Prediction of antifreeze proteins using machine learning

Leveraging deep learning for epigenetic protein prediction: a novel approach for early lung cancer diagnosis and drug discovery

Epigenetic protein (EP) plays a crucial role in influencing disease development, controlling gene expression, and shaping cell identity. They hold potential as targets for future therapies, and studying their mechanisms can lead to improved diagnosis and treatment strategies for various diseases. Anticipating EP is imperative, yet conventional experimental approaches for prediction prove time-intensive and expensive. This work constructed CNN-BiLSTM, computational method for identification of EP prediction. Utilizing primary sequences, two datasets were constructed, and an amphiphilic pseudo amino acid, group dipeptide composition and group amino acid composition were devised to extract numerical features. Model training incorporated a suite of deep learning architectures, including BiLSTM, GRU, and CNN. Notably, an ensemble model combining CNN and BiLSTM, trained using AmpPseAAC features, demonstrated superior performance across both training and testing datasets compared to other predictors. This research contributes to the ongoing efforts to revolutionize therapeutic approaches by facilitating the identification of novel drug targets and improving disease treatment outcomes.

BERT-DomainAFP: Antifreeze protein recognition and classification model based on BERT and structural domain annotation

Antifreeze proteins (AFPs) are crucial for organisms to adapt to low temperatures, with applications in medicine, food storage, aquaculture, and agriculture. Accurate AFP identification is challenging due to structural and sequence diversity. To improve prediction and classification, we propose BERT-DomainAFP, a deep learning model trained on the AntiFreezeDomains dataset created with a novel annotation strategy. The model uses pre-trained ProteinBERT and incorporates oversampling and undersampling techniques to handle unbalanced data, ensuring high predictive ability. BERT-DomainAFP achieves 98.48% accuracy, the highest among existing models, and can classify different AFP types based on structural domain features. This model outperforms current tools, offering a promising solution for AFP recognition and classification in research and applications.

An omics-driven computational model for angiogenic protein prediction: Advancing therapeutic strategies with Ens-deep-AGP

Angiogenic proteins (AGPs) play a critical role in both pathological and physiological activities, making them key therapeutic targets in diseases like cancer, heart disease, and stroke. Traditional methods for identifying AGPs are labor-intensive and time-consuming, creating a need for more efficient approaches. This study addresses this challenge by developing a novel computational model, Ens-Deep-AGP, designed to enhance AGP prediction. The model introduces innovative feature engineering techniques, including Position Specific Scoring Matrix-Decomposition-Discrete Cosine Transform (PSSM-DC-DCT) and Position Specific Scoring Matrix-Auto-Cross-Discrete Wavelet Transform (PSSM-ACC-DWT), which capture comprehensive protein sequence information. The ensemble feature set of these approaches are then fed into Multi-headed Ensemble Residual Convolutional Neural Network (MERCNN), a robust deep learning architecture. Ens-Deep-AGP achieved remarkable accuracy rates of 99.79 % on training dataset and 92.97 % on testing dataset, surpassing Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Bidirectional Long Short-Term Memory Networks (BiLSTM). The successful prediction of AGPs is crucial for accelerating drug development, discovering novel therapeutic targets and deepen our understanding of AGPs' complex roles in healthcare.

Prediction of viral oncoproteins through the combination of generative adversarial networks and machine learning techniques

Viral oncoproteins play crucial roles in transforming normal cells into cancer cells, representing a significant factor in the etiology of various cancers. Traditionally, identifying these oncoproteins is both time-consuming and costly. With advancements in computational biology, bioinformatics tools based on machine learning have emerged as effective methods for predicting biological activities. Here, for the first time, we propose an innovative approach that combines Generative Adversarial Networks (GANs) with supervised learning methods to enhance the accuracy and generalizability of viral oncoprotein prediction. Our methodology evaluated multiple machine learning models, including Random Forest, Multilayer Perceptron, Light Gradient Boosting Machine, eXtreme Gradient Boosting, and Support Vector Machine. In ten-fold cross-validation on our training dataset, the GAN-enhanced Random Forest model demonstrated superior performance metrics: 0.976 accuracy, 0.976 F1 score, 0.977 precision, 0.976 sensitivity, and 1.0 AUC. During independent testing, this model achieved 0.982 accuracy, 0.982 F1 score, 0.982 precision, 0.982 sensitivity, and 1.0 AUC. These results establish our new tool, VirOncoTarget, accessible via a web application. We anticipate that VirOncoTarget will be a valuable resource for researchers, enabling rapid and reliable viral oncoprotein prediction and advancing our understanding of their role in cancer biology.

Improving Antifreeze Proteins Prediction With Protein Language Models and Hybrid Feature Extraction Networks

Accurate identification of antifreeze proteins (AFPs) is crucial in developing biomimetic synthetic anti-icing materials and low-temperature organ preservation materials. Although numerous machine learning-based methods have been proposed for AFPs prediction, the complex and diverse nature of AFPs limits the prediction performance of existing methods. In this study, we propose AFP-Deep, a new deep learning method to predict antifreeze proteins by integrating embedding from protein sequences with pre-trained protein language models and evolutionary contexts with hybrid feature extraction networks. The experimental results demonstrated that the main advantage of AFP-Deep is its utilization of pre-trained protein language models, which can extract discriminative global contextual features from protein sequences. Additionally, the hybrid deep neural networks designed for protein language models and evolutionary context feature extraction enhance the correlation between embeddings and antifreeze pattern. The performance evaluation results show that AFP-Deep achieves superior performance compared to state-of-the-art models on benchmark datasets, achieving an AUPRC of 0.724 and 0.924, respectively.

Hyb_SEnc: An Antituberculosis Peptide Predictor Based on a Hybrid Feature Vector and Stacked Ensemble Learning

Tuberculosis has plagued mankind since ancient times, and the struggle between humans and tuberculosis continues. Mycobacterium tuberculosis is the leading cause of tuberculosis, infecting nearly one-third of the world's population. The rise of peptide drugs has created a new direction in the treatment of tuberculosis. Therefore, for the treatment of tuberculosis, the prediction of anti-tuberculosis peptides is crucial. This paper proposes an anti-tuberculosis peptide prediction method based on hybrid features and stacked ensemble learning. First, a random forest (RF) and extremely randomized tree (ERT) are selected as first-level learning of stacked ensembles. Then, the five best-performing feature encoding methods are selected to obtain the hybrid feature vector, and then the decision tree and recursive feature elimination (DT-RFE) are used to refine the hybrid feature vector. After selection, the optimal feature subset is used as the input of the stacked ensemble model. At the same time, logistic regression (LR) is used as a stacked ensemble secondary learner to build the final stacked ensemble model Hyb_SEnc. The prediction accuracy of Hyb_SEnc achieved 94.68% and 95.74% on the independent test sets of AntiTb_MD and AntiTb_RD, respectively.

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.

DEEP-EP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery

Epigenetic proteins (EP) play a role in the progression of a wide range of diseases, including autoimmune disorders, neurological disorders, and cancer. Recognizing their different functions has prompted researchers to investigate them as potential therapeutic targets and pharmacological targets. This paper proposes a novel deep learning-based model that accurately predicts EP. This study introduces a novel deep learning-based model that accurately predicts EP. Our approach entails generating two distinct datasets for training and evaluating the model. We then use three distinct strategies to transform protein sequences to numerical representations: Dipeptide Deviation from Expected Mean (DDE), Dipeptide Composition (DPC), and Group Amino Acid (GAAC). Following that, we train and compare the performance of four advanced deep learning models algorithms: Ensemble Residual Convolutional Neural Network (ERCNN), Generative Adversarial Network (GAN), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU). The DDE encoding combined with the ERCNN model demonstrates the best performance on both datasets. This study demonstrates deep learning's potential for precisely predicting EP, which can considerably accelerate research and streamline drug discovery efforts. This analytical method has the potential to find new therapeutic targets and advance our understanding of EP activities in disease.

An ensemble computational model for prediction of clathrin protein by coupling machine learning with discrete cosine transform

Clathrin protein (CP) plays a pivotal role in numerous cellular processes, including endocytosis, signal transduction, and neuronal function. Dysregulation of CP has been associated with a spectrum of diseases. Given its involvement in various cellular functions, CP has garnered significant attention for its potential applications in drug design and medicine, ranging from targeted drug delivery to addressing viral infections, neurological disorders, and cancer. The accurate identification of CP is crucial for unraveling its function and devising novel therapeutic strategies. Computational methods offer a rapid, cost-effective, and less labor-intensive alternative to traditional identification methods, making them especially appealing for high-throughput screening. This paper introduces CL-Pred, a novel computational method for CP identification. CL-Pred leverages three feature descriptors: Dipeptide Deviation from Expected Mean (DDE), Bigram Position Specific Scoring Matrix (BiPSSM), and Position Specific Scoring Matrix-Tetra Slice-Discrete Cosine Transform (PSSM-TS-DCT). The model is trained using three classifiers: Support Vector Machine (SVM), Extremely Randomized Tree (ERT), and Light eXtreme Gradient Boosting (LiXGB). Notably, the LiXGB-based model achieves outstanding performance, demonstrating accuracies of 94.63% and 93.65% on the training and testing datasets, respectively. The proposed CL-Pred method is poised to significantly advance our comprehension of clathrin-mediated endocytosis, cellular physiology, and disease pathogenesis. Furthermore, it holds promise for identifying potential drug targets across a spectrum of diseases.

Deep-VEGF: deep stacked ensemble model for prediction of vascular endothelial growth factor by concatenating gated recurrent unit with two-dimensional convolutional neural network

Vascular endothelial growth factor (VEGF) is involved in the development and progression of various diseases, including cancer, diabetic retinopathy, macular degeneration and arthritis. Understanding the role of VEGF in various disorders has led to the development of effective treatments, including anti-VEGF drugs, which have significantly improved therapeutic methods. Accurate VEGF identification is critical, yet experimental identification is expensive and time-consuming. This study presents Deep-VEGF, a novel computational model for VEGF prediction based on deep-stacked ensemble learning. We formulated two datasets using primary sequences. A novel feature descriptor named K-Space Tri Slicing-Bigram position-specific scoring metrix (KSTS-BPSSM) is constructed to extract numerical features from primary sequences. The model training is performed by deep learning techniques, including gated recurrent unit (GRU), generative adversarial network (GAN) and convolutional neural network (CNN). The GRU and CNN are ensembled using stacking learning approach. KSTS-BPSSM-based ensemble model secured the most accurate predictive outcomes, surpassing other competitive predictors across both training and testing datasets. This demonstrates the potential of leveraging deep learning for accurate VEGF prediction as a powerful tool to accelerate research, streamline drug discovery and uncover novel therapeutic targets. This insightful approach holds promise for expanding our knowledge of VEGF's role in health and disease.

Accurate Prediction of Antifreeze Protein from Sequences through Natural Language Text Processing and Interpretable Machine Learning Approaches

Antifreeze proteins (AFPs) bind to growing iceplanes owing to their structural complementarity nature, thereby inhibiting the ice-crystal growth by thermal hysteresis. Classification of AFPs from sequence is a difficult task due to their low sequence similarity, and therefore, the usual sequence similarity algorithms, like Blast and PSI-Blast, are not efficient. Here, a method combining n-gram feature vectors and machine learning models to accelerate the identification of potential AFPs from sequences is proposed. All these n-gram features are extracted from the K-mer counting method. The comparative analysis reveals that, among different machine learning models, Xgboost outperforms others in predicting AFPs from sequence when penta-mers are used as a feature vector. When tested on an independent dataset, our method performed better compared to other existing ones with sensitivity of 97.50%, recall of 98.30%, and f1 score of 99.10%. Further, we used the SHAP method, which provides important insight into the functional activity of AFPs.

VirusHound-I: prediction of viral proteins involved in the evasion of host adaptive immune response using the random forest algorithm and generative adversarial network for data augmentation

Throughout evolution, pathogenic viruses have developed different strategies to evade the response of the adaptive immune system. To carry out successful replication, some pathogenic viruses encode different proteins that manipulate the molecular mechanisms of host cells. Currently, there are different bioinformatics tools for virus research; however, none of them focus on predicting viral proteins that evade the adaptive system. In this work, we have developed a novel tool based on machine and deep learning for predicting this type of viral protein named VirusHound-I. This tool is based on a model developed with the multilayer perceptron algorithm using the dipeptide composition molecular descriptor. In this study, we have also demonstrated the robustness of our strategy for data augmentation of the positive dataset based on generative antagonistic networks. During the 10-fold cross-validation step in the training dataset, the predictive model showed 0.947 accuracy, 0.994 precision, 0.943 F1 score, 0.995 specificity, 0.896 sensitivity, 0.894 kappa, 0.898 Matthew's correlation coefficient and 0.989 AUC. On the other hand, during the testing step, the model showed 0.964 accuracy, 1.0 precision, 0.967 F1 score, 1.0 specificity, 0.936 sensitivity, 0.929 kappa, 0.931 Matthew's correlation coefficient and 1.0 AUC. Taking this model into account, we have developed a tool called VirusHound-I that makes it possible to predict viral proteins that evade the host's adaptive immune system. We believe that VirusHound-I can be very useful in accelerating studies on the molecular mechanisms of evasion of pathogenic viruses, as well as in the discovery of therapeutic targets.

Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting

The identification of druggable proteins (DPs) is significant for the development of new drugs, personalized medicine, understanding of disease mechanisms, drug repurposing, and economic benefits. By identifying new druggable targets, researchers can develop new therapies for a range of diseases, leading to better patient outcomes. Identification of DPs by machine learning strategies is more efficient and cost-effective than conventional methods. In this study, a computational predictor, namely Drug-LXGB, is introduced to enhance the identification of DPs. Features are discovered by composition, transition, and distribution (CTD), composition of K-spaced amino acid pair (CKSAAP), pseudo-position-specific scoring matrix (PsePSSM), and a novel descriptor, called multi-block pseudo amino acid composition (MB-PseAAC). The dimensions of CTD, CKSAAP, PsePSSM, and MB-PseAAC are integrated and utilized the sequential forward selection as feature selection algorithm. The best characteristics are provided by random forest, extreme gradient boosting, and light eXtreme gradient boosting (LXGB). The predictive analysis of these learning methods is measured via 10-fold cross-validation. The LXGB-based model secures the highest results than other existing predictors. Our novel protocol will perform an active role in designing novel drugs and would be fruitful to explore the potential target. This study will help better to capture a more universal view of a potential target.Communicated by Ramaswamy H. Sarma.

Deep-AGP: Prediction of angiogenic protein by integrating two-dimensional convolutional neural network with discrete cosine transform

Angiogenic proteins (AGPs) play a primary role in the formation of new blood vessels from pre-existing ones. AGPs have diverse applications in cancer, including serving as biomarkers, guiding anti-angiogenic therapies, and aiding in tumor imaging. Understanding the role of AGPs in cardiovascular and neurodegenerative diseases is vital for developing new diagnostic tools and therapeutic approaches. Considering the significance of AGPs, in this research, we first time established a computational model using deep learning for identifying AGPs. First, we constructed a sequence-based dataset. Second, we explored features by designing a novel feature encoder, called position-specific scoring matrix-decomposition-discrete cosine transform (PSSM-DC-DCT) and existing descriptors including Dipeptide Deviation from Expected Mean (DDE) and bigram-position-specific scoring matrix (Bi-PSSM). Third, each feature set is fed into two-dimensional convolutional neural network (2D-CNN) and machine learning classifiers. Finally, the performance of each learning model is validated by 10-fold cross-validation (CV). The experimental results demonstrate that 2D-CNN with proposed novel feature descriptor achieved the highest success rate on both training and testing datasets. In addition to being an accurate predictor for identification of angiogenic proteins, our proposed method (Deep-AGP) might be fruitful in understanding cancer, cardiovascular, and neurodegenerative diseases, development of their novel therapeutic methods and drug designing.

Recent Advances in Machine Learning-Based Models for Prediction of Antiviral Peptides

Viruses have killed and infected millions of people across the world. It causes several chronic diseases like COVID-19, HIV, and hepatitis. To cope with such diseases and virus infections, antiviral peptides (AVPs) have been applied in the design of drugs. Keeping in view the significant role in pharmaceutical industry and other research fields, identification of AVPs is highly indispensable. In this connection, experimental and computational methods were proposed to identify AVPs. However, more accurate predictors for boosting AVPs identification are highly desirable. This work presents a thorough study and reports the available predictors of AVPs. We explained applied datasets, feature representation approaches, classification algorithms, and evaluation parameters of performance. In this study, the limitations of the existing studies and the best methods were emphasized. Provided the pros and cons of the applied classifiers. The future insights demonstrate efficient feature encoding approaches, best feature optimization schemes, and effective classification techniques that can improve the performance of novel method for accurate prediction of AVPs.

AFP-SPTS: An Accurate Prediction of Antifreeze Proteins Using Sequential and Pseudo-Tri-Slicing Evolutionary Features with an Extremely Randomized Tree

The development of intracellular ice in the bodies of cold-blooded living organisms may cause them to die. These species yield antifreeze proteins (AFPs) to live in subzero temperature environments. Additionally, AFPs are implemented in biotechnological, industrial, agricultural, and medical fields. Machine learning-based predictors were presented for AFP identification. However, more accurate predictors are still highly desirable for boosting the AFP prediction. This work presents a novel approach, named AFP-SPTS, for the correct prediction of AFPs. We explored the discriminative features with four schemes, namely, dipeptide deviation from the expected mean (DDE), reduced amino acid alphabet (RAAA), grouped dipeptide composition (GDPC), and a novel representative method, called pseudo-position-specific scoring matrix tri-slicing (PseTS-PSSM). Considering the advantages of ensemble learning strategy, we fused each feature vector into different combinations and trained the models with five machine learning algorithms, i.e., multilayer perceptron (MLP), extremely randomized tree (ERT), decision tree (DT), random forest (RF), and AdaBoost. Among all models, PseTS-PSSM + RAAA with an extremely randomized tree attained the best outcomes. The proposed predictor (AFP-SPTS) boosted the accuracies of AFPs in the literature by 1.82 and 4.1%.