Atf7ip and Setdb1 interaction orchestrates the hematopoietic stem and progenitor cell state with diverse lineage differentiation.

Hematopoietic stem and progenitor cells (HSPCs) are a heterogeneous group of cells with expansion, differentiation, and repopulation capacities. How HSPCs orchestrate the stemness state with diverse lineage differentiation at steady condition or acute stress remains largely unknown. Here, we show that zebrafish mutants that are deficient in an epigenetic regulator Atf7ip or Setdb1 methyltransferase undergo excessive myeloid differentiation with impaired HSPC expansion, manifesting a decline in T cells and erythroid lineage. We find that Atf7ip regulates hematopoiesis through Setdb1-mediated H3K9me3 modification and chromatin remodeling. During hematopoiesis, the interaction of Atf7ip and Setdb1 triggers H3K9me3 depositions in hematopoietic regulatory genes including cebpß and cdkn1a, preventing HSPCs from loss of expansion and premature differentiation into myeloid lineage. Concomitantly, loss of Atf7ip or Setdb1 derepresses retrotransposons that instigate the viral sensor Mda5/Rig-I like receptor (RLR) signaling, leading to stress-driven myelopoiesis and inflammation. We find that ATF7IP or SETDB1 depletion represses human leukemic cell growth and induces myeloid differentiation with retrotransposon-triggered inflammation. These findings establish that Atf7ip/Setdb1-mediated H3K9me3 deposition constitutes a genome-wide checkpoint that impedes the myeloid potential and maintains HSPC stemness for diverse blood cell production, providing unique insights into potential intervention in hematological malignancy.

MARPPI: boosting prediction of protein-protein interactions with multi-scale architecture residual network.

Protein-protein interactions (PPIs) are a major component of the cellular biochemical reaction network. Rich sequence information and machine learning techniques reduce the dependence of exploring PPIs on wet experiments, which are costly and time-consuming. This paper proposes a PPI prediction model, multi-scale architecture residual network for PPIs (MARPPI), based on dual-channel and multi-feature. Multi-feature leverages Res2vec to obtain the association information between residues, and utilizes pseudo amino acid composition, autocorrelation descriptors and multivariate mutual information to achieve the amino acid composition and order information, physicochemical properties and information entropy, respectively. Dual channel utilizes multi-scale architecture improved ResNet network which extracts protein sequence features to reduce protein feature loss. Compared with other advanced methods, MARPPI achieves 96.03%, 99.01% and 91.80% accuracy in the intraspecific datasets of Saccharomyces cerevisiae, Human and Helicobacter pylori, respectively. The accuracy on the two interspecific datasets of Human-Bacillus anthracis and Human-Yersinia pestis is 97.29%, and 95.30%, respectively. In addition, results on specific datasets of disease (neurodegenerative and metabolic disorders) demonstrate the ability to detect hidden interactions. To better illustrate the performance of MARPPI, evaluations on independent datasets and PPIs network suggest that MARPPI can be used to predict cross-species interactions. The above shows that MARPPI can be regarded as a concise, efficient and accurate tool for PPI datasets.

CAR T Cell Membrane Camouflaged Nanocatalyst Augments CAR T Cell Therapy Efficacy Against Solid Tumor.

The immunosuppressive tumor microenvironment (TME) reduces the chimeric antigen receptor (CAR) T-cell therapy against solid tumors. Here, a CAR T cell membrane-camouflaged nanocatalyst (ACSP@TCM) is prepared to augment CAR T cell therapy efficacy against solid tumors. ACSP@TCM is prepared by encapsulating core/shell Au/Cu2- xSe and 3-bromopyruvate with a CAR T cell membrane. It is demonstrated that the CAR T cell membrane camouflaging has much better-targeting effect than the homologous tumors cell membrane camouflaging. ACSP@TCM has an appealing synergistic chemodynamic/photothermal therapy (CDT/PTT) effect that can induce the immunogenic cell death (ICD) of NALM 6 cells. Moreover, 3-bromopyruvate can inhibit the efflux of lactic acid by inhibiting the glycolysis process, regulating the acidity of TME, and providing a more favorable environment for the survival of CAR T cells. In addition, the photoacoustic (PA) imaging and computed tomography (CT) imaging performance can guide the ACSP@TCM-mediated tumor therapy. The results demonstrated that the ACSP@TCM significantly enhanced the CAR T cell therapy efficacy against NALM 6 solid tumor mass, and completely eliminated tumors. This work provides an effective tumor strategy for CAR T cell therapy in solid tumors.

Early predictor for differentiation syndrome in newly diagnosed acute promyelocytic leukaemia patients treated with single-agent arsenic trioxide.

Differentiation syndrome (DS) is the second leading cause of death in acute promyelocytic leukaemia (APL) patients. Few studies have tested predictors of DS events. This study aimed to identify optimized predictors of DS events related to APL. The data of 298 consecutive patients who were newly diagnosed with APL between December 2012 and June 2023 were retrospectively investigated. A systematic review of computer-based patient medical records was conducted to obtain clinical data, including baseline characteristics, routine blood examination findings, biochemical indices and clinical manifestations of DS. Among the 298 patients, 158 were classified into the no-DS group, while 140 had DS. Compared with those of patients without DS, the peripheral blast count, age, and WBC count at each time point were significantly different in patients with DS (P < 0.05 for all time points). Generalized linear mixed models (GLMMs) revealed that WBC Double (Coeff. 0.442, P = 0.000) and WBCPeak (Coeff. 0.879, P = 0.000) were independent risk factors for DS. The frequencies of clinical manifestations of unexplained fever (P = 0.003), dyspnoea (P = 0.002), weight gain of more than 5 kg (P = 0.006), pleural effusion (P = 0.001), pulmonary infiltrates (P < 0.001), pericardial effusion (P = 0.002) and renal failure (P = 0.006) were considerably lower in moderate DS patients than in severe DS patients. The WBCDouble occurs earlier than the WBCpeak occurrence, so WBC Double might be a new indicator of DS.

Encapsulation of phenolic acids within food-grade carriers systems: a systematic review.

Phenolic acids are natural compounds with potential therapeutic effects against various diseases. However, their incorporation into food and pharmaceutical products is limited by challenges such as instability, low solubility, and reduced bioavailability. This systematic review summarizes recent advances in phenolic acid encapsulation using food-grade carrier systems, focusing on proteins, lipids, and polysaccharides. Encapsulation efficiency, release behavior, and bioavailability are examined, as well as the potential health benefits of encapsulated phenolic acids in food products. Strategies to address limitations of current encapsulation systems are also proposed. Encapsulation has emerged as a promising method to enhance the stability and bioavailability of phenolic acids in food products, and various encapsulation technologies have been developed for this purpose. The use of proteins, lipids, and carbohydrates as carriers in food-grade encapsulation systems remains a common approach, but it is associated with certain limitations. Future research on phenolic acid encapsulation should focus on developing environmentally friendly, organic solvent-free, low-energy, scalable, and stable encapsulation systems, as well as co-encapsulation methods that combine multiple phenolic acids or phenolic acids with other bioactive substances to produce synergistic effects.

Dynamics of carbon storage driven by land use/land cover transformation in coal mining areas with a high groundwater table: A case study of Yanzhou Coal Mine, China.

Intensive anthropogenic activities have led to drastic changes in land use/land cover (LULC) and impacted the carbon storage in high-groundwater coal basins. In this paper, we conduct a case study on the Yanzhou Coalfield in Shandong Province of China. We further classify waterbodies by using the Google Earth Engine (GEE) to better investigate the process of LULC transformation and the forces driving it in four periods from 1985 to 2020 (i.e., 1985-1995, 1995-2005, 2005-2015, and 2015-2020). We modeled the spatiotemporal dynamics of carbon storage by using InVEST based on the transformation in LULC and its drivers, including mining (M), reclamation (R), urbanization and village relocation (U), and ecological restoration (E). The results indicate that carbon storage had depleted by 19.69 % (321099.06 Mg) owing to intensive transformations in LULC. The area of cropland shrank with the expansion of built-up land and waterbodies, and 56.31 % of the study area underwent transitions in land use in the study period. U was the primary driver of carbon loss while E was the leading driver of carbon gain. While the direct impact of M on carbon loss accounted for only 5.23 % of the total, it affected urbanization and led to village relocation. R led to the recovery of cropland and the reclamation of water for aquaculture, which in turn improved the efficiency of land use. However, it contributed only 2.09 % to the total increase in carbon storage. Numerous complicated and intertwined processes (211) drove the changes in carbon storage in the study area. The work here provides valuable information for decision-makers as well as people involved in reclamation and ecological restoration to better understand the link between carbon storage and the forces influencing it. The results can be used to integrate the goals of carbon sequestration into measures for land management.

Regulation of PM2.5 on mitochondrial damage in H9c2 cells through miR-421/SIRT3 pathway and protective effect of miR-421 inhibitor and resveratrol.

Fine particulate matter (PM2.5) exposure is associated with cardiovascular disease (CVD) morbidity and mortality. Mitochondria are sensitive targets of PM2.5, and mitochondrial dysfunction is closely related to the occurrence of CVD. The epigenetic mechanism of PM2.5-triggered mitochondrial injury of cardiomyocytes is unclear. This study focused on the miR-421/SIRT3 signaling pathway to investigate the regulatory mechanism in cardiac mitochondrial dynamics imbalance in rat H9c2 cells induced by PM2.5. Results illustrated that PM2.5 impaired mitochondrial function and caused dynamics homeostasis imbalance. Besides, PM2.5 up-regulated miR-421 and down-regulated SIRT3 gene expression, along with decreasing p-FOXO3a (SIRT3 downstream target gene) and p-Parkin expression and triggering abnormal expression of fusion gene OPA1 and fission gene Drp1. Further, miR-421 inhibitor (miR-421i) and resveratrol significantly elevated the SIRT3 levels in H9c2 cells after PM2.5 exposure and mediated the expression of SOD2, OPA1 and Drp1, restoring the mitochondrial morphology and function. It suggests that miR-421/SIRT3 pathway plays an epigenetic regulatory role in mitochondrial damage induced by PM2.5 and that miR-421i and resveratrol exert protective effects against PM2.5-incurred cardiotoxicity.

New Insights on the Size-Dependent Inhibition of Enzymes by Gold Nanoparticles.

Particle size might affect the inhibition behaviors of gold nanoparticles (AuNPs) on enzyme activity by influencing the density of binding sites (ρ), the association constant (Ka), the steric hindrance of enzymes by AuNPs, the binding orientations of the enzyme on AuNPs, as well as the structural changes of enzymes. In previous studies, the effects of the above-mentioned factors, which could not be ignored in the applications of enzymatic electrochemistry, were often overshadowed by the effects of surface area. In order to study the size effect on the inhibition types and inhibitory ability of enzymes by AuNPs, we investigated the inhibition behaviors of chymotrypsin (ChT) by AuNPs with three different sizes (D1-AuNCs, D3-AuNPs, and D6-AuNPs) under the same surface area concentration. The results showed that both of the inhibition types and the inhibition ability varied with the particle size of AuNPs. D1-AuNCs inhibited ChT noncompetitively, while D3/D6-AuNPs inhibited ChT competitively. Contrary to the common sense, D6-AuNPs showed a weaker inhibitory ability than D3-AuNPs. By means of zeta potential, agarose gel electrophoresis, isothermal titration calorimetry, synchronous fluorescence spectroscopy, and circular dichroism, the mechanism of the weak inhibitory ability of D6-AuNPs was found to be the standing binding orientation caused by the small curvature. This work had certain guiding significance for the biosafety of AuNPs, the development of nanoinhibitors, as well as the applications of AuNPs in enzymatic electrochemistry.

Quantum Dots Meet Enzymes: Hydrophobicity of Surface Ligands and Size Do Matter.

Colloidal quantum dots (QDs) are a class of representative fluorescent nanomaterials with tunable, bright, and sharp fluorescent emission, with promising biomedical applications. However, their effects on biological systems are not fully elucidated. In this work, we investigated the interactions between QDs with different surface ligands and different particle sizes and α-chymotrypsin (ChT) from the thermodynamic and kinetic perspectives. Enzymatic activity experiments demonstrated that the catalytic activity of ChT was strongly inhibited by QDs coated with dihydrolipoic acid (DHLA-QDs) with noncompetitive inhibitions, whereas the QDs coated with glutathione (GSH-QDs) had weak effects. Furthermore, kinetics studies showed that different particle sizes of DHLA-QDs all had high suppressive effects on the catalytic activity of ChT. It was found that DHLA-QDs with larger particle sizes had stronger inhibition effects because more ChT molecules were bound onto the surface of QDs. This work highlights the importance of hydrophobic ligands and particle sizes of QDs, which should be considered as the primary influencing factors in the assessment of biosafety. Meanwhile, the results herein can also inspire the design of nano inhibitors.

Protein Labeling Facilitates the Understanding of Protein Corona Formation via Fluorescence Resonance Energy Transfer and Fluorescence Correlation Spectroscopy.

Once nanoparticles enter into the biological milieu, nanoparticle-biomacromolecule complexes, especially the protein corona, swiftly form, which cause obvious effects on the physicochemical properties of both nanoparticles and proteins. Here, the thermodynamic parameters of the interactions between water-soluble GSH-CdSe/ZnS core/shell quantum dots (GSH-QDs) and human serum albumin (HSA) were investigated with the aid of labeling fluorescence of HSA. It was proved that the labeling fluorescence originating from a fluorophore (BDP-CN for instance) could be used to investigate the interactions between QDs and HSA. Gel electrophoresis displayed that the binding ratio between HSA and QDs was ∼2:1 by direct visualization. Fluorescence resonance energy transfer (FRET) results indicated that the distance between the QDs and the fluorophore BDP-CN in HSA was 7.2 nm, which indicated that the distance from the fluorophore to the surface of the QDs was ∼4.8 nm. Fluorescence correlation spectroscopy (FCS) results showed that HSA formed a monolayer of a protein corona with a thickness of 5.5 nm. According to the spatial structure of HSA, we could speculate that the binding site of QDs was located at the side edge (not the triangular plane) of HSA with an equilateral triangular prism. The elaboration of the thermodynamic parameters, binding ratio, and interaction orientation will highly improve the fundamental understanding of the formation of protein corona. This work has guiding significance for the exploration of the interactions between proteins and nanomaterials.

Two distinct males absent on the first (MOF)-containing histone acetyltransferases are involved in the epithelial-mesenchymal transition in different ways in human cells.

Human males absent on the first (MOF), a histone acetyltransferase (HAT), forms male-specific lethal (MSL) and non-specific lethal (NSL), two multiprotein HATs, in cells. MSL was originally discovered in dosage compensation study in Drosophila that can specifically acetylate H4K16, while NSL can simultaneously catalyze the H4 at K5, K8, and K16 sites. However, comparative studies of the two HATs in regulating specific biological functions are rarely reported. Here, we present evidence to argue that MSL and NSL function in different ways in the epithelial-to-mesenchymal transition (EMT) process. At first, CRISPR/Cas9-mediated MSL1 (a key subunit of the MSL)-knockout (KO) and NSL3 (a key subunit of the NSL)-KO cells seem to prefer to grow in clusters. Interestingly, the former promotes cell survival and clonal formation, while the latter has the opposite effect on it. Cell staining revealed that MSL1-KO leads to multipolarized spindles, while NSL3-KO causes more lumen-like cells. Furthermore, in Transwell experiments, silencing of MSL1 promotes cell invasion in 293 T, MCF-7, and MDA-MB-231 cells. In contrast, the inhibitory effects on cell invasion are observed in the same NSL3-silenced cells. Consistent with this, mesenchymal biomarkers, like N-cadherin, vimentin, and snail, are negatively correlated with the expression level of MSL1; however, a positive relationship between these proteins and NSL3 in cells has been found. Further studies have clarified that MSL1, but not NSL3, can specifically bind to the E-box-containing Snail promoter region and thereby negatively regulate Snail transactivation. Also, silencing of MSL1 promotes the lung metastasis of B16F10 melanoma cells in mice. Finally, ChIP-Seq analysis indicated that the NSL may be mainly involved in phosphoinositide-mediated signaling pathways. Taken together, the MOF-containing MSL and NSL HATs may regulate the EMT process in different ways in order to respond to different stimuli.

PETrans: De Novo Drug Design with Protein-Specific Encoding Based on Transfer Learning.

Recent years have seen tremendous success in the design of novel drug molecules through deep generative models. Nevertheless, existing methods only generate drug-like molecules, which require additional structural optimization to be developed into actual drugs. In this study, a deep learning method for generating target-specific ligands was proposed. This method is useful when the dataset for target-specific ligands is limited. Deep learning methods can extract and learn features (representations) in a data-driven way with little or no human participation. Generative pretraining (GPT) was used to extract the contextual features of the molecule. Three different protein-encoding methods were used to extract the physicochemical properties and amino acid information of the target protein. Protein-encoding and molecular sequence information are combined to guide molecule generation. Transfer learning was used to fine-tune the pretrained model to generate molecules with better binding ability to the target protein. The model was validated using three different targets. The docking results show that our model is capable of generating new molecules with higher docking scores for the target proteins.

Feedback Modulation between Human INO80 Chromatin Remodeling Complex and miR-372 in HCT116 Cells.

Human INO80 chromatin remodeling complex (INO80 complex) as a transcription cofactor is widely involved in gene transcription regulation and is frequently highly expressed in tumor cells. However, few reports exist on the mutual regulatory mechanism between INO80 complex and non-coding microRNAs. Herein, we showed evidence that the INO80 complex transcriptionally controls microRNA-372 (miR-372) expression through RNA-Seq analysis and a series of biological experiments. Knocking down multiple subunits in the INO80 complex, including the INO80 catalytic subunit, YY1, Ies2, and Arp8, can significantly increase the expression level of miR-372. Interestingly, mimicking miR-372 expression in HCT116 cells, in turn, post-transcriptionally suppressed INO80 and Arp8 expression at both mRNA and protein levels, indicating the existence of a mutual regulatory mechanism between the INO80 complex and miR-372. The target relationship between miR-372 and INO80 complex was verified using luciferase assays in HCT116 colon cancer cells. As expected, miR-372 mimics significantly suppressed the luciferase activity of pMIR-luc/INO80 and pMIR-luc/Arp8 3'-UTR in cells. In contrast, the miR-372 target sites in the 3'-UTRs linked to the luciferase reporter were mutagenized, and both mutant sites lost their response to miR-372. Furthermore, the mutual modulation between the INO80 complex and miR-372 was involved in cell proliferation and the p53/p21 signaling pathway, suggesting the synergistic anti-tumor role of the INO80 complex and miR372. Our results will provide a solid theoretical basis for exploring miR-372 as a biological marker of tumorigenesis.

The Males Absent on the First (MOF) Mediated Acetylation Alters the Protein Stability and Transcriptional Activity of YY1 in HCT116 Cells.

Yin Yang 1 (YY1) is a well-known transcription factor that controls the expression of many genes and plays an important role in the occurrence and development of various cancers. We previously found that the human males absent on the first (MOF)-containing histone acetyltransferase (HAT) complex may be involved in regulating YY1 transcriptional activity; however, the precise interaction between MOF-HAT and YY1, as well as whether the acetylation activity of MOF impacts the function of YY1, has not been reported. Here, we present evidence that the MOF-containing male-specific lethal (MSL) HAT complex regulates YY1 stability and transcriptional activity in an acetylation-dependent manner. First, the MOF/MSL HAT complex was bound to and acetylated YY1, and this acetylation further promoted the ubiquitin-proteasome degradation pathway of YY1. The MOF-mediated degradation of YY1 was mainly related to the 146-270 amino acid residues of YY1. Further research clarified that acetylation-mediated ubiquitin degradation of YY1 mainly occurred through lysine 183. A mutation at the YY1K183 site was sufficient to alter the expression level of p53-mediated downstream target genes, such as CDKN1A (encoding p21), and it also suppressed the transactivation of YY1 on CDC6. Furthermore, a YY1K183R mutant and MOF remarkably antagonized the clone-forming ability of HCT116 and SW480 cells facilitated by YY1, suggesting that the acetylation-ubiquitin mode of YY1 plays an important role in tumor cell proliferation. These data may provide new strategies for the development of therapeutic drugs for tumors with high expression of YY1.

SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction.

BACKGROUND: Protein-protein interactions (PPIs) dominate intracellular molecules to perform a series of tasks such as transcriptional regulation, information transduction, and drug signalling. The traditional wet experiment method to obtain PPIs information is costly and time-consuming. RESULT: In this paper, SDNN-PPI, a PPI prediction method based on self-attention and deep learning is proposed. The method adopts amino acid composition (AAC), conjoint triad (CT), and auto covariance (AC) to extract global and local features of protein sequences, and leverages self-attention to enhance DNN feature extraction to more effectively accomplish the prediction of PPIs. In order to verify the generalization ability of SDNN-PPI, a 5-fold cross-validation on the intraspecific interactions dataset of Saccharomyces cerevisiae (core subset) and human is used to measure our model in which the accuracy reaches 95.48% and 98.94% respectively. The accuracy of 93.15% and 88.33% are obtained in the interspecific interactions dataset of human-Bacillus Anthracis and Human-Yersinia pestis, respectively. In the independent data set Caenorhabditis elegans, Escherichia coli, Homo sapiens, and Mus musculus, all prediction accuracy is 100%, which is higher than the previous PPIs prediction methods. To further evaluate the advantages and disadvantages of the model, the one-core and crossover network are conducted to predict PPIs, and the data show that the model correctly predicts the interaction pairs in the network. CONCLUSION: In this paper, AAC, CT and AC methods are used to encode the sequence, and SDNN-PPI method is proposed to predict PPIs based on self-attention deep learning neural network. Satisfactory results are obtained on interspecific and intraspecific data sets, and good performance is also achieved in cross-species prediction. It can also correctly predict the protein interaction of cell and tumor information contained in one-core network and crossover network.The SDNN-PPI proposed in this paper not only explores the mechanism of protein-protein interaction, but also provides new ideas for drug design and disease prevention.

DCSE:Double-Channel-Siamese-Ensemble model for protein protein interaction prediction.

BACKGROUND: Protein-protein interaction (PPI) is very important for many biochemical processes. Therefore, accurate prediction of PPI can help us better understand the role of proteins in biochemical processes. Although there are many methods to predict PPI in biology, they are time-consuming and lack accuracy, so it is necessary to build an efficiently and accurately computational model in the field of PPI prediction. RESULTS: We present a novel sequence-based computational approach called DCSE (Double-Channel-Siamese-Ensemble) to predict potential PPI. In the encoding layer, we treat each amino acid as a word, and map it into an N-dimensional vector. In the feature extraction layer, we extract features from local and global perspectives by Multilayer Convolutional Neural Network (MCN) and Multilayer Bidirectional Gated Recurrent Unit with Convolutional Neural Networks (MBC). Finally, the output of the feature extraction layer is then fed into the prediction layer to output whether the input protein pair will interact each other. The MCN and MBC are siamese and ensemble based network, which can effectively improve the performance of the model. In order to demonstrate our model's performance, we compare it with four machine learning based and three deep learning based models. The results show that our method outperforms other models in all evaluation criteria. The Accuracy, Precision, [Formula: see text], Recall and MCC of our model are 0.9303, 0.9091, 0.9268, 0.9452, 0.8609. For the other seven models, the highest Accuracy, Precision, [Formula: see text], Recall and MCC are 0.9288, 0.9243, 0.9246, 0.9250, 0.8572. We also test our model in the imbalanced dataset and transfer our model to another species. The results show our model is excellent. CONCLUSION: Our model achieves the best performance by comparing it with seven other models. NLP-based coding method has a good effect on PPI prediction task. MCN and MBC extract protein sequence features from local and global perspectives and these two feature extraction layers are based on siamese and ensemble network structures. Siamese-based network structure can keep the features consistent and ensemble based network structure can effectively improve the accuracy of the model.

Fluorescent Labeling of Human Serum Albumin by Thiol-Cyanimide Addition and Its Application in the Fluorescence Quenching Method for Nanoparticle-Protein Interactions.

A boron-dipyrromethene (BODIPY)-based fluorescent probe, BDP-CN, was synthesized in this work. It had a fluorescence emission maximum at 512 nm and a high quantum yield (48%). As evidenced by agarose gel electrophoresis and liquid chromatography-mass spectrometry, it could realize the fluorescent labeling of human serum albumin (HSA) through a thiol-cyanimide addition. Interestingly, f-HSA, defined as HSA labeled by BDP-CN, had an even higher quantum yield (77%). In addition, BDP-CN would not affect the secondary structure of HSA. Based on the successful formation of f-HSA, it was further applied to study the interactions with nanoparticles. The fluorescence quenching of f-HSA by dihydrolipoic acid-coated gold nanoclusters (DHLA-AuNCs) obeyed a dynamic mechanism, consistent with the intrinsic fluorescence quenching of HSA by DHLA-AuNCs. The association constant Ka between f-HSA and DHLA-AuNCs at 298 K was 1.5 × 105 M-1, which was the same order of magnitude as that between HSA and DHLA-AuNCs. Moreover, the interactions of f-HSA with glutathione-coated gold nanoclusters confirmed that the labeled fluorescence could replace the intrinsic fluorescence to monitor the interactions between proteins and nanoparticles. By this method, strong fluorescence ensures better stability and reproducibility, excitation at a longer wavelength reduces the damage to the proteins, and covalent conjugation with cysteine residues eliminates the inner filter effects to a great extent. Therefore, the strategy for the fluorescent labeling of HSA can be expanded to investigate a broad class of nanoparticle-protein interactions and inspire even more fluorescent labeling methods with organic dyes.

Master equation study of hydrogen abstraction from HCHO by OH via a chemically activated intermediate.

The abstraction reaction of hydrogen from formaldehyde by OH radical plays an important role in formaldehyde oxidation. The reaction involves a bimolecular association to form a chemically activated hydrogen-bonded reaction complex followed by a unimolecular reaction of the complex to generate the products. The reaction rate is usually considered to be pressure-independent by assuming equilibrium between the reactants and the complex. However, our nonequilibrium calculations based on the chemically significant eigenmode of the master equation, carried out with our recently developed TUMME program, indicate that the reaction complex makes the rate constant dependent on pressure at low temperatures (T < 200 K). The calculations include anharmonicity, variational effects, and multi-dimensional tunneling. We find that the reaction rate constant reaches a low-pressure limit at pressures below 10 Torr over the whole investigated temperature range (20-1800 K), which explains why the available low-temperature experiments, which are for pressures below 2 Torr, did not observe the pressure dependence. A new extension of the TUMME master-equation program is used to explore the time evolutions of the concentrations of the OH radical and the complex under pseudo-first-order conditions. The time-dependent evolution of the concentrations of the complex at a low temperature provide direct evidence for the stabilization of the reaction complex at high pressures, and it shows the negligible role of the stabilized reaction complex at low pressures. The picture that emerges is qualitatively consistent with our previous study of the reaction of methanol with OH in that the tunneling in the unimolecular step from the complex to the products affects the phenomenological reaction rate constants differently at high and low pressures and leads to a significant pressure effect.

RGN: Residue-Based Graph Attention and Convolutional Network for Protein-Protein Interaction Site Prediction.

The prediction of a protein-protein interaction site (PPI site) plays a very important role in the biochemical process, and lots of computational methods have been proposed in the past. However, the majority of the past methods are time consuming and lack accuracy. Hence, coming up with an effective computational method is necessary. In this article, we present a novel computational model called RGN (residue-based graph attention and convolutional network) to predict PPI sites. In our paper, the protein is treated as a graph. The amino acid can be seen as the node in the graph structure. The position-specific scoring matrix, hidden Markov model, hydrogen bond estimation algorithm, and ProtBert are applied as node features. The edges are decided by the spatial distance between the amino acids. Then, we utilize a residue-based graph convolutional network and graph attention network to further extract the deeper feature. Finally, the processed node feature is fed into the prediction layer. We show the superiority of our model by comparing it with the other four protein structure-based methods and five protein sequence-based methods. Our model obtains the best performance on all the evaluation metrics (accuracy, precision, recall, F1 score, Matthews correlation coefficient, area under the receiver operating characteristic curve, and area under the precision recall curve). We also conduct a case study to demonstrate that extracting the protein information from the protein structure perspective is effective and points out the difficult aspect of PPI site prediction.

Regulation of the Enzymatic Activities of Lysozyme by the Surface Ligands of Ultrasmall Gold Nanoclusters: The Role of Hydrophobic Interactions.

Nanomaterials for biological applications would inevitably encounter and interact with biomolecules, which have a profound impact on the properties, functions, and even fates of both nanomaterials and biomolecules. Among the biomolecules, lysozyme (Lys) is of great importance in defending the bacterial intruder and maintaining health. Here, the interactions between fluorescent gold nanoclusters (AuNCs) (∼2 nm) capped with different surface ligands and Lys were thoroughly investigated. Fluorescence spectroscopic studies showed that dihydrolipoic acid (DHLA)-capped and glutathione (GSH)-capped AuNCs both quenched the intrinsic fluorescence of Lys by different quenching mechanisms. Agarose gel electrophoresis and zeta-potential assays showed that statistically one DHLA-AuNC could bind one Lys, while one GSH-AuNC could bind 3-4 Lys, providing new examples for the concept of a "protein complex". Activity assays indicated that DHLA-AuNCs heavily inhibited the enzymatic activity of Lys, while GSH-AuNCs had little effect. By synchronous fluorescence and circular dichroism spectroscopic studies, it was deduced that both AuNCs would interact with Lys by electrostatic attractions due to the distinct surface charges, and then DHLA-AuNCs would further interact with Lys by hydrophobic interactions, probably due to the hydrophobic carbon chain of DHLA and the hydrophobic side chains of amino acid residues in Lys, which was proved by the significant secondary structure changes caused by DHLA-AuNCs. Meanwhile, conformational changes induced by GSH-AuNCs with zwitterionic ligands were neglectable. Therefore, this work provided a comprehensive study of the consequences and mechanisms of the interactions between Lys and AuNCs, which was essential for the design and better use of nanomaterials as biological agents.