Multiscale calculations reveal new insights into the reaction mechanism between KRASG12C and α, β-unsaturated carbonyl of covalent inhibitors

Utilizing α,β-unsaturated carbonyl group as Michael acceptors to react with thiols represents a successful strategy for developing KRASG12C inhibitors. Despite this, the precise reaction mechanism between KRASG12C and covalent inhibitors remains a subject of debate, primarily due to the absence of an appropriate residue capable of deprotonating the cysteine thiol as a base. To uncover this reaction mechanism, we first discussed the chemical reaction mechanism in solvent conditions via density functional theory (DFT) calculation. Based on this, we then proposed and validated the enzymatic reaction mechanism by employing quantum mechanics/molecular mechanics (QM/MM) calculation. Our QM/MM analysis suggests that, in biological conditions, proton transfer and nucleophilic addition may proceed through a concerted process to form an enolate intermediate, bypassing the need for a base catalyst. This proposed mechanism differs from previous findings. Following the formation of the enolate intermediate, solvent-assisted tautomerization results in the final product. Our calculations indicate that solvent-assisted tautomerization is the rate-limiting step in the catalytic cycle under biological conditions. On the basis of this reaction mechanism, the calculated kinact/ki for two inhibitors is consistent well with the experimental results. Our findings provide new insights into the reaction mechanism between the cysteine of KRASG12C and the covalent inhibitors and may provide valuable information for designing effective covalent inhibitors targeting KRASG12C and other similar targets.

Development of a Decafluorobiphenyl Cyclized Peptide Targeting the NEMO-IKKα/β Interaction that Enhances Cell Penetration and Attenuates Lipopolysaccharide-Induced Acute Lung Injury

Aberrant canonical NF-κB signaling has been implicated in diseases, such as autoimmune disorders and cancer. Direct disruption of the interaction of NEMO and IKKα/β has been developed as a novel way to inhibit the overactivation of NF-κB. Peptides are a potential solution for disrupting protein-protein interactions (PPIs); however, they typically suffer from poor stability in vivo and limited tissue penetration permeability, hampering their widespread use as new chemical biology tools and potential therapeutics. In this work, decafluorobiphenyl-cysteine SNAr chemistry, molecular modeling, and biological validation allowed the development of peptide PPI inhibitors. The resulting cyclic peptide specifically inhibited canonical NF-κB signaling in vitro and in vivo, and presented positive metabolic stability, anti-inflammatory effects, and low cytotoxicity. Importantly, our results also revealed that cyclic peptides had huge potential in acute lung injury (ALI) treatment, and confirmed the role of the decafluorobiphenyl-based cyclization strategy in enhancing the biological activity of peptide NEMO-IKKα/β inhibitors. Moreover, it provided a promising method for the development of peptide-PPI inhibitors.

Monitoring dynamics of Kyagar Glacier surge and repeated draining of Ice-dammed lake using multi-source remote sensing

Glacier surges, a primary factor contributing to various glacial hazards, has long captivated the attention of the global glaciological community. This study delves into the dynamics of Kyagar Glacier surging and the associated drainage features of its Ice-dammed lake, employing high temporal resolution optical imagery. Our findings indicate that the surge on Kyagar Glacier began in late spring and early summer of 2014 and concluded during the summer of 2016. This surge resulted in the transfer of 0.321 ± 0.012 km3 of glacier mass from the reservoir zone to the receiving zone, leading to the formation of an ice-dammed lake at the glacier's terminus. The lake experienced five outbursts between 2015 and 2019, with the largest discharge occurring in 2017. And the maximum water depth during this period was 112 ± 11 m, resulting in a water storage volume of (158.37 ± 28.32) × 106 m3. On the other hand, our analysis of the relationship between glacier surface velocity and albedo, coupled with an examination of subglacial dynamics, revealed that increased precipitation during the active phase of the Kyagar Glacier results in accumulation of mass in the upper glacier. This accumulation induces changes in basal shear stress, triggering the glacier's transition into an unstable state. Consequently, glacier deformation rates escalate, surface crevasses proliferate, potentially providing conduits for surface meltwater to infiltrate the glacier bed. This, in turn, leaded to elevated basal water pressure, initiating glacier sliding. Furthermore, we postulated that the repetitive drainage of Kyagar Ice-dammed lake was primarily influenced by the opening and closing of subglacial drainage pathways and variations in inflow volumes. Future endeavors necessitate rigorous field observations to enhance glacier surge simulations, deepening our comprehension of glacier surge mechanisms and mitigating the impact of associated glacial hazards.

ML-NPI: Predicting Interactions between Noncoding RNA and Protein Based on Meta-Learning in a Large-Scale Dynamic Graph

Deep learning methods can accurately study noncoding RNA protein interactions (NPI), which is of great significance in gene regulation, human disease, and other fields. However, the computational method for predicting NPI in large-scale dynamic ncRNA protein bipartite graphs is rarely discussed, which is an online modeling and prediction problem. In addition, the results published by researchers on the Web site cannot meet real-time needs due to the large amount of basic data and long update cycles. Therefore, we propose a real-time method based on the dynamic ncRNA-protein bipartite graph learning framework, termed ML-GNN, which can model and predict the NPIs in real time. Our proposed method has the following advantages: first, the meta-learning strategy can alleviate the problem of large prediction errors in sparse neighborhood samples; second, dynamic modeling of newly added data can reduce computational pressure and predict NPIs in real-time. In the experiment, we built a dynamic bipartite graph based on 300000 NPIs from the NPInterv4.0 database. The experimental results indicate that our model achieved excellent performance in multiple experiments. The code for the model is available at https://github.com/taowang11/ML-NPI, and the data can be downloaded freely at http://bigdata.ibp.ac.cn/npinter4.

An Effective Plant Small Secretory Peptide Recognition Model Based on Feature Correction Strategy

Plant small secretory peptides (SSPs) play an important role in the regulation of biological processes in plants. Accurately predicting SSPs enables efficient exploration of their functions. Traditional experimental verification methods are very reliable and accurate, but they require expensive equipment and a lot of time. The method of machine learning speeds up the prediction process of SSPs, but the instability of feature extraction will also lead to further limitations of this type of method. Therefore, this paper proposes a new feature-correction-based model for SSP recognition in plants, abbreviated as SE-SSP. The model mainly includes the following three advantages: First, the use of transformer encoders can better reveal implicit features. Second, design a feature correction module suitable for sequences, named 2-D SENET, to adaptively adjust the features to obtain a more robust feature representation. Third, stack multiple linear modules to further dig out the deep information on the sample. At the same time, the training based on a contrastive learning strategy can alleviate the problem of sparse samples. We construct experiments on publicly available data sets, and the results verify that our model shows an excellent performance. The proposed model can be used as a convenient and effective SSP prediction tool in the future. Our data and code are publicly available at https://github.com/wrab12/SE-SSP/.

An Ontario Health (Cancer Care Ontario) Clinical Practice Guideline: Surveillance Strategies in Patients with Stage I, II, III or Resectable IV Melanoma Who Were Treated with Curative Intent

AIMS

To make recommendations on managing the surveillance of patients with stage I, II, III or resectable IV melanoma who are clinically free of disease following treatment with curative intent.

MATERIALS AND METHODS

This guideline was developed by Ontario Health's (Cancer Care Ontario's) Program in Evidence-Based Care and the Melanoma Disease Site Group (including seven medical oncologists, four surgical oncologists, three dermatologists, one radiation oncologist and one patient representative). The MEDLINE, EMBASE, Cochrane Library, PROSPERO databases and the main relevant guideline websites were searched. Internal and external reviews were conducted, with final approval by the Program in Evidence-Based Care and the Melanoma Disease Site Group. The Grading of Recommendations, Assessment, Development and Evaluation approach was followed, and the Modified Delphi method was used.

RESULTS

Based on the current evidence (eight eligible original study papers and four relevant guidelines) and the clinical opinions of the authors of this guideline, the initial recommendations were made. To reach 75% agreement for each recommendation, the Melanoma Disease Site Group (16 members) voted twice and one recommendation was voted on three times. After a comprehensive internal and external review process (including national and international reviewers), 12 recommendations, three weak recommendations and six qualified statements were ultimately made.

CONCLUSIONS

After a systematic review, a comprehensive internal and external review process and a consensus process, the current guideline has been created. The guideline authors believe that this guideline will help clinicians, patients and policymakers make well-informed healthcare decisions that will guide them in clinical melanoma surveillance and ultimately assist in improving patient outcomes.

Improving drug response prediction via integrating gene relationships with deep learning

Predicting the drug response of cancer cell lines is crucial for advancing personalized cancer treatment, yet remains challenging due to tumor heterogeneity and individual diversity. In this study, we present a deep learning-based framework named Deep neural network Integrating Prior Knowledge (DIPK) (DIPK), which adopts self-supervised techniques to integrate multiple valuable information, including gene interaction relationships, gene expression profiles and molecular topologies, to enhance prediction accuracy and robustness. We demonstrated the superior performance of DIPK compared to existing methods on both known and novel cells and drugs, underscoring the importance of gene interaction relationships in drug response prediction. In addition, DIPK extends its applicability to single-cell RNA sequencing data, showcasing its capability for single-cell-level response prediction and cell identification. Further, we assess the applicability of DIPK on clinical data. DIPK accurately predicted a higher response to paclitaxel in the pathological complete response (pCR) group compared to the residual disease group, affirming the better response of the pCR group to the chemotherapy compound. We believe that the integration of DIPK into clinical decision-making processes has the potential to enhance individualized treatment strategies for cancer patients.

Genetic Algorithm-Based Receptor Ligand: A Genetic Algorithm-Guided Generative Model to Boost the Novelty and Drug-Likeness of Molecules in a Sampling Chemical Space

Deep learning-based de novo molecular design has recently gained significant attention. While numerous DL-based generative models have been successfully developed for designing novel compounds, the majority of the generated molecules lack sufficiently novel scaffolds or high drug-like profiles. The aforementioned issues may not be fully captured by commonly used metrics for the assessment of molecular generative models, such as novelty, diversity, and quantitative estimation of the drug-likeness score. To address these limitations, we proposed a genetic algorithm-guided generative model called GARel (genetic algorithm-based receptor-ligand interaction generator), a novel framework for training a DL-based generative model to produce drug-like molecules with novel scaffolds. To efficiently train the GARel model, we utilized dense net to update the parameters based on molecules with novel scaffolds and drug-like features. To demonstrate the capability of the GARel model, we used it to design inhibitors for three targets: AA2AR, EGFR, and SARS-Cov2. The results indicate that GARel-generated molecules feature more diverse and novel scaffolds and possess more desirable physicochemical properties and favorable docking scores. Compared with other generative models, GARel makes significant progress in balancing novelty and drug-likeness, providing a promising direction for the further development of DL-based de novo design methodology with potential impacts on drug discovery.

AEGNN-M:A 3D Graph-Spatial Co-Representation Model for Molecular Property Prediction

Improving the drug development process can expedite the introduction of more novel drugs that cater to the demands of precision medicine. Accurately predicting molecular properties remains a fundamental challenge in drug discovery and development. Currently, a plethora of computer-aided drug discovery (CADD) methods have been widely employed in the field of molecular prediction. However, most of these methods primarily analyze molecules using low-dimensional representations such as SMILES notations, molecular fingerprints, and molecular graph-based descriptors. Only a few approaches have focused on incorporating and utilizing high-dimensional spatial structural representations of molecules. In light of the advancements in artificial intelligence, we introduce a 3D graph-spatial co-representation model called AEGNN-M, which combines two graph neural networks, GAT and EGNN. AEGNN-M enables learning of information from both molecular graphs representations and 3D spatial structural representations to predict molecular properties accurately. We conducted experiments on seven public datasets, three regression datasets and 14 breast cancer cell line phenotype screening datasets, comparing the performance of AEGNN-M with state-of-the-art deep learning methods. Extensive experimental results demonstrate the satisfactory performance of the AEGNN-M model. Furthermore, we analyzed the performance impact of different modules within AEGNN-M and the influence of spatial structural representations on the model's performance. The interpretability analysis also revealed the significance of specific atoms in determining particular molecular properties.

Acid-Promoted Cyclization of α-Azidobenzyl Ketones through C═N Bond Formation: Synthesis of 6-Substituted Quinoline Derivatives

An acid-promoted cyclization of α-azidobenzyl ketones has been developed for the synthesis of 6-substituted quinoline derivatives. A variety of synthetically useful 6-OTf or -OMs quinoline derivatives were obtained in moderate to good yields. The reaction proceeds via C═N bond formation without organophosphine, providing convenient access to structurally interesting and synthetically important 6-substituted quinoline derivatives in moderate to good yields. A mechanistic perspective that is different from the traditional intramolecular Schmidt reaction has been proposed.

Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization

Antimicrobial peptides (AMPs), short peptides with diverse functions, effectively target and combat various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistance and toxicity, AMPs are considered promising substitutes for traditional antibiotics. While existing deep learning technology enhances AMP generation, it also presents certain challenges. Firstly, AMP generation overlooks the complex interdependencies among amino acids. Secondly, current models fail to integrate crucial tasks like screening, attribute prediction and iterative optimization. Consequently, we develop a integrated deep learning framework, Diff-AMP, that automates AMP generation, identification, attribute prediction and iterative optimization. We innovatively integrate kinetic diffusion and attention mechanisms into the reinforcement learning framework for efficient AMP generation. Additionally, our prediction module incorporates pre-training and transfer learning strategies for precise AMP identification and screening. We employ a convolutional neural network for multi-attribute prediction and a reinforcement learning-based iterative optimization strategy to produce diverse AMPs. This framework automates molecule generation, screening, attribute prediction and optimization, thereby advancing AMP research. We have also deployed Diff-AMP on a web server, with code, data and server details available in the Data Availability section.

Discovery of Novel Inhibitors of BRD4 for Treating Prostate Cancer: A Comprehensive Case Study for Considering Water Networks in Virtual Screening and Drug Design

Androgen receptor (AR) is the primary target for treating prostate cancer (PCa), which inevitably progresses due to drug-resistant mutations. Bromodomain-containing protein 4 (BRD4) has been a new potential drug target for PCa treatment. Herein, we report the rational design and discovery of novel BRD4 inhibitors through computer-aided drug design (CADD), and a hit compound SQ-1 (IC50 = 676 nM) was identified by structure-based virtual screening (SBVS) with the conserved water network. To optimize the structure of SQ-1, the free energy landscape was constructed, and the binding mechanism was explored by characterizing the water profile and the dissociation mechanism. Finally, the compound SQ-17 with improved inhibitory activity (IC50 < 100 nM) was discovered, which showed potent antiproliferative activity against LNCaP. These data highlighted a successful attempt to identify and optimize a small molecule by comprehensive CADD application and provided essential clues for developing novel therapeutics for PCa treatment.

Enhancing Generic Reaction Yield Prediction through Reaction Condition-Based Contrastive Learning

Deep learning (DL)-driven efficient synthesis planning may profoundly transform the paradigm for designing novel pharmaceuticals and materials. However, the progress of many DL-assisted synthesis planning (DASP) algorithms has suffered from the lack of reliable automated pathway evaluation tools. As a critical metric for evaluating chemical reactions, accurate prediction of reaction yields helps improve the practicality of DASP algorithms in the real-world scenarios. Currently, accurately predicting yields of interesting reactions still faces numerous challenges, mainly including the absence of high-quality generic reaction yield datasets and robust generic yield predictors. To compensate for the limitations of high-throughput yield datasets, we curated a generic reaction yield dataset containing 12 reaction categories and rich reaction condition information. Subsequently, by utilizing 2 pretraining tasks based on chemical reaction masked language modeling and contrastive learning, we proposed a powerful bidirectional encoder representations from transformers (BERT)-based reaction yield predictor named Egret. It achieved comparable or even superior performance to the best previous models on 4 benchmark datasets and established state-of-the-art performance on the newly curated dataset. We found that reaction-condition-based contrastive learning enhances the model's sensitivity to reaction conditions, and Egret is capable of capturing subtle differences between reactions involving identical reactants and products but different reaction conditions. Furthermore, we proposed a new scoring function that incorporated Egret into the evaluation of multistep synthesis routes. Test results showed that yield-incorporated scoring facilitated the prioritization of literature-supported high-yield reaction pathways for target molecules. In addition, through meta-learning strategy, we further improved the reliability of the model's prediction for reaction types with limited data and lower data quality. Our results suggest that Egret holds the potential to become an essential component of the next-generation DASP tools.

Unraveling the allosteric inhibition mechanism of PARP-1 CAT and the D766/770A mutation effects via Gaussian accelerated molecular dynamics and Markov state model

PARP-1 (Poly (ADP-ribose) polymerase 1) is a nuclear enzyme and plays a key role in many cellular functions, such as DNA repair, modulation of chromatin structure, and recombination. Developing the PARP-1 inhibitors has emerged as an effective therapeutic strategy for a growing list of cancers. The catalytic structural domain (CAT) of PARP-1 upon binding the inhibitor allosterically regulates the conformational changes of helix domain (HD), affecting its identification with the damaged DNA. The typical type I (EB47) and III (veliparib) inhibitors were able to lengthening or shortening the retention time of this enzyme on DNA damage and thus regulating the cytotoxicity. Nonetheless, the basis underlying allosteric inhibition is unclear, which limits the development of novel PARP-1 inhibitors. Here, to investigate the distinct allosteric changes of EB47 and veliparib against PARP-1 CAT, each complex was simulated via classical and Gaussian accelerated molecular dynamics (cMD and GaMD). To study the reverse allosteric basis and mutation effects, the complexes PARP-1 with UKTT15 and PARP-1 D766/770A mutant with EB47 were also simulated. Importantly, the markov state models were built to identify the transition pathways of crucial substates of allosteric communication and the induction basis of PARP-1 reverse allostery. The conformational change differences of PARP-1 CAT regulated by allosteric inhibitors were concerned with to their interaction at the active site. Energy calculations suggested the energy advantage of EB47 in inhibiting the wild-type PARP-1, compared with D766/770A PARP-1. Secondary structure results showed the change of two key loops (αB-αD and αE-αF) in different systems. This work reported the basis of PARP-1 allostery from both thermodynamic and kinetic views, providing the guidance for the discovery and design of more innovative PARP-1 allosteric inhibitors.

Discovery of a potent and selective PARP1 degrader promoting cell cycle arrest via intercepting CDC25C-CDK1 axis for treating triple-negative breast cancer

PARP1 is a multifaceted component of DNA repair and chromatin remodeling, making it an effective therapeutic target for cancer therapy. The recently reported proteolytic targeting chimera (PROTAC) could effectively degrade PARP1 through the ubiquitin-proteasome pathway, expanding the therapeutic application of PARP1 blocking. In this study, a series of nitrogen heterocyclic PROTACs were designed and synthesized through ternary complex simulation analysis based on our previous work. Our efforts have resulted in a potent PARP1 degrader D6 (DC50 = 25.23 nM) with high selectivity due to nitrogen heterocyclic linker generating multiple interactions with the PARP1-CRBN PPI surface, specifically. Moreover, D6 exhibited strong cytotoxicity to triple negative breast cancer cell line MDA-MB-231 (IC50 = 1.04 µM). And the proteomic results showed that the antitumor mechanism of D6 was found that intensifies DNA damage by intercepting the CDC25C-CDK1 axis to halt cell cycle transition in triple-negative breast cancer cells. Furthermore, in vivo study, D6 showed a promising PK property with moderate oral absorption activity. And D6 could effectively inhibit tumor growth (TGI rate = 71.4 % at 40 mg/kg) without other signs of toxicity in MDA-MB-321 tumor-bearing mice. In summary, we have identified an original scaffold and potent PARP1 PROTAC that provided a novel intervention strategy for the treatment of triple-negative breast cancer.

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics

Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.

StableDNAm: towards a stable and efficient model for predicting DNA methylation based on adaptive feature correction learning

BACKGROUND

DNA methylation, instrumental in numerous life processes, underscores the paramount importance of its accurate prediction. Recent studies suggest that deep learning, due to its capacity to extract profound insights, provides a more precise DNA methylation prediction. However, issues related to the stability and generalization performance of these models persist.

RESULTS

In this study, we introduce an efficient and stable DNA methylation prediction model. This model incorporates a feature fusion approach, adaptive feature correction technology, and a contrastive learning strategy. The proposed model presents several advantages. First, DNA sequences are encoded at four levels to comprehensively capture intricate information across multi-scale and low-span features. Second, we design a sequence-specific feature correction module that adaptively adjusts the weights of sequence features. This improvement enhances the model's stability and scalability, or its generality. Third, our contrastive learning strategy mitigates the instability issues resulting from sparse data. To validate our model, we conducted multiple sets of experiments on commonly used datasets, demonstrating the model's robustness and stability. Simultaneously, we amalgamate various datasets into a single, unified dataset. The experimental outcomes from this combined dataset substantiate the model's robust adaptability.

CONCLUSIONS

Our research findings affirm that the StableDNAm model is a general, stable, and effective instrument for DNA methylation prediction. It holds substantial promise for providing invaluable assistance in future methylation-related research and analyses.

Fragment-based design, synthesis and biological evaluation of theophylline derivatives as ATAD2 inhibitors in BT-549 cells

ATPase family AAA domain-containing protein 2 (ATAD2) has been emerging as a hot anti-cancer drugable target due to its oncogenic epigenetic modification closely associated with cancer cells proliferation, apoptosis, migration and drug resistance. In this study, we design a series of theophylline derivatives as novel ATAD2 inhibitors through fragment-based screening and scaffold growth strategy. A novel potent ATAD2 inhibitor (compound 19f) is discovered with an IC50 value of 0.27 μM against ATAD2, which adopts a combination of classic and atypical binding mode. Additionally, compound 19f could impede ATAD2 activity and c-Myc activation, induced significant apoptosis, and illustrated an anti-migration effect in BT-549 cells. Collectively, these results provide new enlightenment for the development of novel potent ATAD2 inhibitors for triple-negative breast cancer (TNBC) treatment.

Small-Molecule Conformer Generators: Evaluation of Traditional Methods and AI Models on High-Quality Data Sets

Small-molecule conformer generation (SMCG) is an extremely important task in both ligand- and structure-based computer-aided drug design, especially during the hit discovery phase. Recently, a multitude of artificial intelligence (AI) models tailored for SMCG have emerged. Despite developers typically furnishing performance evaluation data upon releasing their AI models, a comprehensive and equitable performance comparison between AI models and conventional methods is still lacking. In this study, we curated a new benchmarking data set comprising 3354 high-quality ligand bioactive conformations. Subsequently, we conducted a systematic assessment of the performance of four widely adopted traditional methods (i.e., ConfGenX, Conformator, OMEGA, and RDKit ETKDG) and five AI models (i.e., ConfGF, DMCG, GeoDiff, GeoMol, and torsional diffusion) in the tasks of reproducing bioactive and low-energy conformations of small molecules. In the former task, the AI models have no advantage, particularly with a maximum ensemble size of 1. Even the best-performing AI model GeoMol is still worse than any of the tested traditional methods. Conversely, in the latter task, the torsional diffusion model shows obvious advantages, surpassing the best-performing traditional method ConfGenX by 26.09 and 12.97% on the COV-R and COV-P metrics, respectively. Furthermore, the influence of force field-based fine-tuning on the quality of the generated conformers was also discussed. Finally, a user-friendly Web server called fastSMCG was developed to enable researchers to rapidly and flexibly generate small-molecule conformers using both traditional and AI methods. We anticipate that our work will offer valuable practical assistance to the scientific community in this field.

CODD-Pred: A Web Server for Efficient Target Identification and Bioactivity Prediction of Small Molecules

Target identification and bioactivity prediction are critical steps in the drug discovery process. Here we introduce CODD-Pred (COmprehensive Drug Design Predictor), an online web server with well-curated data sets from the GOSTAR database, which is designed with a dual purpose of predicting potential protein drug targets and computing bioactivity values of small molecules. We first designed a double molecular graph perception (DMGP) framework for target prediction based on a large library of 646 498 small molecules interacting with 640 human targets. The framework achieved a top-5 accuracy of over 80% for hitting at least one target on both external validation sets. Additionally, its performance on the external validation set comprising 200 molecules surpassed that of four existing target prediction servers. Second, we collected 56 targets closely related to the occurrence and development of cancer, metabolic diseases, and inflammatory immune diseases and developed a multi-model self-validation activity prediction (MSAP) framework that enables accurate bioactivity quantification predictions for small-molecule ligands of these 56 targets. CODD-Pred is a handy tool for rapid evaluation and optimization of small molecules with specific target activity. CODD-Pred is freely accessible at http://codd.iddd.group/.

[Study the involvement of Langerin in mediating epicutaneous sensitization of atopic dermatitis-like mouse model]

Objective: To explore the role of Langerin in mediating epicutaneous sensitization of atopic dermatitis (AD) in mouse model. Methods: Mice were topically treated with calcipotriol (MC903) plus ovalbumin (OVA) on the ears to establish AD mouse models, and mice were divided into wild-type control group, wild-type AD group, Langerin knockout control group, and Langerin knockout AD group. Changes of lesion were daily observed. Infiltration of inflammatory cells, mRNA expression of Tslp, Il4, Il13, Il17a, and Il22, levels of serum total IgE, OVA-specific IgE (sIgE), OVA sIgG1 and OVA sIgG2a, proportion of regulatory T (Treg) cells in cervical draining lymph nodes were evaluated at the end of model preparation. Results: Skin tumidness and thickness, dermal inflammatory cells infiltration, the mRNA expression levels of Tslp, Il4, Il13, Il17a and Il22 in wild-type AD groups were higher than those in wild-type control groups, with (1.80±0.66, 1.64±0.25, 1.71±0.54, 2.41±0.23, 2.49±0.32) and (0.53±0.45, 0.85±0.29, 0.73±0.50, 0.72±0.25, 0.56±0.29), respectively (all P<0.05). In addition, the levels of serum total IgE, OVA sIgE and OVA sIgG1 in wild-type AD groups were higher than those in wild-type control groups, with [(1 216.00±572.70) ng/ml, (597.00±538.30) ng/ml, 1.59±0.09] and [(24.22±35.04) ng/ml, (20.01±41.71) ng/ml, 1.16±0.03], respectively (all P<0.05). In Langerin knockout mice, compared to wild-type mice, skin erythema, skin tumidness, epidermal thickening, inflammatory cell infiltration were more obvious; the mRNA expression levels of Tslp, Il4, Il13, Il17a and Il22 were upregulated with (8.19±6.44, 2.53±0.69, 2.82±0.73, 3.94±1.32, 3.80±1.43) (all P<0.05); the levels of serum total IgE, OVA sIgE and OVA sIgG1 were significantly increased with (2 508.00±657.10) ng/ml, (1 808.00±470.70) ng/ml, (1.73±0.09) (all P<0.05); the number of CD4+CD25+CD127-Treg cells were decreased significantly with (13.25±0.96)% and (15.31±1.47)%, respectively (P<0.05). Conclusion: Langerin is involved in mediating epicutaneous sensitization of the AD mouse model and plays a negative immunoregulatory role.

Generic Interpretable Reaction Condition Predictions with Open Reaction Condition Datasets and Unsupervised Learning of Reaction Center

Effective synthesis planning powered by deep learning (DL) can significantly accelerate the discovery of new drugs and materials. However, most DL-assisted synthesis planning methods offer either none or very limited capability to recommend suitable reaction conditions (RCs) for their reaction predictions. Currently, the prediction of RCs with a DL framework is hindered by several factors, including: (a) lack of a standardized dataset for benchmarking, (b) lack of a general prediction model with powerful representation, and (c) lack of interpretability. To address these issues, we first created 2 standardized RC datasets covering a broad range of reaction classes and then proposed a powerful and interpretable Transformer-based RC predictor named Parrot. Through careful design of the model architecture, pretraining method, and training strategy, Parrot improved the overall top-3 prediction accuracy on catalysis, solvents, and other reagents by as much as 13.44%, compared to the best previous model on a newly curated dataset. Additionally, the mean absolute error of the predicted temperatures was reduced by about 4 °C. Furthermore, Parrot manifests strong generalization capacity with superior cross-chemical-space prediction accuracy. Attention analysis indicates that Parrot effectively captures crucial chemical information and exhibits a high level of interpretability in the prediction of RCs. The proposed model Parrot exemplifies how modern neural network architecture when appropriately pretrained can be versatile in making reliable, generalizable, and interpretable recommendation for RCs even when the underlying training dataset may still be limited in diversity.

Effectiveness of Bladder Filling Control during Online MR-Guided Adaptive Radiotherapy for Rectum Cancer

PURPOSE/OBJECTIVE(S)

MR-guided adaptive radiotherapy (MRgART) treatment sessions at MR-Linac are time-consuming and changes in bladder filling during the session can impact the treatment dosimetry. In this work, we present the procedure implemented in the clinical workflow to stabilize bladder filling during the MR based adaptive radiotherapy sessions and evaluate its effectiveness and the resulting dosimetric impact on the adaptive plan.

MATERIALS/METHODS

Twenty-five rectum cancer patients treated at 1.5T MR-Linac with a short course radiotherapy (25 Gy in 5 fractions of 5 Gy each) were included in this retrospective study. Patients were treated with the adapt-to-shape workflow consisting of a plan adaptation based on the MRI acquired in each session and optimized on the corresponding MR-based synthetic CT. Considering the significant interval time between the acquisition of the first daily MRI used for plan adaptation, and the beam delivery, a bladder catheter was used to stabilize the bladder filling; the procedure consists of emptying the bladder and refilling it with a well-known amount of physiological solution before each MRI acquisition. Two MRIs were acquired at each session: the first was used for plan adaptation and the second was acquired while approving the adapted plan, to be rigidly registered with the first to ensure the appropriateness of the isodoses on the ongoing delivery treatment. A total of 125 sessions and 250 MRI images and bladder contours were analyzed; for each fraction, the time interval between the first and second MRI and the corresponding bladder volumes were recorded; the consistency of bladder volumes and shapes along each online session was assessed with the dice similarity index (DSC) and Hausdorff distance (HD); the impact on plan dosimetry was evaluated by comparing target and bladder DVH cut off points of the plan on the two different MRI datasets.

RESULTS

The time interval between the first and second MRI, averaged over the 125 sessions is 39.0 min, range (18.6-75.8) min. The changes in bladder volumes, DSC index, HD, and the differences between the bladder and target DVH cut-off points are shown in the table below. The DSC and HD are comparable to inter-observer variability in manual contour segmentation, with an average DSC of 0.91 and average HD of 2.13 mm; the average differences in bladder and target dosimetry remain under 0.63% and 0.10%, respectively.

CONCLUSION

The use of a procedure in the clinical workflow of MRgART to stabilize the bladder filling throughout the online session may be helpful to guarantee the accuracy of the ongoing delivered treatment.

Study on the Clinical Use of a Respiratory Navigator Combined with Breath-Hold for MRI- Guided Liver SBRT

PURPOSE/OBJECTIVE(S)

Respiratory movement strongly affects the accuracy of stereotactic body radiation therapy (SBRT) of liver malignancies treated without the use of a respiratory gating system. This study investigates the feasibility and advantages of using a respiratory navigator-guided combined with patient breath-hold for liver SBRT in an adaptive magnetic-resonance guided workflow.

MATERIALS/METHODS

Clinical datasets of 10 liver cancer patients treated with 1.5T MR-Linac with respiratory navigator-guided SBRT combined with patient breath-hold were retrospectively analyzed. All patients underwent simulation CT with and without contrast, and 4D-CT and 3D-T2w MRI without contrast. Patients received a prescription dose ranging from 36 to 50 Gy in 5 to 8 fractions and followed the adapt to shape (ATS) workflow including contours adjustment and a subsequent MR-based synthetic CT (sCT) calculation on the online MRI acquired. The reference treatment plan was optimized on the expiratory phase of the 4D-CT, and during the online session the contours and the adapted plans were performed using the 3D-T2w navigator MRI of the patient's end-expiratory signal; 2D-T2w real-time monitoring MRI was also used as support for the contour's definition. The radiation therapist instructed the patients to hold their breath at the end of the breathing cycle for the time of the beam on. A total of 59 fractions were analyzed. For each fraction the dosimetric parameters of the target and normal liver of the adaptive and reference plans were compared; particularly the volume, the conformity index (CI) and gradient index (GI) for the target, and V5, V10 and Dmean for the normal liver. T-student statistical analysis was performed; a p-value less than 0.05 was considered statistically significant.

RESULTS

In the free breathing state, the 3D-T2w navigator MRI images enable a clear visualization of the tumor and its boundaries. The average target CI of the adaptive and reference plans is not significantly different (p = 0.448), while the GI is significantly higher (p = 0.043). Normal liver V10 and Dmean are lower and V5 is slightly increased, but without statistical differences. The mean values and standard deviation of the dosimetric parameters of the reference and adapted plans are shown in the Table below.

CONCLUSION

The use of a respiratory navigator combined with the breath-hold for MRI- guided liver SBRT allows clear visualization of the tumor, ensures the accuracy of the delivered dose and may be considered an alternative when the respiratory gating system is not available during MRgART sessions.

Study on Lattice Radiotherapy Treatments (LRT) for Head and Neck Bulky Tumors

PURPOSE/OBJECTIVE(S)

Lattice radiotherapy (LRT) exploits various effects of radiation, such as the bystander effect and the abscopal effect, and consists on the administration of high dose fraction in small areas with large tumor masses, helping to solve the problem of treating bulky disease, especially if it is located in a critical anatomical area. The optimization of LRT treatment plans is challenging due to the difficulty to generate spots of high dose within the tumor with consequent high gradient. This study compares the plan dosimetry and delivery time of two delivery techniques VMAT and CyberKnife for LRT treatments of bulky head and neck lesions.

MATERIALS/METHODS

Six patients with giant head and neck tumors who received LRT at our institution were included in this study. Target and OARs were contoured following international guidelines; to allow easy identification of the desired high gradient zones, an artificial geometrical lattice structure with spherical vertices was arranged inside the target volume (GTV), and the vertices of the lattice representing the high dose boost volumes (GTVboost) were delineated. The GTVboost and GTV were prescribed to receive 12 Gy and 3 Gy, respectively in a single fraction. Separate VMAT and CyberKnife LRT plans were optimized for each patient with lattice vertex of 0.5 diameter and center-to-center distances of 1.5 cm (LRT1.5) and 3 cm (LRT3). The dose heterogeneity was measured as the peak-to-valley dose ratio (PVDR), with the traditional definition being replaced by the D10/D90 ratio, where D10 and D90 represent the doses covering 10% and 90% of the GTV, respectively. For each plan generated, the treatment delivery time, the monitor units (MU), and the PVDR were assessed. Pre-treatment plan verifications were performed with ArcCheck array and Gafchromics film for VMAT and CyberKnife, respectively, using gamma analysis criteria of 3%-3mm.

RESULTS

The mean PVDR obtained for VMAT LRT plans were 2.0 and 2.6 for LRT1.5 and LRT3, respectively, and 3.2 and 4.7, respectively for CyberKnife LRT plans. For each pre-treatment plan dose verification, the gamma passing rate (GPR) was higher than 95.0 %; CyberKnife delivery time and MU were more than 10 times higher than that of VMAT, nevertheless, VMAT had a lower PVDR. The detailed results are shown in the table below.

CONCLUSION

CyberKnife LRT has a strong ability to place the peak dose within the target, generating a higher peak-to-valley dose ratio, however its use is partially invalidated by the long beam delivery times and the resulting high MU number; the use of the VMAT LRT technique allows clinically adequate dosimetry with acceptable delivery times.

Unveiling the Selectivity Mechanism of Type-I LRRK2 Inhibitors by Computational Methods: Insights from Binding Thermodynamics and Kinetics Simulation

Understanding the selectivity mechanisms of inhibitors toward highly similar proteins is very important in new drug discovery. Developing highly selective targeting of leucine-rich repeat kinase 2 (LRRK2) kinases for the treatment of Parkinson's disease (PD) is challenging because of the similarity of the kinase ATP binding pocket. During the development of LRRK2 inhibitors, off-target effects on other kinases, especially TTK and JAK2 kinases, have been observed. As a result, significant time and resources have been devoted to improving the selectivity for the LRRK2 target. DNL201 is an LRRK2 kinase inhibitor entering phase I clinical studies. The experiments have shown that DNL201 significantly inhibits LRRK2 kinase activity, with >167-fold selectivity over JAK2 and TTK kinases. However, the potential mechanisms of inhibitor preferential binding to LRRK2 kinase are still not well elucidated. In this work, to reveal the underlying general selectivity mechanism, we carried out several computational methods and comprehensive analyses from both the binding thermodynamics and kinetics on two representative LRRK2 inhibitors (DNL201 and GNE7915) to LRRK2. Our results suggest that the structural and kinetic differences between the proteins may play a key role in determining the activity of the selective small-molecule inhibitor. The selectivity mechanisms proposed in this work could be helpful for the rational design of novel selective LRRK2 kinase inhibitors against PD.

HF-DDI: Predicting Drug-Drug Interaction Events Based on Multimodal Hybrid Fusion

Drug-drug interactions (DDIs) can have a significant impact on patient safety and health. Predicting potential DDIs before administering drugs to patients is a critical step in drug development and can help prevent adverse drug events. In this study, we propose a novel method called HF-DDI for predicting DDI events based on various drug features, including molecular structure, target, and enzyme information. Specifically, we design our model with both early fusion and late fusion strategies and utilize a score calculation module to predict the likelihood of interactions between drugs. Our model was trained and tested on a large data set of known DDIs, achieving an overall accuracy of 0.948. The results suggest that incorporating multiple drug features can improve the accuracy of DDI event prediction and may be useful for improving drug safety and patient outcomes.

Trace Doping: Fluorine-Containing Hydrophobic Lewis Acid Enables Stable Perovskite Solar Cells

With the rapid development in perovskite solar cell (PSC), high efficiency has been achieved, but the long-term operational stability is still the most important challenges for the commercialization of this emerging photovoltaic technology. So far, bi-dopants lithium bis(trifluoromethylsulfonyl)-imide (Li-TFSI)/4-tert-butylpyridine (t-BP)-doped hole-transporting materials (HTM) have led to state-of-the art efficiency in PSCs. However, such dopants have several drawbacks in terms of stability, including the complex oxidation process, undesirable ion migration and ultra-hygroscopic nature. Herein, a fluorine-containing organic Lewis acid dopant bis(pentafluorophenyl)zinc (Zn-FP) with hydrophobic property and high migration barrier has been employed as a potential alternative to widely employed bi-dopants Li-TFSI/t-BP for poly[bis(4-phenyl)(2,4,6-trimethylphenyl)amine] (PTAA). The resulting Zn-FP-based PSCs achieve a maximum PCE of 20.34 % with hysteresis-free current density-voltage (J-V) scans. Specifically, the unencapsulated device exhibits a significantly advanced of operational stability under the International Summit on Organic Photovoltaic Stability protocols (ISOS-L-1), maintaining over 90 % of the original efficiency after operation for 1000 h under continuous 1-sun equivalent illumination in N2 atmosphere in both forward and reverse J-V scan.

Increasing probability of record-population exposure to high temperature and related health-risks in China

Combining the comprehensive effects of temperature and humidity, this study applies a heat stress index to project future population exposure to high temperature and related health-risks over China under different climate change scenarios. Results show that the number of high temperature days, population exposure and their related health-risks will increase significantly in the future compared to the reference period (1985-2014), which is mainly caused by the change of >T99p (the wet bulb globe temperature >99th percentile derived from the reference period). The population effect is absolutely dominant in influencing the decrease in exposure to T90-95p (the wet bulb globe temperature is in the range of (90th, 95th]) and T95-99p (the wet bulb globe temperature is in the range of (95th, 99th]), and the climate effect is the most prominent contributor to the upsurge in exposure to > T99p in most areas. An additional 0.1 billion person-days increase in population exposure to T90-95p, T95-99p and >T99p in a given year is associated with the number of deaths by 1002 (95% CI: 570-1434), 2926 (95% CI: 1783-4069) and 2635 (95% CI: 1345-3925), respectively. Compared with the reference period, total exposure to high temperature under the SSP2-4.5 (SSP5-8.5) scenario will increase to 1.92 (2.01) times in the near-term (2021-2050) and 2.16 (2.35) times in the long-term (2071-2100), which will increase the number of people at heat risk by 1.2266 (95% CI: 0.6341-1.8192) [1.3575 (95% CI: 0.6926-2.0223)] and 1.5885 (95% CI: 0.7869-2.3902) [1.8901 (95% CI:0.9230-2.8572)] million, respectively. Significant geographic variations exist in the changes of exposure and related health-risks. The change is greatest in the southwest and south, whereas it is relatively small in the northeast and north. The findings provide several theoretical references for climate change adaptation.

FFLOM: A Flow-Based Autoregressive Model for Fragment-to-Lead Optimization

Recently, deep generative models have been regarded as promising tools in fragment-based drug design (FBDD). Despite the growing interest in these models, they still face challenges in generating molecules with desired properties in low data regimes. In this study, we propose a novel flow-based autoregressive model named FFLOM for linker and R-group design. In a large-scale benchmark evaluation on ZINC, CASF, and PDBbind test sets, FFLOM achieves state-of-the-art performance in terms of validity, uniqueness, novelty, and recovery of the generated molecules and can recover over 92% of the original molecules in the PDBbind test set (with at least five atoms). FFLOM also exhibits excellent potential applicability in several practical scenarios encompassing fragment linking, PROTAC design, R-group growing, and R-group optimization. In all four cases, FFLOM can perfectly reconstruct the ground-truth compounds and generate over 74% of molecules with novel fragments, some of which have higher binding affinity than the ground truth.

Phytoremediation of Lead-Contaminated Soil in the Westside of Atlanta, GA

Phytoremediation has been explored as a cost-effective method to remediate soil Pb contamination. A greenhouse study was conducted to evaluate the efficacy of Vigna unguiculata, Brassica pekinensis, Gomphrena globose, and Helianthus annuus for removing and immobilizing Pb in soil collected from the Westside Lead Superfund site in Atlanta. Plants were cultivated in sampled soil with a Pb concentration of 515 ± 10 mg/kg for 60 days. Soils growing H. annuus were additionally treated with ethylenediaminetetraacetic acid (EDTA) (0.1 g/kg) or compost (20% soil blend) to assess their capabilities for enhancing phytoremediation. Mean post-phytoremediation Pb concentrations in the four plant species were 23.5, 25.7, 50.0, and 58.1 mg/kg dry weight (DW), respectively, and were substantially higher than 1.55 mg/kg DW in respective plant species grown in control soils with no Pb contamination. The highest Pb concentration, translocation factor, and biomass were found in V. unguiculate among four species without soil amendments. H. annuus treated with EDTA and compost resulted in a significant increase in the total Pb uptake and larger biomass compared to non-treated plants, respectively. Although this study found that V. unguiculata was the best candidate for Pb accumulation and immobilization among four species, soil remediation was limited to 54 mg/kg in a growing season. We find that it is critically important to perform phytostabilization in a secure manner, since Pb bioavailability of edible plant parts implies the potential risk associated with their unintentional consumption. Efficiently and effectively remediating Pb-contaminated soils in a low-cost manner needs to be further studied.

Erratum: Author correction to 'Herbal formula BaWeiBaiDuSan alleviates polymicrobial sepsis-induced liver injury via increasing the gut microbiota Lactobacillus johnsonii and regulating macrophage anti-inflammatory activity in mice' [Acta Pharmaceutica Sinica B 13 (2023) 1164-1179]

[This corrects the article DOI: 10.1016/j.apsb.2022.10.016.].

GCFMCL: predicting miRNA-drug sensitivity using graph collaborative filtering and multi-view contrastive learning

Studies have shown that the mechanism of action of many drugs is related to miRNA. In-depth research on the relationship between miRNA and drugs can provide theoretical foundations and practical approaches for various areas, such as drug target discovery, drug repositioning and biomarker research. Traditional biological experiments to test miRNA-drug susceptibility are costly and time-consuming. Thus, sequence- or topology-based deep learning methods are recognized in this field for their efficiency and accuracy. However, these methods have limitations in dealing with sparse topologies and higher-order information of miRNA (drug) feature. In this work, we propose GCFMCL, a model for multi-view contrastive learning based on graph collaborative filtering. To the best of our knowledge, this is the first attempt that incorporates contrastive learning strategy into the graph collaborative filtering framework to predict the sensitivity relationships between miRNA and drug. The proposed multi-view contrastive learning method is divided into topological contrastive objective and feature contrastive objective: (1) For the homogeneous neighbors of the topological graph, we propose a novel topological contrastive learning method via constructing the contrastive target through the topological neighborhood information of nodes. (2) The proposed model obtains feature contrastive targets from high-order feature information according to the correlation of node features, and mines potential neighborhood relationships in the feature space. The proposed multi-view comparative learning effectively alleviates the impact of heterogeneous node noise and graph data sparsity in graph collaborative filtering, and significantly enhances the performance of the model. Our study employs a dataset derived from the NoncoRNA and ncDR databases, encompassing 2049 experimentally validated miRNA-drug sensitivity associations. Five-fold cross-validation shows that the Area Under the Curve (AUC), Area Under the Precision-Recall Curve (AUPR) and F1-score (F1) of GCFMCL reach 95.28%, 95.66% and 89.77%, which outperforms the state-of-the-art (SOTA) method by the margin of 2.73%, 3.42% and 4.96%, respectively. Our code and data can be accessed at https://github.com/kkkayle/GCFMCL.

Expression Pattern and Prognostic Analysis of Branched-Chain Amino Acid Catabolism-Related Genes in Non-Small Cell Lung Cancer

BACKGROUND

The purpose of our study is to analyze the expression pattern and prognostic value of catabolism-related enzymes of branched-chain amino acids (BCAAs) in non-small cell lung cancer (NSCLC).

METHODS

Differential expression analysis, mutation, copy number variation (CNV), methylation analysis, and survival analysis of BCAAs catabolism-related enzymes in NSCLC were performed using the Cancer Genome Atlas (TCGA) database.

RESULTS

Six and seven differentially expressed genes were obtained in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), respectively. IL4I1 was located at the core regulatory nodes in the gene co-expression networks of both LUAD and LUSC. The AOX1 mutation rate was the highest in both LUAD and LUSC. For CNV, IL4I1 was up-regulated in both LUAD and LUSC with an increase in copy number, whereas AOX1 and ALDH2 were differentially regulated in the two subtypes of lung cancer. In patients with NSCLC, high expression of IL4I1 was associated with lower overall survival (OS), and low expression of ALDH2 predicted shorter disease-free survival (DFS). ALDH2 expression was related with LUSC survival.

CONCLUSIONS

This study explored the biomarkers of BCAAs catabolism related to the prognosis of NSCLC, which provided a theoretical foundation to guide the clinical diagnosis and treatment of NSCLC.

Pharmacokinetics, absorption and transport mechanism for ginseng polysaccharides

BACKGROUND

Ginseng polysaccharide (GP) is one of the most abundant components in Panax ginseng. However, the absorption pathways and mechanisms of GPs have not been investigated systematically due to the challenges of their detection.

METHODS

The fluorescein isothiocyanate derivative (FITC) was employed to label GP and ginseng acidic polysaccharide (GAP) to obtain target samples. HPLC-MS/MS assay was used to determine the pharmacokinetics of GP and GAP in rats. The Caco-2 cell model was used to investigate the uptake and transport mechanisms of GP and GAP in rats.

RESULTS

Our results demonstrated that the absorption of GAP was more than that of GP in rats after gavage administration, while there was no significant difference between both after intravenous administration. In addition, we found that GAP and GP were more distributed in the kidney, liver and genitalia, suggesting that GAP and GP are highly targeted to the liver, kidney and genitalia. Importantly, we explored the uptake mechanism of GAP and GP. GAP and GP are endocytosed into the cell via lattice proteins or niche proteins. Both are transported lysosomally mediated to the endoplasmic reticulum (ER) and then enter the nucleus through the ER, thus completing the process of intracellular uptake and transportation.

CONCLUSION

Our results confirm that the uptake of GPs by small intestinal epithelial cells is primarily mediated via lattice proteins and the cytosolic cellar. The discovery of important pharmacokinetic properties and the uncovering of the absorption mechanism provide a research rationale for the research of GP formulation and clinical promotion.

Deep Generation Model Guided by the Docking Score for Active Molecular Design

A deep generation model, as a novel drug design and discovery tool, shows obvious advantages in generating compounds with novel backbones and has been applied successfully in the field of drug discovery. However, it is still a challenge to generate molecules with expected properties, especially high activity. Here, to obtain compounds both with novelty and high activity to a target, we proposed a conditional molecular generation model COMG by considering the docking score and 3D pharmacophore matching during molecular generation. The proposed model was based on the conditional variational autoencoder architecture constrained by the pharmacophore matching score. During Bayesian optimization, the docking score was applied to enhance the target relevance of generated compounds. Furthermore, to overcome the problem of high structural similarity caused by Bayesian optimization, the idea of the scaffold memory unit was also introduced. The evaluation results of COMG show that our model not only can improve the structural diversity of generated molecules but also can effectively improve the proportion of target-related drug-active molecules. The obtained results indicate that our proposed model COMG is a useful drug design tool.

Myricetin possesses the potency against SARS-CoV-2 infection through blocking viral-entry facilitators and suppressing inflammation in rats and mice

BACKGROUND

Myricetin (3,5,7-trihydroxy-2-(3,4,5-tri hydroxyphenyl)-4-benzopyrone) is a common flavonol extracted from many natural plants and Chinese herb medicines and has been demonstrated to have multiple pharmacological activities, such as anti-microbial, anti-thrombotic, neuroprotective, and anti-inflammatory effects. Previously, myricetin was reported to target Mpro and 3CL-Pro-enzymatic activity to SARS-CoV-2. However, the protective value of myricetin on SARS-Cov-2 infection through viral-entry facilitators has not yet been comprehensively understood.

PURPOSE

The aim of the current study was to evaluate the pharmacological efficacy and the mechanisms of action of myricetin against SARS-CoV-2 infection both in vitro and in vivo.

METHODS

The inhibitory effects of myricetin on SARS-CoV-2 infection and replication were assessed on Vero E6 cells. Molecular docking analysis and bilayer interferometry (BLI) assays, immunocytochemistry (ICC), and pseudoviruses assays were performed to evaluate the roles of myricetin in the intermolecular interaction between the receptor binding domain (RBD) of the SARS-CoV-2 spike (S) protein and angiotensin-converting enzyme 2 (ACE2). The anti-inflammatory potency and mechanisms of myricetin were examined in THP1 macrophages in vitro, as well as in carrageenan-induced paw edema, delayed-type hypersensitivity (DTH) induced auricle edema, and LPS-induced acute lung injury (ALI) animal models.

RESULTS

The results showed that myricetin was able to inhibit binding between the RBD of the SARS-CoV-2 S protein and ACE2 through molecular docking analysis and BLI assay, demonstrating its potential as a viral-entry facilitator blocker. Myricetin could also significantly inhibit SASR-CoV-2 infection and replication in Vero E6 cells (EC₅₀ 55.18 μM), which was further validated with pseudoviruses containing the RBD (wild-type, N501Y, N439K, Y453F) and an S1 glycoprotein mutant (S-D614G). Moreover, myricetin exhibited a marked suppressive action on the receptor-interacting serine/threonine protein kinase 1 (RIPK1)-driven inflammation and NF-kappa B signaling in THP1 macrophages. In animal model studies, myricetin notably ameliorated carrageenan-induced paw edema in rats, DTH induced auricle edema in mice, and LPS-induced ALI in mice.

CONCLUSION

Our findings showed that myricetin inhibited HCoV-229E and SARS-CoV-2 replication in vitro, blocked SARS-CoV-2 virus entry facilitators and relieved inflammation through the RIPK1/NF-κB pathway, suggesting that this flavonol has the potential to be developed as a therapeutic agent against COVID-19.

Small-Molecule Inhibition of Androgen Receptor Dimerization as a Strategy against Prostate Cancer

The clinically used androgen receptor (AR) antagonists for the treatment of prostate cancer (PCa) are all targeting the AR ligand binding pocket (LBP), resulting in various drug-resistant problems. Therefore, a new strategy to combat PCa is urgently needed. Enlightened by the gain-of-function mutations of androgen insensitivity syndrome, we discovered for the first time small-molecule antagonists toward a prospective pocket on the AR dimer interface named the dimer interface pocket (DIP) via molecular dynamics (MD) simulation, structure-based virtual screening, structure-activity relationship exploration, and bioassays. The first-in-class antagonist M17-B15 targeting the DIP is capable of effectively disrupting AR self-association, thereby suppressing AR signaling. Furthermore, M17-B15 exhibits extraordinary anti-PCa efficacy in vitro and also in mouse xenograft tumor models, demonstrating that AR dimerization disruption by small molecules targeting the DIP is a novel and valid strategy against PCa.

Improving drug-target affinity prediction via feature fusion and knowledge distillation

Rapid and accurate prediction of drug-target affinity can accelerate and improve the drug discovery process. Recent studies show that deep learning models may have the potential to provide fast and accurate drug-target affinity prediction. However, the existing deep learning models still have their own disadvantages that make it difficult to complete the task satisfactorily. Complex-based models rely heavily on the time-consuming docking process, and complex-free models lacks interpretability. In this study, we introduced a novel knowledge-distillation insights drug-target affinity prediction model with feature fusion inputs to make fast, accurate and explainable predictions. We benchmarked the model on public affinity prediction and virtual screening dataset. The results show that it outperformed previous state-of-the-art models and achieved comparable performance to previous complex-based models. Finally, we study the interpretability of this model through visualization and find it can provide meaningful explanations for pairwise interaction. We believe this model can further improve the drug-target affinity prediction for its higher accuracy and reliable interpretability.

The α-Synuclein Monomer May Have Different Misfolding Mechanisms in the Induction of α-Synuclein Fibrils with Different Polymorphs

The aggregation of alpha-synuclein (α-Syn) is closely related to the occurrence of some neurodegenerative diseases such as Parkinson's disease. The misfolding of α-Syn monomer plays a key role in the formation of aggregates and extension of fibril. However, the misfolding mechanism of α-Syn remains elusive. Here, three different α-Syn fibrils (isolated from a diseased human brain, generated by in vitro cofactor-tau induction, and obtained by in vitro cofactor-free induction) were selected for the study. The misfolding mechanisms of α-Syn were uncovered by studying the dissociation of the boundary chains based on the conventional molecular dynamics (MD) and Steered MD simulations. The results showed that the dissociation paths of the boundary chains in the three systems were different. According to the reverse process of dissociation, we concluded that in the human brain system, the binding of the monomer and template starts from the C-terminal and gradually misfolds toward the N-terminal. In the cofactor-tau system, the monomer binding starts from residues 58-66 (contain β3), followed by the C-terminal coil (residues 67-79). Then, the N-terminal coil (residues 36-41) and residues 50-57 (contain β2) bind to the template, followed by residues 42-49 (contain β1). In the cofactor-free system, two misfolding paths were found. One is that the monomer binds to the N/C-terminal (β1/β6) and then binds to the remaining residues. The other one is that the monomer binds sequentially from the C- to N-terminal, similar to the human brain system. Furthermore, in the human brain and cofactor-tau systems, electrostatic interactions (especially from residues 58-66) are the main driving force during the misfolding process, whereas in the cofactor-free system, the contributions of electrostatic and van der Waals interactions are comparable. These results may provide a deeper understanding for the misfolding and aggregation mechanism of α-Syn.

Developing an ultrasensitive immunochromatographic assay for authentication of an emergent fraud aminopyrine in herbal tea

Aminopyrine is a nonsteroidal anti-inflammatory drug only for medical purposes, however, it has been illegally added in traditional Chinese herbal teas for fraud activity recently. In this study, a specific antibody against aminopyrine with IC₅₀ of 3.00 ng/mL was obtained for the first time by a rational hapten design. Furthermore, an ultrasensitive gold nanoparticles immunochromatographic assay (AuNPs-ICA) for determination of aminopyrine based on a portable reader was firstly developed, with cut-off value of 100.00 ng/mL, limit of detection (LOD) of 4.80 ng/mL and limit of quantification (LOQ) of 5.71 ng/mL for herbal tea, respectively. The recovery rates ranged from 93.21 % to 105.61 %, with inter-assay coefficient of variation (CV) from 1.08 % to 3.82 %. Additionally, 24 blind samples were examined simultaneously by AuNPs-ICA and LC-MS/MS, demonstrating a good consistency for each other. The proposed AuNPs-ICA is an ultrasensitive and reliable tool for on-site surveillance screening of fraud additives in herbal tea.

EVALUATION OF PRE-ABLATION NLR AND LMR AS PREDICTORS OF DISTANT METASTASES IN PATIENTS WITH DIFFERENTIATED THYROID CANCER

Objective

This research aim was to evaluates the role of the pre-ablation neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR) as predictors of distant metastases in patients with differentiated thyroid cancer (DTC).

Methods

A retrospective analysis was given to 140 patients with DTC who received 131I remnant ablation after surgery. The patients were divided into two groups based on the existence of distant metastasis.

Results

The two groups showed no significant difference in age, gender, WBCs, neutrophils, monocytes, eosinophils, basophils and whether the tumor was multifocal. In the univariate analysis, significant differences were found in tumor size (p=0.021), lymphocyte (p=0.012), NLR (p=0.027), and LMR (p=0.007). According to the ROC curves, NLR had an AUC of 0.612 ± 0.097 with a cut-off value of 1.845, sensitivity of 60.0%, and specificity of 66.2% (p=0.027). LMR had an AUC of 0.638 ± 0.095 with a cut-off value of 4.630, sensitivity of 84.6%, and specificity of 35.4% (p=0.007). In the multivariate analysis, larger tumor size (OR=5.246, 95% CI 1.269-10.907, p=0.009) and higher NLR (OR=2.087, 95% CI 0.977-4.459, p=0.034) were statistically significant for distant metastases.

Conclusion

This research reveals that pre-ablation NLR and tumor size are significantly statistically correlated with distant metastases in patients with DTC.

HeadTailTransfer: An efficient sampling method to improve the performance of graph neural network method in predicting sparse ncRNA-protein interactions

Noncoding RNA (ncRNA) is a functional RNA derived from DNA transcription, and most transcribed genes are transcribed into ncRNA. ncRNA is not directly involved in the translation of proteins, but it can participate in gene expression in cells and affect protein synthesis, thus playing an important role in biological processes such as growth, proliferation, metabolism, and information transmission. Therefore, understanding the interaction between ncRNA and protein is the basis for studying ncRNA regulation of protein-related biological activities. However, it is very expensive and time-consuming to verify ncRNA-protein interaction through biological experiments, and prediction methods based on machine learning have been developed rapidly. Recently, the graph neural network model (GNN) stands out for its excellent performance, but lacks a general framework for predicting ncRNA-protein interactions. We propose a GNN-based framework to predict ncRNA-protein interactions, which can utilize topological structure information to complete prediction tasks faster and more accurately. Meanwhile, for some smaller datasets, many ncRNA nodes lack neighbor information, resulting in lower prediction accuracy. For some larger datasets, the long-tail distribution causes the prediction of the tail nodes (sparse nodes linking few neighbors) to be affected. Therefore, we propose a new sampling method named HeadTailTransfer to mitigate these effects. Experimental results illustrate the effectiveness of this method. Especially for task-specific prediction on the RPI369 dataset in the Graphsage-based neural network framework, the AUC and ACC values increased from 56.8% and 52.2% to 80.2% and 71.8%, respectively. Our data and codes are available: https://github.com/kkkayle/HeadTailTransfer.

Development, validation, and evaluation of a deep learning model to screen cyclin-dependent kinase 12 inhibitors in cancers

Deep learning-based in silico alternatives have been demonstrated to be of significant importance in the acceleration of the drug discovery process and enhancement of success rates. Cyclin-dependent kinase 12 (CDK12) is a transcription-related cyclin-dependent kinase that may act as a biomarker and therapeutic target for cancers. However, currently, there is no high selective CDK12 inhibitor in clinical development and the identification of new specific CDK12 inhibitors has become increasingly challenging due to their similarity with CDK13. In this study, we developed a virtual screening workflow that combines deep learning with virtual screening tools and can be applied rapidly to millions of molecules. We designed a Transformer architecture Drug-Target Interaction (DTI) model with dual-branched self-supervised pre-trained molecular graph models and protein sequence models. Our predictive model produced satisfactory predictions for various targets, including CDK12, with several novel hits. We screened a large compound library consisting of 4.5 million drug-like molecules and recommended a list of potential CDK12 inhibitors for further experimental testing. In kinase assay, compared to the positive CDK12 inhibitor THZ531, the compounds CICAMPA-01, 02, 03 displayed more effective inhibition of CDK12, up to three times as much as THZ531. The compounds CICAMPA-03, 05, 04, 07 showed less inhibition of CDK13 compare to THZ531. In vitro, the IC50 of CICAMPA-01, 04, 05, 06, 09 was less than 3 μM in the HER2 positive CDK12 amplification breast cancer cell line BT-474. Overall, this study provides a highly efficient and end-to-end deep learning protocol, in conjunction with molecular docking, for discovering CDK12 inhibitors in cancers. Additionally, we disclose five novel CDK12 inhibitors. These results may accelerate the discovery of novel chemical-class drugs for cancer treatment.

[Analysis of success rate of organoid construction of nasopharyngeal carcinoma by first-day suspension method]

Objective: To investigate the efficacy of the first-day suspension method for improving the success rate of construction of nasopharyngeal carcinoma-patient derived organoids (NPC-PDO). Methods: The tumor samples of 14 nasopharyngeal carcinoma(NPC) patients, i.e.,13 males and 1 female, with a mean age of 43.0±12.0 years old, were collected from the Affiliated Tumor Hospital of Guangxi Medical University and the First Affiliated Hospital of Guangxi Medical University from January 2022 to July 2022. The tumor samples of 3 patients were digested into single cell suspension and divided into 2 groups, for comparing the efficacy of NPC-PDO construction by the direct inoculation method and the first-day suspension method. The remaining 11 patients were randomized to receive either the direct inoculation method or the first-day suspension method for NPC-PDO construction. The diameter and the number of spheres of NPC-PDO constructed by the two methods were compared by optical microscope; the 3D cell viability detection kit was used to compare the cell viability; the survival rates were compared by trypan blue staining; the success rates of the two construction methods were compared; the number of cases which could be successfully passaged for more than 5 generations and were consistent with the original tissue by pathological examination was counted; and the dynamic changes of cells in suspension overnight were observed by live cell workstation. The independent sample t-test was applied to compare the measurement data of the two groups, and the chi-square test was used to compare the classification data. Results: Compared with the direct inoculation, the diameter and the number of spheres of NPC-PDO constructed by the first-day suspension method were increased, with a higher cell activity, and the success rate of construction was obviously improved (80.0% vs 16.7%, χ²=4.41, P<0.05). In the suspension state, some of the cells aggregated and increased their ability to proliferate. Conclusion: The first-day suspension method can improve the success rate of NPC-PDO construction, especially for those whose original tumor sample size is small.

Herbal formula BaWeiBaiDuSan alleviates polymicrobial sepsis-induced liver injury via increasing the gut microbiota Lactobacillus johnsonii and regulating macrophage anti-inflammatory activity in mice

Sepsis-induced liver injury (SILI) is an important cause of septicemia deaths. BaWeiBaiDuSan (BWBDS) was extracted from a formula of Panax ginseng C. A. Meyer, Lilium brownie F. E. Brown ex Miellez var. viridulum Baker, Polygonatum sibiricum Delar. ex Redoute, Lonicera japonica Thunb., Hippophae rhamnoides Linn., Amygdalus Communis Vas, Platycodon grandiflorus (Jacq.) A. DC., and Cortex Phelloderdri. Herein, we investigated whether the BWBDS treatment could reverse SILI by the mechanism of modulating gut microbiota. BWBDS protected mice against SILI, which was associated with promoting macrophage anti-inflammatory activity and enhancing intestinal integrity. BWBDS selectively promoted the growth of Lactobacillus johnsonii (L. johnsonii) in cecal ligation and puncture treated mice. Fecal microbiota transplantation treatment indicated that gut bacteria correlated with sepsis and was required for BWBDS anti-sepsis effects. Notably, L. johnsonii significantly reduced SILI by promoting macrophage anti-inflammatory activity, increasing interleukin-10+ M2 macrophage production and enhancing intestinal integrity. Furthermore, heat inactivation L. johnsonii (HI-L. johnsonii) treatment promoted macrophage anti-inflammatory activity and alleviated SILI. Our findings revealed BWBDS and gut microbiota L. johnsonii as novel prebiotic and probiotic that may be used to treat SILI. The potential underlying mechanism was at least in part, via L. johnsonii-dependent immune regulation and interleukin-10+ M2 macrophage production.

Unbiased molecular dynamics simulation of a first-in-class small molecule inhibitor binds to oncostatin M

Small molecule inhibitors (SMIs) targeting oncostatin M (OSM) signaling pathway represent new therapeutics to combat cancer, inflammatory bowel disease (IBD) and CNS disease. Recently, the first-in-class SMI named SMI-10B that target OSM and block its interaction with receptor (OSMR) were reported. However, the binding pocket and interaction mode of the compound on OSM remain poorly understood, which hampering the rational design of SMIs that target OSM. Here, using SMI-10B as a probe, the multiple pockets on OSM for small molecules binding were extensively explored by unbiased molecular dynamics (MD) simulations. Then, the near-native structure of the complex was identified by molecular mechanics generalized Born surface area (MM/GBSA) binding energy funnel. Moreover, the binding stabilities of the protein-ligand complexes in near- and non-native conformations were verified by additional independent MD runs and absolute free energy perturbation (FEP) calculation. In summary, the unique feature of SMI-10B spontaneously binds to OSM characterized here not only provide detailed information for understanding the molecular mechanism of SMI-10B binding to OSM, but also will facilitate the rational design of novel and more potent SMIs to block OSM signaling.

From tuberculosis bedside to bench: UBE2B splicing as a potential biomarker and its regulatory mechanism

Alternative splicing (AS) is an important approach for pathogens and hosts to remodel transcriptome. However, tuberculosis (TB)-related AS has not been sufficiently explored. Here we presented the first landscape of TB-related AS by long-read sequencing, and screened four AS events (S100A8-intron1-retention intron, RPS20-exon1-alternaitve promoter, KIF13B-exon4-skipping exon (SE) and UBE2B-exon7-SE) as potential biomarkers in an in-house cohort-1. The validations in an in-house cohort-2 (2274 samples) and public datasets (1557 samples) indicated that the latter three AS events are potential promising biomarkers for TB diagnosis, but not for TB progression and prognosis. The excellent performance of classifiers further underscored the diagnostic value of these three biomarkers. Subgroup analyses indicated that UBE2B-exon7-SE splicing was not affected by confounding factors and thus had relatively stable performance. The splicing of UBE2B-exon7-SE can be changed by heat-killed mycobacterium tuberculosis through inhibiting SRSF1 expression. After heat-killed mycobacterium tuberculosis stimulation, 231 ubiquitination proteins in macrophages were differentially expressed, and most of them are apoptosis-related proteins. Taken together, we depicted a global TB-associated splicing profile, developed TB-related AS biomarkers, demonstrated an optimal application scope of target biomarkers and preliminarily elucidated mycobacterium tuberculosis-host interaction from the perspective of splicing, offering a novel insight into the pathophysiology of TB.

MetalProGNet: a structure-based deep graph model for metalloprotein-ligand interaction predictions

Metalloproteins play indispensable roles in various biological processes ranging from reaction catalysis to free radical scavenging, and they are also pertinent to numerous pathologies including cancer, HIV infection, neurodegeneration, and inflammation. Discovery of high-affinity ligands for metalloproteins powers the treatment of these pathologies. Extensive efforts have been made to develop in silico approaches, such as molecular docking and machine learning (ML)-based models, for fast identification of ligands binding to heterogeneous proteins, but few of them have exclusively concentrated on metalloproteins. In this study, we first compiled the largest metalloprotein-ligand complex dataset containing 3079 high-quality structures, and systematically evaluated the scoring and docking powers of three competitive docking tools (i.e., PLANTS, AutoDock Vina and Glide SP) for metalloproteins. Then, a structure-based deep graph model called MetalProGNet was developed to predict metalloprotein-ligand interactions. In the model, the coordination interactions between metal ions and protein atoms and the interactions between metal ions and ligand atoms were explicitly modelled through graph convolution. The binding features were then predicted by the informative molecular binding vector learned from a noncovalent atom-atom interaction network. The evaluation on the internal metalloprotein test set, the independent ChEMBL dataset towards 22 different metalloproteins and the virtual screening dataset indicated that MetalProGNet outperformed various baselines. Finally, a noncovalent atom-atom interaction masking technique was employed to interpret MetalProGNet, and the learned knowledge accords with our understanding of physics.