Advances in the Prediction of Protein Subcellular Locations with Machine Learning

Optimization of preparation and transformation of protoplasts from Populus simonii × P. nigra leaves and subcellular localization of the major latex protein 328 (MLP328)

BACKGROUND

Populus simonii × P. nigra is an ideal material for studying the molecular mechanisms of woody plants. In recent years, research on Populus simonii × P. nigra has increasingly focused on the application of transgenic technology to improve salt tolerance. However, the rapid characterization of gene functions has been hampered by the long growth cycle and exceedingly poor transformation efficiency. Protoplasts are an important tool for plant gene engineering, which can assist with challenging genetic transformation and the protracted growth cycle of Populus simonii × P. nigra. This study established an optimized system for the preparation and transformation of protoplasts from Populus simonii × P. nigra leaves, making genetic research on Populus simonii × P. nigra faster and more convenient. Major Latex Protein (MLP) family genes play a crucial role in plant salt stress response. In the previous study, we discovered that PsnMLP328 can be induced by salt treatment, which suggested that this gene may be involved in response to salt stress. Protein localization is a suggestion for its function. Therefore, we conducted subcellular localization analysis using protoplasts of Populus simonii × P. nigra to study the function of the PsnMLP328 gene preliminarily.

RESULTS

This study established an optimized system for the preparation and transformation of Populus simonii × P. nigra protoplasts. The research results indicate that the optimal separation scheme for the protoplasts of Populus simonii × P. nigra leaves included 2.5% cellulase R-10, 0.6% macerozyme R-10, 0.3% pectolyase Y-23, and 0.8 M mannitol. After enzymatic digestion for 5 h, the yield of obtained protoplasts could reach up to 2 × 107 protoplasts/gFW, with a high viability of 98%. We carried out the subcellular localization analysis based on the optimized transient transformation system, and the results indicated that the MLP328 protein is localized in the nucleus and cytoplasm; thereby proving the effectiveness of the transformation system.

CONCLUSION

In summary, this study successfully established an efficient system for preparing and transforming leaf protoplasts of Populus simonii × P. nigra, laying the foundation for future research on gene function and expression of Populus simonii × P. nigra.

DGMP: Identifying Cancer Driver Genes by Jointing DGCN and MLP from Multi-omics Genomic Data

Identification of cancer driver genes plays an important role in precision oncology research, which is helpful to understand cancer initiation and progression. However, most existing computational methods mainly used the protein-protein interaction (PPI) networks, or treated the directed gene regulatory networks (GRNs) as the undirected gene-gene association networks to identify the cancer driver genes, which will lose the unique structure regulatory information in the directed GRNs, and then affect the outcome of the cancer driver gene identification. Here, based on the multi-omics pan-cancer data (i.e., gene expression, mutation, copy number variation, and DNA methylation), we propose a novel method (called DGMP) to identify cancer driver genes by jointing directed graph convolutional network (DGCN) and multilayer perceptron (MLP). DGMP learns the multi-omics features of genes as well as the topological structure features in GRN with the DGCN model and uses MLP to weigh more on gene features for mitigating the bias toward the graph topological features in the DGCN learning process. The results on three GRNs show that DGMP outperforms other existing state-of-the-art methods. The ablation experimental results on the DawnNet network indicate that introducing MLP into DGCN can offset the performance degradation of DGCN, and jointing MLP and DGCN can effectively improve the performance of identifying cancer driver genes. DGMP can identify not only the highly mutated cancer driver genes but also the driver genes harboring other kinds of alterations (e.g., differential expression and aberrant DNA methylation) or genes involved in GRNs with other cancer genes. The source code of DGMP can be freely downloaded from https://github.com/NWPU-903PR/DGMP.

CRCF: A Method of Identifying Secretory Proteins of Malaria Parasites

Malaria is a mosquito-borne disease that results in millions of cases and deaths annually. The development of a fast computational method that identifies secretory proteins of the malaria parasite is important for research on antimalarial drugs and vaccines. Thus, a method was developed to identify the secretory proteins of malaria parasites. In this method, a reduced alphabet was selected to recode the original protein sequence. A feature synthesis method was used to synthesise three different types of feature information. Finally, the random forest method was used as a classifier to identify the secretory proteins. In addition, a web server was developed to share the proposed algorithm. Experiments using the benchmark dataset demonstrated that the overall accuracy achieved by the proposed method was greater than 97.8 percent using the 10-fold cross-validation method. Furthermore, the reduced schemes and characteristic performance analyses are discussed.

Multi-scale deep learning for the imbalanced multi-label protein subcellular localization prediction based on immunohistochemistry images

MOTIVATION

The development of microscopic imaging techniques enables us to study protein subcellular locations from the tissue level down to the cell level, contributing to the rapid development of image-based protein subcellular location prediction approaches. However, existing methods suffer from intrinsic limitations, such as poor feature representation ability, data imbalanced issue, and multi-label classification problem, greatly impacting the model performance and generalization.

RESULTS

In this study, we propose MSTLoc, a novel multi-scale end-to-end deep learning model to identify protein subcellular locations in the imbalanced multi-label immunohistochemistry (IHC) images dataset. In our MSTLoc, we deploy a deep convolution neural network to extract multi-scale features from the IHC images, aggregate the high-level features and low-level features via feature fusion to sufficiently exploit the dependencies amongst various subcellular locations, and utilize Vision Transformer (ViT) to model the relationship amongst the features and enhance the feature representation ability. We demonstrate that the proposed MSTLoc achieves better performance than current state-of-the-art models in multi-label subcellular location prediction. Through feature visualization and interpretation analysis, we demonstrate that as compared with the hand-crafted features, the multi-scale deep features learnt from our model exhibit better ability in capturing discriminative patterns underlying protein subcellular locations, and the features from different scales are complementary for the improvement in performance. Finally, case study results indicate that our MSTLoc can successfully identify some biomarkers from proteins that are closely involved with cancer development. For the convenient use of our method, we establish a user-friendly webserver available at http://server.wei-group.net/ MSTLoc.

AVAILABILITY AND IMPLEMENTATION

http://server.wei-group.net/ MSTLoc.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ORS-Pred: An optimized reduced scheme-based identifier for antioxidant proteins

Antioxidant proteins can terminate a chain of reactions caused by free radicals and protect cells from damage. To identify antioxidant proteins rapidly, a computational model was proposed based on the optimized recoding scheme, sequence information and machine learning methods. First, over 600 recoding schemes were collected to build a scheme set. Then, the original sequence was recoded as a reduced expression whose g-gap dipeptides (g = 0, 1, 2) were used as the features of proteins. Furthermore, a random forest method was used to evaluate the classification ability of the obtained dipeptide features. After going through all schemes, the best predictive performance scheme was chosen as the optimized reduction scheme. Finally, for the RF method, a grid search strategy was used to select a better parameter combination to identify antioxidant proteins. In the experiment, the present method correctly recognized 90.13-99.87% of the antioxidant samples. Other experimental results also proved that the present method was efficient to identify antioxidant proteins. Finally, we also developed a web server that was freely accessible to researchers.

Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction

Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation, which tightly controls gene expression. Identification of EPIs can help us better decipher gene regulation and understand disease mechanisms. However, experimental methods to identify EPIs are constrained by funds, time, and manpower, while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literature. First, we briefly introduce existing sequence- based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means, and evaluation strategies. Finally, we concluded with the challenges these methods are confronted with and suggest several future opportunities. We hope this review will provide a useful reference for further studies on enhancer-promoter interactions.

Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms

In the postgenomic age, rapid growth in the number of sequence-known proteins has been accompanied by much slower growth in the number of structure-known proteins (as a result of experimental limitations), and a widening gap between the two is evident. Because protein function is linked to protein structure, successful prediction of protein structure is of significant importance in protein function identification. Foreknowledge of protein structural class can help improve protein structure prediction with significant medical and pharmaceutical implications. Thus, a fast, suitable, reliable, and reasonable computational method for protein structural class prediction has become pivotal in bioinformatics. Here, we review recent efforts in protein structural class prediction from protein sequence, with particular attention paid to new feature descriptors, which extract information from protein sequence, and the use of machine learning algorithms in both feature selection and the construction of new classification models. These new feature descriptors include amino acid composition, sequence order, physicochemical properties, multiprofile Bayes, and secondary structure-based features. Machine learning methods, such as artificial neural networks (ANNs), support vector machine (SVM), K-nearest neighbor (KNN), random forest, deep learning, and examples of their application are discussed in detail. We also present our view on possible future directions, challenges, and opportunities for the applications of machine learning algorithms for prediction of protein structural classes.

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.

Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features

There are a lot of bacteria in the environment, and Gram-positive bacteria are the most common ones. Some Gram-positive bacteria are very harmful to the human body, so it is significant to predict Gram-positive bacterial protein subcellular location. And identification of Gram-positive bacterial protein subcellular location is important for developing effective drugs. In this paper, a new Gram-positive bacterial protein subcellular location dataset was established. The amino acid composition, the gene ontology annotation information, the hydropathy dipeptide composition information, the amino acid dipeptide composition information, and the autocovariance average chemical shift information were selected as characteristic parameters, then these parameters were combined. The locations of Gram-positive bacterial proteins were predicted by the Support Vector Machine (SVM) algorithm, and the overall accuracy (OA) reached 86.1% under the Jackknife test. The overall accuracy (OA) in our predictive model was higher than those in existing methods. This improved method may be helpful for protein function prediction.