Water Network-Augmented Two-State Model for Protein-Ligand Binding Affinity Prediction.

Water network rearrangement from the ligand-unbound state to the ligand-bound state is known to have significant effects on the protein-ligand binding interactions, but most of the current machine learning-based scoring functions overlook these effects. In this study, we endeavor to construct a comprehensive and realistic deep learning model by incorporating water network information into both ligand-unbound and -bound states. In particular, extended connectivity interaction features were integrated into graph representation, and graph transformer operator was employed to extract features of the ligand-unbound and -bound states. Through these efforts, we developed a water network-augmented two-state model called ECIFGraph::HM-Holo-Apo. Our new model exhibits satisfactory performance in terms of scoring, ranking, docking, screening, and reverse screening power tests on the CASF-2016 benchmark. In addition, it can achieve superior performance in large-scale docking-based virtual screening tests on the DEKOIS2.0 data set. Our study highlights that the use of a water network-augmented two-state model can be an effective strategy to bolster the robustness and applicability of machine learning-based scoring functions, particularly for targets with hydrophilic or solvent-exposed binding pockets.

Enhancing Generalizability in Protein-Ligand Binding Affinity Prediction with Multimodal Contrastive Learning.

Improving the generalization ability of scoring functions remains a major challenge in protein-ligand binding affinity prediction. Many machine learning methods are limited by their reliance on single-modal representations, hindering a comprehensive understanding of protein-ligand interactions. We introduce a graph-neural-network-based scoring function that utilizes a triplet contrastive learning loss to improve protein-ligand representations. In this model, three-dimensional complex representations and the fusion of two-dimensional ligand and coarse-grained pocket representations converge while distancing from decoy representations in latent space. After rigorous validation on multiple external data sets, our model exhibits commendable generalization capabilities compared to those of other deep learning-based scoring functions, marking it as a promising tool in the realm of drug discovery. In the future, our training framework can be extended to other biophysical- and biochemical-related problems such as protein-protein interaction and protein mutation prediction.

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives.

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

Global research trends in tongue cancer from 2000 to 2022: bibliometric and visualized analysis.

OBJECTIVES: This study conducts a systematic bibliometric analysis of tongue cancer publications to identify key topics, hotspots, and research distribution. METHODS: We analyzed tongue cancer publications in the Web of Science core collection database, assessing their quantity and quality. We investigated contributors, including countries, affiliations, journals, authors, and categories, within collaborative networks. Additionally, we synthesized key research findings using various analytical techniques, such as alluvial flow, burstness analysis, cluster analysis, co-occurrence network of associations, and network layer overlay. RESULTS: From 2000 to 2022, this bibliometric study covers 2205 articles and reviews across 617 journals, involving 72 countries, 2233 institutions, and 11,266 authors. It shows consistent growth, particularly in 2016. Key contributors include China (499 publications), Karolinska Institute (84 publications), Oral Oncology (144 publications), and Tuula Salo (47 publications). Other notable contributors are the USA (16,747 citations), the National Cancer Institute (NCI) (2597 citations), and the Memorial Sloan-Kettering Cancer Center (MSK) (2231 citations). Additionally, there are significant teams led by Tuula Salo and Dalianis. We have identified six primary clusters: #0 apoptosis, #1 depth of invasion, #2 radiotherapy, #3 hpv, #4 tongue cancer, #5 oral cancer. The top ten highly cited documents primarily pertain to epidemiology, prognostic indicators in early-stage oral tongue cancer, and HPV. Additionally, we observed 16 reference clusters, with depth of invasion (#3), young patients (#4), and tumor budding (#6) gaining prominence since 2012, indicating sustained research interests. CONCLUSIONS: This analysis emphasizes the increasing scholarly interest in tongue cancer research. The bibliometric evaluation highlights pivotal recent research themes such as HPV, depth of invasion, tumor budding, and surgical margins. CLINICAL RELEVANCE: The bibliometric analysis highlights the key topics and studies which have shaped the understanding and management of tongue cancer.

Identification of IGF2 promotes skin wound healing by co-expression analysis.

Oral mucosa is an ideal model for studying scarless wound healing. Researchers have shown that the key factors which promote scarless wound healing already exist in basal state of oral mucosa. Thus, to identify the other potential factors in basal state of oral mucosa will benefit to skin wound healing. In this study, we identified eight gene modules enriched in wound healing stages of human skin and oral mucosa through co-expression analysis, among which the module M8 was only module enriched in basal state of oral mucosa, indicating that the genes in module M8 may have key factors mediating scarless wound healing. Through bioinformatic analysis of genes in module M8, we found IGF2 may be the key factor mediating scarless wound healing of oral mucosa. Then, we purified IGF2 protein by prokaryotic expression, and we found that IGF2 could promote the proliferation and migration of HaCaT cells. Moreover, IGF2 promoted wound re-epithelialization and accelerated wound healing in a full-thickness skin wound model. Our findings identified IGF2 as a factor to promote skin wound healing which provide a potential target for wound healing therapy in clinic.

In-Situ Ultrafast Construction of Zinc Tungstate Interface Layer for Highly Reversible Zinc Anodes.

Constructing artificial solid electrolyte interface on the Zn anode surface is recognized as an appealing method to inhibit zinc dendrites and side reactions, whereas the current techniques are complex and time-consuming. Here, a robust and zincophilic zinc tungstate (ZnWO4) layer has been in situ constructed on the Zn anode surface (denoted as ZWO@Zn) by an ultrafast chemical solution reaction. Comprehensive characterizations and theoretical calculations demonstrate that the ZWO layer can effectively modulate the interfacial electric field distribution and promote the Zn2+ uniform diffusion, thus facilitating the uniform Zn2+ nucleation and suppressing zinc dendrites. Besides, ZWO layer can prevent direct contact between the Zn/water and increase the hydrogen evolution reaction overpotential to eliminate side reactions. Consequently, the in situ constructed ZWO layer facilitates remarkable reversibility in the ZWO@Zn||Ti battery, achieving an impressive Coulombic efficiency of 99.36 % under 1.0 mA cm-2, unprecedented cycling lifespan exceeding 1800 h under 1.0 mA cm-2 in ZWO@Zn||ZWO@Zn battery, and a steady and reliable operation of the overall ZWO@Zn||VS2 battery. The work provides a simple, low cost, and ultrafast pathway to crafting protective layers for driving advancements in aqueous zinc-metal batteries.

Open hepatic artery flow with portal vein clamping protects against bile duct injury compared to pringles maneuver.

BACKGROUND: Conventional hepatic artery and portal vein clamping strategies can prevent blood loss and ischemia-reperfusion liver injury, and such preventative measures are the key to successful liver surgery. However, ischemic-induced damage to cholangiocytes is rarely considered. Here, we aimed to investigate the effect of different hepatic inflow interruption methods on bile duct injury. METHODS: Forty rats were randomly grouped as sham, Pringle maneuver (PM) and hepatic arterial blood flow open (HAFO) groups. We evaluated liver histology and function in liver sections, and biliary histology, cholangiocyte apoptosis and proliferation, cytokine production, and bile composition. RNA sequencing is performed to explore possible molecular mechanisms. The Blood-biliary barrier permeability and tight junctions were analyzed by HRP injection, immunofluorescence staining and analysis of ZO-1 expression by immunoblotting. RESULTS: HAFO significantly attenuated ischemia-induced liver injury and decreased ALT, ALP, TBIL, and DBIL levels in serum. The histopathological observations showed that bile duct injury in the PM group was more serious than that in the HAFO group. The numbers of apoptotic biliary epithelial cells in HAFO-treated rat bile ducta were lower than those in the PM group. RNA-seq showed that tight junctions may be related to the mechanism underlying the protective effect of HAFO, as shown by the reduced HRP levels and increased ZO-1 and claudin-1/3 expression in the HAFO group compared to the PM group. CONCLUSION: Compared with PM, HAFO alleviated the ischemic injury to the biliary system, which was characterized by decreased biliary epithelial cell apoptosis, reduced inflammatory responses and decreased blood-biliary-barrier permeability.

Structure-Based Discovery of a Novel Allosteric Inhibitor against Human Dopamine Transporter.

Human dopamine transporter (hDAT) regulates the reuptake of extracellular dopamine (DA) and is an essential therapeutic target for central nervous system (CNS) diseases. The allosteric modulation of hDAT has been identified for decades. However, the molecular mechanism underlying the transportation is still elusive, which hinders the rational design of allosteric modulators against hDAT. Here, a systematic structure-based method was performed to explore allosteric sites on hDAT in inward-open (IO) conformation and to screen compounds with allosteric affinity. First, the model of the hDAT structure was constructed based on the recently reported Cryo-EM structure of the human serotonin transporter (hSERT) and Gaussian-accelerated molecular dynamics (GaMD) simulation was further utilized for the identification of intermediate energetic stable states of the transporter. Then, with the potential druggable allosteric site on hDAT in IO conformation, virtual screening of seven enamine chemical libraries (∼440,000 compounds) was processed, resulting in 10 compounds being purchased for in vitro assay and with Z1078601926 discovered to allosterically inhibit hDAT (IC50 = 0.527 [0.284; 0.988] µM) when nomifensine was introduced as an orthosteric ligand. Finally, the synergistic effect underlying the allosteric inhibition of hDAT by Z1078601926 and nomifensine was explored using additional GaMD simulation and postbinding free energy analysis. The hit compound discovered in this work not only provides a good starting point for lead optimization but also demonstrates the usability of the method for the structure-based discovery of novel allosteric modulators of other therapeutic targets.

Ligand binding affinity prediction with fusion of graph neural networks and 3D structure-based complex graph.

Accurate prediction of protein-ligand binding affinity is pivotal for drug design and discovery. Here, we proposed a novel deep fusion graph neural networks framework named FGNN to learn the protein-ligand interactions from the 3D structures of protein-ligand complexes. Unlike 1D sequences for proteins or 2D graphs for ligands, the 3D graph of protein-ligand complex enables the more accurate representations of the protein-ligand interactions. Benchmark studies have shown that our fusion models FGNN can achieve more accurate prediction of binding affinity than any individual algorithm. The advantages of fusion strategies have been demonstrated in terms of expressive power of data, learning efficiency and model interpretability. Our fusion models show satisfactory performances on diverse data sets, demonstrating their generalization ability. Given the good performances in both binding affinity prediction and virtual screening, our fusion models are expected to be practically applied for drug screening and design. Our work highlights the potential of the fusion graph neural network algorithm in solving complex prediction problems in computational biology and chemistry. The fusion graph neural networks (FGNN) model is freely available in https://github.com/LinaDongXMU/FGNN.

Discovery of novel BRD4-BD2 inhibitors via in silico approaches: QSAR techniques, molecular docking, and molecular dynamics simulations.

Bromodomain-containing protein 4(BRD4) plays an important role in the occurrence and development of various malignant tumors, which has attracted the attention of scientific research institutions and pharmaceutical companies. The structural modification of most currently available BRD4 inhibitors is relatively simple, but the drug effectiveness is limited. Research has found that the inhibition of BD1 may promote the differentiation of oligodendrocyte progenitor cell; however, the inhibition of BD2 will not cause this outcome. Therefore, newly potential drugs which target BRD4-BD2 need further research. Herein, we initially built QSAR models out of 49 compounds using HQSAR, CoMFA, CoMSIA, and Topomer CoMFA technology. All of the models have shown suitable reliabilities (q2 = 0.778, 0.533, 0.640, 0.702, respectively) and predictive abilities (r2pred = 0.716, 0.6289, 0.6153, 0.7968, respectively) for BRD4-BD2 inhibitors. On the basis of QSAR results and the search of the R-group in the topomer search module, we designed 20 new compounds with high activity that showed appropriate docking score and suitable ADMET. Docking studies and MD simulation were carried out to reveal the amino acid residues (Asn351, Cys347, Tyr350, Pro293, and Asp299) at the active site of BRD4-BD2. Free energy calculations and free energy landscapes verified the stable binding results and indicated stable conformations of the complexes. These theoretical studies provide guidance and theoretical basis for designing and developing novel BRD4-BD2 inhibitors.

QSAR analysis of 3-pyrimidin-4-yl-oxazolidin-2-one derivatives isocitrate dehydrogenase inhibitors using Topomer CoMFA and HQSAR methods.

A series of mIDH1 inhibitors derived from 3-pyrimidine-4-oxazolidin-2-ketone derivatives were studied by QSAR model to explore the key factors that inhibit mIDH1 activity. The generated model was cross-verified and non-cross-verified by Topomer CoMFA and HQSAR methods; the independent test set was verified by PLS method; the Topomer search technology was used for virtual screening and molecular design; and the Surflex-Dock method and ADMET technology were used for molecular docking, pharmacology and toxicity prediction of the designed drug molecules. The Topomer CoMFA and HQSAR cross-validation coefficients q2 are 0.783 and 0.784, respectively, and the non-cross-validation coefficients r2 are 0.978 and 0.934, respectively. Ten new drug molecules have been designed using Topomer search technology. The results of molecular docking and ADMET show that the newly designed drug molecules are effective. The docking situation, pharmacology and toxicity prediction results are good. The model can be used to predict the bioactivity of the same type of new compounds and their derivatives. The prediction results of molecular design, molecular docking and ADMET can provide some ideas for the design and development of novel mIDH1 inhibitor anticancer drugs, and provide certain theoretical basis of the experimental verification of new compounds in the future. Newly designed molecules after docking with corresponding proteins in the PDB library, it can explore the targets of drug molecules acting with large proteins and the related force, which is very helpful for the design of new drugs and the mechanism of drug action.

Computational strategies towards developing novel SARS-CoV-2 Mpro inhibitors against COVID-19.

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains to be a serious threat due to the lack of a specific therapeutic agent. Computational methods are particularly suitable for rapidly fight against SARS-CoV-2. This present research aims to systematically explore the interaction mechanism of a series of novel bicycloproline-containing SARS-CoV-2 Mpro inhibitors through integrated computational approaches. We designed six structurally modified novel SARS-CoV-2 Mpro inhibitors based on the QSAR study. The four designed compounds with higher docking scores were further explored through molecular docking, molecular dynamics (MD) simulations, free energy calculations, and residual energy contributions estimated by the MM-PBSA approach, with comparison to compound 23(PDB entry 7D3I). This research not only provides robust QSAR models as valuable screening tools for the development of anti-COVID-19 drugs, but also proposes the newly designed SARS-CoV-2 Mpro inhibitors with nanomolar activities that can be potentially used for further characterization to treat SARS-CoV-2 virus.

Water-soluble matrine-type alkaloids with potential anti-neuroinflammatory activities from the seeds of Sophora alopecuroides.

A phytochemical investigation on the alkaloids from water-soluble part of Sophora alopecuroides led to obtain forty matrine-type alkaloids (1-40) including eighteen new ones (1-18), which covers almost all positions of the oxygen substitution in matrine-type structure. Notably, eight compounds (1-8) belong to rare bis-amide matrine-type alkaloid. The new structures were determined based on extensive spectroscopic data, electronic circular dichroism (ECD) calculations, and six instances, verified by X-ray crystallography. Most of isolates showed anti-neuroinflammatory activities based on the expression of tumor necrosis factor (TNF)-α and interleukin (IL)-6 in BV2 microglia cells. Especially, compound 39 can suppress those two mediator secretions in a dose-dependent manner with IC50 values of 21.6 ± 0.5 and 16.7 ± 0.8 µM, respectively. Further mechanistic study revealed that 39 suppressed the phosphorylation of IκBα and p65 subunit to regulate the NF-κB signaling pathway.

Structural modification of 4, 5-dihydro-[1, 2, 4] triazolo [4, 3-f] pteridine derivatives as BRD4 inhibitors using 2D/3D-QSAR and molecular docking analysis.

Cancer treatment continues to be one of the most serious public health issues in the world. The overexpression of BRD4 protein has led to a series of malignant tumors, hence the development of small molecule BRD4 protease inhibitors has always been a hot spot in the field of medical research. In this study, a series of 4,5-dihydro-[1, 2, 4] triazolo [4, 3-f] pteridine derivatives were used to establish 3D/2D-QSAR models and to discuss the relationship between inhibitor structure and activity. Four ideal models were established, including the comparative molecular field analysis (CoMFA: [Formula: see text] = 0.574, [Formula: see text] = 0.947) model, comparative molecular similarity index analysis (CoMSIA: [Formula: see text]= 0.622, [Formula: see text] = 0.916) model, topomer CoMFA ([Formula: see text] = 0.691, [Formula: see text]= 0.912) model and hologram quantitative structure-activity relationship (HQSAR: [Formula: see text]= 0.759, [Formula: see text] = 0.963) model. They show quite good external predictive power for the test set, with [Formula: see text] values of 0.602, 0.624, 0.671 and 0.750, respectively. In addition, the contour and color code map given by the 2D/3D-QSAR model with the results of molecular docking analyzed to chalk up modification methods for improving inhibitory activity, which was verified by designing novel compounds. The analysis results are helpful to promote the modification of the inhibitor framework and to provide a reference for the construction of new and promising BRD4 inhibitor compounds.

One new sesquiterpene pyridine alkaloid from the stems and leaves of Euonymus fortunei.

A new sesquiterpene pyridine alkaloid (1), along with four known compounds (2-5), were isolated from the stems and leaves of Euonymus fortunei. The new structure was determined by extensive spectroscopic analyses (IR, UV, NMR, HRESIMS and ECD). In addition, compound 3 showed a stronger anti-respiratory syncytial virus (RSV) activity with an IC50 value of 1.20 ± 0.10 µM than the positive control ribavirin with an IC50 value of 5.62 ± 0.49 µM.[Formula: see text].

[Qualitative analysis of Gynostemma longipes for medicinal usage].

The Qinling-Daba Mountains area is the main producing areas of Gynostemma longipes for medicinal usage, and samples of wild whole plants in Pingli, Shaanxi Province and Qingchuan, Sichuan Province were collected. The ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry(UHPLC-Q-TOF-MS~E) was used to profile the chemical compositions and analyze the similarities and differences of G. longipes samples in these areas. Based on the accurate molecular weight and fragment information obtained from Q-TOF-MS~E, the structures of the main components were identified by combining with the mass spectra, chromatographic behaviors of reference standards and related literatures. The results showed that the components of wild G. longipes from different places among Qinling-Daba Mountains area were similar. Forty-five chemical components were identified in the whole plant of G. longipes from Pingli, Shaanxi Province, including 43 triterpenoid saponins and 2 flavonoids which contain all main peaks in its fingerprint. The main components are dammarane-type triterpenoid saponins, such asgypenoside ⅩLⅨ, gypenoside A and its malonylated product of glycosyl.

Inhibitory Effect of PIK-24 on Respiratory Syncytial Virus Entry by Blocking Phosphatidylinositol-3 Kinase Signaling.

Phosphoinositide-3 kinase signaling modulates many cellular processes, including cell survival, proliferation, differentiation, and apoptosis. Currently, it is known that the establishment of respiratory syncytial virus infection requires phosphoinositide-3 kinase signaling. However, the regulatory pattern of phosphoinositide-3 kinase signaling or its corresponding molecular mechanism during respiratory syncytial virus entry remains unclear. Here, the involvement of phosphoinositide-3 kinase signaling in respiratory syncytial virus entry was studied. PIK-24, a novel compound designed with phosphoinositide-3 kinase as a target, had potent anti-respiratory syncytial virus activity both in vitro and in vivo PIK-24 significantly reduced viral entry into the host cell through blocking the late stage of the fusion process. In a mouse model, PIK-24 effectively reduced the viral load and alleviated inflammation in lung tissue. Subsequent studies on the antiviral mechanism of PIK-24 revealed that viral entry was accompanied by phosphoinositide-3 kinase signaling activation, downstream RhoA and cofilin upregulation, and actin cytoskeleton rearrangement. PIK-24 treatment significantly reversed all these effects. The disruption of actin cytoskeleton dynamics or the modulation of phosphoinositide-3 kinase activity by knockdown also affected viral entry efficacy. Altogether, it is reasonable to conclude that the antiviral activity of PIK-24 depends on the phosphoinositide-3 kinase signaling and that the use of phosphoinositide-3 kinase signaling to regulate actin cytoskeleton rearrangement plays a key role in respiratory syncytial virus entry.

MicroRNA-125b Interacts with Foxp3 to Induce Autophagy in Thyroid Cancer.

Thyroid cancer is rapidly increasing in incidence worldwide. Although most thyroid cancer can be cured with surgery, radioactive iodine, and/or chemotherapy, thyroid cancers still recur and may become chemoresistant. Autophagy is a complex self-degradative process that plays a dual role in cancer development and progression. In this study, we found that miR-125b was downregulated in tissue samples of thyroid cancer as well as in thyroid cancer cell lines, and the expression of Foxp3 was upregulated. Further, we demonstrated that miR-125b could directly act on Foxp3 by binding to its 3' UTR and inhibit the expression of Foxp3. A negative relationship between miR-125b and Foxp3 was thus revealed. Overexpression of miR-125b markedly sensitized thyroid cancer cells to cisplatin treatment by inducing autophagy through an Atg7 pathway in vitro and in vivo. Taken together, our findings demonstrate a novel mechanism by which miR-125b has the potential to negatively regulate Foxp3 to promote autophagy and enhance the efficacy of cisplatin in thyroid cancer. miR-125 may be of therapeutic significance in thyroid cancer.

Matrine-Type Alkaloids from the Roots of Sophora flavescens and Their Antiviral Activities against the Hepatitis B Virus.

Eight new matrine-type alkaloids, flavesines G-J (1-4), alopecurine B (5), 7,11-dehydro-oxymatrine (6), 10-oxy-5,6-dehydromatrine (7), and 10-oxysophoridine (8), along with nine known analogues (9-17) were isolated from the roots of Sophora flavescens. Compounds 1-3 are the first natural matrine-type alkaloids with an open-loop ring D, while compound 4 represents an unprecedented dimerization pattern constructed from matrine and piperidine, and 5 is the first example of a matrine-type alkaloid with cleavage of the C-5-C-6 bond. The new structures were elucidated by means of spectroscopic data analysis (including NMR, MS, IR, and UV), and the absolute configurations were determined using single-crystal X-ray diffraction and ECD data. The isolated alkaloids were evaluated for their antiviral activity against hepatitis B virus, and compounds 1, 4, 5, 10, and 14 exhibited comparable antiviral potencies to matrine.