A novel fluorescence sensor based on Al3+-mediated aggregation of gold nanoclusters for determination of citric acid in beverages.

This paper revealed a new strategy for citric acid (CA) detection using aggregation-induced emission (AIE)-based fluorescent gold nanoclusters (AuNCs). AuNCs was synthesized using glutathione (GSH) as the template and reducing agent and used as the fluorescent probe to detect CA under aluminum ion (Al3+) mediation. The fluorescence intensity of AuNCs increased about 4 times with the addition of Al3+, but the enhanced fluorescence was quenched after the addition of CA. Based on this fluorescence phenomenon, an "on-off" fluorescence strategy was designed for the sensitive determination of CA and a linear detection range for CA was achieved within 0-80.0 µM. In addition, the developed probe exhibited high selectivity and accuracy for determination of CA. The mechanism of fluorescence enhancement and quenching of AuNCs was explored in detail. The established probe was used successfully for CA detection in beverages. The spiked recoveries from 97.50% to 103.67% were gratifying, which indicated the probe had potential prospects for detecting CA in food.

QSAR and molecular docking studies on designing potent inhibitors of SARS-CoVs main protease.

Background: Severe acute respiratory syndrome coronavirus (SARS-CoVs) have emerged as a global health threat, which had caused a high rate of mortality. There is an urgent need to find effective drugs against these viruses. Objective: This study aims to predict the activity of unsymmetrical aromatic disulfides by constructing a QSAR model, and to design new compounds according to the structural and physicochemical attributes responsible for higher activity towards SARS-CoVs main protease. Methods: All molecules were constructed in ChemOffice software and molecular descriptors were calculated by CODESSA software. A regression-based linear heuristic method was established by changing descriptors datasets and calculating predicted IC50 values of compounds. Then, some new compounds were designed according to molecular descriptors from the heuristic method model. The compounds with predicted values smaller than a set point were constantly screened out. Finally, the properties analysis and molecular docking were conducted to further understand the structure-activity relationships of these finalized compounds. Results: The heuristic method explored the various descriptors responsible for bioactivity and gained the best linear model with R2 0.87. The success of the model fully passed the testing set validation, proving that the model has both high statistical significance and excellent predictive ability. A total of 5 compounds with ideal predicted IC50 were found from the 96 newly designed derivatives and their properties analyze was carried out. Molecular docking experiments were conducted for the optimal compound 31a, which has the best compound activity with good target protein binding capability. Conclusion: The heuristic method was quite reliable for predicting IC50 values of unsymmetrical aromatic disulfides. The present research provides meaningful guidance for further exploration of the highly active inhibitors for SARS-CoVs.

Tit Structure-activity Relationship Study and Design of Novel 1, 8- Naphthimide Derivatives as Potential DNA-targeting Chemotherapeutic Agents for Osteosarcoma.

BACKGROUND: 1, 8-naphthimide is a novel tumor inhibitor targeting nuclear DNA, which makes it applicable to the design and development of anti-osteosarcoma drugs. OBJECTIVE: The aim of this study is to establish a satisfactory model based on 1, 8-naphthimide derivatives that makes reliable prediction as DNA-targeted chemotherapy agents for osteosarcoma. METHODS: All compounds are constructed using ChemDraw software and subsequently optimized using Sybyl software. COMSIA method is used to construct QSAR model with the optimized compound in Sybyl software package. A series of new 1, 8-naphthalimide derivatives are designed and their IC50 values are predicted using the QSAR model. Finally, the newly designed compounds are screened according to IC50 values, and molecular docking experiments are conducted on the top ten compounds of IC50. RESULTS: The COMSIA model shows that q2 is 0.529 and the optimum number of components is 6. The model has a high r2 value of 0.993 and a low SEE of 0.033, with the F value and the r2 predicted to be 495.841 and 0.996 respectively. The statistical results and verification results of the model are satisfactory. In addition, analyzing the contour maps is conducive to finding the structural requirements. CONCLUSION: The results of this study can provide guidance for medical chemists and other related workers to develop targeted chemotherapy drugs for osteosarcoma.

3D,2D-QSAR study and docking of novel quinazolines as potential target drugs for osteosarcoma.

Background: Quinazolines are an important class of benzopyrimidine heterocyclic compounds with a promising antitumor activity that can be used for the design and development of osteosarcoma target compounds. Objective: To predict the compound activity of quinazoline compounds by constructing 2D- and 3D-QSAR models, and to design new compounds according to the main influencing factors of compound activity in the two models. Methods: First, heuristic method and GEP (gene expression programming) algorithm were used to construct linear and non-linear 2D-QSAR models. Then a 3D-QSAR model was constructed using CoMSIA method in SYBYL software package. Finally, new compounds were designed according to molecular descriptors of 2D-QSAR model and contour maps of 3D-QSAR model. Several compounds with optimal activity were used for docking experiments with osteosarcoma related targets (FGFR4). Results: The non-linear model constructed by GEP algorithm was more stable and predictive than the linear model constructed by heuristic method. A 3D-QSAR model with high Q2 (0.63) and R 2 (0.987) values and low error values (0.05) was obtained in this study. The success of the model fully passed the external validation formula, proving that the model is very stable and has strong predictive power. 200 quinazoline derivatives were designed according to molecular descriptors and contour maps, and docking experiments were carried out for the most active compounds. Compound 19g.10 has the best compound activity with good target binding capability. Conclusion: To sum up, the two novel QSAR models constructed were very reliable. The combination of descriptors in 2D-QSAR with COMSIA contour maps provides new design ideas for future compound design in osteosarcoma.

Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19.

BACKGROUND AND OBJECTIVE: Efforts to alleviate the ongoing coronavirus disease 2019 (COVID-19) crisis showed that rapid, sensitive, and large-scale screening is critical for controlling the current infection and that of ongoing pandemics. METHODS: Here, we explored the potential of vibrational spectroscopy coupled with machine learning to screen COVID-19 patients in its initial stage. Herein presented is a hybrid classification model called grey wolf optimized support vector machine (GWO-SVM). The proposed model was tested and comprehensively compared with other machine learning models via vibrational spectroscopic fingerprinting including saliva FTIR spectra dataset and serum Raman scattering spectra dataset. RESULTS: For the unknown vibrational spectra, the presented GWO-SVM model provided an accuracy, specificity and F1_score value of 0.9825, 0.9714 and 0.9778 for saliva FTIR spectra dataset, respectively, while an overall accuracy, specificity and F1_score value of 0.9085, 0.9552 and 0.9036 for serum Raman scattering spectra dataset, respectively, which showed superiority than those of state-of-the-art models, thereby suggesting the suitability of the GWO-SVM model to be adopted in a clinical setting for initial screening of COVID-19 patients. CONCLUSIONS: Prospectively, the presented vibrational spectroscopy based GWO-SVM model can facilitate in screening of COVID-19 patients and alleviate the medical service burden. Therefore, herein proof-of-concept results showed the chance of vibrational spectroscopy coupled with GWO-SVM model to help COVID-19 diagnosis and have the potential be further used for early screening of other infectious diseases.

BSA Capped gold Nanoclusters Modulated by Copper ion for Sensitive and Selective Detection of Histidine in Biological Fluid.

This research proposed a sample and environmentally sustainable technique for the synthesis of bovine serum albumin capped gold nanoclusters (BSA-AuNCs) with outstanding fluorescence. The synthesized BSA-AuNCs were investigated using various ways before being combined with Cu2+ to produce a fluorescent switch probe (BSA-AuNCs-Cu2+) for histidine determination. After adding Cu2+, the fluorescence of the BSA-AuNCs was quenched, the fluorescence intensity was enhanced after adding histidine due to good coordination between Cu2+ and histidine. The significant chelation of histidine with Cu2+ demonstrated the viability of developing a selective "switch on" probe for histidine detecting over other amino acids. Unlike existing fluorescent nanomaterial-based approaches for detecting histidine, this study promises good selectivity, high efficiency, and the avoiding of chemical solvents. The designed BSA-AuNCs-Cu2+ fluorescent probe demonstrated an acceptable linear detection range of 0 to 240 µM under optimum circumstances, with a detection limit of 0.9 µM. The BSA-AuNCs-Cu2+ system was investigated in rat serum and human urine, with recoveries ranging from 97.2 to 108.2%, demonstrating its potential applicability for histidine detection with favorable results.

QSAR Study of Novel 1, 8-Naphthimide Derivatives Targeting Nuclear DNA.

BACKGROUND: 1, 8-naphthimide is a novel tumor inhibitor targeting nuclear DNA, which can be used to design and develop anti-osteosarcoma drugs. OBJECTIVE: Quantitative structure-activity relationship (QSAR) model was established to predict the physical properties of compounds. METHODS: In this study, gene expression programming (GEP) was used to build a nonlinear quantitative structureactivity relationship (QSAR) model with descriptors and to predict the activity of a serials novel DNA-targeted chemotherapeutic agents. These descriptors were calculated in CODESSA software and selected from the descriptor pool based on heuristics. Three descriptors were selected to establish a multiple linear regression model. The best nonlinear QSAR model with a correlation coefficient of 0.89 and 0.82 and mean error of 0.02 and 0.06 for the training and test sets were obtained. RESULTS: The results showed that the model established by GEP had better stability and predictive ability. The small molecular docking experiment of 32 compounds was carried out in SYBYL software, and it was found that compound 7A had reliable molecular docking ability. CONCLUSION: The established model reveals the factors affecting the activity of DNA inhibitors and provides direction and guidance for the further design of highly effective DNA-targeting drugs for osteosarcoma.

Design and Screening of New Lead Compounds for Autism Based on QSAR Model and Molecular Docking Studies.

The purpose of the present study aims to develop a satisfactory model for predicting pro-social and pro-cognitive effects on azinesulfonamides of cyclic amine derivatives as potential antipsychotics. The three dimensional-quantitative structure affinity relationship (3D-QSAR) study was performed on a series of azinesulfonamides of cyclic amine derivative using comparative molecular similarity indices analysis (CoMSIA). The best statistical model of CoMSIA q2, r2, SEE and F values are 0.664, 0.973, 0.087, and 82.344, respectively. Based on the model contour maps and the highest activity structure of the 43rd compound, serial new structures were designed and the 43k1 compound was selected as the best structure. The dock results showed a good binding of 43k1 with the protein (PDB ID: 6A93). The QSAR model analysis of the contour maps can help us to provide guidelines for finding novel potential antipsychotics.

Network Pharmacology Integrated Molecular Docking to Reveal the Autism and Mechanism of Baohewan Heshiwei Wen Dan Tang.

BACKGROUND: In recent years, the prevalence and mortality of autism spectrum disorder (ASD) have been increasing. The clinical features are different with different cases, so the treatment ways are different for each one. OBJECTIVE: Baohewan Heshiwei Wen Dan Tang (BHWDT) has been recommended for treating autistic spectrum disorder. To investigate the mechanism of action and how the compounds interact with ASD targets, network pharmacology and molecular docking methods were used in this study. METHODS: Traditional Chinese Medicine Systems Pharmacology (TCMSP) was used to screen the active components according to index of oral bio-activity and drug-likeness. Then, TCMSP and Swiss Target Prediction databases were used to screen potential target genes of active components. The related target genes of ASD were obtained from the Gene Cards database. Matescape database was utilized to get gene ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes pathway annotation of gene targets. Composition- target-pathway (C-T-P) and a protein-protein interaction (PPI) networks were built with Cytoscape 3.8.2 software. RESULTS: The interaction of the main active components of BHWDT was verified by molecular docking. The key targets of MAPK1, IL6, CXCL8 and TP53 of BHWDT were obtained. The key active components Quercetin, Kaempferol and Iuteolin of BHWDT could bind with MAPK1, IL6, CXCL8 and TP53 of BHWDT, respectively. CONCLUSION: BHWDT can be highly effective for treating ASD and this study can help us to understand multiple targets and multiple pathways mechanism.

Study on novel PtNP-sorafenib and its interaction with VEGFR2.

With the developments of nanodrugs, some drugs have combined with nanoparticles (NPs) to reduce their side-effects and increase their therapeutic activities. Here, a novel nanodrug platinum nanoparticle-sorafenib (PtNP-SOR) was proposed for the first time. By means of molecular dynamics simulation, the stability and biocompatibility of PtNP-SOR were investigated. Then, the interaction mechanism between PtNP-SOR and vascular endothelial growth factor receptor 2 (VEGFR2) was explored and compared with that of the peptide 2a coated PtNPs. The results showed that PtNP-SOR could bind to VEGFR2 more stably, which was driven by the Coulombic and strong dispersion interaction between PtNP-SOR and VEGFR2. According to their contributions obtained from the decomposition of binding free energies, the key residues in VEGFR2 were identified to form the specific space, which increased the affinity with PtNP-SOR. This study provided useful insights to the design of PtNP-drugs as well as important theoretical proofs to the interaction between PtNP-SOR and VEGFR2 at a molecular level, which can be of large help during the development and optimization of novel nanodrugs.

Adsorption Behaviors of Typical Proteins on BP, GR, and C2N Surfaces.

The biotoxicity of nanomaterials is very important for the application of nanomaterials in biomedical systems. In this study, proteins with varying secondary structures (α-helices, ß-sheets, and mixed α/ß structures) were employed to investigate the biological properties of three representative two-dimensional (2D) nanomaterials; these nanomaterials consisted of black phosphorus (BP), graphene (GR), and nitrogenized graphene (C2N) and were studied using molecular dynamics simulations. The results showed that the α-helix motif underwent a slight structural change on the BP surface and little structural change on the C2N surface. In contrast, the structure of the ß-sheet motif remained fairly intact on both the BP and C2N surfaces. The α-helix and ß-sheet motifs were able to freely migrate on the BP surface, but they were anchored to the C2N surface. In contrast to BP and C2N, GR severely disrupted the structures of the α-helix and ß-sheet motifs. BBA protein with mixed α/ß structures adsorbed on the BP and C2N surfaces and exhibited biological behaviors that were consistent with those of the α-helix and ß-sheet motifs. In summary, C2N may possess better biocompatibility than BP and GR and is expected to have applications in the biomedical field. This study not only comprehensively evaluated the biological characteristics of nanomaterials but also provided a theoretical strategy to explore and distinguish the surface characteristics of nanomaterials.

Novel quantitative structure-activity relationship model to predict activities of natural products against COVID-19.

Currently, COVID-19 is spreading in a large scale while no efficient vaccine has been produced. A high-effective drug for COVID-19 is very necessary now. We established a satisfied quantitative structure-activity relationship model by gene expression programming to predict the IC50 value of natural compounds. A total of 27 natural products were optimized by heuristic method in CODESSA program to build a liner model. Based on it, only two descriptors were selected and utilized to build a nonlinear model in gene expression programming. The square of correlation coefficient and s2 of heuristic method were 0.80 and 0.10, respectively. In gene expression programming, the square of correlation coefficient and mean square error for training set were 0.91 and 0.04. The square of correlation coefficient and mean square error for test set are 0.86 and 0.1. This nonlinear model has stronger predictive ability to develop the targeted drugs of COVID-19.

Prediction of the inhibitory concentrations of chloroquine derivatives using deep neural networks models.

In recent years, deep neural networks have begun to receive much attention, which has obvious advantages in feature extraction and modeling. However, in the using of deep neural networks for the QSAR modeling process, the selection of various parameters (number of neurons, hidden layers, transfer functions, data set partitioning, number of iterations, etc.) becomes difficult. Thus, we proposed a new and easy method for optimizing the model and selecting Deep Neural Networks (DNN) parameters through uniform design ideas and orthogonal design methods. By using this approach, 222 chloroquine (CQ) derivatives with half maximal inhibitory concentration values reported in different kinds of literature were selected to establish DNN models and a total number of 128,000 DNN models were built to determine the optimized parameters for selecting the better models. Comparing with linear and Artificial Neural Network (ANN) models, we found that DNN models showed better performance.Communicated by Ramaswamy H. Sarma.

Exploring the effect of silicene monolayer on the structure and function of villin headpiece and amyloid fibrils by molecular dynamics simulations.

With the development of various nanomaterial expected to be used in biomedical fields, it is more important to evaluate and understand their potential effects on biological system. In this work, two proteins with different structure, Villin Headpiece (HP35) with α-helix structure and protofibrils Aß1-42 with five ß-strand chains, were selected and their interactions with silicene were studied by means of molecular dynamics (MD) simulation to reveal the potential effect of silicene on the structure and function of biomolecules. The obtained results indicated that silicene could rapidly attract HP35 and Aß1-42 fibrils onto the surface to form a stable binding. The adsorption strength was moderate and no significant structural distortion of HP35 and Aß1-42 fibrils was observed. Moreover, the strength of calculated the H-bonds in neighbor chain of Aß1-42 fibrils indicated that the mild interactions between silicene and fibrils could regularize the structure of Aß1-42 fibrils and stabilize the interactions between five chains of fibrils protein, which might enhance the aggregation of Aß1-42 fibrils. This study provides a new insight for understanding the interaction between nanomaterials and biomolecules and moves forward the development of silicene into biomedical fields.

ABCModeller: an automatic data mining tool based on a consistent voting method with a user-friendly graphical interface.

In order to extract useful information from a huge amount of biological data nowadays, simple and convenient tools are urgently needed for data analysis and modeling. In this paper, an automatic data mining tool, termed as ABCModeller (Automatic Binary Classification Modeller), with a user-friendly graphical interface was developed here, which includes automated functions as data preprocessing, significant feature extraction, classification modeling, model evaluation and prediction. In order to enhance the generalization ability of the final model, a consistent voting method was built here in this tool with the utilization of three popular machine-learning algorithms, as artificial neural network, support vector machine and random forest. Besides, Fibonacci search and orthogonal experimental design methods were also employed here to automatically select significant features in the data space and optimal hyperparameters of the three algorithms to achieve the best model. The reliability of this tool has been verified through multiple benchmark data sets. In addition, with the advantage of a user-friendly graphical interface of this tool, users without any programming skills can easily obtain reliable models directly from original data, which can reduce the complexity of modeling and data mining, and contribute to the development of related research including but not limited to biology. The excitable file of this tool can be downloaded from http://lishuyan.lzu.edu.cn/ABCModeller.rar.

Multi-omics integrative analysis and survival risk model construction of non-small cell lung cancer based on The Cancer Genome Atlas datasets.

Lung cancer is a major cause of cancer-associated mortality worldwide. However, the association between multi-omics data and survival in lung cancer is not fully understood. The present study investigated the performance of the methylation survival risk model in multi-platform integrative molecular subtypes and aimed to identify copy number (CN) variations and mutations that are associated with survival risk. The present study analyzed 439 lung adenocarcinoma cases based on DNA methylation, RNA, microRNA (miRNA), DNA copy number and mutations from The Cancer Genome Atlas datasets. First, six cancer subtypes were identified using integrating DNA methylation, RNA, miRNA and DNA copy number data. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used to extract methylation sites of survival model and calculate the methylation-based survival risk indices for all patients. Survival for patients in the high-risk group was significantly lower compared with that for patients in the low-risk group (P<0.05). The present study also assessed methylation-based survival risks of the six subtypes and analyzed the association between survival risk and non-silent mutation rate, number of segments, fraction of segments altered, aneuploidy score, number of segments with loss of heterozygosity (LOH), fraction of segments with LOH and homologous repair deficiency. Finally, the specific copy number regions and mutant genes associated with the different subtypes were identified (P<0.01). Chromosome regions 17q24.3 and 11p15.5 were identified as those with the most survival risk-associated copy number variation regions, while a total of 29 mutant genes were significantly associated with survival (P<0.01).

Rational design of a near-infrared fluorescence probe for highly selective sensing butyrylcholinesterase (BChE) and its bioimaging applications in living cell.

In the current work, a near-infrared (NIR) fluorescent probe (CyClCP) was developed for fast (35 min), highly sensitive (LOD of 3.75 U/L) and selective response to BChE in vitro and in vivo. Upon the addition of BChE, CyClCP could be efficiently activated with remarkable NIR (λem = 708 nm) fluorescence enhancement and obvious absorbance red shift (581 nm-687 nm). Specifically, according to the subtle differences structural features and substrate preference between BChE and its sister enzyme AChE, CyClCP was constructed by introducing chlorine atom at the ortho-position of the phenolic hydroxyl in the previous reported probe (CyCP). Fortunately, CyClCP exhibited better selectivity towards BChE over AChE compared with CyCP. This molecular design strategy was further rationalized by docking molecular of fluorescence probes (CyClCP and CyCP) and enzymes (BChE and AChE). Finally, CyClCP was membrane permeable and successfully applied to image endogenous BChE level in HepG2 and LO2 cells. Therefore, CyClCP could serve as a promising tool for BChE-related physiological function studies in complex biological systems.

Potential toxicity mechanism of MoS2 nanotube in the interaction between YAP65 WW domain and PRM.

With the widespread application of Molybdenum disulfide (MoS2) in biomedicine, its mechanism of action with biomolecules has attracted increasing attention. Herein, molecular dynamics simulations were performed to investigate the effect of MoS2 nanotube on the binding of the signal protein YAP65, an important Yes kinase-associated protein domain (WW domain), to the proline rich motif ligand (PRM). We designed four systems based on the different initial binding modes among WW domain, PRM and MoS2 nanotube, and observed two ways to affect the binding of WW domain to PRM. The first pathway, the active site in WW domain was occupied by MoS2 nanotube, which prevents WW domain from binding to PRM. In the second pathway, WW domain was bound to PRM with residues W17 and F29 instead of the two highly conserved residues (Y28 and W39), forming an unstable combination. These two results might cause WW domain to lose its original function or to pass the mistaken signal. However, MoS2 nanotube did not destroy the structure and binding of WW domain and PRM in the composite. These findings shed light on the interaction between MoS2 nanotube and signal protein system, while providing another valuable insight into the potential nanotoxicity of MoS2 nanotube.

Quantitative Structure-activity Relationships; Studying the Toxicity of Metal Nanoparticles.

BACKGROUND: Metal nanomaterials are widely used in various fields, including targeted therapy and diagnosis. They are extensively used in targeted drug delivery and local treatments. However, the toxicity associated with these materials could lead to severe adverse health effects. METHODS: In this study, we investigated the relationships between the toxicity and structures of metal nanoparticles by using theoretical calculations and quantitative structure-activity relationships. Twenty four physicochemical descriptors and toxicity data of 23 types of metal nanoparticles were selected as samples, and a multiple linear regression model was established to obtain a toxicity prediction equation with 5 descriptors with an R2 of 0.910. Structures of copper nanoparticles were designed based on the model, and the structure with low toxicity was searched. The multiple nonlinear regression model was used to further improve the prediction accuracy. RESULTS: The R2 values were 0.995 in the training set and 0.988 in the test set, which indicated that the prediction accuracy improved. Based on the result of multiple linear regression, we designed copper nanoparticles with low toxicity. CONCLUSION: The study confirmed that the quantitative structure-activity relationship was a reasonable method for predicting the toxicity and designing the structures with low toxicity of metal nanoparticles.