to measure liver damage; MDA content, superoxide dismutase and glutathione activities, and dihydroethidium staining were used to measure oxidative stress; mRNA expression levels of tumor necrosis factor-α, interleukin (IL)-1β, IL-6, MCP-1, and Ly6G staining were used to measure inflammatory response; and protein expression of Bax, Bcl-2, C-caspase3, and terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling staining were used to measure apoptosis. The experimental results showed that adelmidrol reduced liver I/R injury. In addition, adelmidrol pretreatment elevated AML12 cell activity and reduced I/R-and H/R-induced apoptosis, inflammatory injury, and oxidative stress. ML385, an inhibitor of nuclear factor erythroid2-related factor 2 (Nrf2), reverses liver I/R injury attenuated by adelmidrol. These results suggest that adelmidrol ameliorates liver I/R injury by activating the Nrf2 signaling pathway.

Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer and belongs to the category of malignant tumors of the thyroid gland. Autophagy plays an important role in PTC. The purpose of this study is to develop a novel diagnostic model using autophagy-related genes (ARGs) in patients. In this study, RNA sequencing data of PTC samples and normal samples were obtained from GSE33630 and GSE29265. Then, we analyzed GSE33630 datasets and identified 127 DE-ARGs. Functional enrichment analysis suggested that 127 DE-ARGs were mainly enriched in pathways in cancer, protein processing in endoplasmic reticulum, toll-like receptor pathway, MAPK pathway, apoptosis, neurotrophin signaling pathway, and regulation of autophagy. Subsequently, CALCOCO2, DAPK1, and RAC1 among the 127 DE-ARGs were identified as diagnostic genes by support vector machine recursive feature elimination and least absolute shrinkage and selection operator algorithms. Then, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 and its diagnostic value was confirmed in GSE29265 and our cohorts. Importantly, CALCOCO2 may be a critical regulator involved in immune microenvironment because its expression was related to many types of immune cells. Overall, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 which can be used as diagnostic markers of PTC.

Computational drug development for membrane protein targets

The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.

Improving structure-based protein-ligand affinity prediction by graph representation learning and ensemble learning

Predicting protein-ligand binding affinity presents a viable solution for accelerating the discovery of new lead compounds. The recent widespread application of machine learning approaches, especially graph neural networks, has brought new advancements in this field. However, some existing structure-based methods treat protein macromolecules and ligand small molecules in the same way and ignore the data heterogeneity, potentially leading to incomplete exploration of the biochemical information of ligands. In this work, we propose LGN, a graph neural network-based fusion model with extra ligand feature extraction to effectively capture local features and global features within the protein-ligand complex, and make use of interaction fingerprints. By combining the ligand-based features and interaction fingerprints, LGN achieves Pearson correlation coefficients of up to 0.842 on the PDBbind 2016 core set, compared to 0.807 when using the features of complex graphs alone. Finally, we verify the rationalization and generalization of our model through comprehensive experiments. We also compare our model with state-of-the-art baseline methods, which validates the superiority of our model. To reduce the impact of data similarity, we increase the robustness of the model by incorporating ensemble learning.

Ligand Modification-Free Methods for the Profiling of Protein-Environmental Chemical Interactions

Adverse health outcomes caused by environmental chemicals are often initiated via their interactions with proteins. Essentially, one environmental chemical may interact with a number of proteins and/or a protein may interact with a multitude of environmental chemicals, forming an intricate interaction network. Omics-wide protein-environmental chemical interaction profiling (PECI) is of prominent importance for comprehensive understanding of these interaction networks, including the toxicity mechanisms of action (MoA), and for providing systematic chemical safety assessment. However, such information remains unknown for most environmental chemicals, partly due to their vast chemical diversity. In recent years, with the continuous efforts afforded, especially in mass spectrometry (MS) based omics technologies, several ligand modification-free methods have been developed, and new attention for systematic PECI profiling was gained. In this Review, we provide a comprehensive overview on these methodologies for the identification of ligand-protein interactions, including affinity interaction-based methods of affinity-driven purification, covalent modification profiling, and activity-based protein profiling (ABPP) in a competitive mode, physicochemical property changes assessment methods of ligand-directed nuclear magnetic resonance (ligand-directed NMR), MS integrated with equilibrium dialysis for the discovery of allostery systematically (MIDAS), thermal proteome profiling (TPP), limited proteolysis-coupled mass spectrometry (LiP-MS), stability of proteins from rates of oxidation (SPROX), and several intracellular downstream response characterization methods. We expect that the applications of these ligand modification-free technologies will drive a considerable increase in the number of PECI identified, facilitate unveiling the toxicological mechanisms, and ultimately contribute to systematic health risk assessment of environmental chemicals.

Machine learning assisted rational design of antimicrobial peptides based on human endogenous proteins and their applications for cosmetic preservative system optimization

Preservatives are essential components in cosmetic products, but their safety issues have attracted widespread attention. There is an urgent need for safe and effective alternatives. Antimicrobial peptides (AMPs) are part of the innate immune system and have potent antimicrobial properties. Using machine learning-assisted rational design, we obtained a novel antibacterial peptide, IK-16-1, with significant antibacterial activity and maintaining safety based on β-defensins. IK-16-1 has broad-spectrum antimicrobial properties against Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans, and has no haemolytic activity. The use of IK-16-1 holds promise in the cosmetics industry, since it can serve as a preservative synergist to reduce the amount of other preservatives in cosmetics. This study verified the feasibility of combining computational design with artificial intelligence prediction to design AMPs, achieving rapid screening and reducing development costs.

Discovery of β-sitosterol's effects on molecular changes in rat diabetic wounds and its impact on angiogenesis and macrophages

Diabetes care, particularly for diabetic foot ulcers (DFUs)-related complications, increases treatment costs substantially. Failure to provide timely and appropriate treatment for severe DFUs significantly increases amputation risk. Neovascularization and macrophage polarization play an important role in diabetic wound healing during different stages of the wound repair process. Therefore, a new treatment method that promotes neovascularization and macrophage polarization may accelerate diabetic wound healing. β-sitosterol possesses anti-inflammatory, lipid-lowering, and antidiabetic properties. However, its therapeutic potential in diabetic wound healing remains underexplored. This study evaluated the healing effects of β-sitosterol on diabetic ulcer wounds in rats. We found that β-sitosterol can promote angiogenesis, alternatively activated macrophages (M2 macrophage) proliferation, and collagen synthesis in diabetic wounds. Transcriptomics analysis and proteomics analysis revealed that MAPK, mTOR and VEGF signaling pathways were enriched in β-sitosterol-treated wounds. Molecular docking revealed Ndufb5 maybe the target of β-sitosterol-treated wounds. Our findings confirm the significant diabetic wound healing effects of β-sitosterol in a rat model. β-sitosterol treatment to diabetic wounds accelerates wound healing through promoting M2 macrophage proliferation and angiogenesis. Interestingly, we also found that the process of M2 macrophage proliferation accompanies angiogenesis. Thus, β-sitosterol may be a promising therapeutic approach to enhance diabetic wound healing and reduce amputation in diabetes.