ABLE: Attention based learning for enzyme classification

Classifying proteins into their respective enzyme class is an interesting question for researchers for a variety of reasons. The open source Protein Data Bank (PDB) contains more than 1,60,000 structures, with more being added everyday. This paper proposes an attention-based bidirectional-LSTM model (ABLE) trained on over sampled data generated by SMOTE to analyse and classify a protein into one of the six enzyme classes or a negative class using only the primary structure of the protein described as a string by the FASTA sequence as an input. We achieve the highest F1-score of 0.834 using our proposed model on a dataset of proteins from the PDB. We baseline our model against eighteen other machine learning and deep learning networks, including CNN, LSTM, Bi-LSTM, GRU, and the state-of-the-art DeepEC model. We conduct experiments with two different oversampling techniques, SMOTE and ADASYN. To corroborate the obtained results, we perform extensive experimentation and statistical testing.     

An ensemble learning framework for potential miRNA-disease association prediction with positive-unlabeled data

To explore the pathogenic mechanisms of MicroRNA (miRNA) on diverse diseases, many researchers have concentrated on discovering the potential associations between miRNA and disease using machine learning methods. However, the prediction accuracy of supervised machine learning methods is limited by lacking of experimentally-validated uncorrelated miRNA-disease pairs. Without these negative samples, training a highly accurate model is much more difficult. Different from traditional miRNA-disease prediction models using randomly selected unknown samples as negative training samples, we propose an ensemble learning framework to solve this positive-unlabeled (PU) learning problem. The framework incorporates two steps, i.e., a novel semi-supervised Kmeans (SS-Kmeans) to extract reliable negative samples from unknown miRNA-disease pairs and subagging method to generate diverse training sample sets to make full use of those reliable negative samples for ensemble learning. Combined with effective random vector functional link (RVFL) network as prediction model, the proposed framework showed superior prediction accuracy comparing with other popular approaches. A case study on lung and gastric neoplasms further confirms the framework’s efficacy at identifying miRNA disease associations.     

Prediction of African Swine Fever Virus Inhibitors by Molecular Docking-Driven Machine Learning Models

African swine fever virus (ASFV) causes a highly contagious and severe hemorrhagic viral disease with high mortality in domestic pigs of all ages. Although the virus is harmless to humans, the ongoing ASFV epidemic could have severe economic consequences for global food security. Recent studies have found a few antiviral agents that can inhibit ASFV infections. However, currently, there are no vaccines or antiviral drugs. Hence, there is an urgent need to identify new drugs to treat ASFV. Based on the structural information data on the targets of ASFV, we used molecular docking and machine learning models to identify novel antiviral agents. We confirmed that compounds with high affinity present in the region of interest belonged to subsets in the chemical space using principal component analysis and k-means clustering in molecular docking studies of FDA-approved drugs. These methods predicted pentagastrin as a potential antiviral drug against ASFVs. Finally, it was also observed that the compound had an inhibitory effect on AsfvPolX activity. Results from the present study suggest that molecular docking and machine learning models can play an important role in identifying potential antiviral drugs against ASFVs.     

One-pot rapid synthesis of 4H-1-benzopyran derivatives in a deep eutectic solvent

A method for introducing a biologically active heterocycle, 2-methylquinoline into the 4-position of a 2-amino-4H-1-benzopyran skeleton is described. Choline chloride/glucose (1:1 molar ratio) was used as both the solvent and catalyst in the reaction of a salicylaldehyde, methylquinoline, and cyanoacetate to obtain 2-amino-4H-1-benzopyran derivatives in 48%–80% yields after short reaction times. The effects of the deep eutectic solvent type, substrate molar ratio, cosolvent, temperature, and reaction time were examined. The method has the advantages of simple steps, environmental friendliness, mild conditions, and wide substrate applicability. This is the first attempt to synthesize methylquinoline derivatives of 4H-1-benzopyran.     

Microwave-Assisted One-Pot Lipid Extraction and Glycolipid Production from Oleaginous Yeast Saitozyma podzolica in Sugar Alcohol-Based Media

Glycolipids are non-ionic surfactants occurring in numerous products of daily life. Due to their surface-activity, emulsifying properties, and foaming abilities, they can be applied in food, cosmetics, and pharmaceuticals. Enzymatic synthesis of glycolipids based on carbohydrates and free fatty acids or esters is often catalyzed using certain acyltransferases in reaction media of low water activity, e.g., organic solvents or notably Deep Eutectic Systems (DESs). Existing reports describing integrated processes for glycolipid production from renewables use many reaction steps, therefore this study aims at simplifying the procedure. By using microwave dielectric heating, DESs preparation was first accelerated considerably. A comparative study revealed a preparation time on average 16-fold faster than the conventional heating method in an incubator. Furthermore, lipids from robust oleaginous yeast biomass were successfully extracted up to 70% without using the pre-treatment method for cell disruption, limiting logically the energy input necessary for such process. Acidified DESs consisting of either xylitol or sorbitol and choline chloride mediated the one-pot process, allowing subsequent conversion of the lipids into mono-acylated palmitate, oleate, linoleate, and stearate sugar alcohol esters. Thus, we show strong evidence that addition of immobilized Candida antarctica Lipase B (Novozym 435^®), in acidified DES mixture, enables a simplified and fast glycolipid synthesis using directly oleaginous yeast biomass.     

关注学科知识的职前化学教师自主研修——基于“化学师范生备课组”的活动个案

学科知识是师范生专业素质的重要组成部分,研究基于化学师范生备课组以学科知识强化为主题的研修活动,从备课选题、研修过程、问题批判等方面探讨化学师范生学科知识强化新途径,评析化学师范生备课组的价值。     

基于“学习金字塔理论”的物理化学实验教学模式改革实践

介绍了东北林业大学物理化学实验课程教学中基于"学习金字塔理论"进行教学模式改革实践的情况。在实施实验教学过程中,通过增加"学生通过在线实验视频预习""学生为学生讲解实验""操作前学生小组讨论"和"操作后学生小组总结讨论"等学习环节,建立了一种有效的"以学生为中心"的实验教学模式,促进了学生综合素质和创新能力的培养,取得了良好的教学效果。