pLoc_bal-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by General PseAAC and Quasi-balancing Training Dataset

A review from biological mapping to computation-based subcellular localization

Subcellular localization is crucial to the study of virus and diseases. Specifically, research on protein subcellular localization can help identify clues between virus and host cells that can aid in the design of targeted drugs. Research on RNA subcellular localization is significant for human diseases (such as Alzheimer's disease, colon cancer, etc.). To date, only reviews addressing subcellular localization of proteins have been published, which are outdated for reference, and reviews of RNA subcellular localization are not comprehensive. Therefore, we collated (the most up-to-date) literature on protein and RNA subcellular localization to help researchers understand changes in the field of protein and RNA subcellular localization. Extensive and complete methods for constructing subcellular localization models have also been summarized, which can help readers understand the changes in application of biotechnology and computer science in subcellular localization research and explore how to use biological data to construct improved subcellular localization models. This paper is the first review to cover both protein subcellular localization and RNA subcellular localization. We urge researchers from biology and computational biology to jointly pay attention to transformation patterns, interrelationships, differences, and causality of protein subcellular localization and RNA subcellular localization.

A Novel Method for Identification of Glutarylation Sites Combining Borderline-SMOTE With Tomek Links Technique in Imbalanced Data

Glutarylation is a type of post-translational modification that occurs on lysine residues. It plays an irreplaceable role in various cellular functions. Therefore, identification of glutarylation sites is significant for understanding the molecular mechanism of glutarylation. In this study, we proposed a method named DEXGB_Glu to identify lysine glutarylation sites using XGBoost as classifier which was optimized by differential evolution algorithm. Aiming at the imbalance between positive samples and negative samples, Borderline-SMOTE method was employed to synthesize positive samples, increasing their amount equal to negative samples. Then, Tomek links technique was applied to filter out noise data. Analysis of this method and its results showed that differential evolution algorithm obviously improved the performance and the combination of Borderline-SMOTE and Tomek links effectively solved the imbalance between positive samples and negative samples. Finally, the performance of this method was much better than other methods in prediction of glutarylation sites. The data and code are available on https://github.com/ningq669/DEXGB_Glu.

Multilocation proteins in organelle communication: Based on protein-protein interactions

Protein-protein interaction (PPI) plays a crucial role in most biological processes, including signal transduction and cell apoptosis. Importantly, the knowledge of PPIs can be useful for identification of multimeric protein complexes and elucidation of uncharacterized protein functions. Arabidopsis thaliana, the best-characterized dicotyledonous plant, the steadily increasing amount of information on the levels of its proteome and signaling pathways is progressively enabling more researchers to construct models for cellular processes for the plant, which in turn encourages more experimental data to be generated. In this study, we performed an overview analysis of the 10 major organelles and their associated proteins of the dicotyledonous model plant Arabidopsis thaliana via PPI network, and found that PPI may play an important role in organelle communication. Further, multilocation proteins, especially phosphorylation-related multilocation proteins, can function as a "needle and thread" via PPIs and play an important role in organelle communication. Similar results were obtained in a monocotyledonous model crop, rice. Furthermore, we provide a research strategy for multilocation proteins by LOPIT technique, proteomics, and bioinformatics analysis and also describe their potential role in the field of plant science. The results provide a new view that the phosphorylation-related multilocation proteins play an important role in organelle communication and provide new insight into PPIs and novel directions for proteomic research. The research of phosphorylation-related multilocation proteins may promote the development of organelle communication and provide an important theoretical basis for plant responses to external stress.

Use Chou's 5-steps rule to identify protein post-translational modification and its linkage to secondary metabolism during the floral development of Lonicera japonica Thunb

Lonicera japonica Thunb. is widely used in traditional medicine systems of East Asian and attracts a large amount of studies on the biosynthesis of its active components. Currently, there is little understanding regarding the regulatory mechanisms behind the accumulation of secondary metabolites during its developmental stages. In this study, published transcriptomic and proteomic data were mined to evaluate potential linkage between protein modification and secondary metabolism during the floral development. Stronger correlations were observed between differentially expressed genes (DEGs) and their corresponding differentially abundant proteins (DAPs) in the comparison of juvenile bud stage (JBS)/third green stage (TGS) vs. silver flowering stage (SFS). Seventy-five and 76 cor-rDEGs and cor-rDAPs (CDDs) showed opposite trends at both transcriptional and translational levels when comparing their levels at JBS and TGS relative to those at SFS. CDDs were mainly involved in elements belonging to the protein metabolism and the TCA cycle. Protein-protein interaction analysis indicated that the interacting proteins in the major cluster were primarily involved in TCA cycle and protein metabolism. In the simple phenylpropanoids biosynthetic pathway of SFS, both phospho-2-dehydro-3-deoxyheptonate aldolase (PDA) and glutamate/aspartate-prephenate aminotransferase (AAT) were decreased at the protein level, but increased at the gene level. A confirmatory experiment indicated that protein ubiquitination and succinylation were more prominent during the early floral developmental stages, in correlation with simple phenylpropanoids accumulation. Taken together, those data indicates that phenylpropanoids metabolism and floral development are putatively regulated through the ubiquitination and succinylation modifications of PDA, AAT, and TCA cycle proteins in L. japonica.

Using CHOU'S 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment

Glycosylation of proteins in eukaryote cells is an important and complicated post-translation modification due to its pivotal role and association with crucial physiological functions within most of the proteins. Identification of glycosylation sites in a polypeptide chain is not an easy task due to multiple impediments. Analytical identification of these sites is expensive and laborious. There is a dire need to develop a reliable computational method for precise determination of such sites which can help researchers to save time and effort. Herein, we propose a novel predictor namely iGlycoS-PseAAC by integrating the Chou's Pseudo Amino Acid Composition (PseAAC) and relative/absolute position-based features. The self-consistency results show that the accuracy revealed by the model using the benchmark dataset for prediction of O-linked glycosylation having serine sites is 98.8 percent. The overall accuracy of predictor achieved through 10-fold cross validation by combining the positive and negative results is 97.2 percent. The overall accuracy achieved through Jackknife test is 96.195 percent by aggregating of all the prediction results. Thus the proposed predictor can help in predicting the O-linked glycosylated serine sites in an efficient and accurate way. The overall results show that the accuracy of the iGlycoS-PseAAC is higher than the existing tools.

Molecular Docking and Dynamics Simulation Analysis of Thymoquinone and Thymol Compounds from Nigella sativa L. that Inhibit Cag A and Vac A Oncoprotein of Helicobacter pylori: Probable Treatment of H. pylori Infections

BACKGROUND

Helicobacter pylori infection is accountable for most of the peptic ulcer and intestinal cancers. Due to the uprising resistance towards H. pylori infection through the present and common proton pump inhibitors regimens, the investigation of novel candidates is the inevitable issue. Medicinal plants have always been a source of lead compounds for drug discovery. The research of the related effective enzymes linked with this gram-negative bacterium is critical for the discovery of novel drug targets.

OBJECTIVE

The aim of the study is to identify the best candidate to evaluate the inhibitory effect of thymoquinone and thymol against H. pylori oncoproteins, Cag A and Vac A in comparison to the standard drug, metronidazole by using a computational approach.

MATERIALS AND METHODS

The targeted oncoproteins, Cag A and Vac A were retrieved from RCSB PDB. Lipinski's rule and ADMET toxicity profiling were carried out on the phytoconstituents of the N. sativa. The two compounds of N. sativa were further analyzed by molecular docking and MD simulation studies. The reported phytoconstituents, thymoquinone and thymol present in N. sativa were docked with H. pylori Cag A and Vac A oncoproteins. Structures of ligands were prepared using ChemDraw Ultra 10 software and then changed into their 3D PDB structures using Molinspiration followed by energy minimization by using software Discovery Studio client 2.5.

RESULTS

The docking results revealed the promising inhibitory potential of thymoquinone against Cag A and Vac A with docking energy of -5.81 kcal/mole and -3.61kcal/mole, respectively. On the contrary, the inhibitory potential of thymol against Cag A and Vac A in terms of docking energy was -5.37 kcal/mole and -3.94kcal/mole as compared to the standard drug, metronidazole having docking energy of -4.87 kcal/mole and -3.20 kcal/mole, respectively. Further, molecular dynamic simulations were conducted for 5ns for optimization, flexibility prediction, and determination of folded Cag A and Vac A oncoproteins stability. The Cag A and Vac A oncoproteins-TQ complexes were found to be quite stable with the root mean square deviation value of 0.2nm.

CONCLUSION

The computational approaches suggested that thymoquinone and thymol may play an effective pharmacological role to treat H. pylori infection. Hence, it could be summarized that the ligands thymoquinone and thymol bound and interacted well with the proteins Cag A and Vac A as compared to the ligand MTZ. Our study showed that all lead compounds had good interaction with Cag A and Vac A proteins and suggested them to be a useful target to inhibit H. pylori infection.

RNA Secondary Structure Prediction with Pseudoknots Using Chemical Reaction Optimization Algorithm

RNA molecules play a significant role in cell function especially including pseudoknots. In past decades, several methods have been developed to predict RNA secondary structure with pseudoknots and the most popular one uses minimum free energy. It is a nondeterministic polynomial-time hard (NP-hard) problem. We have proposed an approach based on a metaheuristic algorithm named Chemical Reaction Optimization (CRO) to solve the RNA pseudoknotted structure prediction problem. The reaction operators of CRO algorithm have been redesigned and used on the generated population to find the structure with the minimum free energy. Besides, we have developed an additional operator called Repair operator which has a great influence on our algorithm in increasing accuracy. It helps to increase the true positive base pairs while decreasing the false positive and false negative base pairs. Four energy models have been applied to calculate the energy. To evaluate the performance, we have used four datasets containing RNA pseudoknotted sequences taken from the RNA STRAND and Pseudobase++ database. We have compared the proposed approach with some existing algorithms and shown that our CRO based model is a better prediction method in terms of accuracy and speed.

iPhosH-PseAAC: Identify Phosphohistidine Sites in Proteins by Blending Statistical Moments and Position Relative Features According to the Chou's 5-Step Rule and General Pseudo Amino Acid Composition

Protein phosphorylation is one of the key mechanism in prokaryotes and eukaryotes and is responsible for various biological functions such as protein degradation, intracellular localization, the multitude of cellular processes, molecular association, cytoskeletal dynamics, and enzymatic inhibition/activation. Phosphohistidine (PhosH) has a key role in a number of biological processes, including central metabolism to signalling in eukaryotes and bacteria. Thus, identification of phosphohistidine sites in a protein sequence is crucial, and experimental identification can be expensive, time-taking, and laborious. To address this problem, here, we propose a novel computational model namely iPhosH-PseAAC for prediction of phosphohistidine sites in a given protein sequence using pseudo amino acid composition (PseAAC), statistical moments, and position relative features. The results of the proposed predictor are validated through self-consistency testing, 10-fold cross-validation, and jackknife testing. The self-consistency validation gave the 100 percent accuracy, whereas, for cross-validation, the accuracy achieved is 94.26 percent. Moreover, jackknife testing gave 97.07 percent accuracy for the proposed model. Thus, the proposed model iPhosH-PseAAC for prediction of iPhosH site has the great ability to predict the PhosH sites in given proteins.

Sequence-based Identification of Allergen Proteins Developed by Integration of PseAAC and Statistical Moments via 5-Step Rule

Background:

Allergens are antigens that can stimulate an atopic type I human hypersensitivity reaction by an immunoglobulin E (IgE) reaction. Some proteins are naturally allergenic than others. The challenge for toxicologists is to identify properties that allow proteins to cause allergic sensitization and allergic diseases. The identification of allergen proteins is a very critical and pivotal task. The experimental identification of protein functions is a hectic, laborious and costly task; therefore, computer scientists have proposed various methods in the field of computational biology and bioinformatics using various data science approaches. Objectives: Herein, we report a novel predictor for the identification of allergen proteins.

Methods:

For feature extraction, statistical moments and various position-based features have been incorporated into Chou’s pseudo amino acid composition (PseAAC), and are used for training of a neural network.

Results:

The predictor is validated through 10-fold cross-validation and Jackknife testing, which gave 99.43% and 99.87% accurate results.

Conclusions:

Thus, the proposed predictor can help in predicting the Allergen proteins in an efficient and accurate way and can provide baseline data for the discovery of new drugs and biomarkers.

Identify RNA-associated subcellular localizations based on multi-label learning using Chou's 5-steps rule

BACKGROUND

Biological functions of biomolecules rely on the cellular compartments where they are located in cells. Importantly, RNAs are assigned in specific locations of a cell, enabling the cell to implement diverse biochemical processes in the way of concurrency. However, lots of existing RNA subcellular localization classifiers only solve the problem of single-label classification. It is of great practical significance to expand RNA subcellular localization into multi-label classification problem.

RESULTS

In this study, we extract multi-label classification datasets about RNA-associated subcellular localizations on various types of RNAs, and then construct subcellular localization datasets on four RNA categories. In order to study Homo sapiens, we further establish human RNA subcellular localization datasets. Furthermore, we utilize different nucleotide property composition models to extract effective features to adequately represent the important information of nucleotide sequences. In the most critical part, we achieve a major challenge that is to fuse the multivariate information through multiple kernel learning based on Hilbert-Schmidt independence criterion. The optimal combined kernel can be put into an integration support vector machine model for identifying multi-label RNA subcellular localizations. Our method obtained excellent results of 0.703, 0.757, 0.787, and 0.800, respectively on four RNA data sets on average precision.

CONCLUSION

To be specific, our novel method performs outstanding rather than other prediction tools on novel benchmark datasets. Moreover, we establish user-friendly web server with the implementation of our method.

SubLocEP: a novel ensemble predictor of subcellular localization of eukaryotic mRNA based on machine learning

MOTIVATION

mRNA location corresponds to the location of protein translation and contributes to precise spatial and temporal management of the protein function. However, current assignment of subcellular localization of eukaryotic mRNA reveals important limitations: (1) turning multiple classifications into multiple dichotomies makes the training process tedious; (2) the majority of the models trained by classical algorithm are based on the extraction of single sequence information; (3) the existing state-of-the-art models have not reached an ideal level in terms of prediction and generalization ability. To achieve better assignment of subcellular localization of eukaryotic mRNA, a better and more comprehensive model must be developed.

RESULTS

In this paper, SubLocEP is proposed as a two-layer integrated prediction model for accurate prediction of the location of sequence samples. Unlike the existing models based on limited features, SubLocEP comprehensively considers additional feature attributes and is combined with LightGBM to generated single feature classifiers. The initial integration model (single-layer model) is generated according to the categories of a feature. Subsequently, two single-layer integration models are weighted (sequence-based: physicochemical properties = 3:2) to produce the final two-layer model. The performance of SubLocEP on independent datasets is sufficient to indicate that SubLocEP is an accurate and stable prediction model with strong generalization ability. Additionally, an online tool has been developed that contains experimental data and can maximize the user convenience for estimation of subcellular localization of eukaryotic mRNA.

Bioimage-Based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks

Prediction of protein subcellular location has currently become a hot topic because it has been proven to be useful for understanding both the disease mechanisms and novel drug design. With the rapid development of automated microscopic imaging technology in recent years, classification methods of bioimage-based protein subcellular location have attracted considerable attention for images can describe the protein distribution intuitively and in detail. In the current study, a prediction method of protein subcellular location was proposed based on multi-view image features that are extracted from three different views, including the four texture features of the original image, the global and local features of the protein extracted from the protein channel images after color segmentation, and the global features of DNA extracted from the DNA channel image. Finally, the extracted features were combined together to improve the performance of subcellular localization prediction. From the performance comparison of different combination features under the same classifier, the best ensemble features could be obtained. In this work, a classifier based on Stacked Auto-encoders and the random forest was also put forward. To improve the prediction results, the deep network was combined with the traditional statistical classification methods. Stringent cross-validation and independent validation tests on the benchmark dataset demonstrated the efficacy of the proposed method.

An Insightful 10-year Recollection Since the Emergence of the 5-steps Rule

OBJECTIVE

One of the most challenging and also the most difficult problems is how to formulate a biological sequence with a vector but considerably keep its sequence order information.

METHODS

To address such a problem, the approach of Pseudo Amino Acid Components or PseAAC has been developed.

RESULTS AND CONCLUSION

It has become increasingly clear via the 10-year recollection that the aforementioned proposal has been indeed very powerful.

RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule

By reducing amino acid alphabet, the protein complexity can be significantly simplified, which could improve computational efficiency, decrease information redundancy and reduce chance of overfitting. Although some reduced alphabets have been proposed, different classification rules could produce distinctive results for protein sequence analysis. Thus, it is urgent to construct a systematical frame for reduced alphabets. In this work, we constructed a comprehensive web server called RAACBook for protein sequence analysis and machine learning application by integrating reduction alphabets. The web server contains three parts: (i) 74 types of reduced amino acid alphabet were manually extracted to generate 673 reduced amino acid clusters (RAACs) for dealing with unique protein problems. It is easy for users to select desired RAACs from a multilayer browser tool. (ii) An online tool was developed to analyze primary sequence of protein. The tool could produce K-tuple reduced amino acid composition by defining three correlation parameters (K-tuple, g-gap, λ-correlation). The results are visualized as sequence alignment, mergence of RAA composition, feature distribution and logo of reduced sequence. (iii) The machine learning server is provided to train the model of protein classification based on K-tuple RAAC. The optimal model could be selected according to the evaluation indexes (ROC, AUC, MCC, etc.). In conclusion, RAACBook presents a powerful and user-friendly service in protein sequence analysis and computational proteomics. RAACBook can be freely available at http://bioinfor.imu.edu.cn/raacbook.

Database URL: http://bioinfor.imu.edu.cn/raacbook

Identification of Human Secretome and Membrane Proteome-Based Cancer Biomarkers Utilizing Bioinformatics

Cellular secreted proteins (secretome), together with cellular membrane proteins, collectively referred to as secretory and membrane proteins (SMPs) are a large potential source of biomarkers as they can be used to indicate cell types and conditions. SMPs have been shown to be ideal candidates for several clinically approved drug regimens including for cancer. This study aimed at performing a functional analysis of SMPs within different cancer subtypes to provide great clinical targets for potential prognostic, diagnostic and the therapeutics use. Using an innovative majority decision-based algorithm and transcriptomic data spanning 5 cancer types and over 3000 samples, we quantified the relative difference in SMPs gene expression compared to normal adjacent tissue. A detailed deep data mining analysis revealed a consistent group of downregulated SMP isoforms, enriched in hematopoietic cell lineages (HCL), in multiple cancer types. HCL-associated genes were frequently downregulated in successive cancer stages and high expression was associated with good patient prognosis. In addition, we suggest a potential mechanism by which cancer cells suppress HCL signaling by reducing the expression of immune-related genes. Our data identified potential biomarkers for the cancer immunotherapy. We conclude that our approach may be applicable for the delineation of other types of cancer and illuminate specific targets for therapeutics and diagnostics.

Characterization of the relationship between FLI1 and immune infiltrate level in tumour immune microenvironment for breast cancer

Breast cancer is the most common cancer and the leading cause of cancer death among women in the world. Tumour‐infiltrating lymphocytes were defined as the white blood cells left in the vasculature and localized in tumours. Recently, tumour‐infiltrating lymphocytes were found to be associated with good prognosis and response to immunotherapy in tumours. In this study, to examine the influence of FLI1 in immune system in breast cancer, we interrogated the relationship between the FLI1 expression levels with infiltration levels of 28 immune cell types. By splitting the breast cancer samples into high and low expression FLI1 subtypes, we found that the high expression FLI1 subtype was enriched in many immune cell types, and the up‐regulated differentially expressed genes between them were enriched in immune system processes, immune‐related KEGG pathways and biological processes. In addition, many important immune‐related features were found to be positively correlated with the FLI1 expression level. Furthermore, we found that the FLI1 was correlated with the immune‐related genes. Our findings may provide useful help for recognizing the relationship between tumour immune microenvironment and FLI1, and may unravel clinical outcomes and immunotherapy utility for FLI1 in breast cancer.

Some illuminating remarks on molecular genetics and genomics as well as drug development

Facing the explosive growth of biological sequences unearthed in the post-genomic age, one of the most important but also most difficult problems in computational biology is how to express a biological sequence with a discrete model or a vector, but still keep it with considerable sequence-order information or its special pattern. To deal with such a challenging problem, the ideas of "pseudo amino acid components" and "pseudo K-tuple nucleotide composition" have been proposed. The ideas and their approaches have further stimulated the birth for "distorted key theory", "wenxing diagram", and substantially strengthening the power in treating the multi-label systems, as well as the establishment of the famous "5-steps rule". All these logic developments are quite natural that are very useful not only for theoretical scientists but also for experimental scientists in conducting genetics/genomics analysis and drug development. Presented in this review paper are also their future perspectives; i.e., their impacts will become even more significant and propounding.

Using Chou's General Pseudo Amino Acid Composition to Classify Laccases from Bacterial and Fungal Sources via Chou's Five-Step Rule

Laccases are a group of enzymes with a critical activity in the degradation process of both phenolic and non-phenolic compounds. These enzymes present in a diverse array of species, including fungi and bacteria. Since this enzyme is in the market for different usages from industry to medicine, having a better knowledge of its structures and properties from diverse sources will be useful to select the most appropriate candidate for different purposes. In the current study, sequence- and structure-based characteristics of these enzymes from fungi and bacteria, including pseudo amino acid composition (PseAAC), physicochemical characteristics, and their secondary structures, are being compared and classified. Autodock 4 software was used for docking analysis between these laccases and some phenolic and non-phenolic compounds. The results indicated that features including molecular weight, aliphatic, extinction coefficient, and random coil percentage of these protein groups present high degrees of diversity in most cases. Categorization of these enzymes by the notion of PseAAC, showed over 96% accuracy. The binding free energy between fungal laccases and their substrates showed to be considerably higher than those of bacterial ones. According to the outcomes of the current study, data mining methods by using different machine learning algorithms, especially neural networks, could provide valuable information for a fair comparison between fungal and bacterial laccases. These results also suggested an association between efficacy and physicochemical features of laccase enzymes from different sources.

Identifying Cancer Targets Based on Machine Learning Methods via Chou's 5-steps Rule and General Pseudo Components

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of 'big data' derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks

BACKGROUND

Glioma is the most lethal nervous system cancer. Recent studies have made great efforts to study the occurrence and development of glioma, but the molecular mechanisms are still unclear. This study was designed to reveal the molecular mechanisms of glioma based on protein-protein interaction network combined with machine learning methods. Key differentially expressed genes (DEGs) were screened and selected by using the protein-protein interaction (PPI) networks.

RESULTS

As a result, 19 genes between grade I and grade II, 21 genes between grade II and grade III, and 20 genes between grade III and grade IV. Then, five machine learning methods were employed to predict the gliomas stages based on the selected key genes. After comparison, Complement Naive Bayes classifier was employed to build the prediction model for grade II-III with accuracy 72.8%. And Random forest was employed to build the prediction model for grade I-II and grade III-VI with accuracy 97.1% and 83.2%, respectively. Finally, the selected genes were analyzed by PPI networks, Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and the results improve our understanding of the biological functions of select DEGs involved in glioma growth. We expect that the key genes expressed have a guiding significance for the occurrence of gliomas or, at the very least, that they are useful for tumor researchers.

CONCLUSION

Machine learning combined with PPI networks, GO and KEGG analyses of selected DEGs improve our understanding of the biological functions involved in glioma growth.

Advances in Electrochemistry for Monitoring Cellular Chemical Flux

The transport of organic and inorganic molecules, along with inorganic ions across the plasma membrane results in chemical fluxes that reflect the cellular function in healthy and diseased states. Measurement of these chemical fluxes enables the characterization of protein function and transporter stoichiometry, characterization of a single cell and embryo viability prior to implantation, and screening of pharmaceutical agents. Electrochemical sensors emerge as sensitive and non-invasive tools for measuring chemical fluxes immediately outside the cells in the boundary layer, that are capable of monitoring a diverse range of transported analytes including inorganic ions, gases, neurotransmitters, hormones, and pharmaceutical agents. Used on their own or in combination with other methods, these sensors continue to expand our understanding of the function of rare cells and small tissues. Advances in sensor construction and detection strategies continue to improve sensitivity under physiological conditions, diversify analyte detection, and increase throughput. These advances will be discussed in the context of addressing technical challenges to measuring chemical flux in the boundary layer of cells and measuring the resultant changes to the chemical concentration in the bulk media.

iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou's 5-steps Rule and Pseudo Components

BACKGROUND

The amino acid residues, in protein, undergo post-translation modification (PTM) during protein synthesis, a process of chemical and physical change in an amino acid that in turn alters behavioral properties of proteins. Tyrosine sulfation is a ubiquitous posttranslational modification which is known to be associated with regulation of various biological functions and pathological pro-cesses. Thus its identification is necessary to understand its mechanism. Experimental determination through site-directed mutagenesis and high throughput mass spectrometry is a costly and time taking process, thus, the reliable computational model is required for identification of sulfotyrosine sites.

METHODOLOGY

In this paper, we present a computational model for the prediction of the sulfotyrosine sites named iSulfoTyr-PseAAC in which feature vectors are constructed using statistical moments of protein amino acid sequences and various position/composition relative features. These features are in-corporated into PseAAC. The model is validated by jackknife, cross-validation, self-consistency and in-dependent testing.

RESULTS

Accuracy determined through validation was 93.93% for jackknife test, 95.16% for cross-validation, 94.3% for self-consistency and 94.3% for independent testing.

CONCLUSION

The proposed model has better performance as compared to the existing predictors, how-ever, the accuracy can be improved further, in future, due to increasing number of sulfotyrosine sites in proteins.

iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC via Chou's 5-steps Rule

BACKGROUND

Methylation is one of the most important post-translational modifications in the human body which usually arises on lysine among the most intensely modified residues. It performs a dynamic role in numerous biological procedures, such as regulation of gene expression, regulation of protein function and RNA processing. Therefore, to identify lysine methylation sites is an important challenge as some experimental procedures are time-consuming.

OBJECTIVE

Herein, we propose a computational predictor named iMethylK_pseAAC to identify lysine methylation sites.

METHODS

Firstly, we constructed feature vectors based on PseAAC using position and composition rel-ative features and statistical moments. A neural network is trained based on the extracted features. The performance of the proposed method is then validated using cross-validation and jackknife testing.

RESULTS

The objective evaluation of the predictor showed accuracy of 96.7% for self-consistency, 91.61% for 10-fold cross-validation and 93.42% for jackknife testing.

CONCLUSION

It is concluded that iMethylK_pseAAC outperforms the counterparts to identify lysine methylation sites such as iMethyl_pseACC, BPB_pPMS and PMeS.

Mechanistic Insights of Chemicals and Drugs as Risk Factors for Systemic Lupus Erythematosus

Systemic Lupus Erythematosus (SLE) is a chronic and relapsing heterogenous autoimmune disease that primarily affects women of reproductive age. Genetic and environmental risk factors are involved in the pathogenesis of SLE, and susceptibility genes have recently been identified. However, as gene therapy is far from clinical application, further investigation of environmental risk factors could reveal important therapeutic approaches. We systematically explored two groups of environmental risk factors: chemicals (including silica, solvents, pesticides, hydrocarbons, heavy metals, and particulate matter) and drugs (including procainamide, hydralazine, quinidine, Dpenicillamine, isoniazid, and methyldopa). Furthermore, the mechanisms underlying risk factors, such as genetic factors, epigenetic change, and disrupted immune tolerance, were explored. This review identifies novel risk factors and their underlying mechanisms. Practicable measures for the management of these risk factors will benefit SLE patients and provide potential therapeutic strategies.