iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets

A bi-layer model for identification of piwiRNA using deep neural learning

piwiRNA is a kind of non-coding RNA (ncRNA) that cannot be translated into proteins. It helps in understanding the study of gametes generation and regulation of gene expression over both transcriptional and post-transcriptional levels. piwiRNA has the function of instructing deadenylation, animal fertility, silencing transposons, fighting viruses, and regulating endogenous genes. Due to the great significance of piwiRNA, prediction of piwiRNA is essential for crucial cellular functions. Several predictors were established for prediction of piwiRNA. However, improving the prediction of piwiRNA is highly desirable. In the current study, we developed a more promising predictor named, BLP-piwiRNA. The features are explored by reverse complement k-mer, gapped-k-mer composition, and k-mer composition. The feature set of all descriptors is fused and the best features are selected by cascade and relief feature selection strategies. The best feature sets are provided to random forest (RF), deep neural network (DNN), and support vector machine (SVM). The models validation are examined by 10-fold test. DNN with optimal features of Cascade feature selection approach secured the highest prediction results. The results illustrate that BLP-piwiRNA effectively outperforms the existing studies. The proposed approach would be beneficial for both research community and drug development industry. BLP-piwiRNA would serve as novel biomarkers and therapeutic targets for tumor diagnostics and treatment.Communicated by Ramaswamy H. Sarma.

Toward Nano- and Microplastic Sensors: Identification of Nano- and Microplastic Particles via Artificial Intelligence Combined with a Plasmonic Probe Functionalized with an Estrogen Receptor

Nano- and microplastic particles are a global and emerging environmental issue that might pose potential threats to human health. The present work exploits artificial intelligence (AI) to identify nano- and microplastics in water by monitoring the interaction of the sample with a sensitive surface. An estrogen receptor (ER) grafted onto a gold surface, realized on a nonexpensive and easy-to-produce plastic optical fiber (POF) platform in order to excite a surface plasmon resonance (SPR) phenomenon, has been developed in order to carry out a "smart" sensitive interface (ER-SPR-POF interface). The ER-SPR-POF interface offers output data useful for exploiting a machine learning-based approach to achieve nano- and microplastic particle sensors. This work developed a proof-of-concept sensor through a training phase carried out by different particles, in terms of materials and size. The experimental results have demonstrated that the proposed "smart" ER-SPR-POF interface combined with AI can be used to identify the kind of particles in terms of the materials (polystyrene; poly(methyl methacrylate)) and size (20 μm; 100 nm) with an accuracy of 90.3%.

DeepBSRPred: deep learning-based binding site residue prediction for proteins

MOTIVATION

Proteins-protein interactions (PPIs) are important to govern several cellular activities. Amino acid residues, which are located at the interface are known as the binding sites and the information about binding sites helps to understand the binding affinities and functions of protein-protein complexes.

RESULTS

We have developed a deep neural network-based method, DeepBSRPred, for predicting the binding sites using protein sequence information and predicted structures from AlphaFold2. Specific sequence and structure-based features include position-specific scoring matrix (PSSM), solvent accessible surface area, conservation score and amino acid properties, and residue depth, respectively. Our method predicted the binding sites with an average F1 score of 0.73 in a dataset of 1236 proteins. Further, we compared the performance with other existing methods in the literature using four benchmark datasets and our method outperformed those methods.

AVAILABILITY AND IMPLEMENTATION

The DeepBSRPred web server can be found at https://web.iitm.ac.in/bioinfo2/deepbsrpred/index.html , along with all datasets used in this study. The trained models, the DeepBSRPred standalone source code, and the feature computation pipeline are freely available at https://web.iitm.ac.in/bioinfo2/deepbsrpred/download.html .

Implantable Electrochemical Sensors for Brain Research

Implantable electrochemical sensors provide reliable tools for in vivo brain research. Recent advances in electrode surface design and high-precision fabrication of devices led to significant developments in selectivity, reversibility, quantitative detection, stability, and compatibility of other methods, which enabled electrochemical sensors to provide molecular-scale research tools for dissecting the mechanisms of the brain. In this Perspective, we summarize the contribution of these advances to brain research and provide an outlook on the development of the next generation of electrochemical sensors for the brain.

PrUb-EL: A hybrid framework based on deep learning for identifying ubiquitination sites in Arabidopsis thaliana using ensemble learning strategy

Identification of ubiquitination sites is central to many biological experiments. Ubiquitination is a kind of post-translational protein modification (PTM). It is a key mechanism for increasing protein diversity and plays a vital role in regulating cell function. In recent years, many models have been developed to predict ubiquitination sites in humans, mice and yeast. However, few studies have predicted ubiquitination sites in Arabidopsis thaliana. In view of this, a deep network model named PrUb-EL is proposed to predict ubiquitination sites in Arabidopsis thaliana. Firstly, six features based on the protein sequence are extracted with amino acid index database (AAindex), dipeptide deviates from the expected mean (DDE), dipeptide composition (DPC), blocks substitution matrix (BLOSUM62), enhanced amino acid composition (EAAC) and binary encoding. Secondly, the synthetic minority over-sampling technique (SMOTE) is utilized to process the imbalanced data set. Then a new classifier named DG is presented, which includes Dense block, Residual block and Gated recurrent unit (GRU) block. Finally, each of six feature extraction methods is integrated into the DG model, and the ensemble learning strategy is used to gain the final prediction result. Experimental results show that PrUb-EL has good predictive ability with the accuracy (ACC) and area under the ROC curve (auROC) values of 91.00% and 97.70% using 5-fold cross-validation, respectively. Note that the values of ACC and auROC are 88.58% and 96.09% in the independent test, respectively. Compared with previous studies, our model has significantly improved performance thus it is an excellent method for identifying ubiquitination sites in Arabidopsis thaliana. The datasets and code used for the article are available at https://github.com/Tom-Wangy/PreUb-EL.git.

Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions

Protein-protein interactions play crucial and subtle roles in many biological processes and modifications of their fine mechanisms generally result in severe diseases. Peptide derivatives are very promising therapeutic agents for modulating protein-protein associations with sizes and specificities between those of small compounds and antibodies. For the same reasons, rational design of peptide-based inhibitors naturally borrows and combines computational methods from both protein-ligand and protein-protein research fields. In this chapter, we aim to provide an overview of computational tools and approaches used for identifying and optimizing peptides that target protein-protein interfaces with high affinity and specificity. We hope that this review will help to implement appropriate in silico strategies for peptide-based drug design that builds on available information for the systems of interest.

iPro-GAN: A novel model based on generative adversarial learning for identifying promoters and their strength

BACKGROUND AND OBJECTIVE

Promoter is a component of the gene, which can specifically bind with RNA polymerase and determine where transcription starts, and also determine the transcription efficiency of the gene. Promoters can be divided into strong promoters and weak promoters because their structures and the interaction time interval are quite different. The functional variation of the promoter can lead to a variety of diseases. Therefore, identifying promoters and their strength is necessary and has important biological significance. A novel and promising model based on deep learning is proposed to achieve it.

METHODS

In this work, we build a power model named iPro-GAN for identification of promoters and their strength. First, we collect benchmark datasets and independent datasets for training and testing. Then, Moran-based spatial auto-cross correlation method is used as feature extraction method. Finally, deep convolution generative adversarial network with 10-fold cross validation is applied for classifying. The first layer of the model is used to identify the promoter and the second layer is used to determine its type.

RESULTS

On the benchmark data set, the accuracy of the first layer predictor is 93.15%, and the accuracy of the second layer predictor is 92.30%. On the independent data set, the accuracy of the first layer predictor is 86.77%, and the accuracy of the second layer predictor is 91.66%. In particular, breakthrough progress has been made in the identification of promoters' strength.

CONCLUSIONS

These results are far higher than the existing best predictor, which indicate that our model is serviceable and practicable to identify promoters and their strength. Furthermore, the datasets and source codes are available from this link: https://github.com/Bovbene/iPro-GAN.

ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features

MicroRNAs (miRNAs) play a significant role in plant response to different abiotic stresses. Thus, identification of abiotic stress-responsive miRNAs holds immense importance in crop breeding programmes to develop cultivars resistant to abiotic stresses. In this study, we developed a machine learning-based computational method for prediction of miRNAs associated with abiotic stresses. Three types of datasets were used for prediction, i.e., miRNA, Pre-miRNA, and Pre-miRNA + miRNA. The pseudo K-tuple nucleotide compositional features were generated for each sequence to transform the sequence data into numeric feature vectors. Support vector machine (SVM) was employed for prediction. The area under receiver operating characteristics curve (auROC) of 70.21, 69.71, 77.94 and area under precision-recall curve (auPRC) of 69.96, 65.64, 77.32 percentages were obtained for miRNA, Pre-miRNA, and Pre-miRNA + miRNA datasets, respectively. Overall prediction accuracies for the independent test set were 62.33, 64.85, 69.21 percentages, respectively, for the three datasets. The SVM also achieved higher accuracy than other learning methods such as random forest, extreme gradient boosting, and adaptive boosting. To implement our method with ease, an online prediction server “ASRmiRNA” has been developed. The proposed approach is believed to supplement the existing effort for identification of abiotic stress-responsive miRNAs and Pre-miRNAs.

A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites

Knowledge about protein-protein interactions is beneficial in understanding cellular mechanisms. Protein-protein interactions are usually determined according to their protein-protein interaction sites. Due to the limitations of current techniques, it is still a challenging task to detect protein-protein interaction sites. In this article, we presented a method based on deep learning and XGBoost (called DeepPPISP-XGB) for predicting protein-protein interaction sites. The deep learning model served as a feature extractor to remove redundant information from protein sequences. The Extreme Gradient Boosting algorithm was used to construct a classifier for predicting protein-protein interaction sites. The DeepPPISP-XGB achieved the following results: area under the receiver operating characteristic curve of 0.681, a recall of 0.624, and area under the precision-recall curve of 0.339, being competitive with the state-of-the-art methods. We also validated the positive role of global features in predicting protein-protein interaction sites.

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.

Designing multi-epitope vaccine candidates against functional amyloids in Pseudomonas aeruginosa through immunoinformatic and structural bioinformatics approach

Pseudomonas aeruginosa (P. aeruginosa) displays high drug resistance and biofilm-mediated adaptability, which makes its infections difficult to treat. Alternative intervention methods and targets have made such infections treatment manageable. One of the biofilm components, functional amyloids of Pseudomonas (Fap) is correlated positively with virulence and mucoidy phenotype found in infection in cystic fibrosis (CF) patients. Extracellular accessibility, conservation across P. aeruginosa isolates and linkage with lung infections phenotype in CF patients, makes Fap a promising intervention target. Furthermore, the reported effect of bacterial amyloid on neuronal function and immune response makes it a targetable candidate. In the current study, Fap C protein and its immediate interactions were explored to extract antigenic T-cell and B-cell epitopes. A combination of epitopes and peptide adjuvants has been linked to derive vaccine candidate structures. The vaccine candidates were validated for antigenicity, allergenicity, physiochemical properties, stability and interactions with TLRs and MHC alleles. Immunosimulation studies have demonstrated that vaccines elicit Th1 dominated response, which can assist in good prognosis of infection in CF patients.

DELPHI: accurate deep ensemble model for protein interaction sites prediction

MOTIVATION

Proteins usually perform their functions by interacting with other proteins, which is why accurately predicting protein-protein interaction (PPI) binding sites is a fundamental problem. Experimental methods are slow and expensive. Therefore, great efforts are being made towards increasing the performance of computational methods.

RESULTS

We propose DEep Learning Prediction of Highly probable protein Interaction sites (DELPHI), a new sequence-based deep learning suite for PPI-binding sites prediction. DELPHI has an ensemble structure which combines a CNN and a RNN component with fine tuning technique. Three novel features, HSP, position information and ProtVec are used in addition to nine existing ones. We comprehensively compare DELPHI to nine state-of-the-art programmes on five datasets, and DELPHI outperforms the competing methods in all metrics even though its training dataset shares the least similarities with the testing datasets. In the most important metrics, AUPRC and MCC, it surpasses the second best programmes by as much as 18.5% and 27.7%, respectively. We also demonstrated that the improvement is essentially due to using the ensemble model and, especially, the three new features. Using DELPHI it is shown that there is a strong correlation with protein-binding residues (PBRs) and sites with strong evolutionary conservation. In addition, DELPHI's predicted PBR sites closely match known data from Pfam. DELPHI is available as open-sourced standalone software and web server.

AVAILABILITY AND IMPLEMENTATION

The DELPHI web server can be found at delphi.csd.uwo.ca/, with all datasets and results in this study. The trained models, the DELPHI standalone source code, and the feature computation pipeline are freely available at github.com/lucian-ilie/DELPHI.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

iPro2L-PSTKNC: A Two-Layer Predictor for Discovering Various Types of Promoters by Position Specific of Nucleotide Composition

Promoters are DNA regulatory elements located proximal to the transcription start site, which are in charge of the initiation of specific gene transcription. In Escherichia coli, promoters can be recognized by σ factors that have multiple families based on distinct function and structure, such as σ²⁴, σ²⁸, σ³², σ³⁸, σ⁵⁴ and σ⁷⁰. At present, biological methods are mainly used to identify these promoters. However, because it is time-consuming and material-consuming to do biological experiments, computational biology algorithm has emerged as a more effective way to predict the classification. In this study, we develop a novel two-layer seamless predictor called iPro2L-PSTKNC to identify the promoters of the E. coli genome, which based on the feature extraction model we newly proposed that is named as the position specific tendencies of k-mer nucleotide composition (PSTKNC). On the first layer, it is a binary classification predicting whether a sequence is promoter or not. And the second layer is a multiple classification identifying which type the identified promoter belongs to. The ensemble classification SVM performsbest comparing with other algorithms, which gets a promising accuracy and the Matthews correlation coefficient (MCC) at [Formula: see text] and [Formula: see text]. Our data and code are available at https://github.com/lyuyinuo/iPro2L-PSTKNC.

Prediction of protein-binding residues: dichotomy of sequence-based methods developed using structured complexes versus disordered proteins

MOTIVATION

There are over 30 sequence-based predictors of the protein-binding residues (PBRs). They use either structure-annotated or disorder-annotated training datasets, potentially creating a dichotomy where the structure-/disorder-specific models may not be able to cross-over to accurately predict the other type. Moreover, the structure-trained predictors were shown to substantially cross-predict PBRs among residues that interact with non-protein partners (nucleic acids and small ligands). We address these issues by performing first-of-its-kind comparative study of a representative collection of disorder- and structure-trained predictors using a comprehensive benchmark set with the structure- and disorder-derived annotations of PBRs (to analyze the cross-over) and the protein-, nucleic acid- and small ligand-binding proteins (to study the cross-predictions).

RESULTS

Three predictors provide accurate results: SCRIBER, ANCHOR and disoRDPbind. Some of the structure-trained methods make accurate predictions on the structure-annotated proteins. Similarly, the disorder-trained predictors predict well on the disorder-annotated proteins. However, the considered predictors generally fail to cross-over, with the exception of SCRIBER. Our study also reveals that virtually all methods substantially cross-predict PBRs, except for SCRIBER for the structure-annotated proteins and disoRDPbind for the disorder-annotated proteins. We formulate a novel hybrid predictor, hybridPBRpred, that combines results produced by disoRDPbind and SCRIBER to accurately predict disorder- and structure-annotated PBRs. HybridPBRpred generates accurate results that cross-over structure- and disorder-annotated proteins and produces relatively low amount of cross-predictions, offering an accurate alternative to predict PBRs.

AVAILABILITY AND IMPLEMENTATION

HybridPBRpred webserver, benchmark dataset and supplementary information are available at http://biomine.cs.vcu.edu/servers/hybridPBRpred/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PROBselect: accurate prediction of protein-binding residues from proteins sequences via dynamic predictor selection

Motivation

Knowledge of protein-binding residues (PBRs) improves our understanding of protein−protein interactions, contributes to the prediction of protein functions and facilitates protein−protein docking calculations. While many sequence-based predictors of PBRs were published, they offer modest levels of predictive performance and most of them cross-predict residues that interact with other partners. One unexplored option to improve the predictive quality is to design consensus predictors that combine results produced by multiple methods.

Results

We empirically investigate predictive performance of a representative set of nine predictors of PBRs. We report substantial differences in predictive quality when these methods are used to predict individual proteins, which contrast with the dataset-level benchmarks that are currently used to assess and compare these methods. Our analysis provides new insights for the cross-prediction concern, dissects complementarity between predictors and demonstrates that predictive performance of the top methods depends on unique characteristics of the input protein sequence. Using these insights, we developed PROBselect, first-of-its-kind consensus predictor of PBRs. Our design is based on the dynamic predictor selection at the protein level, where the selection relies on regression-based models that accurately estimate predictive performance of selected predictors directly from the sequence. Empirical assessment using a low-similarity test dataset shows that PROBselect provides significantly improved predictive quality when compared with the current predictors and conventional consensuses that combine residue-level predictions. Moreover, PROBselect informs the users about the expected predictive quality for the prediction generated from a given input protein.

Availability and implementation

PROBselect is available at http://bioinformatics.csu.edu.cn/PROBselect/home/index.

Supplementary information

Supplementary data are available at Bioinformatics online.

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.

KD-KLNMF: Identification of lncRNAs subcellular localization with multiple features and nonnegative matrix factorization

Long non-coding RNAs (lncRNAs) refer to functional RNA molecules with a length more than 200 nucleotides and have minimal or no function to encode proteins. In recent years, more studies show that lncRNAs subcellular localization has valuable clues for their biological functions. So it is count for much to identify lncRNAs subcellular localization. In this paper, a novel statistical model named KD-KLNMF is constructed to predict lncRNAs subcellular localization. Firstly, k-mer and dinucleotide-based spatial autocorrelation are incorporated as the feature vector. Then, Synthetic Minority Over-sampling Technique is used to deal with the imbalance dataset. Next, Kullback-Leibler divergence-based nonnegative matrix factorization is applied to select optimal features. And then we utilize support vector machine as the classifier after comparing with other classifiers. Finally, the jackknife test is performed to evaluate the model. The overall accuracies reach 97.24% and 92.86% on training dataset and independent dataset, respectively. The results are better than the previous methods, which indicate that our model will be a useful and feasible tool to identify lncRNAs subcellular localization. The datasets and source code are freely available at https://github.com/HuijuanQiao/KD-KLNMF.

Boosting Granular Support Vector Machines for the Accurate Prediction of Protein-Nucleotide Binding Sites

AIM AND OBJECTIVE

The accurate identification of protein-ligand binding sites helps elucidate protein function and facilitate the design of new drugs. Machine-learning-based methods have been widely used for the prediction of protein-ligand binding sites. Nevertheless, the severe class imbalance phenomenon, where the number of nonbinding (majority) residues is far greater than that of binding (minority) residues, has a negative impact on the performance of such machine-learning-based predictors.

MATERIALS AND METHODS

In this study, we aim to relieve the negative impact of class imbalance by Boosting Multiple Granular Support Vector Machines (BGSVM). In BGSVM, each base SVM is trained on a granular training subset consisting of all minority samples and some reasonably selected majority samples. The efficacy of BGSVM for dealing with class imbalance was validated by benchmarking it with several typical imbalance learning algorithms. We further implemented a protein-nucleotide binding site predictor, called BGSVM-NUC, with the BGSVM algorithm.

RESULTS

Rigorous cross-validation and independent validation tests for five types of proteinnucleotide interactions demonstrated that the proposed BGSVM-NUC achieves promising prediction performance and outperforms several popular sequence-based protein-nucleotide binding site predictors. The BGSVM-NUC web server is freely available at http://csbio.njust.edu.cn/bioinf/BGSVM-NUC/ for academic use.

Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features

Post Translational Modification (PTM) is defined as the alteration of protein sequence upon interaction with different macromolecules after the translation process. Glutarylation is considered one of the most important PTMs, which is associated with a wide range of cellular functioning, including metabolism, translation, and specified separate subcellular localizations. During the past few years, a wide range of computational approaches has been proposed to predict Glutarylation sites. However, despite all the efforts that have been made so far, the prediction performance of the Glutarylation sites has remained limited. One of the main challenges to tackle this problem is to extract features with significant discriminatory information. To address this issue, we propose a new machine learning method called BiPepGlut using the concept of a bi-peptide-based evolutionary method for feature extraction. To build this model, we also use the Extra-Trees (ET) classifier for the classification purpose, which, to the best of our knowledge, has never been used for this task. Our results demonstrate BiPepGlut is able to significantly outperform previously proposed models to tackle this problem. BiPepGlut achieves 92.0%, 84.8%, 95.6%, 0.82, and 0.88 in accuracy, sensitivity, specificity, Matthew's Correlation Coefficient, and F1-score, respectively. BiPepGlut is implemented as a publicly available online predictor.

Predicting Self-Interacting Proteins Using a Recurrent Neural Network and Protein Evolutionary Information

Self-interacting proteins (SIPs) play crucial roles in biological activities of organisms. Many high-throughput methods can be used to identify SIPs. However, these methods are both time-consuming and expensive. How to develop effective computational approaches for identifying SIPs is a challenging task. In the article, we present a novel computational method called RRN-SIFT, which combines the recurrent neural network (RNN) with scale invariant feature transform (SIFT) to predict SIPs based on protein evolutionary information. The main advantage of the proposed RNN-SIFT model is that it uses SIFT for extracting key feature by exploring the evolutionary information embedded in Position-Specific Iterated BLAST–constructed position-specific scoring matrix and employs an RNN classifier to perform classification based on extracted features. Extensive experiments show that the RRN-SIFT obtained average accuracy of 94.34% and 97.12% on the yeast and human dataset, respectively. We also compared our performance with the back propagation neural network (BPNN), the state-of-the-art support vector machine (SVM), and other existing methods. By comparing with experimental results, the performance of RNN-SIFT is significantly better than that of the BPNN, SVM, and other previous methods in the domain. Therefore, we conclude that the proposed RNN-SIFT model is a useful tool for predicting SIPs, as well to solve other bioinformatics tasks. To facilitate widely studies and encourage future proteomics research, a freely available web server called RNN-SIFT-SIPs was developed at http://219.219.62.123:8888/RNNSIFT/ including the source code and the SIP datasets.

SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences

Motivation

Accurate predictions of protein-binding residues (PBRs) enhances understanding of molecular-level rules governing protein–protein interactions, helps protein–protein docking and facilitates annotation of protein functions. Recent studies show that current sequence-based predictors of PBRs severely cross-predict residues that interact with other types of protein partners (e.g. RNA and DNA) as PBRs. Moreover, these methods are relatively slow, prohibiting genome-scale use.

Results

We propose a novel, accurate and fast sequence-based predictor of PBRs that minimizes the cross-predictions. Our SCRIBER (SeleCtive pRoteIn-Binding rEsidue pRedictor) method takes advantage of three innovations: comprehensive dataset that covers multiple types of binding residues, novel types of inputs that are relevant to the prediction of PBRs, and an architecture that is tailored to reduce the cross-predictions. The dataset includes complete protein chains and offers improved coverage of binding annotations that are transferred from multiple protein–protein complexes. We utilize innovative two-layer architecture where the first layer generates a prediction of protein-binding, RNA-binding, DNA-binding and small ligand-binding residues. The second layer re-predicts PBRs by reducing overlap between PBRs and the other types of binding residues produced in the first layer. Empirical tests on an independent test dataset reveal that SCRIBER significantly outperforms current predictors and that all three innovations contribute to its high predictive performance. SCRIBER reduces cross-predictions by between 41% and 69% and our conservative estimates show that it is at least 3 times faster. We provide putative PBRs produced by SCRIBER for the entire human proteome and use these results to hypothesize that about 14% of currently known human protein domains bind proteins.

Availability and implementation

SCRIBER webserver is available at http://biomine.cs.vcu.edu/servers/SCRIBER/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features

Post Translational Modification (PTM) is considered an important biological process with a tremendous impact on the function of proteins in both eukaryotes, and prokaryotes cells. During the past decades, a wide range of PTMs has been identified. Among them, malonylation is a recently identified PTM which plays a vital role in a wide range of biological interactions. Notwithstanding, this modification plays a potential role in energy metabolism in different species including Homo Sapiens. The identification of PTM sites using experimental methods is time-consuming and costly. Hence, there is a demand for introducing fast and cost-effective computational methods. In this study, we propose a new machine learning method, called Mal-Light, to address this problem. To build this model, we extract local evolutionary-based information according to the interaction of neighboring amino acids using a bi-peptide based method. We then use Light Gradient Boosting (LightGBM) as our classifier to predict malonylation sites. Our results demonstrate that Mal-Light is able to significantly improve malonylation site prediction performance compared to previous studies found in the literature. Using Mal-Light we achieve Matthew's correlation coefficient (MCC) of 0.74 and 0.60, Accuracy of 86.66% and 79.51%, Sensitivity of 78.26% and 67.27%, and Specificity of 95.05% and 91.75%, for Homo Sapiens and Mus Musculus proteins, respectively. Mal-Light is implemented as an online predictor which is publicly available at: (http://brl.uiu.ac.bd/MalLight/).

iPseU-Layer: Identifying RNA Pseudouridine Sites Using Layered Ensemble Model

Pseudouridine represents one of the most prevalent post-transcriptional RNA modifications. The identification of pseudouridine sites is an essential step toward understanding RNA functions, RNA structure stabilization, translation process, and RNA stability; however, high-throughput experimental techniques remain expensive and time-consuming in lab explorations and biochemical processes. Thus, how to develop an efficient pseudouridine site identification method based on machine learning is very important both in academic research and drug development. Motived by this, we present an effective layered ensemble model designated as iPseU-Layer for identification of RNA pseudouridine sites. The proposed iPseU-Layer approach is essentially based on three different machine learning layers including: feature selection layer, feature extraction and fusion layer, and prediction layer. The feature selection layer reduces the dimensionality, which can be regarded as a data pre-processing stage. The feature extraction and fusion layer utilizes an ensemble method which is implemented through various machine learning algorithms to generate some outputs. The prediction layer applies classic random forest to identify the final results. Furthermore, we systematically conduct the validation experiments using cross-validation tests and independent test with the current state-of-the-art models. The proposed iPseU-Layer provides a promising predictive performance in terms of sensitivity, specificity, accuracy and Matthews correlation coefficient. Collectively, these findings indicate that the framework of iPseU-Layer is a feasible and effective strategy for the prediction of RNA pseudouridine sites.

Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix

BACKGROUND

The biological process known as post-translational modification (PTM) is a condition whereby proteomes are modified that affects normal cell biology, and hence the pathogenesis. A number of PTMs have been discovered in the recent years and lysine phosphoglycerylation is one of the fairly recent developments. Even with a large number of proteins being sequenced in the post-genomic era, the identification of phosphoglycerylation remains a big challenge due to factors such as cost, time consumption and inefficiency involved in the experimental efforts. To overcome this issue, computational techniques have emerged to accurately identify phosphoglycerylated lysine residues. However, the computational techniques proposed so far hold limitations to correctly predict this covalent modification.

RESULTS

We propose a new predictor in this paper called Bigram-PGK which uses evolutionary information of amino acids to try and predict phosphoglycerylated sites. The benchmark dataset which contains experimentally labelled sites is employed for this purpose and profile bigram occurrences is calculated from position specific scoring matrices of amino acids in the protein sequences. The statistical measures of this work, such as sensitivity, specificity, precision, accuracy, Mathews correlation coefficient and area under ROC curve have been reported to be 0.9642, 0.8973, 0.8253, 0.9193, 0.8330, 0.9306, respectively.

CONCLUSIONS

The proposed predictor, based on the feature of evolutionary information and support vector machine classifier, has shown great potential to effectively predict phosphoglycerylated and non-phosphoglycerylated lysine residues when compared against the existing predictors. The data and software of this work can be acquired from https://github.com/abelavit/Bigram-PGK.

Prediction of apoptosis protein subcellular location based on position-specific scoring matrix and isometric mapping algorithm

Apoptosis proteins are related to many diseases. Obtaining the subcellular localization information of apoptosis proteins is helpful to understand the mechanism of diseases and to develop new drugs. At present, the researchers mainly focus on the primary protein sequences, so there is still room for improvement in the prediction accuracy of the subcellular localization of apoptosis proteins. In this paper, a new method named ERT-ECT-PSSM-IS is proposed to predict apoptosis proteins based on the position-specific scoring matrix (PSSM). First, the local and global features of different directions are extracted by evolutionary row transformation (ERT) and cross-covariance of evolutionary column transformation (ECT) based on PSSM (ERT-ECT-PSSM). Second, an improved isometric mapping algorithm (I-SMA) is used to eliminate redundant features. Finally, we adopt a support vector machine (SVM) to classify our results, and the prediction accuracy is evaluated by jackknife cross-validation tests. The experimental results show that the proposed method not only extracts more abundant feature expression but also has better predictive performance and robustness for the subcellular localization of apoptosis proteins in ZD98, ZW225, and CL317 databases. Graphical abstract Framework of the proposed prediction model.

Recent Advancement in Predicting Subcellular Localization of Mycobacterial Protein with Machine Learning Methods

Mycobacterium tuberculosis (MTB) can cause the terrible tuberculosis (TB), which is reported as one of the most dreadful epidemics. Although many biochemical molecular drugs have been developed to cope with this disease, the drug resistance-especially the multidrug-resistant (MDR) and extensively drug-resistance (XDR)-poses a huge threat to the treatment. However, traditional biochemical experimental method to tackle TB is time-consuming and costly. Benefited by the appearance of the enormous genomic and proteomic sequence data, TB can be treated via sequence-based biological computational approach-bioinformatics. Studies on predicting subcellular localization of mycobacterial protein (MBP) with high precision and efficiency may help figure out the biological function of these proteins and then provide useful insights for protein function annotation as well as drug design. In this review, we reported the progress that has been made in computational prediction of subcellular localization of MBP including the following aspects: 1) Construction of benchmark datasets. 2) Methods of feature extraction. 3) Techniques of feature selection. 4) Application of several published prediction algorithms. 5) The published results. 6) The further study on prediction of subcellular localization of MBP.

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.

EvolStruct-Phogly: incorporating structural properties and evolutionary information from profile bigrams for the phosphoglycerylation prediction

Background

Post-translational modification (PTM), which is a biological process, tends to modify proteome that leads to changes in normal cell biology and pathogenesis. In the recent times, there has been many reported PTMs. Out of the many modifications, phosphoglycerylation has become particularly the subject of interest. The experimental procedure for identification of phosphoglycerylated residues continues to be an expensive, inefficient and time-consuming effort, even with a large number of proteins that are sequenced in the post-genomic period. Computational methods are therefore being anticipated in order to effectively predict phosphoglycerylated lysines. Even though there are predictors available, the ability to detect phosphoglycerylated lysine residues still remains inadequate.

Results

We have introduced a new predictor in this paper named EvolStruct-Phogly that uses structural and evolutionary information relating to amino acids to predict phosphoglycerylated lysine residues. Benchmarked data is employed containing experimentally identified phosphoglycerylated and non-phosphoglycerylated lysines. We have then extracted the three structural information which are accessible surface area of amino acids, backbone torsion angles, amino acid’s local structure conformations and profile bigrams of position-specific scoring matrices.

Conclusion

EvolStruct-Phogly showed a noteworthy improvement in regards to the performance when compared with the previous predictors. The performance metrics obtained are as follows: sensitivity 0.7744, specificity 0.8533, precision 0.7368, accuracy 0.8275, and Mathews correlation coefficient of 0.6242. The software package and data of this work can be obtained from https://github.com/abelavit/EvolStruct-Phogly or www.alok-ai-lab.com

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.

Identification of Phage Viral Proteins With Hybrid Sequence Features

The uniqueness of bacteriophages plays an important role in bioinformatics research. In real applications, the function of the bacteriophage virion proteins is the main area of interest. Therefore, it is very important to classify bacteriophage virion proteins and non-phage virion proteins accurately. Extracting comprehensive and effective sequence features from proteins plays a vital role in protein classification. In order to more fully represent protein information, this paper is more comprehensive and effective by combining the features extracted by the feature information representation algorithm based on sequence information (CCPA) and the feature representation algorithm based on sequence and structure information. After extracting features, the Max-Relevance-Max-Distance (MRMD) algorithm is used to select the optimal feature set with the strongest correlation between class labels and low redundancy between features. Given the randomness of the samples selected by the random forest classification algorithm and the randomness features for producing each node variable, a random forest method is employed to perform 10-fold cross-validation on the bacteriophage protein classification. The accuracy of this model is as high as 93.5% in the classification of phage proteins in this study. This study also found that, among the eight physicochemical properties considered, the charge property has the greatest impact on the classification of bacteriophage proteins These results indicate that the model discussed in this paper is an important tool in bacteriophage protein research.

Prediction of aptamer-protein interacting pairs based on sparse autoencoder feature extraction and an ensemble classifier

Aptamer-protein interacting pairs play important roles in physiological functions and structural characterization. Identifying aptamer-protein interacting pairs is challenging and limited, despite of the tremendous applications of aptamers. Therefore, it is vital to construct a high prediction performance model for identifying aptamer-target interacting pairs. In this study, a novel ensemble method is presented to predict aptamer-protein interacting pairs by integrating sequence characteristics derived from aptamers and the target proteins. The features extracted for aptamers were the compositions of amino acids and pseudo K-tuple nucleotides. In addition, a sparse autoencoder was used to characterize features for the target protein sequences. To remove redundant features, gradient boosting decision tree (GBDT) and incremental feature selection (IFS) methods were used to obtain the optimum combination of sequence characters. Based on 616 selected features, an ensemble of three sub- support vector machine (SVM) classifiers was used to construct our prediction model. Evaluated on an independent dataset, our predictor obtained an accuracy of 75.7%, Matthew's Correlation Coefficient of 0.478, and Youden's Index of 0.538, which were superior to the values reached using other existing predictors. The results show that our model can be used to distinguishing novel aptamer-protein interacting pairs and revealing the interrelation between aptamers and proteins.

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.

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.