Prediction of Extracellular Matrix Proteins by Fusing Multiple Feature Information, Elastic Net, and Random Forest Algorithm

Res-GCN: Identification of protein phosphorylation sites using graph convolutional network and residual network

An essential post-translational modification, phosphorylation is intimately related with a wide range of biological activities. The advancement of effective computational methods for correctly recognizing phosphorylation sites is important for in-depth understanding of various physiological phenomena. However, the traditional method of identifying phosphorylation sites experimentally is time-consuming and laborious, which makes it difficult to meet the processing demands of today's big data. This research proposes the use of a novel model, Res-GCN, to recognize the phosphorylation sites of SARS-CoV-2. Firstly, eight feature extraction strategies are utilized to digitize the protein sequence from multiple viewpoints, including amino acid property encodings (AAindex), pseudo-amino acid composition (PseAAC), adapted normal distribution bi-profile Bayes (ANBPB), dipeptide composition (DC), binary encoding (BE), enhanced amino acid composition (EAAC), Word2Vec, and BLOSUM62 matrices. Secondly, elastic net is utilized to eliminate redundant data in the fused matrix. Finally, a combination of graph convolutional network (GCN) and residual network (ResNet) is used to classify the phosphorylated sites and output predictions using a fully connected layer (FC). The performance of Res-GCN is tested by 5-fold cross-validation and independent testing, and excellent results are obtained on S/T and Y datasets. This demonstrates that the Res-GCN model exhibits exceptional predictive performance and generalizability.

Using Convolutional Neural Networks to Build a Lightweight Flood Height Prediction Model with Grad-Cam for the Selection of Key Grid Cells in Radar Echo Maps

Recent climate change has brought extremely heavy rains and widescale flooding to many areas around the globe. However, previous flood prediction methods usually require a lot of computation to obtain the prediction results and impose a heavy burden on the unit cost of the prediction. This paper proposes the use of a deep learning model (DLM) to overcome these problems. We alleviated the high computational overhead of this approach by developing a novel framework for the construction of lightweight DLMs. The proposed scheme involves training a convolutional neural network (CNN) by using a radar echo map in conjunction with historical flood records at target sites and using Grad-Cam to extract key grid cells from these maps (representing regions with the greatest impact on flooding) for use as inputs in another DLM. Finally, we used real radar echo maps of five locations and the flood heights record to verify the validity of the method proposed in this paper. The experimental results show that our proposed lightweight model can achieve similar or even better prediction accuracy at all locations with only about 5~15% of the operation time and about 30~35% of the memory space of the CNN.

BERT-m7G: A Transformer Architecture Based on BERT and Stacking Ensemble to Identify RNA N7-Methylguanosine Sites from Sequence Information

As one of the most prevalent posttranscriptional modifications of RNA, N7-methylguanosine (m7G) plays an essential role in the regulation of gene expression. Accurate identification of m7G sites in the transcriptome is invaluable for better revealing their potential functional mechanisms. Although high-throughput experimental methods can locate m7G sites precisely, they are overpriced and time-consuming. Hence, it is imperative to design an efficient computational method that can accurately identify the m7G sites. In this study, we propose a novel method via incorporating BERT-based multilingual model in bioinformatics to represent the information of RNA sequences. Firstly, we treat RNA sequences as natural sentences and then employ bidirectional encoder representations from transformers (BERT) model to transform them into fixed-length numerical matrices. Secondly, a feature selection scheme based on the elastic net method is constructed to eliminate redundant features and retain important features. Finally, the selected feature subset is input into a stacking ensemble classifier to predict m7G sites, and the hyperparameters of the classifier are tuned with tree-structured Parzen estimator (TPE) approach. By 10-fold cross-validation, the performance of BERT-m7G is measured with an ACC of 95.48% and an MCC of 0.9100. The experimental results indicate that the proposed method significantly outperforms state-of-the-art prediction methods in the identification of m7G modifications.

Prediction of protein ubiquitination sites via multi-view features based on eXtreme gradient boosting classifier

Ubiquitination is a common and reversible post-translational protein modification that regulates apoptosis and plays an important role in protein degradation and cell diseases. However, experimental identification of protein ubiquitination sites is usually time-consuming and labor-intensive, so it is necessary to establish effective predictors. In this study, we propose a ubiquitination sites prediction method based on multi-view features, namely UbiSite-XGBoost. Firstly, we use seven single-view features encoding methods to convert protein sequence fragments into digital information. Secondly, the least absolute shrinkage and selection operator (LASSO) is applied to remove the redundant information and get the optimal feature subsets. Finally, these features are inputted into the eXtreme gradient boosting (XGBoost) classifier to predict ubiquitination sites. Five-fold cross-validation shows that the AUC values of Set1-Set6 datasets are 0.8258, 0.7592, 0.7853, 0.8345, 0.8979 and 0.8901, respectively. The synthetic minority oversampling technique (SMOTE) is employed in Set4-Set6 unbalanced datasets, and the AUC values are 0.9777, 0.9782 and 0.9860, respectively. In addition, we have constructed three independent test datasets which the AUC values are 0.8007, 0.6897 and 0.7280, respectively. The results show that the proposed method UbiSite-XGBoost is superior to other ubiquitination prediction methods and it provides new guidance for the identification of ubiquitination sites. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/UbiSite-XGBoost/.

FEGS: a novel feature extraction model for protein sequences and its applications

BACKGROUND

Feature extraction of protein sequences is widely used in various research areas related to protein analysis, such as protein similarity analysis and prediction of protein functions or interactions.

RESULTS

In this study, we introduce FEGS (Feature Extraction based on Graphical and Statistical features), a novel feature extraction model of protein sequences, by developing a new technique for graphical representation of protein sequences based on the physicochemical properties of amino acids and effectively employing the statistical features of protein sequences. By fusing the graphical and statistical features, FEGS transforms a protein sequence into a 578-dimensional numerical vector. When FEGS is applied to phylogenetic analysis on five protein sequence data sets, its performance is notably better than all of the other compared methods.

CONCLUSION

The FEGS method is carefully designed, which is practically powerful for extracting features of protein sequences. The current version of FEGS is developed to be user-friendly and is expected to play a crucial role in the related studies of protein sequence analyses.

Anticancer peptides prediction with deep representation learning features

Anticancer peptides constitute one of the most promising therapeutic agents for combating common human cancers. Using wet experiments to verify whether a peptide displays anticancer characteristics is time-consuming and costly. Hence, in this study, we proposed a computational method named identify anticancer peptides via deep representation learning features (iACP-DRLF) using light gradient boosting machine algorithm and deep representation learning features. Two kinds of sequence embedding technologies were used, namely soft symmetric alignment embedding and unified representation (UniRep) embedding, both of which involved deep neural network models based on long short-term memory networks and their derived networks. The results showed that the use of deep representation learning features greatly improved the capability of the models to discriminate anticancer peptides from other peptides. Also, UMAP (uniform manifold approximation and projection for dimension reduction) and SHAP (shapley additive explanations) analysis proved that UniRep have an advantage over other features for anticancer peptide identification. The python script and pretrained models could be downloaded from https://github.com/zhibinlv/iACP-DRLF or from http://public.aibiochem.net/iACP-DRLF/.

NonClasGP-Pred: robust and efficient prediction of non-classically secreted proteins by integrating subset-specific optimal models of imbalanced data

Non-classically secreted proteins (NCSPs) are proteins that are located in the extracellular environment, although there is a lack of known signal peptides or secretion motifs. They usually perform different biological functions in intracellular and extracellular environments, and several of their biological functions are linked to bacterial virulence and cell defence. Accurate protein localization is essential for all living organisms, however, the performance of existing methods developed for NCSP identification has been unsatisfactory and in particular suffer from data deficiency and possible overfitting problems. Further improvement is desirable, especially to address the lack of informative features and mining subset-specific features in imbalanced datasets. In the present study, a new computational predictor was developed for NCSP prediction of gram-positive bacteria. First, to address the possible prediction bias caused by the data imbalance problem, ten balanced subdatasets were generated for ensemble model construction. Then, the F-score algorithm combined with sequential forward search was used to strengthen the feature representation ability for each of the training subdatasets. Third, the subset-specific optimal feature combination process was adopted to characterize the original data from different aspects, and all subdataset-based models were integrated into a unified model, NonClasGP-Pred, which achieved an excellent performance with an accuracy of 93.23 %, a sensitivity of 100 %, a specificity of 89.01 %, a Matthew's correlation coefficient of 87.68 % and an area under the curve value of 0.9975 for ten-fold cross-validation. Based on assessment on the independent test dataset, the proposed model outperformed state-of-the-art available toolkits. For availability and implementation, see: http://lab.malab.cn/~wangchao/softwares/NonClasGP/.

PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron

Amyloid is generally an aggregate of insoluble fibrin; its abnormal deposition is the pathogenic mechanism of various diseases, such as Alzheimer's disease and type II diabetes. Therefore, accurately identifying amyloid is necessary to understand its role in pathology. We proposed a machine learning-based prediction model called PredAmyl-MLP, which consists of the following three steps: feature extraction, feature selection, and classification. In the step of feature extraction, seven feature extraction algorithms and different combinations of them are investigated, and the combination of SVMProt-188D and tripeptide composition (TPC) is selected according to the experimental results. In the step of feature selection, maximum relevant maximum distance (MRMD) and binomial distribution (BD) are, respectively, used to remove the redundant or noise features, and the appropriate features are selected according to the experimental results. In the step of classification, we employed multilayer perceptron (MLP) to train the prediction model. The 10-fold cross-validation results show that the overall accuracy of PredAmyl-MLP reached 91.59%, and the performance was better than the existing methods.

Identifying Antioxidant Proteins by Using Amino Acid Composition and Protein-Protein Interactions

Excessive oxidative stress responses can threaten our health, and thus it is essential to produce antioxidant proteins to regulate the body’s oxidative responses. The low number of antioxidant proteins makes it difficult to extract their representative features. Our experimental method did not use structural information but instead studied antioxidant proteins from a sequenced perspective while focusing on the impact of data imbalance on sensitivity, thus greatly improving the model’s sensitivity for antioxidant protein recognition. We developed a method based on the Composition of k-spaced Amino Acid Pairs (CKSAAP) and the Conjoint Triad (CT) features derived from the amino acid composition and protein-protein interactions. SMOTE and the Max-Relevance-Max-Distance algorithm (MRMD) were utilized to unbalance the training data and select the optimal feature subset, respectively. The test set used 10-fold crossing validation and a random forest algorithm for classification according to the selected feature subset. The sensitivity was 0.792, the specificity was 0.808, and the average accuracy was 0.8.

Discrimination of Thermophilic Proteins and Non-thermophilic Proteins Using Feature Dimension Reduction

Thermophilicity is a very important property of proteins, as it sometimes determines denaturation and cell death. Thus, methods for predicting thermophilic proteins and non-thermophilic proteins are of interest and can contribute to the design and engineering of proteins. In this article, we describe the use of feature dimension reduction technology and LIBSVM to identify thermophilic proteins. The highest accuracy obtained by cross-validation was 96.02% with 119 parameters. When using only 16 features, we obtained an accuracy of 93.33%. We discuss the importance of the different characteristics in identification and report a comparison of the performance of support vector machine to that of other methods.

Identification and Classification of Enhancers Using Dimension Reduction Technique and Recurrent Neural Network

Enhancers are noncoding fragments in DNA sequences, which play an important role in gene transcription and translation. However, due to their high free scattering and positional variability, the identification and classification of enhancers have a higher level of complexity than those of coding genes. In order to solve this problem, many computer studies have been carried out in this field, but there are still some deficiencies in these prediction models. In this paper, we use various feature extraction strategies, dimension reduction technology, and a comprehensive application of machine model and recurrent neural network model to achieve an accurate prediction of enhancer identification and classification with the accuracy of was 76.7% and 84.9%, respectively. The model proposed in this paper is superior to the previous methods in performance index or feature dimension, which provides inspiration for the prediction of enhancers by computer technology in the future.