Methods For Protein Subcellular Localization Research Articles

The Development and Progress in Machine Learning for Protein Subcellular Localization Prediction

Protein subcellular localization is a novel and promising area and is defined as searching for the specific location of proteins inside the cell, such as in the nucleus, in the cytoplasm or on the cell membrane. With the rapid development of next-generation sequencing technology, more and more new protein sequences have been continuously discovered. It is no longer sufficient to merely use traditional wet experimental methods to predict the subcellular localization of these new proteins. Therefore, it is urgent to develop high-throughput computational methods to achieve quick and precise protein subcellular localization predictions. This review summarizes the development of prediction methods for protein subcellular localization over the past decades, expounds on the application of various machine learning methods in this field, and compares the properties and performance of various well-known predictors. The narrative of this review mainly revolves around three main types of methods, namely, the sequence-based methods, the knowledge-based methods, and the fusion methods. A special focus is on the gene ontology (GO)-based methods and the PLoc series methods. Finally, this review looks forward to the future development directions of protein subcellular localization prediction.

A multi-scale multi-model deep neural network via ensemble strategy on high-throughput microscopy image for protein subcellular localization

Protein subcellular locations are closely related to the function of proteins. By detecting the abnormalities in subcellular locations, we can infer the occurrence of some diseases and mine new drug targets. A large number of single-cell high-throughput microscopy images provide us with relevant resources for studying protein distribution patterns. However, in the existing image-based protein subcellular localization methods, the traditional techniques are lack of efficiency and accuracy, and the potential of deep learning methods has not been fully tapped. So in this study, we propose a multi-scale multi-model deep neural network via ensemble strategy for protein subcellular localization on single-cell high-throughput images. First of all, we employ a deep convolutional neural network as multi-scale feature extractor and use global average pooling to map extracted features at different stages into feature vectors, then concatenate these multi-scale features to form a multi-model structure for image classification. In addition, we add Squeeze-and-Excitation Blocks to the network to emphasize more informative features. What is more, we use an ensemble method to fuse the classification results from the multi-model structure to obtain the final subcellular location of each single-cell image. Experiments show the validity and effectiveness of our method on yeast cell images, it can significantly improve the accuracy of high-throughput microscopy image-based protein subcellular localization, and we achieve the classification accuracy of 0.9098 on the high-throughput microscopy images of yeast cells. In the work of protein subcellular localization, our method provides a framework for processing and classifying microscope images, and further lays the foundation for the study of protein and gene functions.

A Novel Protein Subcellular Localization Method With CNN-XGBoost Model for Alzheimer's Disease.

The disorder distribution of protein in the compartment or organelle leads to many human diseases, including neurodegenerative diseases such as Alzheimer's disease. The prediction of protein subcellular localization play important roles in the understanding of the mechanism of protein function, pathogenes and disease therapy. This paper proposes a novel subcellular localization method by integrating the Convolutional Neural Network (CNN) and eXtreme Gradient Boosting (XGBoost), where CNN acts as a feature extractor to automatically obtain features from the original sequence information and a XGBoost classifier as a recognizer to identify the protein subcellular localization based on the output of the CNN. Experiments are implemented on three protein datasets. The results prove that the CNN-XGBoost method performs better than the general protein subcellular localization methods.

Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC

Predicting protein subcellular location with support vector machine has been a popular research area recently because of the dramatic explosion of bioinformation. Though substantial achievements have been obtained, few researchers considered the problem of data imbalance before classification, which will lead to low accuracy for some categories. So in this work, we combined oversampling method with SVM to deal with the protein subcellular localization of unbalanced data sets.To capture valuable information of a protein, a PseAAC (Pseudo Amino Acid Composition) has been extracted from PSSM(Position-Specific Scoring Matrix) as a feature vector, and then be selected by principal component analysis (PCA). Next, samples which are treated by oversampling method to eliminate the imbalance of sample numbers in different classes are fed into support vector machine to predict the protein subcellular location. To evaluate the performance of proposed method, Jackknife tests are performed on three benchmark datasets (ZD98, CL317 and ZW225).Results of SVM experiments with and without oversampling gained by Jackknife tests show that oversampling methods have successfully decrease the imbalance of data sets, and the prediction accuracy of each class in each dataset is higher than 88.9%. With comparison with other protein subcellular localization methods, the method in this work reaches the best performance. The overall accuracies of ZD98, CL317 and ZW225 are 93.2%, 96.00% and 92.15% respectively, which are 2.4%, 8.0% and 8.2% higher than the best methods in the comparison. The excellent overall accuracy gained by the proposed method indicates that the feature representation and selection capture useful information of protein sequence and oversampling methods successfully solve the imbalance of sample numbers in SVM classification.

MLASSO-Hum: A LASSO-based interpretable human-protein subcellular localization predictor

Knowing the subcellular compartments of human proteins is essential to shed light on the mechanisms of a broad range of human diseases. In computational methods for protein subcellular localization, knowledge-based methods (especially gene ontology (GO) based methods) are known to perform better than sequence-based methods. However, existing GO-based predictors often lack interpretability and suffer from overfitting due to the high dimensionality of feature vectors. To address these problems, this paper proposes an interpretable multi-label predictor, namely mLASSO-Hum, which can yield sparse and interpretable solutions for large-scale prediction of human protein subcellular localization. By using the one-vs-rest LASSO-based classifiers, 87 out of more than 8000 GO terms are found to play more significant roles in determining the subcellular localization. Based on these 87 essential GO terms, we can decide not only where a protein resides within a cell, but also why it is located there. To further exploit information from the remaining GO terms, a method based on the GO hierarchical information derived from the depth distance of GO terms is proposed. Experimental results show that mLASSO-Hum performs significantly better than state-of-the-art predictors. We also found that in addition to the GO terms from the cellular component category, GO terms from the other two categories also play important roles in the final classification decisions. For readers׳ convenience, the mLASSO-Hum server is available online at http://bioinfo.eie.polyu.edu.hk/mLASSOHumServer/.

다중레이블 조합을 사용한 단백질 세포내 위치 예측

단백질이 존재하는 세포내 위치에 대한 지식은 단백질의 기능과 관련된 중요한 정보이다. 본 논문은 개선된 레이블 멱집합 다중레이블 분류방법을 제안하여 단백질이 존재하는 세포내의 다중 위치를 예측한다. 다중레이블 분류 방법 중에서 레이블 멱집합 방법은 특정 생물학적 기능을 수행하는 단백질의 세포내 위치간의 연관 관계를 효과적으로 모델링할 수 있다. 본 논문은 다중레이블을 다른 다중레이블들의 선형조합으로 나타낼 때의 조합가중치를 제약조건이 있는 최적화를 통하여 구하고, 이를 사용하여 여러 다중레이블의 예측 확률들을 조합하여 최종적인 예측을 수행한다. 인간 단백질 자료에 대한 실험에서 제안한 방법이 다른 단백질 세포내 위치 예측 방법에 비하여 높은 성능을 보였다. 이는 제안한 방법이 레이블 멱집합 방법에서 사용되는 다중레이블들내에 존재하는 중복 정보를 이용하여 다중 레이블의 예측확률을 성공적으로 강화할 수 있기 때문이다. Knowledge about protein subcellular localization provides important information about protein function. This paper improves a label power-set multi-label classification for the accurate prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular locations. Among multi-label classification methods, label power-set method can effectively model the correlation between subcellular locations of proteins performing certain biological function. With constrained optimization, this paper calculates combination weights which are used in the linear combination representation of a multi-label by other multi-labels. Using these weights, the prediction probabilities of multi-labels are combined to give final prediction results. Experimental results on human protein dataset show that the proposed method achieves higher performance than other prediction methods for protein subcellular localization. This shows that the proposed method can successfully enrich the prediction probability of multi-labels by exploiting the overlapping information between multi-labels.

A Novel Numerical Feature Extraction Method for Protein Subcellular Localization

A Novel Numerical Feature Extraction Method for Protein Subcellular Localization

Multilabel Learning via Random Label Selection for Protein Subcellular Multilocations Prediction

Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. In the past few years, only a few methods have been proposed to tackle proteins with multiple locations. However, they only adopt a simple strategy, that is, transforming the multilocation proteins to multiple proteins with single location, which does not take correlations among different subcellular locations into account. In this paper, a novel method named random label selection (RALS) (multilabel learning via RALS), which extends the simple binary relevance (BR) method, is proposed to learn from multilocation proteins in an effective and efficient way. RALS does not explicitly find the correlations among labels, but rather implicitly attempts to learn the label correlations from data by augmenting original feature space with randomly selected labels as its additional input features. Through the fivefold cross-validation test on a benchmark data set, we demonstrate our proposed method with consideration of label correlations obviously outperforms the baseline BR method without consideration of label correlations, indicating correlations among different subcellular locations really exist and contribute to improvement of prediction performance. Experimental results on two benchmark data sets also show that our proposed methods achieve significantly higher performance than some other state-of-the-art methods in predicting subcellular multilocations of proteins. The prediction web server is available at http://levis.tongji.edu.cn:8080/bioinfo/MLPred-Euk/ for the public usage.

Virus-ECC-mPLoc: A Multi-Label Predictor for Predicting the Subcellular Localization of Virus Proteins with Both Single and Multiple Sites Based on a General Form of Chou's Pseudo Amino Acid Composition

Protein subcellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of subcellular localization of viral proteins in a host cell or virus-infected cell is important because it is closely related to their destructive tendencies and consequences. Prediction of viral protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods specialized for viral proteins are only used to deal with the single-location proteins. To better reflect the characteristics of multiplex proteins, a new predictor, called Virus-ECC-mPLoc, has been developed that can be used to deal with the systems containing both singleplex and multiplex proteins by introducing a powerful multi-label learning approach which exploits correlations between subcellular locations and by hybridizing the gene ontology information with the dipeptide composition information. It can be utilized to identify viral proteins among the following six locations: (1) viral capsid, (2) host cell membrane, (3) host endoplasmic reticulum, (4) host cytoplasm, (5) host nucleus, and (6) secreted. Experimental results show that the overall success rates thus obtained by Virus-ECC-mPLoc are 86.9% for jackknife test and 87.2% for independent data set test, which are significantly higher than that by any of the existing predictors. As a user-friendly web-server, Virus-ECCmPLoc is freely accessible to the public at the web-site http://levis.tongji.edu.cn:8080/bioinfo/Virus-ECC-mPLoc/.

Multilabel Learning for Protein Subcellular Location Prediction

Protein subcellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of subcellular localization of proteins indicates protein functions and helps in identifying drug targets. Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. To better reflect the characteristics of multiplex proteins, we formulate prediction of subcellular localization of multiplex proteins as a multilabel learning problem. We present and compare two multilabel learning approaches, which exploit correlations between labels and leverage label-specific features, respectively, to induce a high quality prediction model. Experimental results on six protein data sets under various organisms show that our described methods achieve significantly higher performance than any of the existing methods. Among the different multilabel learning methods, we find that methods exploiting label correlations performs better than those leveraging label-specific features.

ProLoc-GO: Utilizing informative Gene Ontology terms for sequence-based prediction of protein subcellular localization

BackgroundGene Ontology (GO) annotation, which describes the function of genes and gene products across species, has recently been used to predict protein subcellular and subnuclear localization. Existing GO-based prediction methods for protein subcellular localization use the known accession numbers of query proteins to obtain their annotated GO terms. An accurate prediction method for predicting subcellular localization of novel proteins without known accession numbers, using only the input sequence, is worth developing.ResultsThis study proposes an efficient sequence-based method (named ProLoc-GO) by mining informative GO terms for predicting protein subcellular localization. For each protein, BLAST is used to obtain a homology with a known accession number to the protein for retrieving the GO annotation. A large number n of all annotated GO terms that have ever appeared are then obtained from a large set of training proteins. A novel genetic algorithm based method (named GOmining) combined with a classifier of support vector machine (SVM) is proposed to simultaneously identify a small number m out of the n GO terms as input features to SVM, where m <<n. The m informative GO terms contain the essential GO terms annotating subcellular compartments such as GO:0005634 (Nucleus), GO:0005737 (Cytoplasm) and GO:0005856 (Cytoskeleton). Two existing data sets SCL12 (human protein with 12 locations) and SCL16 (Eukaryotic proteins with 16 locations) with <25% sequence identity are used to evaluate ProLoc-GO which has been implemented by using a single SVM classifier with the m = 44 and m = 60 informative GO terms, respectively. ProLoc-GO using input sequences yields test accuracies of 88.1% and 83.3% for SCL12 and SCL16, respectively, which are significantly better than the SVM-based methods, which achieve < 35% test accuracies using amino acid composition (AAC) with acid pairs and AAC with dipedtide composition. For comparison, ProLoc-GO using known accession numbers of query proteins yields test accuracies of 90.6% and 85.7%, which is also better than Hum-PLoc (85.0%) and Euk-OET-PLoc (83.7%) using ensemble classifiers with hybridization of GO terms and amphiphilic pseudo amino acid composition for SCL12 and SCL16, respectively.ConclusionThe growth of Gene Ontology in size and popularity has increased the effectiveness of GO-based features. GOmining can serve as a tool for selecting informative GO terms in solving sequence-based prediction problems. The prediction system using ProLoc-GO with input sequences of query proteins for protein subcellular localization has been implemented (see Availability).

A simple and effective method for protein subcellular localization using Agrobacterium-mediated transformation of onion epidermal cells

A modified Agrobacterium-mediated transformation protocol has been successfully used for transient expression of the intrinsically fluorescent proteins and their fusion proteins in onion epidermis. The mean of the transformed cells rate per peel is about 10.5±0.9%, while that of the particle bombardment method is at the range 2.0±0.4%. To compare with the prevailing method of micro-projectile bombardment, the modified Agrobacterium-mediated transformation may provide with higher efficiency and even more simplified manipulability on a lower budget.