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Xiao H, Zou Y, Wang J, Wan S. A Review for Artificial Intelligence Based Protein Subcellular Localization. Biomolecules 2024; 14:409. [PMID: 38672426 PMCID: PMC11048326 DOI: 10.3390/biom14040409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 03/21/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024] Open
Abstract
Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. Mislocalized proteins may lead to a broad range of diseases, such as cancer and Alzheimer's disease. Knowing where a target protein resides within a cell will give insights into tailored drug design for a disease. As the gold validation standard, the conventional wet lab uses fluorescent microscopy imaging, immunoelectron microscopy, and fluorescent biomarker tags for protein subcellular location identification. However, the booming era of proteomics and high-throughput sequencing generates tons of newly discovered proteins, making protein subcellular localization by wet-lab experiments a mission impossible. To tackle this concern, in the past decades, artificial intelligence (AI) and machine learning (ML), especially deep learning methods, have made significant progress in this research area. In this article, we review the latest advances in AI-based method development in three typical types of approaches, including sequence-based, knowledge-based, and image-based methods. We also elaborately discuss existing challenges and future directions in AI-based method development in this research field.
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Affiliation(s)
- Hanyu Xiao
- Department of Genetics, Cell Biology and Anatomy, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA;
| | - Yijin Zou
- College of Veterinary Medicine, China Agricultural University, Beijing 100193, China;
| | - Jieqiong Wang
- Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA;
| | - Shibiao Wan
- Department of Genetics, Cell Biology and Anatomy, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA;
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Fu X, Suo H, Zhang J, Chen D. Machine-learning-guided Directed Evolution for AAV Capsid Engineering. Curr Pharm Des 2024; 30:811-824. [PMID: 38445704 DOI: 10.2174/0113816128286593240226060318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/07/2024] [Accepted: 02/13/2024] [Indexed: 03/07/2024]
Abstract
Target gene delivery is crucial to gene therapy. Adeno-associated virus (AAV) has emerged as a primary gene therapy vector due to its broad host range, long-term expression, and low pathogenicity. However, AAV vectors have some limitations, such as immunogenicity and insufficient targeting. Designing or modifying capsids is a potential method of improving the efficacy of gene delivery, but hindered by weak biological basis of AAV, complexity of the capsids, and limitations of current screening methods. Artificial intelligence (AI), especially machine learning (ML), has great potential to accelerate and improve the optimization of capsid properties as well as decrease their development time and manufacturing costs. This review introduces the traditional methods of designing AAV capsids and the general steps of building a sequence-function ML model, highlights the applications of ML in the development workflow, and summarizes its advantages and challenges.
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Affiliation(s)
- Xianrong Fu
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Hairui Suo
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Jiachen Zhang
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Dongmei Chen
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China
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3
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Agrawal S, Sisodia DS, Nagwani NK. Augmented sequence features and subcellular localization for functional characterization of unknown protein sequences. Med Biol Eng Comput 2021; 59:2297-2310. [PMID: 34545514 DOI: 10.1007/s11517-021-02436-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Accepted: 08/29/2021] [Indexed: 11/24/2022]
Abstract
Advances in high-throughput techniques lead to evolving a large number of unknown protein sequences (UPS). Functional characterization of UPS is significant for the investigation of disease symptoms and drug repositioning. Protein subcellular localization is imperative for the functional characterization of protein sequences. Diverse techniques are used on protein sequences for feature extraction. However, many times a single feature extraction technique leads to poor prediction performance. In this paper, two feature augmentations are described through sequence induced, physicochemical, and evolutionary information of the amino acid residues. While augmented features preserve the sequence-order-information and protein-residue-properties. Two bacterial protein datasets Gram-Positive (G +) and Gram-Negative (G-) are utilized for the experimental work. After performing essential preprocessing on protein datasets, two sets of feature vectors are obtained. These feature vectors are used separately to train the different individual and ensembles such as decision tree (C 4.5), k-nearest neighbor (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), support vector machine (SVM), AdaBoost, gradient boosting machine (GBM), and random forest (RF) with fivefold cross-validation. Prediction results of the model demonstrate that overall accuracy reported by C4.5 is highest 99.57% on G + and 97.47% on G- datasets with known protein sequences. Similarly, for the UPS overall accuracy of G + is 85.17% with SVM and 82.45% with G- dataset using MLP.
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Affiliation(s)
- Saurabh Agrawal
- Department of Computer Science & Engineering, National Institute of Technology Raipur, GE Road, Raipur, Chhattisgarh, 492010, India.
| | - Dilip Singh Sisodia
- Department of Computer Science & Engineering, National Institute of Technology Raipur, GE Road, Raipur, Chhattisgarh, 492010, India
| | - Naresh Kumar Nagwani
- Department of Computer Science & Engineering, National Institute of Technology Raipur, GE Road, Raipur, Chhattisgarh, 492010, India
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Prediction of Drug-Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method. Molecules 2021; 26:molecules26175359. [PMID: 34500792 PMCID: PMC8433937 DOI: 10.3390/molecules26175359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 08/27/2021] [Accepted: 08/30/2021] [Indexed: 11/17/2022] Open
Abstract
Identification of drug–target interactions (DTIs) is vital for drug discovery. However, traditional biological approaches have some unavoidable shortcomings, such as being time consuming and expensive. Therefore, there is an urgent need to develop novel and effective computational methods to predict DTIs in order to shorten the development cycles of new drugs. In this study, we present a novel computational approach to identify DTIs, which uses protein sequence information and the dual-tree complex wavelet transform (DTCWT). More specifically, a position-specific scoring matrix (PSSM) was performed on the target protein sequence to obtain its evolutionary information. Then, DTCWT was used to extract representative features from the PSSM, which were then combined with the drug fingerprint features to form the feature descriptors. Finally, these descriptors were sent to the Rotation Forest (RoF) model for classification. A 5-fold cross validation (CV) was adopted on four datasets (Enzyme, Ion Channel, GPCRs (G-protein-coupled receptors), and NRs (Nuclear Receptors)) to validate the proposed model; our method yielded high average accuracies of 89.21%, 85.49%, 81.02%, and 74.44%, respectively. To further verify the performance of our model, we compared the RoF classifier with two state-of-the-art algorithms: the support vector machine (SVM) and the k-nearest neighbor (KNN) classifier. We also compared it with some other published methods. Moreover, the prediction results for the independent dataset further indicated that our method is effective for predicting potential DTIs. Thus, we believe that our method is suitable for facilitating drug discovery and development.
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Wang S, Deng L, Xia X, Cao Z, Fei Y. Predicting antifreeze proteins with weighted generalized dipeptide composition and multi-regression feature selection ensemble. BMC Bioinformatics 2021; 22:340. [PMID: 34162327 PMCID: PMC8220696 DOI: 10.1186/s12859-021-04251-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 06/09/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Antifreeze proteins (AFPs) are a group of proteins that inhibit body fluids from growing to ice crystals and thus improve biological antifreeze ability. It is vital to the survival of living organisms in extremely cold environments. However, little research is performed on sequences feature extraction and selection for antifreeze proteins classification in the structure and function prediction, which is of great significance. RESULTS In this paper, to predict the antifreeze proteins, a feature representation of weighted generalized dipeptide composition (W-GDipC) and an ensemble feature selection based on two-stage and multi-regression method (LRMR-Ri) are proposed. Specifically, four feature selection algorithms: Lasso regression, Ridge regression, Maximal information coefficient and Relief are used to select the feature sets, respectively, which is the first stage of LRMR-Ri method. If there exists a common feature subset among the above four sets, it is the optimal subset; otherwise we use Ridge regression to select the optimal subset from the public set pooled by the four sets, which is the second stage of LRMR-Ri. The LRMR-Ri method combined with W-GDipC was performed both on the antifreeze proteins dataset (binary classification), and on the membrane protein dataset (multiple classification). Experimental results show that this method has good performance in support vector machine (SVM), decision tree (DT) and stochastic gradient descent (SGD). The values of ACC, RE and MCC of LRMR-Ri and W-GDipC with antifreeze proteins dataset and SVM classifier have reached as high as 95.56%, 97.06% and 0.9105, respectively, much higher than those of each single method: Lasso, Ridge, Mic and Relief, nearly 13% higher than single Lasso for ACC. CONCLUSION The experimental results show that the proposed LRMR-Ri and W-GDipC method can significantly improve the accuracy of antifreeze proteins prediction compared with other similar single feature methods. In addition, our method has also achieved good results in the classification and prediction of membrane proteins, which verifies its widely reliability to a certain extent.
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Affiliation(s)
- Shunfang Wang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, China.
| | - Lin Deng
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, China
| | - Xinnan Xia
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, China.
| | - Zicheng Cao
- School of Public Health (Shenzhen), Sun Yat-Sen University, Guangzhou, 510006, China
| | - Yu Fei
- School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, 650221, China.
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Protein Subnuclear Localization Based on Radius-SMOTE and Kernel Linear Discriminant Analysis Combined with Random Forest. ELECTRONICS 2020. [DOI: 10.3390/electronics9101566] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Protein subnuclear localization plays an important role in proteomics, and can help researchers to understand the biologic functions of nucleus. To date, most protein datasets used by studies are unbalanced, which reduces the prediction accuracy of protein subnuclear localization—especially for the minority classes. In this work, a novel method is therefore proposed to predict the protein subnuclear localization of unbalanced datasets. First, the position-specific score matrix is used to extract the feature vectors of two benchmark datasets and then the useful features are selected by kernel linear discriminant analysis. Second, the Radius-SMOTE is used to expand the samples of minority classes to deal with the problem of imbalance in datasets. Finally, the optimal feature vectors of the expanded datasets are classified by random forest. In order to evaluate the performance of the proposed method, four index evolutions are calculated by Jackknife test. The results indicate that the proposed method can achieve better effect compared with other conventional methods, and it can also improve the accuracy for both majority and minority classes effectively.
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Yuan F, Liu G, Yang X, Wang S, Wang X. Prediction of oxidoreductase subfamily classes based on RFE-SND-CC-PSSM and machine learning methods. J Bioinform Comput Biol 2020; 17:1950029. [PMID: 31617464 DOI: 10.1142/s021972001950029x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Oxidoreductase is an enzyme that widely exists in organisms. It plays an important role in cellular energy metabolism and biotransformation processes. Oxidoreductases have many subclasses with different functions, creating an important classification task in bioinformatics. In this paper, a dataset of 2640 oxidoreductase sequences was used to perform an analysis and comparison. The idea of dipeptides was introduced to process the Position Specific Score Matrix (PSSM), since each dipeptide consists of two amino acids and each column of PSSM corresponds to the information of one amino acid. Two kinds of dipeptide scores were proposed, the Standardization Normal Distribution PSSM (SND-PSSM) and the Correlation Coefficient PSSM (CC-PSSM). Recursive Feature Elimination (RFE) is used to extract features from the SND-PSSM and CC-PSSM, and the two sets of extracted features are combined to form a new feature matrix, the RFE-SND-CC-PSSM. The results show that, with the proposed method and a kernel-based nonlinear SVM classifier, the accuracy can reach 95.56% by the Jackknife test. Our method greatly improves the accuracy of oxidoreductase subclass prediction. Using this method to predict the categories of the 6 major types of enzymes effectively improves its prediction accuracy to 94.54%, indicating that this method has general applicability to other protein problems. The results show that our method is effective and universally applicable, and might be complementary to the existing methods.
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Affiliation(s)
- Fang Yuan
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Kunming Medical University, Kunming 650500, P. R. China
| | - Gan Liu
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, P. R. China
| | - Xiwen Yang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, P. R. China
| | - Shunfang Wang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, P. R. China
| | - Xueren Wang
- School of Mathematics and Statistics, Yunnan University, Kunming 650504, P. R. China
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Du L, Meng Q, Chen Y, Wu P. Subcellular location prediction of apoptosis proteins using two novel feature extraction methods based on evolutionary information and LDA. BMC Bioinformatics 2020; 21:212. [PMID: 32448129 PMCID: PMC7245797 DOI: 10.1186/s12859-020-3539-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 05/06/2020] [Indexed: 11/13/2022] Open
Abstract
Background Apoptosis, also called programmed cell death, refers to the spontaneous and orderly death of cells controlled by genes in order to maintain a stable internal environment. Identifying the subcellular location of apoptosis proteins is very helpful in understanding the mechanism of apoptosis and designing drugs. Therefore, the subcellular localization of apoptosis proteins has attracted increased attention in computational biology. Effective feature extraction methods play a critical role in predicting the subcellular location of proteins. Results In this paper, we proposed two novel feature extraction methods based on evolutionary information. One of the features obtained the evolutionary information via the transition matrix of the consensus sequence (CTM). And the other utilized the evolutionary information from PSSM based on absolute entropy correlation analysis (AECA-PSSM). After fusing the two kinds of features, linear discriminant analysis (LDA) was used to reduce the dimension of the proposed features. Finally, the support vector machine (SVM) was adopted to predict the protein subcellular locations. The proposed CTM-AECA-PSSM-LDA subcellular location prediction method was evaluated using the CL317 dataset and ZW225 dataset. By jackknife test, the overall accuracy was 99.7% (CL317) and 95.6% (ZW225) respectively. Conclusions The experimental results show that the proposed method which is hopefully to be a complementary tool for the existing methods of subcellular localization, can effectively extract more abundant features of protein sequence and is feasible in predicting the subcellular location of apoptosis proteins.
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Affiliation(s)
- Lei Du
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, China.,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China
| | - Qingfang Meng
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, China. .,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China.
| | - Yuehui Chen
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, China.,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China
| | - Peng Wu
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, China.,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China
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Wang S, Cao Z, Li M, Yue Y. G-DipC: An Improved Feature Representation Method for Short Sequences to Predict the Type of Cargo in Cell-Penetrating Peptides. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:739-747. [PMID: 31352350 DOI: 10.1109/tcbb.2019.2930993] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Cell-penetrating peptides (CPPs) are functional short peptides with high carrying capacity. CPP sequences with targeting functions for the highly efficient delivery of drugs to target cells. In this paper, which is focused on the prediction of the cargo category of CPPs, a biocomputational model is constructed to efficiently distinguish the category of cargo carried by CPPs as macromolecular carriers among the seven known deliverable cargo categories. Based on dipeptide composition (DipC), an improved feature representation method, general dipeptide composition (G-DipC) is proposed for short peptide sequences and can effectively increase the abundance of features represented. Then linear discriminant analysis (LDA) is applied to mine some important low-dimensional features of G-DipC and a predictive model is built with the XGBoost algorithm. Experimental results with five-fold cross validation show that G-DipC improves accuracy by 25 and 5 percent compared with amino acid composition (AAC) and DipC, respectively. G-DipC is even found to be better than tripeptide composition (TipC). Thus, the proposed model provides a novel resource for the study of cell-penetrating peptides, and the improved dipeptide composition G-DipC can be widely adapted to determine the feature representation of other biological sequences.
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Zhao W, Li GP, Wang J, Zhou YK, Gao Y, Du PF. Predicting protein sub-Golgi locations by combining functional domain enrichment scores with pseudo-amino acid compositions. J Theor Biol 2019; 473:38-43. [DOI: 10.1016/j.jtbi.2019.04.025] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 04/22/2019] [Accepted: 04/29/2019] [Indexed: 12/11/2022]
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Wang S, Yue Y. Protein subnuclear localization based on a new effective representation and intelligent kernel linear discriminant analysis by dichotomous greedy genetic algorithm. PLoS One 2018; 13:e0195636. [PMID: 29649330 PMCID: PMC5896989 DOI: 10.1371/journal.pone.0195636] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 03/26/2018] [Indexed: 01/03/2023] Open
Abstract
A wide variety of methods have been proposed in protein subnuclear localization to improve the prediction accuracy. However, one important trend of these means is to treat fusion representation by fusing multiple feature representations, of which, the fusion process takes a lot of time. In view of this, this paper novelly proposed a method by combining a new single feature representation and a new algorithm to obtain good recognition rate. Specifically, based on the position-specific scoring matrix (PSSM), we proposed a new expression, correlation position-specific scoring matrix (CoPSSM) as the protein feature representation. Based on the classic nonlinear dimension reduction algorithm, kernel linear discriminant analysis (KLDA), we added a new discriminant criterion and proposed a dichotomous greedy genetic algorithm (DGGA) to intelligently select its kernel bandwidth parameter. Two public datasets with Jackknife test and KNN classifier were used for the numerical experiments. The results showed that the overall success rate (OSR) with single representation CoPSSM is larger than that with many relevant representations. The OSR of the proposed method can reach as high as 87.444% and 90.3361% for these two datasets, respectively, outperforming many current methods. To show the generalization of the proposed algorithm, two extra standard datasets of protein subcellular were chosen to conduct the expending experiment, and the prediction accuracy by Jackknife test and Independent test is still considerable.
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Affiliation(s)
- Shunfang Wang
- School of Information Science and Engineering, Yunnan University, Kunming, PR China
- * E-mail:
| | - Yaoting Yue
- School of Information Science and Engineering, Yunnan University, Kunming, PR China
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12
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Abstract
Computational identification of special protein molecules is a key issue in understanding protein function. It can guide molecular experiments and help to save costs. I assessed 18 papers published in the special issue of Int. J. Mol. Sci., and also discussed the related works. The computational methods employed in this special issue focused on machine learning, network analysis, and molecular docking. New methods and new topics were also proposed. There were in addition several wet experiments, with proven results showing promise. I hope our special issue will help in protein molecules identification researches.
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Zou Q, He W. Special Protein Molecules Computational Identification. Int J Mol Sci 2018; 19:ijms19020536. [PMID: 29439426 PMCID: PMC5855758 DOI: 10.3390/ijms19020536] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Revised: 02/02/2018] [Accepted: 02/10/2018] [Indexed: 01/29/2023] Open
Abstract
Computational identification of special protein molecules is a key issue in understanding protein function. It can guide molecular experiments and help to save costs. I assessed 18 papers published in the special issue of Int. J. Mol. Sci., and also discussed the related works. The computational methods employed in this special issue focused on machine learning, network analysis, and molecular docking. New methods and new topics were also proposed. There were in addition several wet experiments, with proven results showing promise. I hope our special issue will help in protein molecules identification researches.
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Affiliation(s)
- Quan Zou
- School of Computer Science and Technology, Tianjin University, Tianjin 300354, China.
| | - Wenying He
- School of Computer Science and Technology, Tianjin University, Tianjin 300354, China.
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14
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An Ensemble Classifier with Random Projection for Predicting Protein–Protein Interactions Using Sequence and Evolutionary Information. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8010089] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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15
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Wang S, Nie B, Yue K, Fei Y, Li W, Xu D. Protein Subcellular Localization with Gaussian Kernel Discriminant Analysis and Its Kernel Parameter Selection. Int J Mol Sci 2017; 18:E2718. [PMID: 29244758 PMCID: PMC5751319 DOI: 10.3390/ijms18122718] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 12/04/2017] [Accepted: 12/05/2017] [Indexed: 11/16/2022] Open
Abstract
Kernel discriminant analysis (KDA) is a dimension reduction and classification algorithm based on nonlinear kernel trick, which can be novelly used to treat high-dimensional and complex biological data before undergoing classification processes such as protein subcellular localization. Kernel parameters make a great impact on the performance of the KDA model. Specifically, for KDA with the popular Gaussian kernel, to select the scale parameter is still a challenging problem. Thus, this paper introduces the KDA method and proposes a new method for Gaussian kernel parameter selection depending on the fact that the differences between reconstruction errors of edge normal samples and those of interior normal samples should be maximized for certain suitable kernel parameters. Experiments with various standard data sets of protein subcellular localization show that the overall accuracy of protein classification prediction with KDA is much higher than that without KDA. Meanwhile, the kernel parameter of KDA has a great impact on the efficiency, and the proposed method can produce an optimum parameter, which makes the new algorithm not only perform as effectively as the traditional ones, but also reduce the computational time and thus improve efficiency.
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Affiliation(s)
- Shunfang Wang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, China.
| | - Bing Nie
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, China.
| | - Kun Yue
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, China.
| | - Yu Fei
- School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming 650221, China.
| | - Wenjia Li
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, China.
| | - Dongshu Xu
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, China.
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In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences. Sci Rep 2017; 7:11174. [PMID: 28894115 PMCID: PMC5593914 DOI: 10.1038/s41598-017-10724-0] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Accepted: 08/14/2017] [Indexed: 01/09/2023] Open
Abstract
Analysis of drug–target interactions (DTIs) is of great importance in developing new drug candidates for known protein targets or discovering new targets for old drugs. However, the experimental approaches for identifying DTIs are expensive, laborious and challenging. In this study, we report a novel computational method for predicting DTIs using the highly discriminative information of drug-target interactions and our newly developed discriminative vector machine (DVM) classifier. More specifically, each target protein sequence is transformed as the position-specific scoring matrix (PSSM), in which the evolutionary information is retained; then the local binary pattern (LBP) operator is used to calculate the LBP histogram descriptor. For a drug molecule, a novel fingerprint representation is utilized to describe its chemical structure information representing existence of certain functional groups or fragments. When applying the proposed method to the four datasets (Enzyme, GPCR, Ion Channel and Nuclear Receptor) for predicting DTIs, we obtained good average accuracies of 93.16%, 89.37%, 91.73% and 92.22%, respectively. Furthermore, we compared the performance of the proposed model with that of the state-of-the-art SVM model and other previous methods. The achieved results demonstrate that our method is effective and robust and can be taken as a useful tool for predicting DTIs.
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17
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Wang Y, You Z, Li X, Chen X, Jiang T, Zhang J. PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein-Protein Interactions from Protein Sequences. Int J Mol Sci 2017; 18:ijms18051029. [PMID: 28492483 PMCID: PMC5454941 DOI: 10.3390/ijms18051029] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Revised: 04/24/2017] [Accepted: 04/29/2017] [Indexed: 01/08/2023] Open
Abstract
Protein–protein interactions (PPIs) are essential for most living organisms’ process. Thus, detecting PPIs is extremely important to understand the molecular mechanisms of biological systems. Although many PPIs data have been generated by high-throughput technologies for a variety of organisms, the whole interatom is still far from complete. In addition, the high-throughput technologies for detecting PPIs has some unavoidable defects, including time consumption, high cost, and high error rate. In recent years, with the development of machine learning, computational methods have been broadly used to predict PPIs, and can achieve good prediction rate. In this paper, we present here PCVMZM, a computational method based on a Probabilistic Classification Vector Machines (PCVM) model and Zernike moments (ZM) descriptor for predicting the PPIs from protein amino acids sequences. Specifically, a Zernike moments (ZM) descriptor is used to extract protein evolutionary information from Position-Specific Scoring Matrix (PSSM) generated by Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then, PCVM classifier is used to infer the interactions among protein. When performed on PPIs datasets of Yeast and H. Pylori, the proposed method can achieve the average prediction accuracy of 94.48% and 91.25%, respectively. In order to further evaluate the performance of the proposed method, the state-of-the-art support vector machines (SVM) classifier is used and compares with the PCVM model. Experimental results on the Yeast dataset show that the performance of PCVM classifier is better than that of SVM classifier. The experimental results indicate that our proposed method is robust, powerful and feasible, which can be used as a helpful tool for proteomics research.
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Affiliation(s)
- Yanbin Wang
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China.
| | - Zhuhong You
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China.
| | - Xiao Li
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China.
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
| | - Tonghai Jiang
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China.
| | - Jingting Zhang
- Department of Mathematics and Statistics, Henan University, Kaifeng 100190, China.
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Jiao YS, Du PF. Predicting protein submitochondrial locations by incorporating the positional-specific physicochemical properties into Chou's general pseudo-amino acid compositions. J Theor Biol 2017; 416:81-87. [DOI: 10.1016/j.jtbi.2016.12.026] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Revised: 12/06/2016] [Accepted: 12/30/2016] [Indexed: 11/26/2022]
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19
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Chen M, Yang F, Kang J, Yang X, Lai X, Gao Y. Multi-Layer Identification of Highly-Potent ABCA1 Up-Regulators Targeting LXRβ Using Multiple QSAR Modeling, Structural Similarity Analysis, and Molecular Docking. Molecules 2016; 21:molecules21121639. [PMID: 27916850 PMCID: PMC6273961 DOI: 10.3390/molecules21121639] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 11/21/2016] [Accepted: 11/26/2016] [Indexed: 12/19/2022] Open
Abstract
In this study, in silico approaches, including multiple QSAR modeling, structural similarity analysis, and molecular docking, were applied to develop QSAR classification models as a fast screening tool for identifying highly-potent ABCA1 up-regulators targeting LXRβ based on a series of new flavonoids. Initially, four modeling approaches, including linear discriminant analysis, support vector machine, radial basis function neural network, and classification and regression trees, were applied to construct different QSAR classification models. The statistics results indicated that these four kinds of QSAR models were powerful tools for screening highly potent ABCA1 up-regulators. Then, a consensus QSAR model was developed by combining the predictions from these four models. To discover new ABCA1 up-regulators at maximum accuracy, the compounds in the ZINC database that fulfilled the requirement of structural similarity of 0.7 compared to known potent ABCA1 up-regulator were subjected to the consensus QSAR model, which led to the discovery of 50 compounds. Finally, they were docked into the LXRβ binding site to understand their role in up-regulating ABCA1 expression. The excellent binding modes and docking scores of 10 hit compounds suggested they were highly-potent ABCA1 up-regulators targeting LXRβ. Overall, this study provided an effective strategy to discover highly potent ABCA1 up-regulators.
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Affiliation(s)
- Meimei Chen
- College of Chemistry and Chemical Engineering, Fujian Normal University, Fuzhou 350007, Fujian, China.
- College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, Fujian, China.
| | - Fafu Yang
- College of Chemistry and Chemical Engineering, Fujian Normal University, Fuzhou 350007, Fujian, China.
| | - Jie Kang
- College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, Fujian, China.
| | - Xuemei Yang
- College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, Fujian, China.
| | - Xinmei Lai
- College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, Fujian, China.
| | - Yuxing Gao
- College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, Fujian, China.
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20
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Li ZW, You ZH, Chen X, Gui J, Nie R. Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics. Int J Mol Sci 2016; 17:ijms17091396. [PMID: 27571061 PMCID: PMC5037676 DOI: 10.3390/ijms17091396] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Revised: 08/13/2016] [Accepted: 08/16/2016] [Indexed: 12/19/2022] Open
Abstract
Protein-protein interactions (PPIs) occur at almost all levels of cell functions and play crucial roles in various cellular processes. Thus, identification of PPIs is critical for deciphering the molecular mechanisms and further providing insight into biological processes. Although a variety of high-throughput experimental techniques have been developed to identify PPIs, existing PPI pairs by experimental approaches only cover a small fraction of the whole PPI networks, and further, those approaches hold inherent disadvantages, such as being time-consuming, expensive, and having high false positive rate. Therefore, it is urgent and imperative to develop automatic in silico approaches to predict PPIs efficiently and accurately. In this article, we propose a novel mixture of physicochemical and evolutionary-based feature extraction method for predicting PPIs using our newly developed discriminative vector machine (DVM) classifier. The improvements of the proposed method mainly consist in introducing an effective feature extraction method that can capture discriminative features from the evolutionary-based information and physicochemical characteristics, and then a powerful and robust DVM classifier is employed. To the best of our knowledge, it is the first time that DVM model is applied to the field of bioinformatics. When applying the proposed method to the Yeast and Helicobacter pylori (H. pylori) datasets, we obtain excellent prediction accuracies of 94.35% and 90.61%, respectively. The computational results indicate that our method is effective and robust for predicting PPIs, and can be taken as a useful supplementary tool to the traditional experimental methods for future proteomics research.
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Affiliation(s)
- Zheng-Wei Li
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, China.
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China.
| | - Xing Chen
- School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 21116, China.
| | - Jie Gui
- Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China.
| | - Ru Nie
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, China.
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21
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An JY, You ZH, Meng FR, Xu SJ, Wang Y. RVMAB: Using the Relevance Vector Machine Model Combined with Average Blocks to Predict the Interactions of Proteins from Protein Sequences. Int J Mol Sci 2016; 17:ijms17050757. [PMID: 27213337 PMCID: PMC4881578 DOI: 10.3390/ijms17050757] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2016] [Revised: 04/29/2016] [Accepted: 05/04/2016] [Indexed: 11/16/2022] Open
Abstract
Protein-Protein Interactions (PPIs) play essential roles in most cellular processes. Knowledge of PPIs is becoming increasingly more important, which has prompted the development of technologies that are capable of discovering large-scale PPIs. Although many high-throughput biological technologies have been proposed to detect PPIs, there are unavoidable shortcomings, including cost, time intensity, and inherently high false positive and false negative rates. For the sake of these reasons, in silico methods are attracting much attention due to their good performances in predicting PPIs. In this paper, we propose a novel computational method known as RVM-AB that combines the Relevance Vector Machine (RVM) model and Average Blocks (AB) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the AB feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We performed five-fold cross-validation experiments on yeast and Helicobacter pylori datasets, and achieved very high accuracies of 92.98% and 95.58% respectively, which is significantly better than previous works. In addition, we also obtained good prediction accuracies of 88.31%, 89.46%, 91.08%, 91.55%, and 94.81% on other five independent datasets C. elegans, M. musculus, H. sapiens, H. pylori, and E. coli for cross-species prediction. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM-AB method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool. To facilitate extensive studies for future proteomics research, we developed a freely available web server called RVMAB-PPI in Hypertext Preprocessor (PHP) for predicting PPIs. The web server including source code and the datasets are available at http://219.219.62.123:8888/ppi_ab/.
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Affiliation(s)
- Ji-Yong An
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, China.
| | - Zhu-Hong You
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, China.
| | - Fan-Rong Meng
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, China.
| | - Shu-Juan Xu
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, China.
| | - Yin Wang
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, China.
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