Deazaflavin-dependent nitroreductase (Ddn) is a crucial enzyme involved in mycolic acid biosynthesis, a vital component of the cell wall in Mycobacterium tuberculosis (MTB)-the bacterial pathogen responsible for tuberculosis. Over the past two decades, nitroimidazole oxazine scaffold (NOS) derivatives have been investigated as potential therapeutic agents targeting Ddn in MTB, with a focus on enhancing drug efficacy, minimizing toxicity, and combating drug resistance. In this study, we performed an extensive theoretical investigation combining three-dimensional quantitative structure-activity relationship (3D-QSAR) studies, all-atom molecular docking, and atomic-level molecular dynamics (MD) simulations. Additionally, we analyzed the binding free energies and their decomposed terms between inhibitors and Ddn to elucidate the structure-activity relationships (SARs) and mechanisms of a series of NOS derivatives developed for MTB inhibition. The CoMFA and CoMSIA models demonstrated strong performance, with cross-validation coefficients (R cv 2) of 0.591 and 0.629, respectively, and prediction coefficients (R pred 2) of 0.7698 and 0.6848 for CoMFA and CoMSIA, respectively. These models effectively predicted the minimum inhibitory concentration (MIC) values of the compounds against MTB based on the NOS scaffold. Molecular docking followed by MD simulations was employed to validate the binding modes of these derivatives at the active site of Ddn, providing detailed insights into their interaction patterns. Notably, our analysis revealed that residues Tyr65, Ser78, Tyr130, Tyr133, and Tyr136 played critical roles in determining the potency of the compounds by contributing significantly to their binding energies. These findings provide valuable guidance for the rational design of novel NOS inhibitors with enhanced potential as effective anti-tuberculosis agents.

Vascular endothelial growth factor receptor 2 (VEGFR-2) is a key target for cancer treatment. VEGFR-2 inhibitors, which would initiate apoptosis, have been identified. To derive novel and potent VEGFR-2 inhibitors, a series of quinoxaline derivatives were studied using a combination of molecular modeling approaches, including three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, and molecular dynamics (MD) simulations. We developed robust 3D-QSAR models (CoMFA: R2cv = 0.663, R2pred = 0.6126; CoMSIA: R2cv = 0.631, R2pred = 0.6974) that reveal the structural determinants for VEGFR-2 inhibition. These models were validated for the reliability and predictability. Contour maps provided insights into the requirements for substituents on the common substructure. Molecular docking was conducted to validate the developed models and provided initial conformation for following MD simulations. MD simulations identified vital amino acid residues (Leu838、Phe916、Leu976) involved in ligand-receptor interactions. This workflow can be applied to optimize and design novel and potent VEGFR-2 inhibitors.

Noncoding RNA (NcRNA)-protein interactions (NPIs) play fundamentally important roles in carrying out cellular activities. Although various predictors based on molecular features and graphs have been published to boost the identification of NPIs, most of them often ignore the information between known NPIs or exhibit insufficient learning ability from graphs, posing a significant challenge in effectively identifying NPIs. To develop a more reliable and accurate predictor for NPIs, in this article, we propose NPI-DCGNN, an end-to-end NPI predictor based on a dual-channel graph neural network (DCGNN). NPI-DCGNN initially treats the known NPIs as an ncRNA-protein bipartite graph. Subsequently, for each ncRNA-protein pair, NPI-DCGNN extracts two local subgraphs centered around the ncRNA and protein, respectively, from the bipartite graph. After that, it utilizes a dual-channel graph representation learning layer based on GNN to generate high-level feature representations for the ncRNA-protein pair. Finally, it employs a fully connected network and output layer to predict whether an interaction exists between the pair of ncRNA and protein. Experimental results on four experimentally validated datasets demonstrate that NPI-DCGNN outperforms several state-of-the-art NPI predictors. Our case studies on the NPInter database further demonstrate the prediction power of NPI-DCGNN in predicting NPIs. With the availability of the source codes (https://github.com/zhangxin11111/NPI-DCGNN), we anticipate that NPI-DCGNN could facilitate the studies of ncRNA interactome by providing highly reliable NPI candidates for further experimental validation.

In recent years, Alzheimer disease (AD) as a neurodegenerative disorder has been increasing annually with the aging of the global population, therefore, development of novel anti-AD drugs is imperative. Studies have proven that glycogen synthase kinase-3 beta (GSK-3 beta) is a pivotal factor in the development of AD. Therefore, GSK-3 beta inhibitors would provide powerful means to treat the disorders, such as AD. To in-depth study the structure-activity relationship of a series of oxadiazole derivatives as multifunctional anti-Alzheimer agents, computational three dimensional quantitative structure-activity relationship (3D-QSAR) studies, molecular docking and molecular dynamics were conducted. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods were conducted to build up the 3D-QSAR models, and exhibited significant results (Rcv2 = 0.692, Rpred2 = 0.6885/CoMFA, Rcv2 = 0.696, Rpred2 = 0.6887/CoMSIA). The accuracy of the 3D-QSAR models was validated by external validation and applicability domain analysis. The derived contour maps provided structural information for designing novel compounds to improve the inhibitory activities. Additionally, molecular docking and molecular dynamics were also employed to investigate the bonding interactions and stability of this series of inhibitors in the active site of GSK-3 beta, and the results revealed that the importance of residues Ile62, Asn64 Val70, Tyr128, Val129 and Leu182 for ligand binding to the receptor GSK-3 beta. All the results would be of great help for the discovery of new GSK-3 beta agents that can solve the problem of AD. Effective 3D-QSAR models, molecular docking, molecular dynamics were employed for novel oxadiazole derivatives, the derived structural information will be helpful in the designing of novel ligands with more potent activity.

A wealth of experimental evidence has suggested that open chromatin regions (OCRs) are involved in many critical biological activities, such as DNA replication, enhancer activity, and gene transcription. Accurately identifying OCRs in livestock species can provide critical insights into the distribution and characteristics of OCRs for disease treatment in livestock, thereby improving animal welfare. However, most current machine-learning methods for OCR prediction were originally designed for a limited number of model organisms, such as humans and some model organisms, and thus their performance on non-model organisms, specifically livestock, is often unsatisfactory. To bridge this gap, we propose DeepOCR, a lightweight depth-separable residual network model for predicting OCRs in livestock, including chicken, cattle, and sheep. DeepOCR integrates a single convolution layer and two improved residue structure blocks to extract and learn important features from the input DNA sequences. A fully connected layer was also employed to further process the extracted features and improve the robustness of the entire network. Our benchmarking experiments demonstrated superior prediction performance of DeepOCR compared to state-of-the-art approaches on testing datasets of the three species. The source code of DeepOCR is freely available for academic purposes at https://github.com/jasonzhao371/DeepOCR/. We anticipate DeepOCR servers as a practical and reliable computational tool for OCR-related studies in livestock species.

The vascular endothelial growth factor receptor-2 kinases (VEGFR-2) expressed on tumor cells and vessels are attractive targets for cancer treatment. Potent inhibitors for the VEGFR-2 receptor are novel strategies to develop anti-cancer drugs. In this work, template ligand-based 3D-QSAR studies were performed on a series of benzoxazole derivatives toward different cell lines (HepG2, HCT-116 and MCF-7). Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques were used to generate 3D-QSAR models. Good predictability was derived for the optimal CoMFA models (HepG2: R-cv(2) = 0.509, R-pred(2) = 0.5128; HCT-116: R-cv(2) = 0.574, R-pred(2) = 0.5597; MCF-7: R-cv(2) = 0.568, R-pred(2) = 0.5057) and CoMSIA models (HepG2: R-cv(2) = 0.711, R-pred(2) = 0.6198; HCT-116: R-cv(2) = 0.531, R-pred(2) = 0.5804; MCF-7: R-cv(2) = 0.669, R-pred(2) = 0.6577). In addition, the contour maps derived from CoMFA and CoMSIA models were also generated to illustrate the relationship between different fields and the inhibitory activities. Moreover, molecular docking and molecular dynamics (MD) simulations were also conducted to understand the binding modes and the potential interactions between the receptor and the inhibitors. Some key residues (Leu35, Val43, Lys63, Leu84, Gly117, Leu180 and Asp191) were pointed out for stabilizing the inhibitors in the binding pocket. The binding free energies for the inhibitors agreed well with the experimental inhibitory activity and indicated that steric, electrostatic and hydrogen bond interactions are the main driving force for inhibitor-receptor binding. Overall, a good consistency between theoretical 3D-SQAR and molecular docking and MD simulation studies would provide directions for the design of new candidates, avoiding time-consuming and costly synthesis and biological evaluations. On the whole, the results derived from this study could expand the understanding of benzoxazole derivatives as anticancer agents and would be of great help in lead optimization for early drug discovery of highly potent anticancer activity targeting VEGFR-2.

ExplorationVolume 3, Issue 5 20230503 INSIDE BACK COVEROpen Access Inside Back Cover: Small-molecule nanoprodrug with high drug loading and EGFR, PI3K/AKT dual-inhibiting properties for bladder cancer treatment (EXP2 5/2023) Guoyin Li, Guoyin LiSearch for more papers by this authorZewen Song, Zewen SongSearch for more papers by this authorYi Ru, Yi RuSearch for more papers by this authorJing Zhang, Jing ZhangSearch for more papers by this authorLianxiang Luo, Lianxiang LuoSearch for more papers by this authorWei Yang, Wei YangSearch for more papers by this authorHao Wu, Hao WuSearch for more papers by this authorHaibao Jin, Haibao JinSearch for more papers by this authorXuanwen Bao, Xuanwen BaoSearch for more papers by this authorDi Wei, Di WeiSearch for more papers by this authorZhao Yan, Zhao YanSearch for more papers by this authorHaijing Qu, Haijing QuSearch for more papers by this authorZheng Zhu, Zheng ZhuSearch for more papers by this authorGang Zhou, Gang ZhouSearch for more papers by this author Guoyin Li, Guoyin LiSearch for more papers by this authorZewen Song, Zewen SongSearch for more papers by this authorYi Ru, Yi RuSearch for more papers by this authorJing Zhang, Jing ZhangSearch for more papers by this authorLianxiang Luo, Lianxiang LuoSearch for more papers by this authorWei Yang, Wei YangSearch for more papers by this authorHao Wu, Hao WuSearch for more papers by this authorHaibao Jin, Haibao JinSearch for more papers by this authorXuanwen Bao, Xuanwen BaoSearch for more papers by this authorDi Wei, Di WeiSearch for more papers by this authorZhao Yan, Zhao YanSearch for more papers by this authorHaijing Qu, Haijing QuSearch for more papers by this authorZheng Zhu, Zheng ZhuSearch for more papers by this authorGang Zhou, Gang ZhouSearch for more papers by this author First published: 17 October 2023 https://doi.org/10.1002/EXP.20230503AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Graphical Abstract Overexpression of the EGFR is found in up to 74% of bladder cancer tissue. Two natural products that were found, triptolide (represented by lightning) and hesperidin (represented by orange), could inhibit EGFR pathways. They are linked together into a prodrug that can form nanoparticles (represented by oranges containing lightning), which not only remarkably improved their bioavailability and therapeutic but also reduced their systemic toxicity. Volume3, Issue5October 202320230503 RelatedInformation

In epigenetic mechanisms, DNA methyltransferase 3 alpha (DNMT3A) acts as an initiator for DNA methylation and prevents the downstream genes from expressing. Perturbations of DNMT3A functions may cause uncontrolled gene expression, resulting in pathogenic consequences such as cancers. It is, therefore, vitally important to understand the catalytic process of DNMT3A in its biological macromolecule assembly, viz., heterotetramer: (DNMT3A-3 L)dimer. In this study, we utilized molecular dynamics (MD) simulations, Markov State Models (MSM), and quantum mechanics/molecular mechanics simulations (QM/MM) to investigate the de novo methyl transfer process. We identified the dynamics of the key residues relevant to the insertion of the target cytosine (dC) into the catalytic domain of DNMT3A, and the detailed potential energy surface of the seven-step reaction referring to methyl transfer. Our calculated potential energy barrier (22.51 kcal/mol) approximates the former experimental data (23.12 kcal/mol). The conformational change of the 5-methyl-cytosine (5mC) intermediate was found necessary in forming a four-water chain for the elimination step, which is unique to the other DNMTs. The biological assembly facilitates the creation of such a water chain, and the elimination occurs in an asynchronized mechanism in the two catalytic pockets. We anticipate the findings can enable a better understanding of the general mechanisms of the de novo methyl transfer for fulfilling the key enzymatic functions in epigenetics. And the unique elimination of DNMT3A might ignite novel methods for designing anti-cancer and tumor inhibitors of DNMTs.

The perturbations of DNA methyltransferase 3 alpha (DNMT3A) may cause uncontrolled gene expression, resulting in cancers and tumors. The DNMT inhibitors Azacytidine (AZA) and Zebularine (ZEB) inhibit the DNMT family with no specificities, and consequently would bring side effects during the treatment. Therefore, it is vital to understand the inhibitory mechanisms in DNMT3A to inform the new inhibitor design for DNMTs. Herein, we carried out molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) simulations to investigate the inhibitory mechanisms of the AZA and ZEB. The results were compared to the methyl transfer of cytosine. We showed how the AZA might stop the methyl transfer process, whereas the ZEB might be stuck in a methyl-transferred intermediate (IM3). The IM3 state then fails the elimination due to the unique protein dynamics that result in missing the catalytic water chain. Our results brought atomic-level insights into the mechanisms of the two drugs in DNMT3A, which could benefit the new generation of drug design for the DNMTs.

Bladder cancer (BCa) is one of the most common malignancies worldwide. Although multiple efforts have been made, the 5-year survival rate of patients with BCa remains unchanged in recent years. Overexpression of the epidermal growth factor receptor (EGFR) is found in ≈74% of BCa tissue specimens; however, current EGFR-based targeted therapies show little benefit for BCa patients, as the EGFR downstream pathways appear to be circumvented by other receptor tyrosine kinases (RTKs). In this study, two natural products are identified, namely triptolide (TPL) and hesperidin (HSP), that target and inhibit the EGFR and its downstream PI3K/AKT pathway in BCa. To synergistically combine triptolide and hesperidin, a succinic acid linker was employed to conjugate them and formed an amphiphilic TPL-HSP EGFR-targeting prodrug (THE), which further self-assembled to generate nanoparticles (THE NPs). These NPs allowed the EGFR-targeted delivery of the triptolide and hesperidin, and simultaneous inhibition of the EGFR and PI3K/AKT both in vitro and in vivo. This study provides a promising EGFR-targeted delivery approach with the dual inhibition of the EGFR and PI3K/AKT, while also exhibiting a high drug loading and low toxicity. Our formulation may be a suitable option to deliver natural products for BCa treatment by EGFR-targeted therapy.

The metal-dependent M17 aminopeptidases are conserved throughout all kingdoms of life. This large enzyme family is characterized by a conserved binuclear metal center and a distinctive homohexameric arrangement. Recently, we showed that hexamer formation in Plasmodium M17 aminopeptidases was controlled by the metal ion environment, although the functional necessity for hexamer formation is still unclear. To further understand the mechanistic role of the hexameric as-sembly, here we undertook an investigation of the structure and dynamics of the M17 aminopeptidase from Plasmodium fal-ciparum, PfA-M17. We describe a novel structure of PfA-M17, which shows that the active sites of each trimer are linked by a dynamic loop, and loop movement is coupled with a drastic rearrangement of the binuclear metal center and substrate -binding pocket, rendering the protein inactive. Molecular dy-namics simulations and biochemical analyses of PfA-M17 vari-ants demonstrated that this rearrangement is inherent to PfA-M17, and that the transition between the active and inactive states is metal dependent and part of a dynamic regulatory mechanism. Key to the mechanism is a remodeling of the binuclear metal center, which occurs in response to a signal from the neighboring active site and serves to moderate the rate of proteolysis under different environmental conditions. In conclusion, this work identifies a precise mechanism by which oligomerization contributes to PfA-M17 function. Furthermore, it describes a novel role for metal cofactors in the regulation of enzymes, with implications for the wide range of metal-loenzymes that operate via a two-metal ion catalytic center, including DNA processing enzymes and metalloproteases.

Epidermal growth factor receptor (EGFR) is a promising target for the treatment of different types of malignant tumors. Therefore, a combined molecular modeling study was performed on a series of quinazoline derivatives as EGFR inhibitors. The optimum ligand-based CoMFA and CoMSIA models showed reliable and satisfactory predictability (with R2cv=0.681, R2ncv=0.844, R2pred=0.8702 and R2cv=0.643, R2ncv=0.874, R2pred=0.6423). The derived contour maps provide structural features to improve inhibitory activity. Furthermore, the contour maps, molecular docking, and molecular dynamics (MD) simulations have good consistency, illustrating that the derived models are reliable. In addition, MD simulations and binding free energy calculations were also carried out to understand the conformational fluctuations at the binding pocket of the receptor. The results indicate that hydrogen bond, hydrophobic and electrostatic interactions play significant roles on activity and selectivity. Furthermore, amino acids Val31, Lys50, Thr95, Leu149 and Asp160 are considered as essential residues to participate in the ligand-receptor interactions. Overall, this work would offer reliable theoretical basis for future structural modification, design and synthesis of novel EGFR inhibitors with good potency.Communicated by Ramaswamy H. Sarma

DNA gyrase B (GyrB) plays a critical role in DNA replication, repair, recombination, and transcription and has become an attractive target for a number of antibacterial agents. In the present work, to better comprehend the structure–activity relationship and mechanism of action of novel series of GyrB inhibitors, a theoretical study of three-dimensional-quantitative structure–activity relationship (3D-QSAR), molecular docking, molecular dynamics (MD) simulations, and binding free energy analysis were performed. The results showed that CoMFA (R2cv = 0.591, R2pred = 0.7698) and CoMSIA (R2cv = 0.629, R2pred = 0.6848) models possessed robust stability and predictability. The derived contour maps of steric, electrostatic, and hydrogen bond donor field further displayed the modified information of these inhibitors. Molecular docking was further conducted to provide the initial binding conformation for MD simulations. Subsequently, MD simulations were applied to explore the key amino acids and binding modes at the active site. The binding free energy decomposition analysis further indicates that the residues Ile54, Glu55, Arg83, Ala85, Val86 and Thr128 are essential for the high selectivity of inhibitors. Overall, these results would serve as a significant guideline for the discovery and design of novel GyrB inhibitors.

Effective computational approaches for bitter-tasting peptides have been developed and analyzed in the present work.

Src homology 2 (SH2)-containing protein tyrosine phosphatase 2 (SHP2) as a major phosphatase would affect the development of tumors by regulating several cellular processes, and is a significant potential target for cancer treatment. In the present work, a series of pyridine derivatives possessing a wide range of inhibitory activity was employed to investigate the structural requirements by developing three dimensional quantitative structure–activity relationship (3D-QSAR) models using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. The results show that CoMFA (R cv 2 = 0.646, R pred 2 = 0.5587) and CoMSIA (R cv 2 = 0.777, R pred 2 = 0.7131) have excellent stability and predictability. The relationship between the inhibitory activity and structure of the inhibitors was analyzed by the derived contour maps. Furthermore, the QSAR models were validated by molecular docking and molecular dynamics simulations, which were also applied to reveal the potential molecular mechanism of these inhibitors. It was found that Arg110, Asn216, Thr218, Thr252 and Pro490 play a crucial role in stabilizing the inhibitors. Additionally, MM/PBSA calculations provided the binding free energy were also conducted to explain the discrepancy of binding activities. Overall, the outcomes of this work could provide useful information and theoretical guidance for the development of novel and potent SHP2 inhibitors.

Focal adhesion kinase (FAK) is a promising target for developing more effective anticancer drugs. To better understand the structure-activity relationships and mechanism of actions of FAK inhibitors, a molecular modeling study using 3D-QSAR, molecular docking, molecular dynamics simulations, and binding free energy analysis were conducted. Two types of satisfactory 3D-QSAR models were generated, comprising the CoMFA model (R2cv = 0.528, R2pred = 0.7557) and CoMSIA model (R2cv = 0.757, R2pred = 0.8362), for predicting the inhibitory activities of novel inhibitors. The derived contour maps indicate structural characteristics for substituents on the template. Molecular docking, molecular dynamic simulations and binding free energy calculations further reveal that the binding of inhibitors to FAK is mainly contributed from hydrophobic, electrostatic and hydrogen bonding interactions. In addition, some key residues (Arg14, Glu88, Cys90, Arg138, Asn139, Leu141, and Leu155) responsible for ligand-receptor binding are highlighted. All structural information obtained from 3D-QSAR models and molecular dynamics is consist with the available experimental activities. All the results will facilitate the optimization of this series of FAK inhibitors with higher inhibitory activities.

In the process of cancer treatment, due to the complexity, diversity and heterogeneity of tumors, most conventional anticancer drugs show a narrow therapeutic window. Therefore, the current trend of clinical research has gradually shifted from a single treatment to a dual-drug or multi-modal combination therapy with different therapeutic effects. Janusparticles can independently control the release of different drugs, reducing mutual interference between modes. In this review, the preparation methods and mechanisms of Janusnanomaterials, the application of the Janusnanoplatform based on chemotherapy in the field of multi-mode combined anti-tumor applications, and the challenges faced by Janusnanomaterials are summarized.

Dityrosine is the product of oxidation that has been linked to a number of serious pathological conditions. Evidence indicates that high amounts of dityrosine exist in oxidized milk powders and some milk related foodstuffs, further reducing the nutritional value of oxidized proteins. Therefore, we hypothesize that some receptors related to special diseases would be targets for dityrosine. However, the mechanisms of the interaction of dityrosine with probable targets are still unknown. In the present work, an inverse virtual screening approach was performed to screen possible novel targets for dityrosine. Molecular docking studies were performed on a panel of targets extracted from the potential drug target database (PDTD) to optimize and validate the screening results. Firstly, two different conformations cis- and trans- were found for dityrosine during minimization. Moreover, Tubulin (αT) (−11.0 kcal/mol) was identified as a target for cis-dityrosine (CDT), targets including αT (−11.2 kcal/mol) and thyroid hormone receptor beta-1 (−10.7 kcal/mol) presented high binding affinities for trans-dityrosine (TDT). Furthermore, in order to provide binding complexes with higher precision, the three docked systems were further refined by performing thermo dynamic simulations. A series of techniques for searching for the most stable binding pose and the calculation of binding free energy are elaborately provided in this work. The major interactions between these targets and dityrosine were hydrophobic, electrostatic and hydrogen bonding. The application of inverse virtual screening method may facilitate the prediction of unknown targets for known ligands, and direct future experimental assays.