EGPDI: identifying protein-DNA binding sites based on multi-view graph embedding fusion.

Mechanisms of protein-DNA interactions are involved in a wide range of biological activities and processes. Accurately identifying binding sites between proteins and DNA is crucial for analyzing genetic material, exploring protein functions, and designing novel drugs. In recent years, several computational methods have been proposed as alternatives to time-consuming and expensive traditional experiments. However, accurately predicting protein-DNA binding sites still remains a challenge. Existing computational methods often rely on handcrafted features and a single-model architecture, leaving room for improvement. We propose a novel computational method, called EGPDI, based on multi-view graph embedding fusion. This approach involves the integration of Equivariant Graph Neural Networks (EGNN) and Graph Convolutional Networks II (GCNII), independently configured to profoundly mine the global and local node embedding representations. An advanced gated multi-head attention mechanism is subsequently employed to capture the attention weights of the dual embedding representations, thereby facilitating the integration of node features. Besides, extra node features from protein language models are introduced to provide more structural information. To our knowledge, this is the first time that multi-view graph embedding fusion has been applied to the task of protein-DNA binding site prediction. The results of five-fold cross-validation and independent testing demonstrate that EGPDI outperforms state-of-the-art methods. Further comparative experiments and case studies also verify the superiority and generalization ability of EGPDI.

iProL: identifying DNA promoters from sequence information based on Longformer pre-trained model.

Promoters are essential elements of DNA sequence, usually located in the immediate region of the gene transcription start sites, and play a critical role in the regulation of gene transcription. Its importance in molecular biology and genetics has attracted the research interest of researchers, and it has become a consensus to seek a computational method to efficiently identify promoters. Still, existing methods suffer from imbalanced recognition capabilities for positive and negative samples, and their recognition effect can still be further improved. We conducted research on E. coli promoters and proposed a more advanced prediction model, iProL, based on the Longformer pre-trained model in the field of natural language processing. iProL does not rely on prior biological knowledge but simply uses promoter DNA sequences as plain text to identify promoters. It also combines one-dimensional convolutional neural networks and bidirectional long short-term memory to extract both local and global features. Experimental results show that iProL has a more balanced and superior performance than currently published methods. Additionally, we constructed a novel independent test set following the previous specification and compared iProL with three existing methods on this independent test set.

GCRFLDA: scoring lncRNA-disease associations using graph convolution matrix completion with conditional random field.

Long noncoding RNAs (lncRNAs) play important roles in various biological regulatory processes, and are closely related to the occurrence and development of diseases. Identifying lncRNA-disease associations is valuable for revealing the molecular mechanism of diseases and exploring treatment strategies. Thus, it is necessary to computationally predict lncRNA-disease associations as a complementary method for biological experiments. In this study, we proposed a novel prediction method GCRFLDA based on the graph convolutional matrix completion. GCRFLDA first constructed a graph using the available lncRNA-disease association information. Then, it constructed an encoder consisting of conditional random field and attention mechanism to learn efficient embeddings of nodes, and a decoder layer to score lncRNA-disease associations. In GCRFLDA, the Gaussian interaction profile kernels similarity and cosine similarity were fused as side information of lncRNA and disease nodes. Experimental results on four benchmark datasets show that GCRFLDA is superior to other existing methods. Moreover, we conducted case studies on four diseases and observed that 70 of 80 predicted associated lncRNAs were confirmed by the literature.

DeepASDPred: a CNN-LSTM-based deep learning method for Autism spectrum disorders risk RNA identification.

BACKGROUND: Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders characterized by difficulty communicating with society and others, behavioral difficulties, and a brain that processes information differently than normal. Genetics has a strong impact on ASD associated with early onset and distinctive signs. Currently, all known ASD risk genes are able to encode proteins, and some de novo mutations disrupting protein-coding genes have been demonstrated to cause ASD. Next-generation sequencing technology enables high-throughput identification of ASD risk RNAs. However, these efforts are time-consuming and expensive, so an efficient computational model for ASD risk gene prediction is necessary. RESULTS: In this study, we propose DeepASDPerd, a predictor for ASD risk RNA based on deep learning. Firstly, we use K-mer to feature encode the RNA transcript sequences, and then fuse them with corresponding gene expression values to construct a feature matrix. After combining chi-square test and logistic regression to select the best feature subset, we input them into a binary classification prediction model constructed by convolutional neural network and long short-term memory for training and classification. The results of the tenfold cross-validation proved our method outperformed the state-of-the-art methods. Dataset and source code are available at https://github.com/Onebear-X/DeepASDPred is freely available. CONCLUSIONS: Our experimental results show that DeepASDPred has outstanding performance in identifying ASD risk RNA genes.

IHCP: interpretable hepatitis C prediction system based on black-box machine learning models.

BACKGROUND: Hepatitis C is a prevalent disease that poses a high risk to the human liver. Early diagnosis of hepatitis C is crucial for treatment and prognosis. Therefore, developing an effective medical decision system is essential. In recent years, many computational methods have been proposed to identify hepatitis C patients. Although existing hepatitis prediction models have achieved good results in terms of accuracy, most of them are black-box models and cannot gain the trust of doctors and patients in clinical practice. As a result, this study aims to use various Machine Learning (ML) models to predict whether a patient has hepatitis C, while also using explainable models to elucidate the prediction process of the ML models, thus making the prediction process more transparent. RESULT: We conducted a study on the prediction of hepatitis C based on serological testing and provided comprehensive explanations for the prediction process. Throughout the experiment, we modeled the benchmark dataset, and evaluated model performance using fivefold cross-validation and independent testing experiments. After evaluating three types of black-box machine learning models, Random Forest (RF), Support Vector Machine (SVM), and AdaBoost, we adopted Bayesian-optimized RF as the classification algorithm. In terms of model interpretation, in addition to using common SHapley Additive exPlanations (SHAP) to provide global explanations for the model, we also utilized the Local Interpretable Model-Agnostic Explanations with stability (LIME_stabilitly) to provide local explanations for the model. CONCLUSION: Both the fivefold cross-validation and independent testing show that our proposed method significantly outperforms the state-of-the-art method. IHCP maintains excellent model interpretability while obtaining excellent predictive performance. This helps uncover potential predictive patterns of the model and enables clinicians to better understand the model's decision-making process.

StackEPI: identification of cell line-specific enhancer-promoter interactions based on stacking ensemble learning.

BACKGROUND: Understanding the regulatory role of enhancer-promoter interactions (EPIs) on specific gene expression in cells contributes to the understanding of gene regulation, cell differentiation, etc., and its identification has been a challenging task. On the one hand, using traditional wet experimental methods to identify EPIs often means a lot of human labor and time costs. On the other hand, although the currently proposed computational methods have good recognition effects, they generally require a long training time. RESULTS: In this study, we studied the EPIs of six human cell lines and designed a cell line-specific EPIs prediction method based on a stacking ensemble learning strategy, which has better prediction performance and faster training speed, called StackEPI. Specifically, by combining different encoding schemes and machine learning methods, our prediction method can extract the cell line-specific effective information of enhancer and promoter gene sequences comprehensively and in many directions, and make accurate recognition of cell line-specific EPIs. Ultimately, the source code to implement StackEPI and experimental data involved in the experiment are available at https://github.com/20032303092/StackEPI.git . CONCLUSIONS: The comparison results show that our model can deliver better performance on the problem of identifying cell line-specific EPIs and outperform other state-of-the-art models. In addition, our model also has a more efficient computation speed.

Does tourism increase CO2 emissions and health spending in Mexico? New evidence from nonlinear ARDL approach.

This study investigates the nexus between tourism, CO2 emissions and health spending in Mexico. We applied a nonlinear ARDL approach for the empirical analysis for the time period 1996-2018. Mexico receives a large number of tourists each year, tourism improves foreign exchange earnings and contributes positively to the economic growth. However, tourist activities impose a serious environmental cost in terms of CO2 emissions which increase health spending. The empirical findings suggest that tourism leads to CO2 emissions which resultantly causes a high level of health spending in Mexico. Both short-run and long-run findings reported a significant positive association between tourism, CO2 emissions, and health expenditures. Therefore, the government needs legislation to reduce CO2 emissions, besides the use of renewable energy could also help to reduce the CO2 emissions and health expenditures in society. This study does not support to reduce the health expenditure, rather it suggests optimal utilization of the funds allocated to the health sector.

Using multi-layer perceptron to identify origins of replication in eukaryotes via informative features.

BACKGROUND: The origin is the starting site of DNA replication, an extremely vital part of the informational inheritance between parents and children. More importantly, accurately identifying the origin of replication has great application value in the diagnosis and treatment of diseases related to genetic information errors, while the traditional biological experimental methods are time-consuming and laborious. RESULTS: We carried out research on the origin of replication in a variety of eukaryotes and proposed a unique prediction method for each species. Throughout the experiment, we collected data from 7 species, including Homo sapiens, Mus musculus, Drosophila melanogaster, Arabidopsis thaliana, Kluyveromyces lactis, Pichia pastoris and Schizosaccharomyces pombe. In addition to the commonly used sequence feature extraction methods PseKNC-II and Base-content, we designed a feature extraction method based on TF-IDF. Then the two-step method was utilized for feature selection. After comparing a variety of traditional machine learning classification models, the multi-layer perceptron was employed as the classification algorithm. Ultimately, the data and codes involved in the experiment are available at https://github.com/Sarahyouzi/EukOriginPredict . CONCLUSIONS: The prediction accuracy of the training set of the above-mentioned seven species after 100 times fivefold cross validation reach 92.60%, 90.80%, 91.22%, 96.15%, 96.72%, 99.86%, 96.72%, respectively. It denotes that compared with other methods, the methods we designed could accomplish superior performance. In addition, our experiments reveals that the models of multiple species could predict each other with high accuracy, and the results of STREME shows that they have a certain common motif.

PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation.

BACKGROUND: With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. RESULTS: A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. CONCLUSIONS: Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

Batch-feeding whole-cell catalytic synthesis of α-arbutin by amylosucrase from Xanthomonas campestris.

α-Arbutin is an effective skin-whitening cosmetic ingredient and can be synthesized through hydroquinone glycosylation. In this study, amylosucrase (Amy-1) from Xanthomonas campestris pv. campestris 8004 was newly identified as a sucrose-utilizing glycosylating hydroquinone enzyme. Its kinetic parameters showed a seven-time higher affinity to hydroquinone than maltose-utilizing α-glycosidase. The glycosylation of HQ can be quickly achieved with over 99% conversion when a high molar ratio of glycoside donor to acceptor (80:1) was used. A batch-feeding catalysis method was designed to eliminate HQ inhibition with high productivity (> 36.4 mM h-1). Besides, to eliminate the serious inhibition caused by the accumulated hydroquinone oxidation products, the whole-cell catalysis was further proposed. 306 mM of α-arbutin was finally achieved with 95% molar conversion rate within 15 h. Hence, the batch-feeding whole-cell biocatalysis by Amy-1 is a promising technology for α-arbutin production with enhanced yield and molar conversion rate.

Anti-Leishmanial and Cytotoxic Activities of a Series of Maleimides: Synthesis, Biological Evaluation and Structure-Activity Relationship.

In the present study, 45 maleimides have been synthesized and evaluated for anti-leishmanial activities against L. donovani in vitro and cytotoxicity toward THP1 cells. All compounds exhibited obvious anti-leishmanial activities. Among the tested compounds, there were 10 maleimides with superior anti-leishmanial activities to standard drug amphotericin B, and 32 maleimides with superior anti-leishmanial activities to standard drug pentamidine, especially compounds 16 (IC50 < 0.0128 µg/mL) and 42 (IC50 < 0.0128 µg/mL), which showed extraordinary efficacy in an in vitro test and low cytotoxicities (CC50 > 10 µg/mL). The anti-leishmanial activities of 16 and 42 were 10 times better than that of amphotericin B. The structure and activity relationship (SAR) studies revealed that 3,4-non-substituted maleimides displayed the strongest anti-leishmanial activities compared to those for 3-methyl-maleimides and 3,4-dichloro-maleimides. 3,4-dichloro-maleimides were the least cytotoxic compared to 3-methyl-maleimides and 3,4-non-substituted maleimides. The results show that several of the reported compounds are promising leads for potential anti-leishmanial drug development.

IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction.

BACKGROUND: Non-coding RNAs (ncRNAs) play crucial roles in many biological processes, such as post-transcription of gene regulation. ncRNAs mainly function through interaction with RNA binding proteins (RBPs). To understand the function of a ncRNA, a fundamental step is to identify which protein is involved into its interaction. Therefore it is promising to computationally predict RBPs, where the major challenge is that the interaction pattern or motif is difficult to be found. RESULTS: In this study, we propose a computational method IPMiner (Interaction Pattern Miner) to predict ncRNA-protein interactions from sequences, which makes use of deep learning and further improves its performance using stacked ensembling. One of the IPMiner's typical merits is that it is able to mine the hidden sequential interaction patterns from sequence composition features of protein and RNA sequences using stacked autoencoder, and then the learned hidden features are fed into random forest models. Finally, stacked ensembling is used to integrate different predictors to further improve the prediction performance. The experimental results indicate that IPMiner achieves superior performance on the tested lncRNA-protein interaction dataset with an accuracy of 0.891, sensitivity of 0.939, specificity of 0.831, precision of 0.945 and Matthews correlation coefficient of 0.784, respectively. We further comprehensively investigate IPMiner on other RNA-protein interaction datasets, which yields better performance than the state-of-the-art methods, and the performance has an increase of over 20 % on some tested benchmarked datasets. In addition, we further apply IPMiner for large-scale prediction of ncRNA-protein network, that achieves promising prediction performance. CONCLUSION: By integrating deep neural network and stacked ensembling, from simple sequence composition features, IPMiner can automatically learn high-level abstraction features, which had strong discriminant ability for RNA-protein detection. IPMiner achieved high performance on our constructed lncRNA-protein benchmark dataset and other RNA-protein datasets. IPMiner tool is available at http://www.csbio.sjtu.edu.cn/bioinf/IPMiner .

LabCaS: labeling calpain substrate cleavage sites from amino acid sequence using conditional random fields.

The calpain family of Ca(2+) -dependent cysteine proteases plays a vital role in many important biological processes which is closely related with a variety of pathological states. Activated calpains selectively cleave relevant substrates at specific cleavage sites, yielding multiple fragments that can have different functions from the intact substrate protein. Until now, our knowledge about the calpain functions and their substrate cleavage mechanisms are limited because the experimental determination and validation on calpain binding are usually laborious and expensive. In this work, we aim to develop a new computational approach (LabCaS) for accurate prediction of the calpain substrate cleavage sites from amino acid sequences. To overcome the imbalance of negative and positive samples in the machine-learning training which have been suffered by most of the former approaches when splitting sequences into short peptides, we designed a conditional random field algorithm that can label the potential cleavage sites directly from the entire sequences. By integrating the multiple amino acid features and those derived from sequences, LabCaS achieves an accurate recognition of the cleave sites for most calpain proteins. In a jackknife test on a set of 129 benchmark proteins, LabCaS generates an AUC score 0.862. The LabCaS program is freely available at: http://www.csbio.sjtu.edu.cn/bioinf/LabCaS. Proteins 2013. © 2012 Wiley Periodicals, Inc.

An improved COVID-19 classification model on chest radiography by dual-ended multiple attention learning.

As a highly contagious disease, COVID-19 has not only had a great impact on the life, study and work of hundreds of millions of people around the world, but also had a huge impact on the global health care system. Therefore, any technical tool that allows for rapid screening and high-precision diagnosis of COVID-19 infections can be of vital help. In order to reduce the burden on health care system, the computer-aided diagnosis of COVID-19 has become a current research hotspot. X-ray imaging is a common and low-cost tool that can help with the COVID-19 diagnosis. The data used for this study has 15,153 CXR images, containing 10,192 normal lungs, 3,631 COVID-19 positive cases and 1,345 images of viral pneumonia. For this computer-aided task, we propose the dual-ended multiple attention learning model (DMAL). The model incorporates multiple attention learning into both networks, and the two networks are linked using an integration module. Specifically, in both networks, the backbone network is used to extract global features and the branch network captures local area information; the integration module combines multi-stage features; and the attention module containing element, channel and spatial attention prompts the model to focus on multi-scale information relevant to the disease. We evaluate the proposed DMAL network using relevant competitive methods as well as ten advanced deep learning models in the image domain and obtain the best performance with 99.67%, 99.53%, 99.66%, 99.60% and 99.76% in terms of Accuracy, Precision, Sensitivity, F1 Scores and Specificity. The proposed method will help in the rapid screening and high-precision diagnosis of COVID-19, given the general trend of such severe global infections. Our code and model are available in [https://github.com/Graziagh/DMALNet].

ELMo4m6A: A Contextual Language Embedding-Based Predictor for Detecting RNA N6-Methyladenosine Sites.

N6-methyladenosine (m6A) is a universal post-transcriptional modification of RNAs, and it is widely involved in various biological processes. Identifying m6A modification sites accurately is indispensable to further investigate m6A-mediated biological functions. How to better represent RNA sequences is crucial for building effective computational methods for detecting m6A modification sites. However, traditional encoding methods require complex biological prior knowledge and are time-consuming. Furthermore, most of the existing m6A sites prediction methods are limited to single species, and few methods are able to predict m6A sites across different species and tissues. Thus, it is necessary to design a more efficient computational method to predict m6A sites across multiple species and tissues. In this paper, we proposed ELMo4m6A, a contextual language embedding-based method for predicting m6A sites from RNA sequences without any prior knowledge. ELMo4m6A first learns embeddings of RNA sequences using a language model ELMo, then uses a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) to identify m6A sites. The results of 5-fold cross-validation and independent testing demonstrate that ELMo4m6A is superior to state-of-the-art methods. Moreover, we applied integrated gradients to find potential sequence patterns contributing to m6A sites.

An interpretable machine learning framework for diagnosis and prognosis of COVID-19.

Coronaviruses have affected the lives of people around the world. Increasingly, studies have indicated that the virus is mutating and becoming more contagious. Hence, the pressing priority is to swiftly and accurately predict patient outcomes. In addition, physicians and patients increasingly need interpretability when building machine models in healthcare. We propose an interpretable machine framework(KISM) that can diagnose and prognose patients based on blood test datasets. First, we use k-nearest neighbors, isolated forests, and SMOTE to pre-process the original blood test datasets. Seven machine learning tools Support Vector Machine, Extra Tree, Random Forest, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, Logistic Regression, and ensemble learning were then used to diagnose and predict COVID-19. In addition, we used SHAP and scikit-learn post-hoc interpretability to report feature importance, allowing healthcare professionals and artificial intelligence models to interact to suggest biomarkers that some doctors may have missed. The 10-fold cross-validation of two public datasets shows that the performance of KISM is better than that of the current state-of-the-art methods. In the diagnostic COVID-19 task, an AUC value of 0.9869 and an accuracy of 0.9787 were obtained, and ultimately Leukocytes, platelets, and Proteina C reativa mg/dL were found to be the most indicative biomarkers for the diagnosis of COVID-19. An AUC value of 0.9949 and an accuracy of 0.9677 were obtained in the prognostic COVID-19 task and Age, LYMPH, and WBC were found to be the most indicative biomarkers for identifying the severity of the patient.

Design, synthesis, and antifungal activity of novel dithiin tetracarboximide derivatives as potential succinate dehydrogenase inhibitors.

BACKGROUND: Succinate dehydrogenase inhibitor (SDHI) fungicides are an important class of agricultural fungicides with the advantages of high efficiency and a broad bactericidal spectrum. To pursue novel SDHIs, a series of N-substituted dithiin tetracarboximide derivatives were designed, synthesized, and characterized by 1 H NMR, 13 C NMR, and high resolution mass spectrum (HRMS). RESULTS: These engineered compounds displayed potent fungicidal activity against phytopathogens, including Sclerotinia sclerotiorum, Botrytis cinerea, and Rhizoctonia solani, comparable with that of the commercial SDHI fungicide boscalid. In particular, compound 18 stood out with prominent activity against S. sclerotiorum with a half-maximal effective concentration (EC50 ) value of 1.37 µg ml-1 . Compound 1 exhibited the most potent antifungal activity against B. cinerea with EC50 values of 5.02 µg ml-1 . As for R. solani, 12 and 13 exhibited remarkably inhibitory activity with EC50 values of 4.26 and 5.76 µg ml-1 , respectively. In the succinate dehydrogenase (SDH) inhibition assay, 13 presented significant inhibitory activity with a half-maximal inhibitory concentration (IC50 ) value of 15.3 µm, which was approximately equivalent to that of boscalid (14.2 µm). Furthermore, molecular docking studies revealed that 13 could anchor in the binding site of SDH. CONCLUSION: Taken together, results suggested that the dithiin tetracarboximide scaffold possessed a huge potential to be developed as novel fungicides and SDHIs. © 2023 Society of Chemical Industry.

Predicting protein-ATP binding sites from primary sequence through fusing bi-profile sampling of multi-view features.

BACKGROUND: Adenosine-5'-triphosphate (ATP) is one of multifunctional nucleotides and plays an important role in cell biology as a coenzyme interacting with proteins. Revealing the binding sites between protein and ATP is significantly important to understand the functionality of the proteins and the mechanisms of protein-ATP complex. RESULTS: In this paper, we propose a novel framework for predicting the proteins' functional residues, through which they can bind with ATP molecules. The new prediction protocol is achieved by combination of sequence evolutional information and bi-profile sampling of multi-view sequential features and the sequence derived structural features. The hypothesis for this strategy is single-view feature can only represent partial target's knowledge and multiple sources of descriptors can be complementary. CONCLUSIONS: Prediction performances evaluated by both 5-fold and leave-one-out jackknife cross-validation tests on two benchmark datasets consisting of 168 and 227 non-homologous ATP binding proteins respectively demonstrate the efficacy of the proposed protocol. Our experimental results also reveal that the residue structural characteristics of real protein-ATP binding sites are significant different from those normal ones, for example the binding residues do not show high solvent accessibility propensities, and the bindings prefer to occur at the conjoint points between different secondary structure segments. Furthermore, results also show that performance is affected by the imbalanced training datasets by testing multiple ratios between positive and negative samples in the experiments. Increasing the dataset scale is also demonstrated useful for improving the prediction performances.

Signal-BNF: a Bayesian network fusing approach to predict signal peptides.

A signal peptide is a short peptide chain that directs the transport of a protein and has become the crucial vehicle in finding new drugs or reprogramming cells for gene therapy. As the avalanche of new protein sequences generated in the postgenomic era, the challenge of identifying new signal sequences has become even more urgent and critical in biomedical engineering. In this paper, we propose a novel predictor called Signal-BNF to predict the N-terminal signal peptide as well as its cleavage site based on Bayesian reasoning network. Signal-BNF is formed by fusing the results of different Bayesian classifiers which used different feature datasets as its input through weighted voting system. Experiment results show that Signal-BNF is superior to the popular online predictors such as Signal-3L and PrediSi. Signal-BNF is featured by high prediction accuracy that may serve as a useful tool for further investigating many unclear details regarding the molecular mechanism of the zip code protein-sorting system in cells.

Synthesis of a series of validoxylamine A esters and their biological activities.

BACKGROUND: The worldwide reduction in food production due to pests and diseases is still an important challenge facing today. Validoxylamine A (VAA) is a natural polyhydroxyl compound derived from validamycin, acting as an efficient trehalase inhibitor with insecticidal and antifungal activities. To extend the application and discover green pesticide, a series of ester derivatives were prepared based on VAA as a lead compound. Their biological activities were investigated against three typically agricultural disease, Rhizoctonia solani, Sclerotinia sclerotiorum and Aphis craccivora. RESULTS: This study involved 30 novel validoxylamine A fatty acid esters (VAFAEs) synthesized by Novozym 435 and they were characterized with high-resolution electrospray ionization mass spectrometry (HR-ESI-MS) and proton nuclear magnetic resonance (1 H-NMR). Of these 30 derivatives, most compounds showed improved antifungal activity, and 12 novel compounds showed improved insecticidal activity. When reacted with pentadecanoic acid, compound 14 showed the highest inhibitory activity against R. solani [median effective concentration (EC50 ) 0.01 µmol L-1 ], while the EC50 value of VAA was 34.99 µmol L-1 . Furthermore, 21 novel VAFAEs showed higher inhibitory activity against S. sclerotiorum. Validoxylamine A oleic acid ester, compound 21, exhibited the highest insecticidal activity against A. craccivora [median lethal concentration (LC50 ) 39.63 µmol L-1 ], while the LC50 value of Pymetrozine was 50.45 µmol L-1 , a commercialized pesticide against A. craccivora. CONCLUSION: Combining our results, esterification of VAA by introducing different acyl donors was beneficial for the development of new eco-friendly drugs in the field of pesticides.