Current Proteomics - Volume 17, Issue 4, 2020

Background: DNA-binding proteins are very important to many biomolecular functions. The traditional experimental methods are expensive and time-consuming, so, computational methods that can predict whether a protein is a DNA-binding protein or not are very helpful to researchers. Machine learning has been widely used in many research areas. Many researchers have proposed machine learning methods for DNA-binding protein prediction, and this paper highlights their advantages and disadvantages. Objective: There are many computational methods that can predict DNA-binding proteins. Every method uses different features and different classifier algorithms. In this paper, a review of these methods is provided to find out some common procedures that can help researchers to develop more accurate methods. Methods: Firstly, the information stored in the protein sequence and gene sequence is presented. That information is the basis to find out the patterns leading to binding. Then, feature extraction methods and classifier algorithms are discussed. At last, some commonly used benchmark datasets are analysed and evaluated by methods. Conclusion: In this review, we analyzed some popular computational methods to predict DNAbinding protein. From those methods, we highlighted many features necessary to build up an accurate DNA-binding protein classifier. This can also help researchers to build up more useful computational tools. Currently, there are some machine learning methods with good performance in predicting DNAbinding proteins. The performance can be improved by using different kinds of features and classifiers.

Background: Protein-Protein Interactions (PPIs) play a key role in various biological processes. Many methods have been developed to predict protein-protein interactions and protein interaction networks. However, many existing applications are limited, because of relying on a large number of homology proteins and interaction marks. Methods: In this paper, we propose a novel integrated learning approach (RF-Ada-DF) with the sequence-based feature representation, for identifying protein-protein interactions. Our method firstly constructs a sequence-based feature vector to represent each pair of proteins, via Multivariate Mutual Information (MMI) and Normalized Moreau-Broto Autocorrelation (NMBAC). Then, we feed the 638- dimentional features into an integrated learning model for judging interaction pairs and non-interaction pairs. Furthermore, this integrated model embeds Random Forest in AdaBoost framework and turns weak classifiers into a single strong classifier. Meanwhile, we also employ double fault detection in order to suppress over-adaptation during the training process. Results: To evaluate the performance of our method, we conduct several comprehensive tests for PPIs prediction. On the H. pylori dataset, our method achieves 88.16% accuracy and 87.68% sensitivity, the accuracy of our method is increased by 0.57%. On the S. cerevisiae dataset, our method achieves 95.77% accuracy and 93.36% sensitivity, the accuracy of our method is increased by 0.76%. On the Human dataset, our method achieves 98.16% accuracy and 96.80% sensitivity, the accuracy of our method is increased by 0.6%. Experiments show that our method achieves better results than other outstanding methods for sequence-based PPIs prediction. The datasets and codes are available at https://github.com/guofei-tju/RF-Ada-DF.git.

Background: The ligand-receptor interaction plays an important role in signal transduction required for cellular differentiation, proliferation, and immune response process. The analysis of ligand-receptor interactions is helpful to provide a deeper understanding of cellular proliferation/ differentiation and other cell processes. Methods: The computational technique would be used to promote ligand-receptor interactions research in future proteomics research. In this paper, we propose a novel computational method to predict ligand-receptor interactions from amino acid sequences by a machine learning approach. We extract features from ligand and receptor sequences by Histogram of Oriented Gradient (HOG) and Discrete Cosine Transform (DCT). Then, these features are fed into the Fuzzy C-Means (FCM) clustering algorithm for clustering, and also we get multiple training subsets to generate the same number of sub-classifiers. We choose an optimal sub-classifier for predicting ligand-receptor interactions according to the similarity from one sample to training subsets. Observations: In order to verify the performance, we perform five-fold cross-validation experiments on a ligand-receptor interactions dataset and achieve 80.08% accuracy, 82.98% sensitivity and 80.02% specificity. Then, we test our extracted feature method on two Protein-Protein Interactions (PPIs) datasets, and achieve accuracies of 93.79% and 87.46%, respectively. Conclusion: Our proposed method can be a useful tool for identifying of ligand-receptor interactions. Related data sets and source code are available at https://github.com/guofei-tju/ligand-receptorinteractions. git.

Background: The DNA-binding proteins is an important process in multiple biomolecular functions. However, the tradition experimental methods for DNA-binding proteins identification are still time consuming and extremely expensive. Objective: In past several years, various computational methods have been developed to detect DNAbinding proteins. However, most of them do not integrate multiple information. Methods: In this study, we propose a novel computational method to predict DNA-binding proteins by two steps Multiple Kernel Support Vector Machine (MK-SVM) and sequence information. Firstly, we extract several feature and construct multiple kernels. Then, multiple kernels are linear combined by Multiple Kernel Learning (MKL). At last, a final SVM model, constructed by combined kernel, is built to predict DNA-binding proteins. Results: The proposed method is tested on two benchmark data sets. Compared with other existing method, our approach is comparable, even better than other methods on some data sets. Conclusion: We can conclude that MK-SVM is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

During the last three decades or so, many efforts have been made to study the protein cleavage sites by some disease-causing enzyme, such as HIV (Human Immunodeficiency Virus) protease and SARS (Severe Acute Respiratory Syndrome) coronavirus main proteinase. It has become increasingly clear via this mini-review that the motivation driving the aforementioned studies is quite wise, and that the results acquired through these studies are very rewarding, particularly for developing peptide drugs.

Background: Alterations in airway smooth muscle cells cause an increase in their mass and result in a significant impact on airway remodeling diseases such as asthma and chronic obstructive pulmonary disease. Several studies have used platelet-derived growth factors to stimulate the alterations of airway smooth muscle cells. Objective: This review discusses the platelet-derived growth factor-stimulated alterations of airway smooth muscle cells, diversity of inhibitors and inhibitory actions against these alterations and their related mechanisms, and how this diversity presents an avenue for the development of multifarious therapeutic targets for airway remodeling diseases especially asthma and chronic obstructive pulmonary disease. Methods: A comprehensive search of PubMed and Medscape database for studies that investigated the stimulation of the alterations of airway smooth muscle cells in asthma and chronic obstructive pulmonary disease by platelet-derived growth factors and inhibitions of these alterations. Results: Marked platelet-derived growth factor-stimulated alterations of airway smooth muscle cells are proliferation, migration and proliferative phenotype with diverse inhibitors and inhibitory actions against these alterations. Inhibitory actions are the result of the activation of protein kinase, overexpression of Tripartite motif protein, human transporter sub-family ABCA1 protein and miRNAs, knockdown of an isoform of reticulon 4 and follistatin protein, exogenous applications of recombinant proteins, supplements and active metabolite of retinoic acid, flavonoid extracts and polysaccharides extract. Conclusion: The multifarious inhibitors and inhibitory actions with varied mechanisms in platelet-derived growth factors-stimulated alterations of airway smooth muscle cells present a potential for diverse therapeutic targets for the treatment of airway remodeling diseases.

Background:S. dysenteriae is the causative agent of shigellosis, a severe form of bacillary dysentery and this infectious disease is still a health problem worldwide, especially in children. The most important proteins of the Shigella type III secretion system are IpaB and IpaD, which attach to the intestinal epithelial cells and provide the possibility of invasion and disease. These two proteins with immunogenic properties can be a suitable target to design and manufacture subunit recombinant vaccines. Objective: The aim of this study is to design an immunogenic chimeric protein against IpaB and IpaD as a subunit vaccine candidate through an in silico study. Methods: Firstly, the immunogenic epitopes of amino acid sequences, physico-chemical parameters, and the allergenicity of the chimeric protein were determined. Then the tertiary structure and the potential ability of the chimeric protein were predicted and evaluated in terms of inducing B cells’ immune responses with effective epitopes. Finally, the optimization of the chimeric protein was examined as the index affecting the protein expression. Results: Data showed an instability index of 37.18 and a well-established predicted third structure for the chimeric protein, with a z-score of -6.11. Also, more than 99% of its amino acids were in the optimal range. Minimum energy for mRNA structure increased to -317.9 and the Codon Adaptive Index (CAI) rose to 88%. The designed protein had no IgE specific B cell epitopes. Conclusion: Overall, the results of this study show that the designed protein can be considered as an immunogen vaccine candidate against S. dysenteriae.

Background: The protozoan Giardia duodenalis, which causes giardiasis, is an intestinal parasite that commonly affects humans, mainly pre-school children. Although there are asymptomatic cases, the main clinical features are chronic and acute diarrhea, nausea, abdominal pain, and malabsorption syndrome. Little is currently known about the virulence of the parasite, but some cases of chronic gastrointestinal alterations post-infection have been reported even when the infection was asymptomatic, suggesting that the cathepsin L proteases of the parasite may be involved in the damage at the level of the gastrointestinal mucosa. Objective: The aim of this study was the in silico identification and characterization of extracellular cathepsin L proteases in the proteome of G. duodenalis. Methods: The NP_001903 sequence of cathepsin L protease from Homo sapiens was searched against the Giardia duodenalis proteome. The subcellular localization of Giardia duodenalis cathepsin L proteases was performed in the DeepLoc-1.0 server. The construction of a phylogenetic tree of the extracellular proteins was carried out using the Molecular Evolutionary Genetics Analysis software (MEGA X). The Robetta server was used for the construction of the three-dimensional models. The search for possible inhibitors of the extracellular cathepsin L proteases of Giardia duodenalis was performed by entering the three-dimensional structures in the FINDSITEcomb drug discovery tool. Results: Based on the amino acid sequence of cathepsin L from Homo sapiens, 8 protein sequences were identified that have in their modular structure the Pept_C1A domain characteristic of cathepsins and two of these proteins (XP_001704423 and XP_001704424) are located extracellularly. Threedimensional models were designed for both extracellular proteins and several inhibitory ligands with a score greater than 0.9 were identified. In vitro studies are required to corroborate if these two extracellular proteins play a role in the virulence of Giardia duodenalis and to discover ligands that may be useful as therapeutic targets that interfere in the mechanism of pathogenesis generated by the parasite. Conclusion: In silico analysis identified two proteins in the Giardia duodenalis protein repertoire whose characteristics allowed them to be classified as cathepsin L proteases, which may be secreted into the extracellular medium to act as virulence factors. Three-dimensional models of both proteins allowed the identification of inhibitory ligands with a high score. The results suggest that administration of those compounds might be used to block the endopeptidase activity of the extracellular cathepsin L proteases, interfering with the mechanisms of pathogenesis of the protozoan parasite Giardia duodenalis.