Combinatorial Chemistry & High Throughput Screening - Volume 23, Issue 4, 2020

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

Background: Drugs are very important for human life because they can provide treatment, cure, prevention, or diagnosis of different diseases. However, they also cause side effects, which can increase the risks for humans and pharmaceuticals companies. It is essential to identify drug side effects in drug discovery. To date, lots of computational methods have been proposed to predict the side effects of drugs and most of them used the fact that similar drugs always have similar side effects. However, previous studies did not analyze which substructures are highly related to which kind of side effect. Method: In this study, we conducted a computational investigation. In this regard, we extracted a drug set for each side effect, which consisted of drugs having the side effect. Also, for each substructure, a set was constructed by picking up drugs owing such substructure. The relationship between one side effect and one substructure was evaluated based on linkages between drugs in their corresponding drug sets, resulting in an Es value. Then, the statistical significance of Es value was measured by a permutation test. Results and Conclusion: A number of highly related pairs of side effects and substructures were obtained and some were extensively analyzed to confirm the reliability of the results reported in this study.

Aim and Objective: Leukemia is the second common blood cancer after lymphoma, and its incidence rate has an increasing trend in recent years. Leukemia can be classified into four types: acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and chronic myelogenous leukemia (CML). More than forty drugs are applicable to different types of leukemia based on the discrepant pathogenesis. Therefore, the identification of specific drug-targeted biological processes and pathways is helpful to determinate the underlying pathogenesis among such four types of leukemia. Methods: In this study, the gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were highly related to drugs for leukemia were investigated for the first time. The enrichment scores for associated GO terms and KEGG pathways were calculated to evaluate the drugs and leukemia. The feature selection method, minimum redundancy maximum relevance (mRMR), was used to analyze and identify important GO terms and KEGG pathways. Results: Twenty Go terms and two KEGG pathways with high scores have all been confirmed to effectively distinguish four types of leukemia. Conclusion: This analysis may provide a useful tool for the discrepant pathogenesis and drug design of different types of leukemia.

Background: As a newly uncovered post-translational modification on the ε-amino group of lysine residue, protein malonylation was found to be involved in metabolic pathways and certain diseases. Apart from experimental approaches, several computational methods based on machine learning algorithms were recently proposed to predict malonylation sites. However, previous methods failed to address imbalanced data sizes between positive and negative samples. Objective: In this study, we identified the significant features of malonylation sites in a novel computational method which applied machine learning algorithms and balanced data sizes by applying synthetic minority over-sampling technique. Method: Four types of features, namely, amino acid (AA) composition, position-specific scoring matrix (PSSM), AA factor, and disorder were used to encode residues in protein segments. Then, a two-step feature selection procedure including maximum relevance minimum redundancy and incremental feature selection, together with random forest algorithm, was performed on the constructed hybrid feature vector. Results: An optimal classifier was built from the optimal feature subset, which featured an F1-measure of 0.356. Feature analysis was performed on several selected important features. Conclusion: Results showed that certain types of PSSM and disorder features may be closely associated with malonylation of lysine residues. Our study contributes to the development of computational approaches for predicting malonyllysine and provides insights into molecular mechanism of malonylation.

Background: The analysis of retinal images can help to detect retinal abnormalities that are caused by cardiovascular and retinal disorders. Objective: In this paper, we propose methods based on texture features for mining and analyzing information from retinal images. Methods: The recognition of the retinal mask region is a prerequisite for retinal image processing. However, there is no way to automatically recognize the retinal region. By quantifying and analyzing texture features, a method is proposed to automatically identify the retinal region. The boundary of the circular retinal region is detected based on the image texture contrast feature, followed by the filling of the closed circular area, and then the detected circular retinal mask region can be obtained. Results: The experimental results show that the method based on the image contrast feature can be used to detect the retinal region automatically. The average accuracy of retinal mask region detection of images from the Digital Retinal Images for Vessel Extraction (DRIVE) database was 99.34%. Conclusion: This is the first time these texture features of retinal images are analyzed, and texture features are used to recognize the circular retinal region automatically.

Background: Evidence have increasingly indicated that for human disease, cell metabolism are deeply associated with proteins. Structural mutations and dysregulations of these proteins contribute to the development of the complex disease. Free radicals are unstable molecules that seek for electrons from the surrounding atoms for stability. Once a free radical binds to an atom in the body, a chain reaction occurs, which causes damage to cells and DNA. An antioxidant protein is a substance that protects cells from free radical damage. Accurate identification of antioxidant proteins is important for understanding their role in delaying aging and preventing and treating related diseases. Therefore, computational methods to identify antioxidant proteins have become an effective prior-pinpointing approach to experimental verification. Methods: In this study, support vector machines was used to identify antioxidant proteins, using amino acid compositions and 9-gap dipeptide compositions as feature extraction, and feature reduction by Principal Component Analysis. Results: The prediction accuracy Acc of this experiment reached 98.38%, the recall rate Sn of the positive sample was found to be 99.27%, the recall rate Sp of the negative sample reached 97.54%, and the MCC value was 0.9678. To evaluate our proposed method, the predictive performance of 20 antioxidant proteins from the National Center for Biotechnology Information(NCBI) was studied. As a result, 20 antioxidant proteins were correctly predicted by our method. Experimental results demonstrate that the performance of our method is better than the state-of-the-art methods for identification of antioxidant proteins. Conclusion: We collected experimental protein data from Uniport, including 253 antioxidant proteins and 1552 non-antioxidant proteins. The optimal feature extraction used in this paper is composed of amino acid composition and 9-gap dipeptide. The protein is identified by support vector machine, and the model evaluation index is obtained based on 5-fold cross-validation. Compared with the existing classification model, it is further explained that the SVM recognition model constructed in this paper is helpful for the recognition of antioxidized proteins.

Background: Many forms of variations exist in the genome, which are the main causes of individual phenotypic differences. The detection of variants, especially those located in the tumor genome, still faces many challenges due to the complexity of the genome structure. Thus, the performance assessment of variation detection tools using next-generation sequencing platforms is urgently needed. Method: We have created a software package called the Multi-Variation Simulator of Cancer genomes (MVSC) to simulate common genomic variants, including single nucleotide polymorphisms, small insertion and deletion polymorphisms, and structural variations (SVs), which are analogous to human somatically acquired variations. Three sets of variations embedded in genomic sequences in different periods were dynamically and sequentially simulated one by one. Results: In cancer genome simulation, complex SVs are important because this type of variation is characteristic of the tumor genome structure. Overlapping variations of different sizes can also coexist in the same genome regions, adding to the complexity of cancer genome architecture. Our results show that MVSC can efficiently simulate a variety of genomic variants that cannot be simulated by existing software packages. Conclusion: The MVSC-simulated variants can be used to assess the performance of existing tools designed to detect SVs in next-generation sequencing data, and we also find that MVSC is memory and time-efficient compared with similar software packages.

Background: Panax notoginseng, a Chinese herbal medicine, has been widely used to treat vascular diseases. Diabetic retinopathy (DR) is one of the complications of diabetic microangiopathy. According to recent studies, the application of Panax notoginseng extract and related Chinese patent medicine preparations can significantly improve DR. However, the pharmacological mechanisms remain unclear. Therefore, the purpose of this study was to decipher the potential mechanism of Panax notoginseng treatment of DR using network pharmacology. Methods: We evaluated and screened the active compounds of Panax notoginseng using the Traditional Chinese Medicine Systems Pharmacology database and collected potential targets of the compounds by target fishing. A multi-source database was also used to organize targets of DR. The potential targets as the treatment of DR with Panax notoginseng were then obtained by matching the compound targets with the DR targets. Using protein-protein interaction networks and topological analysis, interactions between potential targets were identified. In addition, we also performed gene ontology-biological process and pathway enrichment analysis for the potential targets by using the Biological Information Annotation Database. Results: Eight active ingredients of Panax notoginseng and 31 potential targets for the treatment of DR were identified. The screening and enrichment analysis revealed that the treatment of DR using Panax notoginseng primarily involved 28 biological processes and 10 related pathways. Further analyses indicated that angiogenesis, inflammatory reactions, and apoptosis may be the main processes involved in the treatment of DR with Panax notoginseng. In addition, we determined that the mechanism of intervention of Panax notoginseng in treating DR may involve five core targets, VEGFA, MMP-9, MMP-2, FGF2, and COX-2. Conclusion: Panax notoginseng may treat diabetic retinopathy through the mechanism of network pharmacological analysis. The underlying molecular mechanisms were closely related to the intervention of angiogenesis, inflammation, and apoptosis with VEGFA, MMP-9, MMP-2, FGF2, and COX-2 being possible targets.

Aims and Objective: In this work ZnO-nanorod (ZnO-NR) as reusable catalyst promoted Strecker-type reaction of 2,4-dihydroxyacetophenone, isopropenylacetylene, trimethylsilyl cyanide (TMSCN), primary amines and isocyanides at ambient temperature under solvent-free conditions and produced α-amino nitriles benzofuran derivatives in high yields. These synthesized compounds may have antioxidant ability. Materials and Methods: ZnO-NRs in these reactions were prepared according to reported article. 2,4-dihydroxyacetophenone 1 (2 mmol) and isopropenylacetylene 2 (2 mmol) were mixed and stirred for 30 min in the presence of ZnO-NR (10 mol%) under solvent-free conditions at room temperature. After 30 min, primary amine 3 (2 mmol) was added to the mixture gently and the mixture was stirred for 15 min. After this time TMSCN 4 (2 mmol) was added to the mixture and stirred for 15 min. After completion of the reaction, as indicated by TLC, isocyanides 5 was added to mixture in the presence of catalyst. Results: In the first step of this research, the reaction of 2,4-dihydroxyacetophenone 1, isopropenylacetylene 2, methyl amine 3a, trimethylsilyle cyanide 4 and tert-butyl isocyanides 5a was used as a sample reaction to attain the best reaction conditions. The results showed this reaction performed with catalyst and did not have any product without catalyst after 12 h. Conclusion: In conclusion, we investigate multicomponent reaction of 2,4-dihydroxyacetophenone 1, isopropenylacetylene 2, primary amines 3, trimethylsilyl cyanide 4 and isocyanides along with ZnO-NRs as reusable catalyst at room temperature under solvent-free conditions which generates α-amino nitrile benzofuran derivatives in high yields. The advantages of our method are high atom economy, green reaction conditions, higher yield, shorter reaction times, and easy work-up, which are in good agreement with some principles of green chemistry. The compounds 8c exhibit excellent DPPH radical scavenging activity and FRAP compared to synthetic antioxidants BHT and TBHQ.