Clathrin proteins, key elements of the vesicle coat, play a crucial role in various cellular processes, including neural function, signal transduction, and endocytosis. Disruptions in clathrin protein functions have been associated with a wide range of diseases, such as Alzheimer's, neurodegeneration, viral infection, and cancer. Therefore, correctly identifying clathrin protein functions is critical to unravel the mechanism of these fatal diseases and designing drug targets. This paper presents a novel computational method, named TargetCLP, to precisely identify clathrin proteins. TargetCLP leverages four single-view feature representation methods, including two transformed feature sets (PSSM-CLBP and RECM-CLBP), one qualitative characteristics feature, and one deep-learned-based embedding using ESM. The single-view features are integrated based on their weights using differential evolution, and the BTG feature selection algorithm is utilized to generate a more optimal and reduced subset. The model is trained using various classifiers, among which the proposed SnBiLSTM achieved remarkable performance. Experimental and comparative results on both training and independent datasets show that the proposed TargetCLP offers significant improvements in terms of both prediction accuracy and generalization to unseen data, furthering advancements in the research field.

Accurate prediction of protein subcellular localization is critical for understanding cellular functions and guiding drug design. However, current computational methods have limited and insufficient performance and as such, there exist few efficient vision learners based on self-supervised learning for extracting deep and informative features. To address it, we propose a novel bioimage-based method, termed PScL-SDNNMAE, to effectively predict the subcellular localizations of proteins in human cells. PScL-SDNNMAE first extracts classical features using traditional image descriptors. Next, the masked autoencoder (MAE) is first trained using the training image data and then used to extract the MAE-based deep features. In the feature selection phase, PScL-SDNNMAE applies the Analysis of Variance (ANOVA), Mutual Information (MI) and stepwise discriminant analysis (SDA) to select the optimal features from the classical feature sets. Finally, PScL-SDNNMAE trains the deep neural network (DNN) classifier using the super feature set generated by integrating all the classical optimal and MAE-based deep features. Extensive benchmark experiments including 10-fold cross-validation on the training dataset and independent test on the independent dataset illustrate more advanced performance and generalization capability of PScL-SDNNMAE than other existing state-of-the-art predictors. Moreover, the experiments also demonstrate the effectiveness of self-supervised learning methods in learning representations of IHC images, as well as the significant potential for pre-training on massive unlabeled datasets in the future.

MOTIVATION: Despite the extensive manufacturing of antiviral drugs and vaccination, viral infections continue to be a major human ailment. Antiviral peptides (AVPs) have emerged as potential candidates in the pursuit of novel antiviral drugs. These peptides show vigorous antiviral activity against a diverse range of viruses by targeting different phases of the viral life cycle. Therefore, the accurate prediction of AVPs is an essential yet challenging task. Lately, many machine learning-based approaches have developed for this purpose; however, their limited capabilities in terms of feature engineering, accuracy, and generalization make these methods restricted. RESULTS: In the present study, we aim to develop an efficient machine learning-based approach for the identification of AVPs, referred to as DeepAVP-TPPred, to address the aforementioned problems. First, we extract two new transformed feature sets using our designed image-based feature extraction algorithms and integrate them with an evolutionary information-based feature. Next, these feature sets were optimized using a novel feature selection approach called binary tree growth Algorithm. Finally, the optimal feature space from the training dataset was fed to the deep neural network to build the final classification model. The proposed model DeepAVP-TPPred was tested using stringent 5-fold cross-validation and two independent dataset testing methods, which achieved the maximum performance and showed enhanced efficiency over existing predictors in terms of both accuracy and generalization capabilities. AVAILABILITY AND IMPLEMENTATION: https://github.com/MateeullahKhan/DeepAVP-TPPred.

MOTIVATION: Over the past decades, a variety of in silico methods have been developed to predict protein subcellular localization within cells. However, a common and major challenge in the design and development of such methods is how to effectively utilize the heterogeneous feature sets extracted from bioimages. In this regards, limited efforts have been undertaken. RESULTS: We propose a new two-level stacked autoencoder network (termed 2L-SAE-SM) to improve its performance by integrating the heterogeneous feature sets. In particular, in the first level of 2L-SAE-SM, each optimal heterogeneous feature set is fed to train our designed stacked autoencoder network (SAE-SM). All the trained SAE-SMs in the first level can output the decision sets based on their respective optimal heterogeneous feature sets, known as 'intermediate decision' sets. Such intermediate decision sets are then ensembled using the mean ensemble method to generate the 'intermediate feature' set for the second-level SAE-SM. Using the proposed framework, we further develop a novel predictor, referred to as PScL-2LSAESM, to characterize image-based protein subcellular localization. Extensive benchmarking experiments on the latest benchmark training and independent test datasets collected from the human protein atlas databank demonstrate the effectiveness of the proposed 2L-SAE-SM framework for the integration of heterogeneous feature sets. Moreover, performance comparison of the proposed PScL-2LSAESM with current state-of-the-art methods further illustrates that PScL-2LSAESM clearly outperforms the existing state-of-the-art methods for the task of protein subcellular localization. AVAILABILITY AND IMPLEMENTATION: https://github.com/csbio-njust-edu/PScL-2LSAESM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Characterization of protein subcellular localization has become an important and long-standing task in bioinformatics and computational biology, which provides valuable information for elucidating various cellular functions of proteins and guiding drug design. RESULTS: Here, we develop a novel bioimage-based computational approach, termed PScL-DDCFPred, to accurately predict protein subcellular localizations in human tissues. PScL-DDCFPred first extracts multiview image features, including global and local features, as base or pure features; next, it applies a new integrative feature selection method based on stepwise discriminant analysis and generalized discriminant analysis to identify the optimal feature sets from the extracted pure features; Finally, a classifier based on deep neural network (DNN) and deep-cascade forest (DCF) is established. Stringent 10-fold cross-validation tests on the new protein subcellular localization training dataset, constructed from the human protein atlas databank, illustrates that PScL-DDCFPred achieves a better performance than several existing state-of-the-art methods. Moreover, the independent test set further illustrates the generalization capability and superiority of PScL-DDCFPred over existing predictors. In-depth analysis shows that the excellent performance of PScL-DDCFPred can be attributed to three critical factors, namely the effective combination of the DNN and DCF models, complementarity of global and local features, and use of the optimal feature sets selected by the integrative feature selection algorithm. AVAILABILITY AND IMPLEMENTATION: https://github.com/csbio-njust-edu/PScL-DDCFPred. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Protein subcellular localization plays a crucial role in characterizing the function of proteins and understanding various cellular processes. Therefore, accurate identification of protein subcellular location is an important yet challenging task. Numerous computational methods have been proposed to predict the subcellular location of proteins. However, most existing methods have limited capability in terms of the overall accuracy, time consumption and generalization power. To address these problems, in this study, we developed a novel computational approach based on human protein atlas (HPA) data, referred to as PScL-HDeep, for accurate and efficient image-based prediction of protein subcellular location in human tissues. We extracted different handcrafted and deep learned (by employing pretrained deep learning model) features from different viewpoints of the image. The step-wise discriminant analysis (SDA) algorithm was applied to generate the optimal feature set from each original raw feature set. To further obtain a more informative feature subset, support vector machine-based recursive feature elimination with correlation bias reduction (SVM-RFE + CBR) feature selection algorithm was applied to the integrated feature set. Finally, the classification models, namely support vector machine with radial basis function (SVM-RBF) and support vector machine with linear kernel (SVM-LNR), were learned on the final selected feature set. To evaluate the performance of the proposed method, a new gold standard benchmark training dataset was constructed from the HPA databank. PScL-HDeep achieved the maximum performance on 10-fold cross validation test on this dataset and showed a better efficacy over existing predictors. Furthermore, we also illustrated the generalization ability of the proposed method by conducting a stringent independent validation test.