iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC

Predictive modeling for ubiquitin proteins through advanced machine learning technique

Ubiquitination is an essential post-translational modification mechanism involving the ubiquitin protein's bonding to a substrate protein. It is crucial in a variety of physiological activities including cell survival and differentiation, and innate and adaptive immunity. Any alteration in the ubiquitin system leads to the development of various human diseases. Numerous researches show the highly reversibility and dynamic of ubiquitin system, making the experimental identification quite difficult. To solve this issue, this article develops a model using a machine learning approach, tending to improve the ubiquitin protein prediction precisely. We deeply investigate the ubiquitination data that is proceed through different features extraction methods, followed by the classification. The evaluation and assessment are conducted considering Jackknife tests and 10-fold cross-validation. The proposed method demonstrated the remarkable performance in terms of 100 %, 99.88 %, and 99.84 % accuracy on Dataset-I, Dataset-II, and Dataset-III, respectively. Using Jackknife test, the method achieves 100 %, 99.91 %, and 99.99 % for Dataset-I, Dataset-II and Dataset-III, respectively. This analysis concludes that the proposed method outperformed the state-of-the-arts to identify the ubiquitination sites and helpful in the development of current clinical therapies. The source code and datasets will be made available at Github.

An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma

When the mutation affects the melanocytes of the body, a condition called melanoma results which is one of the deadliest skin cancers. Early detection of cutaneous melanoma is vital for raising the chances of survival. Melanoma can be due to inherited defective genes or due to environmental factors such as excessive sun exposure. The accuracy of the state-of-the-art computer-aided diagnosis systems is unsatisfactory. Moreover, the major drawback of medical imaging is the shortage of labeled data. Generalized classifiers are required to diagnose melanoma to avoid overfitting the dataset. To address these issues, blending ensemble-based deep learning (BEDLM-CMS) model is proposed to detect mutation of cutaneous melanoma by integrating long short-term memory (LSTM), Bi-directional LSTM (BLSTM) and gated recurrent unit (GRU) architectures. The dataset used in the proposed study contains 2608 human samples and 6778 mutations in total along with 75 types of genes. The most prominent genes that function as biomarkers for early diagnosis and prognosis are utilized. Multiple extraction techniques are used in this study to extract the most-prominent features. Afterwards, we applied different DL models optimized through grid search technique to diagnose melanoma. The validity of the results is confirmed using several techniques, including tenfold cross validation (10-FCVT), independent set (IST), and self-consistency (SCT). For validation of the results multiple metrics are used which include accuracy, specificity, sensitivity, and Matthews's correlation coefficient. BEDLM gives the highest accuracy of 97% in the independent set test whereas in self-consistency test and tenfold cross validation test it gives 94% and 93% accuracy, respectively. Accuracy of in self-consistency test, independent set test, and tenfold cross validation test is LSTM (96%, 94%, 92%), GRU (93%, 94%, 91%), and BLSTM (99%, 98%, 93%), respectively. The findings demonstrate that the proposed BEDLM-CMS can be used effectively applied for early diagnosis and treatment efficacy evaluation of cutaneous melanoma.

DeepBCE: Evaluation of deep learning models for identification of immunogenic B-cell epitopes

B-Cell epitopes (BCEs) can identify and bind with receptor proteins (antigens) to initiate an immune response against pathogens. Understanding antigen-antibody binding interactions has many applications in biotechnology and biomedicine, including designing antibodies, therapeutics, and vaccines. Lab-based experimental identification of these proteins is time-consuming and challenging. Computational techniques have been proposed to discover BCEs, but most lack of significant accomplishments. This work uses classical and deep learning models (DLMs) with sequence-based features to predict immunity stimulator BCEs from proteomics sequences. The proposed convolutional neural network-based model outperforms other models with an accuracy (ACC) of 0.878, an F-measure of 0.871, and an area under the receiver operating characteristic curve (AUC) of 0.945. The proposed strategy achieves 58.7% better results on average than other state-of-the-art approaches based on the Mathews Correlation Coefficient (MCC) results. The established model is accessible through a web application located at http://deeplbcepred.pythonanywhere.com.

A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns

Enhancers regulate gene expression, by playing a crucial role in the synthesis of RNAs and proteins. They do not directly encode proteins or RNA molecules. In order to control gene expression, it is important to predict enhancers and their potency. Given their distance from the target gene, lack of common motifs, and tissue/cell specificity, enhancer regions are thought to be difficult to predict in DNA sequences. Recently, a number of bioinformatics tools were created to distinguish enhancers from other regulatory components and to pinpoint their advantages. However, because the quality of its prediction method needs to be improved, its practical application value must also be improved. Based on nucleotide composition and statistical moment-based features, the current study suggests a novel method for identifying enhancers and non-enhancers and evaluating their strength. The proposed study outperformed state-of-the-art techniques using fivefold and tenfold cross-validation in terms of accuracy. The accuracy from the current study results in 86.5% and 72.3% in enhancer site and its strength prediction respectively. The results of the suggested methodology point to the potential for more efficient and successful outcomes when statistical moment-based features are used. The current study's source code is available to the research community at https://github.com/csbioinfopk/enpred.

CNNLSTMac4CPred: A Hybrid Model for N4-Acetylcytidine Prediction

N4-Acetylcytidine (ac4C) is a highly conserved post-transcriptional and an extensively existing RNA modification, playing versatile roles in the cellular processes. Due to the limitation of techniques and knowledge, large-scale identification of ac4C is still a challenging task. RNA sequences are like sentences containing semantics in the natural language. Inspired by the semantics of language, we proposed a hybrid model for ac4C prediction. The model used long short-term memory and convolution neural network to extract the semantic features hidden in the sequences. The semantic and the two traditional features (k-nucleotide frequencies and pseudo tri-tuple nucleotide composition) were combined to represent ac4C or non-ac4C sequences. The eXtreme Gradient Boosting was used as the learning algorithm. Five-fold cross-validation over the training set consisting of 1160 ac4C and 10,855 non-ac4C sequences obtained the area under the receiver operating characteristic curve (AUROC) of 0.9004, and the independent test over 469 ac4C and 4343 non-ac4C sequences reached an AUROC of 0.8825. The model obtained a sensitivity of 0.6474 in the five-fold cross-validation and 0.6290 in the independent test, outperforming two state-of-the-art methods. The performance of semantic features alone was better than those of k-nucleotide frequencies and pseudo tri-tuple nucleotide composition, implying that ac4C sequences are of semantics. The proposed hybrid model was implemented into a user-friendly web-server which is freely available to scientific communities: http://47.113.117.61/ac4c/ . The presented model and tool are beneficial to identify ac4C on large scale.

iPhosS(Deep)-PseAAC: Identification of Phosphoserine Sites in Proteins Using Deep Learning on General Pseudo Amino Acid Compositions

Among all the PTMs, the protein phosphorylation is pivotal for various pathological and physiological processes. About 30 percent of eukaryotic proteins undergo the phosphorylation modification, leading to various changes in conformation, function, stability, localization, and so forth. In eukaryotic proteins, phosphorylation occurs on serine (S), Threonine (T) and Tyrosine (Y) residues. Among these all, serine phosphorylation has its own importance as it is associated with various importance biological processes, including energy metabolism, signal transduction pathways, cell cycling, and apoptosis. Thus, its identification is important, however, the in vitro, ex vivo and in vivo identification can be laborious, time-taking and costly. There is a dire need of an efficient and accurate computational model to help researchers and biologists identifying these sites, in an easy manner. Herein, we propose a novel predictor for identification of Phosphoserine sites (PhosS) in proteins, by integrating the Chou's Pseudo Amino Acid Composition (PseAAC) with deep features. We used well-known DNNs for both the tasks of learning a feature representation of peptide sequences and performing classifications. Among different DNNs, the best score is shown by Covolutional Neural Network based model which renders CNN based prediction model the best for Phosphoserine prediction. Based on these results, it is concluded that the proposed model can help to identify PhosS sites in a very efficient and accurate manner which can help scientists understand the mechanism of this modification in proteins.

iAcety–SmRF: Identification of Acetylation Protein by Using Statistical Moments and Random Forest

Acetylation is the most important post-translation modification (PTM) in eukaryotes; it has manifold effects on the level of protein that transform an acetyl group from an acetyl coenzyme to a specific site on a polypeptide chain. Acetylation sites play many important roles, including regulating membrane protein functions and strongly affecting the membrane interaction of proteins and membrane remodeling. Because of these properties, its correct identification is essential to understand its mechanism in biological systems. As such, some traditional methods, such as mass spectrometry and site-directed mutagenesis, are used, but they are tedious and time-consuming. To overcome such limitations, many computer models are being developed to correctly identify their sequences from non-acetyl sequences, but they have poor efficiency in terms of accuracy, sensitivity, and specificity. This work proposes an efficient and accurate computational model for predicting Acetylation using machine learning approaches. The proposed model achieved an accuracy of 100 percent with the 10-fold cross-validation test based on the Random Forest classifier, along with a feature extraction approach using statistical moments. The model is also validated by the jackknife, self-consistency, and independent test, which achieved an accuracy of 100, 100, and 97, respectively, results far better as compared to the already existing models available in the literature.

A comprehensive tool for accurate identification of methyl-Glutamine sites

Methylation is a biochemical process involved in nearly all of the human body functions. Glutamine is considered an indispensable amino acid that is susceptible to methylation via post-translational modification (PTM). Modern research has proved that methylation plays a momentous role in the progression of most types of cancers. Therefore, there is a need for an effective method to predict glutamine sites vulnerable to methylation accurately and inexpensively. The motive of this study is the formulation of an accurate method that could predict such sites with high accuracy. Various computationally intelligent classifiers were employed for their formulation and evaluation. Rigorous validations prove that deep learning performs best as compared to other classifiers. The accuracy (ACC) and the area under the receiver operating curve (AUC) obtained by 10-fold cross-validation was 0.962 and 0.981, while with the jackknife testing, it was 0.968 and 0.980, respectively. From these results, it is concluded that the proposed methodology works sufficiently well for the prediction of methyl-glutamine sites. The webserver's code, developed for the prediction of methyl-glutamine sites, is freely available at https://github.com/s20181080001/WebServer.git. The code can easily be set up by any intermediate-level Python user.

Identification of stress response proteins through fusion of machine learning models and statistical paradigms

Proteins are a vital component of cells that perform physiological functions to ensure smooth operations of bodily functions. Identification of a protein's function involves a detailed understanding of the structure of proteins. Stress proteins are essential mediators of several responses to cellular stress and are categorized based on their structural characteristics. These proteins are found to be conserved across many eukaryotic and prokaryotic linkages and demonstrate varied crucial functional activities inside a cell. The in-vivo, ex vivo, and in-vitro identification of stress proteins are a time-consuming and costly task. This study is aimed at the identification of stress protein sequences with the aid of mathematical modelling and machine learning methods to supplement the aforementioned wet lab methods. The model developed using Random Forest showed remarkable results with 91.1% accuracy while models based on neural network and support vector machine showed 87.7% and 47.0% accuracy, respectively. Based on evaluation results it was concluded that random-forest based classifier surpassed all other predictors and is suitable for use in practical applications for the identification of stress proteins. Live web server is available at http://biopred.org/stressprotiens , while the webserver code available is at https://github.com/abdullah5naveed/SRP_WebServer.git.

iTAGPred: A Two-Level Prediction Model for Identification of Angiogenesis and Tumor Angiogenesis Biomarkers

A crucial biological process called angiogenesis plays a vital role in migration, growth, and wound healing of endothelial cells and other processes that are controlled by chemical signals. Angiogenesis is the process that controls the growth of blood vessels within tissues while angiogenesis proteins play a significant role in the proper working of this process. The balancing of these signals is necessary for the proper working of angiogenesis. Unbalancing of these signals increases blood vessel formation, which causes abnormal growth or several diseases including cancer. The proposed work focuses on developing a two-layered prediction model using different classifiers like random forest (RF), neural network, and support vector machine. The first level performs in silico identification of angiogenesis proteins based on the primary structure. In the case the protein is an angiogenesis protein, then the second level predicts whether the protein is linked with tumor angiogenesis or not. The performance of the model is evaluated through various validation techniques. The model was evaluated using k-fold cross-validation, independent, self-consistency, and jackknife testing. The overall accuracy using an RF classifier for angiogenesis at the first level was 97.8% and for tumor angiogenesis at the second level was 99.5%, ANN showed 94.1% accuracy for angiogenesis and 79.9% for tumor angiogenesis, and the accuracy of SVM for angiogenesis was 78.8% and for tumor angiogenesis was 65.19%.

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.

Evaluating machine learning methodologies for identification of cancer driver genes

Cancer is driven by distinctive sorts of changes and basic variations in genes. Recognizing cancer driver genes is basic for accurate oncological analysis. Numerous methodologies to distinguish and identify drivers presently exist, but efficient tools to combine and optimize them on huge datasets are few. Most strategies for prioritizing transformations depend basically on frequency-based criteria. Strategies are required to dependably prioritize organically dynamic driver changes over inert passengers in high-throughput sequencing cancer information sets. This study proposes a model namely PCDG-Pred which works as a utility capable of distinguishing cancer driver and passenger attributes of genes based on sequencing data. Keeping in view the significance of the cancer driver genes an efficient method is proposed to identify the cancer driver genes. Further, various validation techniques are applied at different levels to establish the effectiveness of the model and to obtain metrics like accuracy, Mathew's correlation coefficient, sensitivity, and specificity. The results of the study strongly indicate that the proposed strategy provides a fundamental functional advantage over other existing strategies for cancer driver genes identification. Subsequently, careful experiments exhibit that the accuracy metrics obtained for self-consistency, independent set, and cross-validation tests are 91.08%., 87.26%, and 92.48% respectively.

NPalmitoylDeep-PseAAC: A Predictor of N-Palmitoylation Sites in Proteins Using Deep Representations of Proteins and PseAAC via Modified 5-Steps Rule

Background:

Among all the major Post-translational modification, lipid modifications possess special significance due to their widespread functional importance in eukaryotic cells. There exist multiple types of lipid modifications and Palmitoylation, among them, is one of the broader types of modification, having three different types. The N-Palmitoylation is carried out by attachment of palmitic acid to an N-terminal cysteine. Due to the association of N-Palmitoylation with various biological functions and diseases such as Alzheimer’s and other neurodegenerative diseases, its identification is very important.

Objective:

The in vitro, ex vivo and in vivo identification of Palmitoylation is laborious, time-taking and costly. There is a dire need for an efficient and accurate computational model to help researchers and biologists identify these sites, in an easy manner. Herein, we propose a novel prediction model for the identification of N-Palmitoylation sites in proteins.

Method:

The proposed prediction model is developed by combining the Chou’s Pseudo Amino Acid Composition (PseAAC) with deep neural networks. We used well-known deep neural networks (DNNs) for both the tasks of learning a feature representation of peptide sequences and developing a prediction model to perform classification.

Results:

Among different DNNs, Gated Recurrent Unit (GRU) based RNN model showed the highest scores in terms of accuracy, and all other computed measures, and outperforms all the previously reported predictors.

Conclusion:

The proposed GRU based RNN model can help to identify N-Palmitoylation in a very efficient and accurate manner which can help scientists understand the mechanism of this modification in proteins.

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.

Identification of Lysine Carboxylation Sites in Proteins by Integrating Statistical Moments and Position Relative Features via General PseAAC

Background:

Carboxylation is one of the most biologically important post-translational modifications and occurs on lysine, arginine, and glutamine residues of a protein. Among all these three, the covalent attachment of the carboxyl group with the lysine side chain is the most frequent and biologically important type of carboxylation. For studying such biological functions, it is essential to correctly determine the lysine sites sensitive to carboxylation.

Objective:

Herein, we present a computational model for the prediction of the carboxylysine site which is based on machine learning.

Methods:

Various position and composition relative features have been incorporated into the Pse- AAC for construction of feature vectors and a neural network is employed as a classifier. The model is validated by jackknife, cross-validation, self-consistency, and independent testing.

Results:

The results of the self-consistency test elaborated that model has 99.76% Acc, 99.76% Sp, 99.76% Sp, and 0.99 MCC. Using the jackknife method, prediction model validation gave 97.07% Acc, while for 10-fold cross-validation, prediction model validation gave 95.16% Acc.

Conclusion:

The results of independent dataset testing were 94.3% which illustrated that the proposed model has better performance as compared to the existing model PreLysCar; however, the accuracy can be improved further, in the future, due to the increasing number of carboxylysine sites in proteins.

Identification of 4-carboxyglutamate residue sites based on position based statistical feature and multiple classification

Glutamic acid is an alpha-amino acid used by all living beings in protein biosynthesis. One of the important glutamic acid modifications is post-translationally modified 4-carboxyglutamate. It has a significant role in blood coagulation. 4-carboxyglumates are required for the binding of calcium ions. On the contrary, this modification can also cause different diseases such as bone resorption, osteoporosis, papilloma, and plaque atherosclerosis. Considering its importance, it is necessary to predict the occurrence of glutamic acid carboxylation in amino acid stretches. As there is no computational based prediction model available to identify 4-carboxyglutamate modification, this study is, therefore, designed to predict 4-carboxyglutamate sites with a less computational cost. A machine learning model is devised with a Multilayered Perceptron (MLP) classifier using Chou's 5-step rule. It may help in learning statistical moments and based on this learning, the prediction is to be made accurately either it is 4-carboxyglutamate residue site or detected residue site having no 4-carboxyglutamate. Prediction accuracy of the proposed model is 94% using an independent set test, while obtained prediction accuracy is 99% by self-consistency tests.

Insights into Machine Learning-based Approaches for Virtual Screening in Drug Discovery: Existing Strategies and Streamlining Through FP-CADD

BACKGROUND

Machine learning is an active area of research in computer science by the availability of big data collection of all sorts prompting interest in the development of novel tools for data mining. Machine learning methods have wide applications in computer-aided drug discovery methods. Most incredible approaches to machine learning are used in drug designing, which further aid the process of biological modelling in drug discovery. Mainly, two main categories are present which are Ligand-Based Virtual Screening (LBVS) and Structure-Based Virtual Screening (SBVS), however, the machine learning approaches fall mostly in the category of LBVS.

OBJECTIVES

This study exposits the major machine learning approaches being used in LBVS. Moreover, we have introduced a protocol named FP-CADD which depicts a 4-steps rule of thumb for drug discovery, the four protocols of computer-aided drug discovery (FP-CADD). Various important aspects along with SWOT analysis of FP-CADD are also discussed in this article.

CONCLUSION

By this thorough study, we have observed that in LBVS algorithms, Support Vector Machines (SVM) and Random Forest (RF) are those which are widely used due to high accuracy and efficiency. These virtual screening approaches have the potential to revolutionize the drug designing field. Also, we believe that the process flow presented in this study, named FP-CADD, can streamline the whole process of computer-aided drug discovery. By adopting this rule, the studies related to drug discovery can be made homogeneous and this protocol can also be considered as an evaluation criterion in the peer-review process of research articles.

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

A Sequence-Based Predictor of Zika Virus Proteins Developed by Integration of PseAAC and Statistical Moments

BACKGROUND

ZIKV has been a well-known global threat, which hits almost all of the American countries and posed a serious threat to the entire globe in 2016. The first outbreak of ZIKV was reported in 2007 in the Pacific area, followed by another severe outbreak, which occurred in 2013/2014 and subsequently, ZIKV spread to all other Pacific islands. A broad spectrum of ZIKV associated neurological malformations in neonates and adults has driven this deadly virus into the limelight. Though tremendous efforts have been focused on understanding the molecular basis of ZIKV, the viral proteins of ZIKV have still not been studied extensively.

OBJECTIVES

Herein, we report the first and the novel predictor for the identification of ZIKV proteins.

METHODS

We have employed Chou's pseudo amino acid composition (PseAAC), statistical moments and various position-based features.

RESULTS

The predictor is validated through 10-fold cross-validation and Jackknife testing. In 10- fold cross-validation, 94.09% accuracy, 93.48% specificity, 94.20% sensitivity and 0.80 MCC were achieved while in Jackknife testing, 96.62% accuracy, 94.57% specificity, 97.00% sensitivity and 0.88 MCC were achieved.

CONCLUSION

Thus, ZIKVPred-PseAAC can help in predicting the ZIKV proteins efficiently and accurately and can provide baseline data for the discovery of new drugs and biomarkers against ZIKV.

iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou's 5-Steps Rule and Informative Physicochemical Properties

Understanding of quorum-sensing peptides (QSPs) in their functional mechanism plays an essential role in finding new opportunities to combat bacterial infections by designing drugs. With the avalanche of the newly available peptide sequences in the post-genomic age, it is highly desirable to develop a computational model for efficient, rapid and high-throughput QSP identification purely based on the peptide sequence information alone. Although, few methods have been developed for predicting QSPs, their prediction accuracy and interpretability still requires further improvements. Thus, in this work, we proposed an accurate sequence-based predictor (called iQSP) and a set of interpretable rules (called IR-QSP) for predicting and analyzing QSPs. In iQSP, we utilized a powerful support vector machine (SVM) cooperating with 18 informative features from physicochemical properties (PCPs). Rigorous independent validation test showed that iQSP achieved maximum accuracy and MCC of 93.00% and 0.86, respectively. Furthermore, a set of interpretable rules IR-QSP was extracted by using random forest model and the 18 informative PCPs. Finally, for the convenience of experimental scientists, the iQSP web server was established and made freely available online. It is anticipated that iQSP will become a useful tool or at least as a complementary existing method for predicting and analyzing QSPs.

iCrotoK-PseAAC: Identify lysine crotonylation sites by blending position relative statistical features according to the Chou's 5-step rule

Among different post-translational modifications (PTMs), one of the most important one is the lysine crotonylation in proteins. Its importance cannot be undermined related to different diseases and essential biological practice. The key step for finding the hidden mechanisms of crotonylation along with their occurrence sites is to completely apprehend the mechanism behind this biological process. In previously reported studies, researchers have used different techniques, like position weighted matrix (PWM), support vector machine (SVM), k nearest neighbors (KNN), and many others. However, the maximum prediction accuracy achieved was not such high. To address this, herein, we propose an improved predictor for lysine crotonylation sites named iCrotoK-PseAAC, in which we have incorporated various position and composition relative features along with statistical moments into PseAAC. The results of self-consistency testing were 100% accurate, while the 10-fold cross validation gave 99.0% accuracy. Based on the validation and comparison of model, it is concluded that the iCrotoK-PseAAC is more accurate than the previously proposed models.

SDBP-Pred: Prediction of single-stranded and double-stranded DNA-binding proteins by extending consensus sequence and K-segmentation strategies into PSSM

Identification of DNA-binding proteins (DNA-BPs) is a hot issue in protein science due to its key role in various biological processes. These processes are highly concerned with DNA-binding protein types. DNA-BPs are classified into single-stranded DNA-binding proteins (SSBs) and double-stranded DNA-binding proteins (DSBs). SSBs mainly involved in DNA recombination, replication, and repair, while DSBs regulate transcription process, DNA cleavage, and chromosome packaging. In spite of the aforementioned significance, few methods have been proposed for discrimination of SSBs and DSBs. Therefore, more predictors with favorable performance are indispensable. In this work, we present an innovative predictor, called SDBP-Pred with a novel feature descriptor, named consensus sequence-based K-segmentation position-specific scoring matrix (CSKS-PSSM). We encoded the local discriminative features concealed in PSSM via K-segmentation strategy and the global potential features by applying the notion of the consensus sequence. The obtained feature vector then input to support vector machine (SVM) with linear, polynomial and radial base function (RBF) kernels. Our model with SVM-RBF achieved the highest accuracies on three tests namely jackknife, 10-fold, and independent tests, respectively than the recent method. The obtained prediction results illustrate the superlative prediction performance of SDBP-Pred over existing studies in the literature so far.

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.

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.

iNR-2L: A two-level sequence-based predictor developed via Chou's 5-steps rule and general PseAAC for identifying nuclear receptors and their families

Nuclear receptor proteins (NRPs) perform a vital role in regulating gene expression. With the rapidity growth of NRPs in post-genomic era, it is highly recommendable to identify NRPs and their sub-families accurately from their primary sequences. Several conventional methods have been used for discrimination of NRPs and their sub-families, but did not achieve considerable results. In a sequel, a two-level new computational model "iNR-2 L" is developed. Two discrete methods namely: Dipeptide Composition and Tripeptide Composition were used to formulate NRPs sequences. Further, both the descriptor spaces were merged to construct hybrid space. Furthermore, feature selection technique minimum redundancy and maximum relevance was employed in order to select salient features as well as reduce the noise and redundancy. The experiential outcomes exhibited that the proposed model iNR-2 L achieved outstanding results. It is anticipated that the proposed computational model might be a practical and effective tool for academia and research community.