pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC

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.

Metabolism of Oxalate in Humans: A Potential Role Kynurenine Aminotransferase/Glutamine Transaminase/Cysteine Conjugate Beta-lyase Plays in Hyperoxaluria

Hyperoxaluria, excessive urinary oxalate excretion, is a significant health problem worldwide. Disrupted oxalate metabolism has been implicated in hyperoxaluria and accordingly, an enzymatic disturbance in oxalate biosynthesis can result in the primary hyperoxaluria. Alanine glyoxylate aminotransferase-1 and glyoxylate reductase, the enzymes involving glyoxylate (precursor for oxalate) metabolism, have been related to primary hyperoxalurias. Some studies suggest that other enzymes such as glycolate oxidase and alanine glyoxylate aminotransferase-2 might be associated with primary hyperoxaluria as well, but evidence of a definitive link is not strong between the clinical cases and gene mutations. There are still some idiopathic hyperoxalurias, which require a further study for the etiologies. Some aminotransferases, particularly kynurenine aminotransferases, can convert glyoxylate to glycine. Based on biochemical and structural characteristics, expression level, subcellular localization of some aminotransferases, a number of them appear able to catalyze the transamination of glyoxylate to glycine more efficiently than alanine glyoxylate aminotransferase-1. The aim of this minireview is to explore other undermining causes of primary hyperoxaluria and stimulate research toward achieving a comprehensive understanding of underlying mechanisms leading to the disease. Herein, we reviewed all aminotransferases in the liver for their functions in glyoxylate metabolism. Particularly, kynurenine aminotransferase-I and III were carefully discussed regarding their biochemical and structural characteristics, cellular localization, and enzyme inhibition. Kynurenine aminotransferase-III is, so far, the most efficient putative mitochondrial enzyme to transaminate glyoxylate to glycine in mammalian livers, might be an interesting enzyme to look over in hyperoxaluria etiology of primary hyperoxaluria and should be carefully investigated for its involvement in oxalate metabolism.

iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding

An enhancer is a short (50-1500bp) region of DNA that plays an important role in gene expression and the production of RNA and proteins. Genetic variation in enhancers has been linked to many human diseases, such as cancer, disorder or inflammatory bowel disease. Due to the importance of enhancers in genomics, the classification of enhancers has become a popular area of research in computational biology. Despite the few computational tools employed to address this problem, their resulting performance still requires improvements. In this study, we treat enhancers by the word embeddings, including sub-word information of its biological words, which then serve as features to be fed into a support vector machine algorithm to classify them. We present iEnhancer-5Step, a web server containing two-layer classifiers to identify enhancers and their strength. We are able to attain an independent test accuracy of 79% and 63.5% in the two layers, respectively. Compared to current predictors on the same dataset, our proposed method is able to yield superior performance as compared to the other methods. Moreover, this study provides a basis for further research that can enrich the field of applying natural language processing techniques in biological sequences. iEnhancer-5Step is freely accessible via http://biologydeep.com/fastenc/.

SNARE-CNN: a 2D convolutional neural network architecture to identify SNARE proteins from high-throughput sequencing data

Deep learning has been increasingly and widely used to solve numerous problems in various fields with state-of-the-art performance. It can also be applied in bioinformatics to reduce the requirement for feature extraction and reach high performance. This study attempts to use deep learning to predict SNARE proteins, which is one of the most vital molecular functions in life science. A functional loss of SNARE proteins has been implicated in a variety of human diseases (e.g., neurodegenerative, mental illness, cancer, and so on). Therefore, creating a precise model to identify their functions is a crucial problem for understanding these diseases, and designing the drug targets. Our SNARE-CNN model which uses two-dimensional convolutional neural networks and position-specific scoring matrix profiles could identify SNARE proteins with achieved sensitivity of 76.6%, specificity of 93.5%, accuracy of 89.7%, and MCC of 0.7 in cross-validation dataset. We also evaluate the performance of our model via an independent dataset and the result shows that we are able to solve the overfitting problem. Compared with other state-of-the-art methods, this approach achieved significant improvement in all of the metrics. Throughout the proposed study, we provide an effective model for identifying SNARE proteins and a basis for further research that can apply deep learning in bioinformatics, especially in protein function prediction. SNARE-CNN are freely available at https://github.com/khanhlee/snare-cnn.

PPI-Detect: A support vector machine model for sequence-based prediction of protein-protein interactions

The prediction of peptide-protein or protein-protein interactions (PPI) is a challenging task, especially if amino acid sequences are the only information available. Machine learning methods allow us to exploit the information content in PPI datasets. However, the numerical codification of these datasets often influences the performance of data mining approaches. Here, we introduce a procedure for the general-purpose numerical codification of polypeptides. This procedure transforms pairs of amino acid sequences into a machine learning-friendly vector, whose elements represent numerical descriptors of residues in proteins. We used this numerical encoding procedure for the development of a support vector machine model (PPI-Detect), which allows predicting whether two proteins will interact or not. PPI-Detect (https://ppi-detect.zmb.uni-due.de/) outperforms state of the art sequence-based predictors of PPI. We employed PPI-Detect for the analysis of derivatives of EPI-X4, an endogenous peptide inhibitor of CXCR4, a G-protein-coupled receptor. There, we identified with high accuracy those peptides which bind better than EPI-X4 to the receptor. Also using PPI-Detect, we designed a novel peptide and then experimentally established its anti-CXCR4 activity. © 2019 Wiley Periodicals, Inc.

Structural Variability in the RLR-MAVS Pathway and Sensitive Detection of Viral RNAs

Cells need high-sensitivity detection of non-self molecules in order to fight against pathogens. These cellular sensors are thus of significant importance to medicinal purposes, especially for treating novel emerging pathogens. RIG-I-like receptors (RLRs) are intracellular sensors for viral RNAs (vRNAs). Their active forms activate mitochondrial antiviral signaling protein (MAVS) and trigger downstream immune responses against viral infection. Functional and structural studies of the RLR-MAVS signaling pathway have revealed significant supramolecular variability in the past few years, which revealed different aspects of the functional signaling pathway. Here I will discuss the molecular events of RLR-MAVS pathway from the angle of detecting single copy or a very low copy number of vRNAs in the presence of non-specific competition from cytosolic RNAs, and review key structural variability in the RLR / vRNA complexes, the MAVS helical polymers, and the adapter-mediated interactions between the active RLR / vRNA complex and the inactive MAVS in triggering the initiation of the MAVS filaments. These structural variations may not be exclusive to each other, but instead may reflect the adaptation of the signaling pathways to different conditions or reach different levels of sensitivity in its response to exogenous vRNAs.

Bioinformatics Applications in Advancing Animal Virus Research

Viruses serve as infectious agents for all living entities. There have been various research groups that focus on understanding the viruses in terms of their host-viral relationships, pathogenesis and immune evasion. However, with the current advances in the field of science, now the research field has widened up at the ‘omics’ level. Apparently, generation of viral sequence data has been increasing. There are numerous bioinformatics tools available that not only aid in analysing such sequence data but also aid in deducing useful information that can be exploited in developing preventive and therapeutic measures. This chapter elaborates on bioinformatics tools that are specifically designed for animal viruses as well as other generic tools that can be exploited to study animal viruses. The chapter further provides information on the tools that can be used to study viral epidemiology, phylogenetic analysis, structural modelling of proteins, epitope recognition and open reading frame (ORF) recognition and tools that enable to analyse host-viral interactions, gene prediction in the viral genome, etc. Various databases that organize information on animal and human viruses have also been described. The chapter will converse on overview of the current advances, online and downloadable tools and databases in the field of bioinformatics that will enable the researchers to study animal viruses at gene level.

Characterize the difference between TMPRSS2-ERG and non-TMPRSS2-ERG fusion patients by clinical and biological characteristics in prostate cancer

The TMPRSS2-ERG gene fusion were frequently found in prostate cancer, and thought to play some fundamental mechanisms for the development of prostate cancer. However, until now, the clinical and prognostic significance of TMPRSS2-ERG gene fusion was not fully understood. In this study, based on the 281 prostate cancers that constructed from a historical watchful waiting cohort, the statistically significant associations between TMPRSS2-ERG gene fusion and clinicopathologic characteristics were identified. In addition, the Elastic Net algorithm was used to predict the patients with TMPRSS2-ERG fusion status, and good predictive results were obtained, indicating that this algorithm was suitable to this prediction problem. The differential gene network was constructed from the network, and the KEGG enrichment analysis demonstrated that the module genes were significantly enriched in several important pathways.

Hydrogen bond analysis of the EGFR-ErbB3 heterodimer related to non-small cell lung cancer and drug resistance

Lung cancer is the predominant cause of cancer deaths on a worldwide scale. A mutation in the epidermal growth factor receptor (EGFR) can cause non-small cell lung cancer (NSCLC). The L858R one-point mutation in exon 21 in EGFR is the most prevalent in NSCLC. For over 60% of EGFR-muted NSCLC, another mutation T790M can cause drug resistance. In this paper, we consider EGFR and ErbB3 heterodimers involving three structures of EGFR, wild-type, with L858R mutation, and with L858R and T790M mutations. We perform molecular dynamics (MD) simulations to analyze hydrogen bonds in all three instances. The hydrogen bonds contribute to the conformational stability of the protein and molecular recognition. Several other parameters are also investigated in the present study, which reveals significant changes in the dimer at different levels of mutation. The knowledge and results obtained from this study lead to useful insight into the mechanism of NSCLC drug resistance.

The Multiple Applications and Possible Mechanisms of the Hyperbaric Oxygenation Therapy

Hyperbaric Oxygenation Therapy (HBOT) is used as an adjunctive method for multiple diseases. The method meets the routine treating and is non-invasive, as well as provides 100% pure oxygen (O2), which is at above-normal atmospheric pressure in a specialized chamber. It is well known that in the condition of O2 deficiency, it will induce a series of adverse events. In order to prevent the injury induced by anoxia, the capability of offering pressurized O2 by HBOT seems involuntary and significant. In recent years, HBOT displays particular therapeutic efficacy in some degree, and it is thought to be beneficial to the conditions of angiogenesis, tissue ischemia and hypoxia, nerve system disease, diabetic complications, malignancies, Carbon monoxide (CO) poisoning and chronic radiation-induced injury. Single and combination HBOT are both applied in previous studies, and the manuscript is to review the current applications and possible mechanisms of HBOT. The applicability and validity of HBOT for clinical treatment remain controversial, even though it is regarded as an adjunct to conventional medical treatment with many other clinical benefits. There also exists a negative side effect of accepting pressurized O2, such as oxidative stress injury, DNA damage, cellular metabolic, activating of coagulation, endothelial dysfunction, acute neurotoxicity and pulmonary toxicity. Then it is imperative to comprehensively consider the advantages and disadvantages of HBOT in order to obtain a satisfying therapeutic outcome.

PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids

The biological process known as post-translational modification (PTM) contributes to diversifying the proteome hence affecting many aspects of normal cell biology and pathogenesis. There have been many recently reported PTMs, but lysine phosphoglycerylation has emerged as the most recent subject of interest. Despite a large number of proteins being sequenced, the experimental method for detection of phosphoglycerylated residues remains an expensive, time-consuming and inefficient endeavor in the post-genomic era. Instead, the computational methods are being proposed for accurately predicting phosphoglycerylated lysines. Though a number of predictors are available, performance in detecting phosphoglycerylated lysine residues is still limited. In this paper, we propose a new predictor called PhoglyStruct that utilizes structural information of amino acids alongside a multilayer perceptron classifier for predicting phosphoglycerylated and non-phosphoglycerylated lysine residues. For the experiment, we located phosphoglycerylated and non-phosphoglycerylated lysines in our employed benchmark. We then derived and integrated properties such as accessible surface area, backbone torsion angles, and local structure conformations. PhoglyStruct showed significant improvement in the ability to detect phosphoglycerylated residues from non-phosphoglycerylated ones when compared to previous predictors. The sensitivity, specificity, accuracy, Mathews correlation coefficient and AUC were 0.8542, 0.7597, 0.7834, 0.5468 and 0.8077, respectively. The data and Matlab/Octave software packages are available at https://github.com/abelavit/PhoglyStruct .

Construction and analysis of dysregulated lncRNA-associated ceRNA network in colorectal cancer

Colorectal cancer (CRC) is one of the most frequently diagnosed digestive system cancer. The aim of the present study was to investigate the interactions among messenger RNAs (mRNAs), microRNAs (miRNAs), and long noncoding RNAs (lncRNAs) in CRC to reveal the mechanisms of CRC. Differentially expressed genes (DEGs) were identified from public gene expression data sets. One thousand eighty-one common dysregulated mRNAs in two data sets were identified. Gene function analysis and protein-protein interaction network analysis indicated that these DEGs might play important roles in CRC. LINC00365 was selected through coding- noncoding network analysis and its expression was validated upregulated in 22 paired clinical samples and four CRC cell lines. A competing endogenous RNA network composed of 70 miRNAs, nine mRNAs, and LINC00365 was constructed. Eight of nine mRNAs were validated upregulated in The Cancer Genome Atlas data set. Our results suggested that LINC00365 was an oncogene in CRC and it could regulate the expression of several mRNAs through sponging miRNAs.

Analysis and prediction of animal toxins by various Chou's pseudo components and reduced amino acid compositions

The animal toxin proteins are one of the disulfide rich small peptides that detected in venomous species. They are used as pharmacological tools and therapeutic agents in medicine for the high specificity of their targets. The successful analysis and prediction of toxin proteins may have important signification for the pharmacological and therapeutic researches of toxins. In this study, significant differences were found between the toxins and the non-toxins in amino acid compositions and several important biological properties. The random forest was firstly proposed to predict the animal toxin proteins by selecting 400 pseudo amino acid compositions and the dipeptide compositions of reduced amino acid alphabet as the input parameters. Based on dipeptide composition of reduced amino acid alphabet with 13 reduced amino acids, the best overall accuracy of 85.71% was obtained. These results indicated that our algorithm was an efficient tool for the animal toxin prediction.

Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou's general PseAAC

Identifying the location of proteins in a cell plays an important role in understanding their functions, such as drug design, therapeutic target discovery and biological research. However, the traditional subcellular localization experiments are time-consuming, laborious and small scale. With the development of next-generation sequencing technology, the number of proteins has grown exponentially, which lays the foundation of the computational method for identifying protein subcellular localization. Although many methods for predicting subcellular localization of proteins have been proposed, most of them are limited to single-location. In this paper, we propose a multi-kernel SVM to predict subcellular localization of both multi-location and single-location proteins. First, we make use of the evolutionary information extracted from position specific scoring matrix (PSSM) and physicochemical properties of proteins, by Chou's general PseAAC and other efficient functions. Then, we propose a multi-kernel support vector machine (SVM) model to identify multi-label protein subcellular localization. As a result, our method has a good performance on predicting subcellular localization of proteins. It achieves an average precision of 0.7065 and 0.6889 on two human datasets, respectively. All results are higher than those achieved by other existing methods. Therefore, we provide an efficient system via a novel perspective to study the protein subcellular localization.

Predicting lysine lipoylation sites using bi-profile bayes feature extraction and fuzzy support vector machine algorithm

Lipoylation is a highly conserved post-translational modification which has been found to be involved in many biological processes and closely associated with various metabolic diseases. The accurate identification of lipoylation sites is necessary to elucidate the underlying molecular mechanisms of lipoylation. As the traditional experimental methods are time consuming and expensive, it is desired to develop computational methods to predict lipoylation sites. In this study, a novel predictor named LipoPred is proposed to predict lysine lipoylation sites. On the one hand, an effective feature extraction method, bi-profile bayes encoding, is employed to encode lipoylation sites. On the other hand, a fuzzy support vector machine algorithm is proposed to solve the class imbalance and noise problem in the prediction of lipoylation sites. As illustrated by 10-fold cross-validation, LipoPred achieves an excellent performance with a Matthew's correlation coefficient of 0.9930. Therefore, LipoPred can be a useful bioinformatics tool for the prediction of lipoylation sites. Feature analysis shows that some residues around lipoylation sites may play an important role in the prediction. The results of analysis and prediction could offer useful information for elucidating the molecular mechanisms of lipoylation. A user-friendly web-server for LipoPred is established at 123.206.31.171/LipoPred/.

Large-scale frequent stem pattern mining in RNA families

Functionally similar non-coding RNAs are expected to be similar in certain regions of their secondary structures. These similar regions are called common structure motifs, and are structurally conserved throughout evolution to maintain their functional roles. Common structure motif identification is one of the critical tasks in RNA secondary structure analysis. Nevertheless, current approaches suffer several limitations, and/or do not scale with both structure size and the number of input secondary structures. In this work, we present a method to transform the conserved base pair stems into transaction items and apply frequent itemset mining to identify common structure motifs existing in a majority of input structures. Our experimental results on telomerase and ribosomal RNA secondary structures report frequent stem patterns that are of biological significance. Moreover, the algorithms utilized in our method are scalable and frequent stem patterns can be identified efficiently among many large structures.

Effective DNA binding protein prediction by using key features via Chou's general PseAAC

DNA-binding proteins (DBPs) are responsible for several cellular functions, starting from our immunity system to the transport of oxygen. In the recent studies, scientists have used supervised machine learning based methods that use information from the protein sequence only to classify the DBPs. Most of the methods work effectively on the train sets but performance of most of them degrades in the independent test set. It shows a room for improving the prediction method by reducing over-fitting. In this paper, we have extracted several features solely using the protein sequence and carried out two different types of feature selection on them. Our results have proven comparable on training set and significantly improved on the independent test set. On the independent test set our accuracy was 82.26% which is 1.62% improved compared to the previous best state-of-the-art methods. Performance in terms of sensitivity and area under receiver operating characteristic curve for the independent test set was also higher and they were 0.95 and 0.823 respectively.

iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou's PseAAC to pseudo-tri-nucleotide composition

Nucleosome is a central element of eukaryotic chromatin, which composes of histone proteins and DNA molecules. It performs vital roles in many eukaryotic intra-nuclear processes, for instance, chromatin structure and transcriptional regulation formation. Identification of nucleosome positioning via wet lab is difficult; so, the attention is diverted towards the accurate intelligent automated prediction. In this regard, a novel intelligent automated model "iNuc-ext-PseTNC" is developed to identify the nucleosome positioning in genomes accurately. In this predictor, the sequences of DNA are mathematically represented by two different discrete feature extraction techniques, namely pseudo-tri-nucleotide composition (PseTNC) and pseudo-di-nucleotide composition. Several contemporary machine learning algorithms were examined. Further, the predictions of individual classifiers were integrated through an evolutionary genetic algorithm. The success rates of the ensemble model are higher than individual classifiers. After analyzing the prediction results, it is noticed that iNuc-ext-PseTNC model has achieved better performance in combination with PseTNC feature space, which are 94.3%, 93.14%, and 88.60% of accuracies using six-fold cross-validation test for the three benchmark datasets S₁, S₂, and S₃, respectively. The achieved outcomes exposed that the results of iNuc-ext-PseTNC model are prominent compared to the existing methods so far notifiable in the literature. It is ascertained that the proposed model might be more fruitful and a practical tool for rudimentary academia and research.

Implications of Newly Identified Brain eQTL Genes and Their Interactors in Schizophrenia

Schizophrenia (SCZ) is a devastating genetic mental disorder. Identification of the SCZ risk genes in brains is helpful to understand this disease. Thus, we first used the minimum Redundancy-Maximum Relevance (mRMR) approach to integrate the genome-wide sequence analysis results on SCZ and the expression quantitative trait locus (eQTL) data from ten brain tissues to identify the genes related to SCZ. Second, we adopted the variance inflation factor regression algorithm to identify their interacting genes in brains. Third, using multiple analysis methods, we explored and validated their roles. By means of the aforementioned procedures, we have found that (1) the cerebellum may play a crucial role in the pathogenesis of SCZ and (2) ITIH4 may be utilized as a clinical biomarker for the diagnosis of SCZ. These interesting findings may stimulate novel strategy for developing new drugs against SCZ. It has not escaped our notice that the approach reported here is of use for studying many other genome diseases as well.

BlaPred: Predicting and classifying β-lactamase using a 3-tier prediction system via Chou's general PseAAC

Antibiotics of β-lactam class account for nearly half of the global antibiotic use. The β-lactamase enzyme is a major element of the bacterial arsenals to escape the lethal effect of β-lactam antibiotics. Different variants of β-lactamases have evolved to counter the different types of β-lactam antibiotics. Extensive research has been done to isolate and characterize different variants of β-lactamases. Unfortunately, identification and classification of the β-lactamase enzyme are purely based on experiments, which is both time- and resource-consuming. Thus, there is a need for fast and accurate computational methods to identify and classify new β-lactamase enzymes from the avalanche of sequence data generated in the post-genomic era. Based on these considerations, we have developed a support vector machine based three-tier prediction system, BlaPred, to predict and classify (as per Ambler classification) β-lactamases solely from their protein sequences. The input features used were amino acid composition, classic and amphiphilic pseudo amino acid compositions. The results show that the classic pseudo amino acid composition-based models performed better than the other models. Following a leave-one-out cross-validation procedure, the accuracy to discriminate β-lactamases from non-β-lactamases was 93.57% (tier-I); accuracies for prediction of class A β-lactamases was 93.27%, 95.52% for class B, 96.86% for class C and 97.31% for class D (tier-II); and at tier-III the accuracies for prediction were 84.78%, 95.65% and 89.13% for subclasses B1, B2 and B3, respectively. The comparative results on an independent dataset suggests that our method works efficiently to distinguish β-lactamases from non-β-lactamases, with an overall accuracy of 93.09%, and is further able to classify β-lactamase sequences into their respective Ambler classes and subclasses with accuracy higher than 92% and 87%, respectively. Comparative performance of BlaPred on an independent benchmark dataset also shows a significant improvement over other existing methods. Finally, BlaPred is available as a webserver, as well as standalone software, which can be accessed at http://proteininformatics.org/mkumar/blapred.

iPromoter-FSEn: Identification of bacterial σ70 promoter sequences using feature subspace based ensemble classifier

Sigma promoter sequences in bacterial genomes are important due to their role in transcription initiation. Sigma 70 is one of the most important and crucial sigma factors. In this paper, we address the problem of identification of σ⁷⁰ promoter sequences in bacterial genome. We propose iPromoter-FSEn, a novel predictor for identification of σ⁷⁰ promoter sequences. Our proposed method is based on a feature subspace based ensemble classifier. A large set of of features extracted from the sequence of nucleotides are divided into subsets and each subset is given to individual single classifiers to learn. Based on the decisions of the ensemble an aggregate decision is made by the ensemble voting classifier. We tested our method on a standard benchmark dataset extracted from experimentally validated results. Experimental results shows that iPromoter-FSEn significantly improves over the state-of-the art σ⁷⁰ promoter sequence predictors. The accuracy and area under receiver operating characteristic curve of iPromoter-FSEn are 86.32% and 0.9319 respectively. We have also made our method readily available for use as an web application from: http://ipromoterfsen.pythonanywhere.com/server.

Characterization of proteins in different subcellular localizations for Escherichia coli K12

Knowing the comprehensive knowledge about the protein subcellular localization is an important step to understand the function of the proteins. Recent advances in system biology have allowed us to develop more accurate methods for characterizing the proteins at subcellular localization level. In this study, the analysis method was developed to characterize the topological properties and biological properties of the cytoplasmic proteins, inner membrane proteins, outer membrane proteins and periplasmic proteins in Escherichia coli (E. coli). Statistical significant differences were found in all topological properties and biological properties among proteins in different subcellular localizations. In addition, investigation was carried out to analyze the differences in 20 amino acid compositions for four protein categories. We also found that there were significant differences in all of the 20 amino acid compositions. These findings may be helpful for understanding the comprehensive relationship between protein subcellular localization and biological function.

iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou's Pseudo components

Recombination hotspots in a genome are unevenly distributed. Hotspots are regions in a genome that show higher rates of meiotic recombinations. Computational methods for recombination hotspot prediction often use sophisticated features that are derived from physico-chemical or structure based properties of nucleotides. In this paper, we propose iRSpot-SF that uses sequence based features which are computationally cheap to generate. Four feature groups are used in our method: k-mer composition, gapped k-mer composition, TF-IDF of k-mers and reverse complement k-mer composition. We have used recursive feature elimination to select 17 top features for hotspot prediction. Our analysis shows the superiority of gapped k-mer composition and reverse complement k-mer composition features over others. We have used SVM with RBF kernel as a classification algorithm. We have tested our algorithm on standard benchmark datasets. Compared to other methods iRSpot-SF is able to produce significantly better results in terms of accuracy, Mathew's Correlation Coefficient and sensitivity which are 84.58%, 0.6941 and 84.57%. We have made our method readily available to use as a python based tool and made the datasets and source codes available at: https://github.com/abdlmaruf/iRSpot-SF. An web application is developed based on iRSpot-SF and freely available to use at: http://irspot.pythonanywhere.com/server.html.

PrESOgenesis: A two-layer multi-label predictor for identifying fertility-related proteins using support vector machine and pseudo amino acid composition approach

Successful spermatogenesis and oogenesis are the two genetically independent processes preceding embryo development. To date, several fertility-related proteins have been described in mammalian species. Nevertheless, further studies are required to discover more proteins associated with the development of germ cells and embryogenesis in order to shed more light on the processes. This work builds on our previous software (OOgenesis_Pred), mainly focusing on algorithms beyond what was previously done, in particular new fertility-related proteins and their classes (embryogenesis, spermatogenesis and oogenesis) based on the support vector machine according to the concept of Chou's pseudo-amino acid composition features. The results of five-fold cross validation, as well as the independent test demonstrated that this method is capable of predicting the fertility-related proteins and their classes with accuracy of more than 80%. Moreover, by using feature selection methods, important properties of fertility-related proteins were identified that allowed for their accurate classification. Based on the proposed method, a two-layer classifier software, named as "PrESOgenesis" ( https://github.com/mrb20045/PrESOgenesis ) was developed. The tool identified a query sequence (protein or transcript) as fertility or non-fertility-related protein at the first layer and then classified the predicted fertility-related protein into different classes of embryogenesis, spermatogenesis or oogenesis at the second layer.

iRNA-3typeA: Identifying Three Types of Modification at RNA's Adenosine Sites

RNA modifications are additions of chemical groups to nucleotides or their local structural changes. Knowledge about the occurrence sites of these modifications is essential for in-depth understanding of the biological functions and mechanisms and for treating some genomic diseases as well. With the avalanche of RNA sequences generated in the post-genomic age, many computational methods have been proposed for identifying various types of RNA modifications one by one. However, so far no method whatsoever has been developed for simultaneously identifying several different types of RNA modifications. To address such a challenge, we developed a predictor called "iRNA-3typeA," by which we can simultaneously identify the occurrence sites of the following three most frequently observed modifications in RNA: (1) N1-methyladenosine (m1A), (2) N6-methyladenosine (m6A), and (3) adenosine to inosine (A-to-I). It has been shown via rigorous cross-validations for the RNA sequences from Homo sapiens and Mus musculus transcriptomes that the success rates achieved by the powerful new predictor are quite high. For the convenience of broad experimental scientists, a user-friendly web server for iRNA-3typeA has been established at http://lin-group.cn/server/iRNA-3typeA/. It is anticipated that iRNA-3typeA may become a useful high throughput tool for genome analysis.

A proteome-wide systems toxicological approach deciphers the interaction network of chemotherapeutic drugs in the cardiovascular milieu

Onco-cardiology is critical for the management of cancer therapeutics since many of the anti-cancer agents are associated with cardiotoxicity. Therefore, the major aim of the current study is to employ a novel in silico method combined with experimental validation to explore off-targets and prioritize the enriched molecular pathways related to the specific cardiovascular events other than their intended targets by deriving relationship between drug-target-pathways and cardiovascular complications in order to help onco-cardiologists for the management of strategies to minimize cardiotoxicity. A systems biological understanding of the multi-target effects of a drug requires prior knowledge of proteome-wide binding profiles. In order to achieve the above, we have utilized PharmMapper, a web-based tool that uses a reverse pharmacophore mapping approach (spatial arrangement of features essential for a molecule to interact with a specific target receptor), along with KEGG for exploring the pathway relationship. In the validation part of the study, predicted protein targets and signalling pathways were strengthened with existing datasets of DrugBank and antibody arrays specific to vascular endothelial growth factor (VEGF) signalling in the case of 5-fluorouracil as direct experimental evidence. The current systems toxicological method illustrates the potential of the above big-data in supporting the knowledge of onco-cardiological indications which may lead to the generation of a decision making catalogue in future therapeutic prescription.

pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC

Knowledge of protein subcellular localization is vitally important for both basic research and drug development. With the avalanche of protein sequences emerging in the post-genomic age, it is highly desired to develop computational tools for timely and effectively identifying their subcellular localization purely based on the sequence information alone. Recently, a predictor called "pLoc-mGpos" was developed for identifying the subcellular localization of Gram-positive bacterial proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems in which some proteins, called "multiplex proteins", may simultaneously occur in two or more subcellular locations. Although it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mGpos was trained by an extremely skewed dataset in which some subset (subcellular location) was over 11 times the size of the other subsets. Accordingly, it cannot avoid the bias consequence caused by such an uneven training dataset. To alleviate such bias consequence, we have developed a new and bias-reducing predictor called pLoc_bal-mGpos by quasi-balancing the training dataset. Rigorous target jackknife tests on exactly the same experiment-confirmed dataset have indicated that the proposed new predictor is remarkably superior to pLoc-mGpos, the existing state-of-the-art predictor in identifying the subcellular localization of Gram-positive bacterial proteins. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_bal-mGpos/, by which users can easily get their desired results without the need to go through the detailed mathematics.

DPP-PseAAC: A DNA-binding protein prediction model using Chou's general PseAAC

A DNA-binding protein (DNA-BP) is a protein that can bind and interact with a DNA. Identification of DNA-BPs using experimental methods is expensive as well as time consuming. As such, fast and accurate computational methods are sought for predicting whether a protein can bind with a DNA or not. In this paper, we focus on building a new computational model to identify DNA-BPs in an efficient and accurate way. Our model extracts meaningful information directly from the protein sequences, without any dependence on functional domain or structural information. After feature extraction, we have employed Random Forest (RF) model to rank the features. Afterwards, we have used Recursive Feature Elimination (RFE) method to extract an optimal set of features and trained a prediction model using Support Vector Machine (SVM) with linear kernel. Our proposed method, named as DNA-binding Protein Prediction model using Chou's general PseAAC (DPP-PseAAC), demonstrates superior performance compared to the state-of-the-art predictors on standard benchmark dataset. DPP-PseAAC achieves accuracy values of 93.21%, 95.91% and 77.42% for 10-fold cross-validation test, jackknife test and independent test respectively. The source code of DPP-PseAAC, along with relevant dataset and detailed experimental results, can be found at https://github.com/srautonu/DNABinding. A publicly accessible web interface has also been established at: http://77.68.43.135:8080/DPP-PseAAC/.

Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC

This study examines accurate and efficient computational method for identification of 5-methylcytosine sites in RNA modification. The occurrence of 5-methylcytosine (m⁵C) plays a vital role in a number of biological processes. For better comprehension of the biological functions and mechanism it is necessary to recognize m⁵C sites in RNA precisely. The laboratory techniques and procedures are available to identify m⁵C sites in RNA, but these procedures require a lot of time and resources. This study develops a new computational method for extracting the features of RNA sequence. In this method, first the RNA sequence is encoded via composite feature vector, then, for the selection of discriminate features, the minimum-redundancy-maximum-relevance algorithm was used. Secondly, the classification method used has been based on a support vector machine by using jackknife cross validation test. The suggested method efficiently identifies m⁵C sites from non- m⁵C sites and the outcome of the suggested algorithm is 93.33% with sensitivity of 90.0 and specificity of 96.66 on bench mark datasets. The result exhibits that proposed algorithm shown significant identification performance compared to the existing computational techniques. This study extends the knowledge about the occurrence sites of RNA modification which paves the way for better comprehension of the biological uses and mechanism.