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Wang J, Zhou H, Wang Y, Xu M, Yu Y, Wang J, Liu Y. Prediction of submitochondrial proteins localization based on Gene Ontology. Comput Biol Med 2023; 167:107589. [PMID: 37883850 DOI: 10.1016/j.compbiomed.2023.107589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/28/2023] [Accepted: 10/17/2023] [Indexed: 10/28/2023]
Abstract
Mitochondria, which are double-membrane bound organelles commonly found in eukaryotic cells, play a fundamental role as sites for cellular energy production. Within the mitochondria, there exist substructures called submitochondria, and specific proteins associated with submitochondria have been implicated in various human diseases. Therefore, comprehending the precise localization of these submitochondrial proteins is of utmost importance. Such knowledge not only aids in unraveling their role in the pathogenesis of diseases but also facilitates the development of therapeutic drugs and diagnostic methods. In this study, we proposed a novel method based on Gene Ontology (GO) to predict the localization of the submitochondrial proteins, called GO-Submito. More specifically, the GO-Submito fine-tuned pre-training Bidirectional Encoder Representations from Transformers models to encode GO annotations into vectors. Subsequently, the Multi-head Attention Mechanism was employed to fuse these encoded vectors of GO annotations, enabling precise localization prediction. Through comprehensive evaluation, our results demonstrated that GO-Submito outperforms existing methods, offering a reliable and efficient tool for precisely localizing submitochondrial proteins.
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Affiliation(s)
- Jingyu Wang
- Department of Epidemiology, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China.
| | - Haihang Zhou
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China.
| | - Yuxiang Wang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China.
| | - Mengdie Xu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China.
| | - Yun Yu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China; Institute of Medical Informatics and Management, Nanjing Medical University, 101 Longmian Avenu, Nanjing, 210029, Jiangsu, China.
| | - Junjie Wang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China; Institute of Medical Informatics and Management, Nanjing Medical University, 101 Longmian Avenu, Nanjing, 210029, Jiangsu, China.
| | - Yun Liu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China; Department of Information, the First Affiliated Hospital, Nanjing Medical University, No. 300 Guang Zhou Road, Nanjing, 210029, Jiangsu, China; Institute of Medical Informatics and Management, Nanjing Medical University, 101 Longmian Avenu, Nanjing, 210029, Jiangsu, China.
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Liu S, Cui C, Chen H, Liu T. Ensemble Learning-Based Feature Selection for Phage Protein Prediction. Front Microbiol 2022; 13:932661. [PMID: 35910662 PMCID: PMC9335128 DOI: 10.3389/fmicb.2022.932661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 06/14/2022] [Indexed: 11/14/2022] Open
Abstract
Phage has high specificity for its host recognition. As a natural enemy of bacteria, it has been used to treat super bacteria many times. Identifying phage proteins from the original sequence is very important for understanding the relationship between phage and host bacteria and developing new antimicrobial agents. However, traditional experimental methods are both expensive and time-consuming. In this study, an ensemble learning-based feature selection method is proposed to find important features for phage protein identification. The method uses four types of protein sequence-derived features, quantifies the importance of each feature by adding perturbations to the features to influence the results, and finally splices the important features among the four types of features. In addition, we analyzed the selected features and their biological significance.
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Affiliation(s)
- Songbo Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Chengmin Cui
- Beijing Institute of Control Engineering, China Academy of Space Technology, Beijing, China
| | - Huipeng Chen
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
- *Correspondence: Huipeng Chen
| | - Tong Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
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3
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Cong H, Liu H, Chen Y, Cao Y. Self-evoluting framework of deep convolutional neural network for multilocus protein subcellular localization. Med Biol Eng Comput 2020; 58:3017-3038. [PMID: 33078303 DOI: 10.1007/s11517-020-02275-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Accepted: 10/14/2020] [Indexed: 12/12/2022]
Abstract
In the present paper, deep convolutional neural network (DCNN) is applied to multilocus protein subcellular localization as it is more suitable for multi-class classification. There are two main problems with this application. First, the appropriate features for correlation between multiple sites are hard to find. Second, the classifier structure is difficult to determine as it is greatly affected by the distribution of classified data. To solve these problems, a self-evoluting framework using DCNNs for multilocus protein subcellular localization is proposed. It has three characteristics that the previous algorithms do not. The first is that it combines the ant colony algorithm with the DCNN to form a self-evoluting algorithm for multilocus protein subcellular localization. The second is that it randomly groups subcellular sites using a limited random k-labelsets multi-label classification method. It also solves complex problems in a divide-and-conquer approach and proposes a flexible expansion model. The third is that it realizes the random selection feature extraction method in the positioning process and avoids the defects in individual feature extraction methods. The algorithm in the present paper is tested on the human database, and the overall correct rate is 67.17%, which is higher than that for the stacked self-encoder (SAE), support vector machine (SVM), random forest classifier (RF), or single deep convolutional neural network.Graphical abstract The algorithm mentioned in the present paper mainly includes four parts. They are protein sequence data preprocessing, integrated DCNN model construction, finding optimal DCNN combination by ant colony optimization, and protein subcellular localization for sequences. These parts are sequential relationships and the data obtained in the previous part is the basis for the latter part of the function. In the part of data preprocessing, the limited RAkEL multi-label classification method is used to randomly group subcellular sites. At the same time, the feature fusion of protein sequences is carried out by using multiple feature extraction methods. Each combination including features and sites information corresponds to a DCNN model. In the part of finding optimal DCNN combination by ant colony optimization, the main purpose is to find the best combination of DCNN models through the global optimization ability of the ant colony algorithm. The positioning of sequences is mainly to obtain multilocus subcellular localization by the optimal model combination.
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Affiliation(s)
- Hanhan Cong
- School of Information Science and Engineering, Shandong Normal University, No. 88, Wenhua East Road, Jinan City, China.,Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Shandong Normal University, Jinan, China
| | - Hong Liu
- School of Information Science and Engineering, Shandong Normal University, No. 88, Wenhua East Road, Jinan City, China. .,Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Shandong Normal University, Jinan, China.
| | - Yuehui Chen
- School of Information Science and Engineering, University of Jinan, Jinan, China.,Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China
| | - Yi Cao
- School of Information Science and Engineering, University of Jinan, Jinan, China.,Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China
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Savojardo C, Bruciaferri N, Tartari G, Martelli PL, Casadio R. DeepMito: accurate prediction of protein sub-mitochondrial localization using convolutional neural networks. Bioinformatics 2020; 36:56-64. [PMID: 31218353 PMCID: PMC6956790 DOI: 10.1093/bioinformatics/btz512] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 05/31/2019] [Accepted: 06/17/2019] [Indexed: 11/18/2022] Open
Abstract
Motivation The correct localization of proteins in cell compartments is a key issue for their function. Particularly, mitochondrial proteins are physiologically active in different compartments and their aberrant localization contributes to the pathogenesis of human mitochondrial pathologies. Many computational methods exist to assign protein sequences to subcellular compartments such as nucleus, cytoplasm and organelles. However, a substantial lack of experimental evidence in public sequence databases hampered so far a finer grain discrimination, including also intra-organelle compartments. Results We describe DeepMito, a novel method for predicting protein sub-mitochondrial cellular localization. Taking advantage of powerful deep-learning approaches, such as convolutional neural networks, our method is able to achieve very high prediction performances when discriminating among four different mitochondrial compartments (matrix, outer, inner and intermembrane regions). The method is trained and tested in cross-validation on a newly generated, high-quality dataset comprising 424 mitochondrial proteins with experimental evidence for sub-organelle localizations. We benchmark DeepMito towards the only one recent approach developed for the same task. Results indicate that DeepMito performances are superior. Finally, genomic-scale prediction on a highly-curated dataset of human mitochondrial proteins further confirms the effectiveness of our approach and suggests that DeepMito is a good candidate for genome-scale annotation of mitochondrial protein subcellular localization. Availability and implementation The DeepMito web server as well as all datasets used in this study are available at http://busca.biocomp.unibo.it/deepmito. A standalone version of DeepMito is available on DockerHub at https://hub.docker.com/r/bolognabiocomp/deepmito. DeepMito source code is available on GitHub at https://github.com/BolognaBiocomp/deepmito Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Castrense Savojardo
- Biocomputing Group, Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Bologna, Italy
| | - Niccolò Bruciaferri
- Biocomputing Group, Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Bologna, Italy
| | - Giacomo Tartari
- Biocomputing Group, Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Bologna, Italy.,Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IBIOM), Italian National Research Council (CNR), Bari, Italy
| | - Pier Luigi Martelli
- Biocomputing Group, Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Bologna, Italy
| | - Rita Casadio
- Biocomputing Group, Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Bologna, Italy.,Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IBIOM), Italian National Research Council (CNR), Bari, Italy
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6
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Some illuminating remarks on molecular genetics and genomics as well as drug development. Mol Genet Genomics 2020; 295:261-274. [PMID: 31894399 DOI: 10.1007/s00438-019-01634-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 12/05/2019] [Indexed: 02/07/2023]
Abstract
Facing the explosive growth of biological sequences unearthed in the post-genomic age, one of the most important but also most difficult problems in computational biology is how to express a biological sequence with a discrete model or a vector, but still keep it with considerable sequence-order information or its special pattern. To deal with such a challenging problem, the ideas of "pseudo amino acid components" and "pseudo K-tuple nucleotide composition" have been proposed. The ideas and their approaches have further stimulated the birth for "distorted key theory", "wenxing diagram", and substantially strengthening the power in treating the multi-label systems, as well as the establishment of the famous "5-steps rule". All these logic developments are quite natural that are very useful not only for theoretical scientists but also for experimental scientists in conducting genetics/genomics analysis and drug development. Presented in this review paper are also their future perspectives; i.e., their impacts will become even more significant and propounding.
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Shao YT, Liu XX, Lu Z, Chou KC. pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning. ACTA ACUST UNITED AC 2020. [DOI: 10.4236/ns.2020.127042] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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8
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Ismailoglu F, Cavill R, Smirnov E, Zhou S, Collins P, Peeters R. Heterogeneous Domain Adaptation for IHC Classification of Breast Cancer Subtypes. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:347-353. [PMID: 30369448 DOI: 10.1109/tcbb.2018.2877755] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Increasingly, multiple parallel omics datasets are collected from biological samples. Integrating these datasets for classification is an open area of research. Additionally, whilst multiple datasets may be available for the training samples, future samples may only be measured by a single technology requiring methods which do not rely on the presence of all datasets for sample prediction. This enables us to directly compare the protein and the gene profiles. New samples with just one set of measurements (e.g., just protein) can then be mapped to this latent common space where classification is performed. Using this approach, we achieved an improvement of up to 12 percent in accuracy when classifying samples based on their protein measurements compared with baseline methods which were trained on the protein data alone. We illustrate that the additional inclusion of the gene expression or protein expression in the training process enabled the separation between the classes to become clearer.
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9
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Shao Y, Chou KC. pLoc_Deep-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by Deep Learning. ACTA ACUST UNITED AC 2020. [DOI: 10.4236/ns.2020.126034] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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10
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Chou KC. Advances in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs. Curr Med Chem 2019; 26:4918-4943. [PMID: 31060481 DOI: 10.2174/0929867326666190507082559] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 01/29/2019] [Accepted: 01/31/2019] [Indexed: 12/16/2022]
Abstract
The smallest unit of life is a cell, which contains numerous protein molecules. Most
of the functions critical to the cell’s survival are performed by these proteins located in its different
organelles, usually called ‘‘subcellular locations”. Information of subcellular localization
for a protein can provide useful clues about its function. To reveal the intricate pathways at the
cellular level, knowledge of the subcellular localization of proteins in a cell is prerequisite.
Therefore, one of the fundamental goals in molecular cell biology and proteomics is to determine
the subcellular locations of proteins in an entire cell. It is also indispensable for prioritizing
and selecting the right targets for drug development. Unfortunately, it is both timeconsuming
and costly to determine the subcellular locations of proteins purely based on experiments.
With the avalanche of protein sequences generated in the post-genomic age, it is highly
desired to develop computational methods for rapidly and effectively identifying the subcellular
locations of uncharacterized proteins based on their sequences information alone. Actually,
considerable progresses have been achieved in this regard. This review is focused on those
methods, which have the capacity to deal with multi-label proteins that may simultaneously
exist in two or more subcellular location sites. Protein molecules with this kind of characteristic
are vitally important for finding multi-target drugs, a current hot trend in drug development.
Focused in this review are also those methods that have use-friendly web-servers established so
that the majority of experimental scientists can use them to get the desired results without the
need to go through the detailed mathematics involved.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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11
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Abstract
The smallest unit of life is a cell, which contains numerous protein molecules. Most
of the functions critical to the cell’s survival are performed by these proteins located in its different
organelles, usually called ‘‘subcellular locations”. Information of subcellular localization
for a protein can provide useful clues about its function. To reveal the intricate pathways at the
cellular level, knowledge of the subcellular localization of proteins in a cell is prerequisite.
Therefore, one of the fundamental goals in molecular cell biology and proteomics is to determine
the subcellular locations of proteins in an entire cell. It is also indispensable for prioritizing
and selecting the right targets for drug development. Unfortunately, it is both timeconsuming
and costly to determine the subcellular locations of proteins purely based on experiments.
With the avalanche of protein sequences generated in the post-genomic age, it is highly
desired to develop computational methods for rapidly and effectively identifying the subcellular
locations of uncharacterized proteins based on their sequences information alone. Actually,
considerable progresses have been achieved in this regard. This review is focused on those
methods, which have the capacity to deal with multi-label proteins that may simultaneously
exist in two or more subcellular location sites. Protein molecules with this kind of characteristic
are vitally important for finding multi-target drugs, a current hot trend in drug development.
Focused in this review are also those methods that have use-friendly web-servers established so
that the majority of experimental scientists can use them to get the desired results without the
need to go through the detailed mathematics involved.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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12
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Yu B, Qiu W, Chen C, Ma A, Jiang J, Zhou H, Ma Q. SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting. Bioinformatics 2019; 36:1074-1081. [DOI: 10.1093/bioinformatics/btz734] [Citation(s) in RCA: 98] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 09/04/2019] [Accepted: 09/25/2019] [Indexed: 11/13/2022] Open
Abstract
Abstract
Motivation
Mitochondria are an essential organelle in most eukaryotes. They not only play an important role in energy metabolism but also take part in many critical cytopathological processes. Abnormal mitochondria can trigger a series of human diseases, such as Parkinson's disease, multifactor disorder and Type-II diabetes. Protein submitochondrial localization enables the understanding of protein function in studying disease pathogenesis and drug design.
Results
We proposed a new method, SubMito-XGBoost, for protein submitochondrial localization prediction. Three steps are included: (i) the g-gap dipeptide composition (g-gap DC), pseudo-amino acid composition (PseAAC), auto-correlation function (ACF) and Bi-gram position-specific scoring matrix (Bi-gram PSSM) are employed to extract protein sequence features, (ii) Synthetic Minority Oversampling Technique (SMOTE) is used to balance samples, and the ReliefF algorithm is applied for feature selection and (iii) the obtained feature vectors are fed into XGBoost to predict protein submitochondrial locations. SubMito-XGBoost has obtained satisfactory prediction results by the leave-one-out-cross-validation (LOOCV) compared with existing methods. The prediction accuracies of the SubMito-XGBoost method on the two training datasets M317 and M983 were 97.7% and 98.9%, which are 2.8–12.5% and 3.8–9.9% higher than other methods, respectively. The prediction accuracy of the independent test set M495 was 94.8%, which is significantly better than the existing studies. The proposed method also achieves satisfactory predictive performance on plant and non-plant protein submitochondrial datasets. SubMito-XGBoost also plays an important role in new drug design for the treatment of related diseases.
Availability and implementation
The source codes and data are publicly available at https://github.com/QUST-AIBBDRC/SubMito-XGBoost/.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Bin Yu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
- School of Life Sciences, University of Science and Technology of China, Hefei 230027, China
- Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
- School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha 410114, China
| | - Wenying Qiu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
- Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
| | - Cheng Chen
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
- Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
| | - Anjun Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Jing Jiang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
- School of Aerospace Engineering, Xiamen University, Xiamen 361001, China
| | - Hongyan Zhou
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
- Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
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13
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Chou KC. Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses. Int J Pept Res Ther 2019. [DOI: 10.1007/s10989-019-09910-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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15
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Xiao X, Cheng X, Chen G, Mao Q, Chou KC. pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset. Med Chem 2019; 15:496-509. [DOI: 10.2174/1573406415666181217114710] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 10/23/2018] [Accepted: 12/12/2018] [Indexed: 12/17/2022]
Abstract
Background/Objective:Knowledge of protein subcellular localization is vitally important for both basic research and drug development. Facing the avalanche of protein sequences emerging in the post-genomic age, it is urgent to develop computational tools for timely and effectively identifying their subcellular localization based on the sequence information alone. Recently, a predictor called “pLoc-mVirus” was developed for identifying the subcellular localization of virus proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems in which some proteins, known as “multiplex proteins”, may simultaneously occur in, or move between two or more subcellular location sites. Despite the fact that it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mVirus was trained by an extremely skewed dataset in which some subset was over 10 times the size of the other subsets. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset.Methods:Using the Chou's general PseAAC (Pseudo Amino Acid Composition) approach and the IHTS (Inserting Hypothetical Training Samples) treatment to balance out the training dataset, we have developed a new predictor called “pLoc_bal-mVirus” for predicting the subcellular localization of multi-label virus proteins.Results:Cross-validation tests on exactly the same experiment-confirmed dataset have indicated that the proposed new predictor is remarkably superior to pLoc-mVirus, the existing state-of-theart predictor for the same purpose.Conclusion:Its user-friendly web-server is available at http://www.jci-bioinfo.cn/pLoc_balmVirus/, by which the majority of experimental scientists can easily get their desired results without the need to go through the detailed complicated mathematics. Accordingly, pLoc_bal-mVirus will become a very useful tool for designing multi-target drugs and in-depth understanding of the biological process in a cell.
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Affiliation(s)
- Xuan Xiao
- Gordon Life Science Institute, Boston, MA 02478, United States
| | - Xiang Cheng
- Gordon Life Science Institute, Boston, MA 02478, United States
| | - Genqiang Chen
- College of Chemistry, Chemical Engineering and Biotechnology, Donghua University, Shanghai 201620, China
| | - Qi Mao
- College of Information Science and Technology, Donghua University, Shanghai, China
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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Yang W, Zhu XJ, Huang J, Ding H, Lin H. A Brief Survey of Machine Learning Methods in Protein Sub-Golgi Localization. Curr Bioinform 2019. [DOI: 10.2174/1574893613666181113131415] [Citation(s) in RCA: 111] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background:The location of proteins in a cell can provide important clues to their functions in various biological processes. Thus, the application of machine learning method in the prediction of protein subcellular localization has become a hotspot in bioinformatics. As one of key organelles, the Golgi apparatus is in charge of protein storage, package, and distribution.Objective:The identification of protein location in Golgi apparatus will provide in-depth insights into their functions. Thus, the machine learning-based method of predicting protein location in Golgi apparatus has been extensively explored. The development of protein sub-Golgi apparatus localization prediction should be reviewed for providing a whole background for the fields.Method:The benchmark dataset, feature extraction, machine learning method and published results were summarized.Results:We briefly introduced the recent progresses in protein sub-Golgi apparatus localization prediction using machine learning methods and discussed their advantages and disadvantages.Conclusion:We pointed out the perspective of machine learning methods in protein sub-Golgi localization prediction.
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Affiliation(s)
- Wuritu Yang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China
| | - Xiao-Juan Zhu
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China
| | - Jian Huang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China
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17
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Xiao X, Xu ZC, Qiu WR, Wang P, Ge HT, Chou KC. iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition. Genomics 2018; 111:1785-1793. [PMID: 30529532 DOI: 10.1016/j.ygeno.2018.12.001] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 11/20/2018] [Accepted: 12/04/2018] [Indexed: 12/20/2022]
Abstract
The promoter is a regulatory DNA region about 81-1000 base pairs long, usually located near the transcription start site (TSS) along upstream of a given gene. By combining a certain protein called transcription factor, the promoter provides the starting point for regulated gene transcription, and hence plays a vitally important role in gene transcriptional regulation. With explosive growth of DNA sequences in the post-genomic age, it has become an urgent challenge to develop computational method for effectively identifying promoters because the information thus obtained is very useful for both basic research and drug development. Although some prediction methods were developed in this regard, most of them were limited at merely identifying whether a query DNA sequence being of a promoter or not. However, based on their strength-distinct levels for transcriptional activation and expression, promoter should be divided into two categories: strong and weak types. Here a new two-layer predictor, called "iPSW(2L)-PseKNC", was developed by fusing the physicochemical properties of nucleotides and their nucleotide density into PseKNC (pseudo K-tuple nucleotide composition). Its 1st-layer serves to predict whether a query DNA sequence sample is of promoter or not, while its 2nd-layer is able to predict the strength of promoters. It has been observed through rigorous cross-validations that the 1st-layer sub-predictor is remarkably superior to the existing state-of-the-art predictors in identifying the promoters and non-promoters, and that the 2nd-layer sub-predictor can do what is beyond the reach of the existing predictors. Moreover, the web-server for iPSW(2L)-PseKNC has been established at http://www.jci-bioinfo.cn/iPSW(2L)-PseKNC, by which the majority of experimental scientists can easily get the results they need.
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Affiliation(s)
- Xuan Xiao
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China; The Gordon Life Science Institute, Boston, MA 02478, USA.
| | - Zhao-Chun Xu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.
| | - Wang-Ren Qiu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China; The Gordon Life Science Institute, Boston, MA 02478, USA
| | - Peng Wang
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Hui-Ting Ge
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Kuo-Chen Chou
- The Gordon Life Science Institute, Boston, MA 02478, USA; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
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Kumar R, Kumari B, Kumar M. Proteome-wide prediction and annotation of mitochondrial and sub-mitochondrial proteins by incorporating domain information. Mitochondrion 2018; 42:11-22. [DOI: 10.1016/j.mito.2017.10.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Revised: 07/21/2017] [Accepted: 10/06/2017] [Indexed: 12/22/2022]
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Hoseini ASH, Mirzarezaee M. Prediction of Protein Sub-Mitochondria Locations Using Protein Interaction Networks. IRANIAN JOURNAL OF BIOTECHNOLOGY 2018; 16:e1933. [PMID: 31457027 PMCID: PMC6697825 DOI: 10.15171/ijb.1933] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2017] [Revised: 01/11/2018] [Accepted: 01/13/2018] [Indexed: 01/09/2023]
Abstract
Background Prediction of the protein localization is among the most important issues in the bioinformatics that is used for the prediction of the proteins in the cells and organelles such as mitochondria. In this study, several machine learning algorithms are applied for the prediction of the intracellular protein locations. These algorithms use the features extracted from protein sequences. In contrast, protein interactions have been less investigated. Objectives As protein interactions usually occur in the same or adjacent places, using this feature to find the location would be efficient and impressive. This study did not aim at increasing the total accuracy of the conducted research. The study has focused on the features of the proteins’ interaction and their employment which lead to a higher accuracy. Materials and Methods In this study, we have examined the protein interaction network as one of the features for prediction of the protein localization and its effects on the prediction results. In this regards, we have gathered some of the most common features including Amino Acid Composition, Dipeptide Compositions, Pseudo Amino Acid Compositions (PseAAC), Position Specific Scoring Matrix (PSSM), Functional Domain, Gene Ontology information, and the Pair-wise sequence alignment. The results of the classification are compared to the ones using protein interactions. For achieving this goal different machine learning algorithms were tested. Results The best-obtained results of using single feature set obtained using SVM classifier for PseAAC feature. The accuracy of combining all features with PPI data, using the Decision Tree and Random Forest classifiers, was 82.49% and 83.35%, respectively. In another experiment, using just protein interaction data with the different cutting points resulted in obtaining an accuracy of 93.035% for the protein location prediction. Conclusion In total, it was shown that protein(s) interaction has a significant impact on the prediction of the mitochondrial proteins’ location. This feature can separately distinguish the locations well. Using this feature the accuracy of the results is raised up to 5%.
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Affiliation(s)
| | - Mitra Mirzarezaee
- Department of Computer Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran.,School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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Uddin MR, Sharma A, Farid DM, Rahman MM, Dehzangi A, Shatabda S. EvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features. J Theor Biol 2018; 443:138-146. [DOI: 10.1016/j.jtbi.2018.02.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2017] [Revised: 01/18/2018] [Accepted: 02/03/2018] [Indexed: 12/21/2022]
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21
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Wang Y, Song J, Marquez-Lago TT, Leier A, Li C, Lithgow T, Webb GI, Shen HB. Knowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites. Sci Rep 2017; 7:5755. [PMID: 28720874 PMCID: PMC5515926 DOI: 10.1038/s41598-017-06219-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Accepted: 06/08/2017] [Indexed: 11/24/2022] Open
Abstract
Matrix Metalloproteases (MMPs) are an important family of proteases that play crucial roles in key cellular and disease processes. Therefore, MMPs constitute important targets for drug design, development and delivery. Advanced proteomic technologies have identified type-specific target substrates; however, the complete repertoire of MMP substrates remains uncharacterized. Indeed, computational prediction of substrate-cleavage sites associated with MMPs is a challenging problem. This holds especially true when considering MMPs with few experimentally verified cleavage sites, such as for MMP-2, -3, -7, and -8. To fill this gap, we propose a new knowledge-transfer computational framework which effectively utilizes the hidden shared knowledge from some MMP types to enhance predictions of other, distinct target substrate-cleavage sites. Our computational framework uses support vector machines combined with transfer machine learning and feature selection. To demonstrate the value of the model, we extracted a variety of substrate sequence-derived features and compared the performance of our method using both 5-fold cross-validation and independent tests. The results show that our transfer-learning-based method provides a robust performance, which is at least comparable to traditional feature-selection methods for prediction of MMP-2, -3, -7, -8, -9 and -12 substrate-cleavage sites on independent tests. The results also demonstrate that our proposed computational framework provides a useful alternative for the characterization of sequence-level determinants of MMP-substrate specificity.
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Affiliation(s)
- Yanan Wang
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, 3800, Australia
| | - Jiangning Song
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, 3800, Australia
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, 3800, Australia
- ARC Centre of Excellence for Advanced Molecular Imaging, Monash University, Melbourne, VIC, 3800, Australia
| | - Tatiana T Marquez-Lago
- Informatics Institute, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
- Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - André Leier
- Informatics Institute, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
- Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - Chen Li
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, 3800, Australia
| | - Trevor Lithgow
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, 3800, Australia.
| | - Geoffrey I Webb
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, 3800, Australia.
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China.
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Liu B, Wu H, Chou KC. Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences. ACTA ACUST UNITED AC 2017. [DOI: 10.4236/ns.2017.94007] [Citation(s) in RCA: 91] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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23
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Protein subcellular localization of fluorescence microscopy images: Employing new statistical and Texton based image features and SVM based ensemble classification. Inf Sci (N Y) 2016. [DOI: 10.1016/j.ins.2016.01.064] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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24
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Breckels LM, Holden SB, Wojnar D, Mulvey CM, Christoforou A, Groen A, Trotter MWB, Kohlbacher O, Lilley KS, Gatto L. Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics. PLoS Comput Biol 2016; 12:e1004920. [PMID: 27175778 PMCID: PMC4866734 DOI: 10.1371/journal.pcbi.1004920] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Accepted: 04/16/2016] [Indexed: 11/19/2022] Open
Abstract
Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis. Sub-cellular localisation of proteins is critical to their function in all cellular processes; proteins localising to their intended micro-environment, e.g organelles, vesicles or macro-molecular complexes, will meet the interaction partners and biochemical conditions suitable to pursue their molecular function. Therefore, sound data and methods to reliably and systematically study protein localisation, and hence their mis-localisation and the disruption of protein trafficking, that are relied upon by the cell biology community, are essential. Here we present a method to infer protein localisation relying on the optimal integration of experimental mass spectrometry-based data and auxiliary sources, such as GO annotation, outputs from third-party software, protein-protein interactions or immunocytochemistry data. We found that the application of transfer learning algorithms across these diverse data sources considerably improves on the quantity and reliability of sub-cellular protein assignment, compared to single data classifiers previously applied to infer sub-cellular localisation using experimental data only. We show how our method does not compromise biologically relevant experimental-specific signal after integration with heterogeneous freely available third-party resources. The integration of different data sources is an important challenge in the data intensive world of biology and we anticipate the transfer learning methods presented here will prove useful to many areas of biology, to unify data obtained from different but complimentary sources.
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Affiliation(s)
- Lisa M. Breckels
- Computational Proteomics Unit, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
| | - Sean B. Holden
- Computer Laboratory, University of Cambridge, Cambridge, United Kingdom
| | - David Wojnar
- Quantitative Biology Center, Universität Tübingen, Tübingen, Germany
| | - Claire M. Mulvey
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
| | - Andy Christoforou
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
| | - Arnoud Groen
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
| | | | - Oliver Kohlbacher
- Quantitative Biology Center, Universität Tübingen, Tübingen, Germany
- Center for Bioinformatics, Universität Tübingen, Tübingen, Germany
- Biomolecular Interactions, Max Planck Institute for Developmental Biology, Tübingen, Germany
| | - Kathryn S. Lilley
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
| | - Laurent Gatto
- Computational Proteomics Unit, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
- * E-mail:
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25
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Classifying Multifunctional Enzymes by Incorporating Three Different Models into Chou’s General Pseudo Amino Acid Composition. J Membr Biol 2016; 249:551-7. [DOI: 10.1007/s00232-016-9904-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2015] [Accepted: 04/11/2016] [Indexed: 10/21/2022]
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26
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Liu B, Liu F, Fang L, Wang X, Chou KC. repRNA: a web server for generating various feature vectors of RNA sequences. Mol Genet Genomics 2015; 291:473-81. [DOI: 10.1007/s00438-015-1078-7] [Citation(s) in RCA: 102] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Accepted: 06/04/2015] [Indexed: 10/23/2022]
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27
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Liu B, Chen J, Wang X. Protein remote homology detection by combining Chou’s distance-pair pseudo amino acid composition and principal component analysis. Mol Genet Genomics 2015; 290:1919-31. [DOI: 10.1007/s00438-015-1044-4] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Accepted: 04/06/2015] [Indexed: 02/07/2023]
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28
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Kumar R, Srivastava A, Kumari B, Kumar M. Prediction of β-lactamase and its class by Chou’s pseudo-amino acid composition and support vector machine. J Theor Biol 2015; 365:96-103. [DOI: 10.1016/j.jtbi.2014.10.008] [Citation(s) in RCA: 125] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2014] [Revised: 10/01/2014] [Accepted: 10/06/2014] [Indexed: 01/01/2023]
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29
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Chen W, Lin H, Chou KC. Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. MOLECULAR BIOSYSTEMS 2015; 11:2620-34. [DOI: 10.1039/c5mb00155b] [Citation(s) in RCA: 262] [Impact Index Per Article: 29.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
With the avalanche of DNA/RNA sequences generated in the post-genomic age, it is urgent to develop automated methods for analyzing the relationship between the sequences and their functions.
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Affiliation(s)
- Wei Chen
- Department of Physics
- School of Sciences
- and Center for Genomics and Computational Biology
- Hebei United University
- Tangshan 063000
| | - Hao Lin
- Gordon Life Science Institute
- Boston
- USA
- Key Laboratory for Neuro-Information of Ministry of Education
- Center of Bioinformatics
| | - Kuo-Chen Chou
- Department of Physics
- School of Sciences
- and Center for Genomics and Computational Biology
- Hebei United University
- Tangshan 063000
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30
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Li L, Yu S, Xiao W, Li Y, Hu W, Huang L, Zheng X, Zhou S, Yang H. Protein submitochondrial localization from integrated sequence representation and SVM-based backward feature extraction. MOLECULAR BIOSYSTEMS 2015; 11:170-7. [PMID: 25335193 DOI: 10.1039/c4mb00340c] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Mitochondrion, a tiny energy factory, plays an important role in various biological processes of most eukaryotic cells.
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Affiliation(s)
- Liqi Li
- Department of General Surgery
- Xinqiao Hospital
- Third Military Medical University
- Chongqing 400037
- China
| | - Sanjiu Yu
- Institute of Cardiovascular Diseases of PLA
- Xinqiao Hospital
- Third Military Medical University
- Chongqing 400037
- China
| | - Weidong Xiao
- Department of General Surgery
- Xinqiao Hospital
- Third Military Medical University
- Chongqing 400037
- China
| | - Yongsheng Li
- Institute of Cancer
- Xinqiao Hospital
- Third Military Medical University
- Chongqing 400037
- China
| | - Wenjuan Hu
- Department of Pathophysiology and High Altitude Pathology
- College of High Altitude Military Medicine
- Third Military Medical University
- Chongqing 400038
- China
| | - Lan Huang
- Institute of Cardiovascular Diseases of PLA
- Xinqiao Hospital
- Third Military Medical University
- Chongqing 400037
- China
| | - Xiaoqi Zheng
- Department of Mathematics
- Shanghai Normal University
- Shanghai 200234
- China
| | - Shiwen Zhou
- National Drug Clinical Trial Institution
- Xinqiao Hospital
- Third Military Medical University
- Chongqing 400037
- China
| | - Hua Yang
- Department of General Surgery
- Xinqiao Hospital
- Third Military Medical University
- Chongqing 400037
- China
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31
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Qiu WR, Xiao X, Lin WZ, Chou KC. iUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model. J Biomol Struct Dyn 2014; 33:1731-42. [PMID: 25248923 DOI: 10.1080/07391102.2014.968875] [Citation(s) in RCA: 126] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
As one of the most important posttranslational modifications (PTMs), ubiquitination plays an important role in regulating varieties of biological processes, such as signal transduction, cell division, apoptosis, and immune response. Ubiquitination is also named "lysine ubiquitination" because it occurs when an ubiquitin is covalently attached to lysine (K) residues of targeting proteins. Given an uncharacterized protein sequence that contains many lysine residues, which one of them is the ubiquitination site, and which one is of non-ubiquitination site? With the avalanche of protein sequences generated in the postgenomic age, it is highly desired for both basic research and drug development to develop an automated method for rapidly and accurately annotating the ubiquitination sites in proteins. In view of this, a new predictor called "iUbiq-Lys" was developed based on the evolutionary information, gray system model, as well as the general form of pseudo-amino acid composition. It was demonstrated via the rigorous cross-validations that the new predictor remarkably outperformed all its counterparts. As a web-server, iUbiq-Lys is accessible to the public at http://www.jci-bioinfo.cn/iUbiq-Lys . For the convenience of most experimental scientists, we have further provided a protocol of step-by-step guide, by which users can easily get their desired results without the need to follow the complicated mathematics that were presented in this paper just for the integrity of its development process.
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Affiliation(s)
- Wang-Ren Qiu
- a Computer Department, Jing-De-Zhen Ceramic Institute , Jing-De-Zhen 333403 , China
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Pacharawongsakda E, Theeramunkong T. Predict subcellular locations of singleplex and multiplex proteins by semi-supervised learning and dimension-reducing general mode of Chou's PseAAC. IEEE Trans Nanobioscience 2014; 12:311-20. [PMID: 23864226 DOI: 10.1109/tnb.2013.2272014] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Predicting protein subcellular location is one of major challenges in Bioinformatics area since such knowledge helps us understand protein functions and enables us to select the targeted proteins during drug discovery process. While many computational techniques have been proposed to improve predictive performance for protein subcellular location, they have several shortcomings. In this work, we propose a method to solve three main issues in such techniques; i) manipulation of multiplex proteins which may exist or move between multiple cellular compartments, ii) handling of high dimensionality in input and output spaces and iii) requirement of sufficient labeled data for model training. Towards these issues, this work presents a new computational method for predicting proteins which have either single or multiple locations. The proposed technique, namely iFLAST-CORE, incorporates the dimensionality reduction in the feature and label spaces with co-training paradigm for semi-supervised multi-label classification. For this purpose, the Singular Value Decomposition (SVD) is applied to transform the high-dimensional feature space and label space into the lower-dimensional spaces. After that, due to limitation of labeled data, the co-training regression makes use of unlabeled data by predicting the target values in the lower-dimensional spaces of unlabeled data. In the last step, the component of SVD is used to project labels in the lower-dimensional space back to those in the original space and an adaptive threshold is used to map a numeric value to a binary value for label determination. A set of experiments on viral proteins and gram-negative bacterial proteins evidence that our proposed method improve the classification performance in terms of various evaluation metrics such as Aiming (or Precision), Coverage (or Recall) and macro F-measure, compared to the traditional method that uses only labeled data.
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Xu Y, Wen X, Wen LS, Wu LY, Deng NY, Chou KC. iNitro-Tyr: prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition. PLoS One 2014; 9:e105018. [PMID: 25121969 PMCID: PMC4133382 DOI: 10.1371/journal.pone.0105018] [Citation(s) in RCA: 167] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Accepted: 07/16/2014] [Indexed: 12/31/2022] Open
Abstract
Nitrotyrosine is one of the post-translational modifications (PTMs) in proteins that occurs when their tyrosine residue is nitrated. Compared with healthy people, a remarkably increased level of nitrotyrosine is detected in those suffering from rheumatoid arthritis, septic shock, and coeliac disease. Given an uncharacterized protein sequence that contains many tyrosine residues, which one of them can be nitrated and which one cannot? This is a challenging problem, not only directly related to in-depth understanding the PTM’s mechanism but also to the nitrotyrosine-based drug development. Particularly, with the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop a high throughput tool in this regard. Here, a new predictor called “iNitro-Tyr” was developed by incorporating the position-specific dipeptide propensity into the general pseudo amino acid composition for discriminating the nitrotyrosine sites from non-nitrotyrosine sites in proteins. It was demonstrated via the rigorous jackknife tests that the new predictor not only can yield higher success rate but also is much more stable and less noisy. A web-server for iNitro-Tyr is accessible to the public at http://app.aporc.org/iNitro-Tyr/. For the convenience of most experimental scientists, we have further provided a protocol of step-by-step guide, by which users can easily get their desired results without the need to follow the complicated mathematics that were presented in this paper just for the integrity of its development process. It has not escaped our notice that the approach presented here can be also used to deal with the other PTM sites in proteins.
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Affiliation(s)
- Yan Xu
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, China
- * E-mail:
| | - Xin Wen
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, China
| | - Li-Shu Wen
- College of Sciences, Liaoning Shiyou University, FuShun, China
| | - Ling-Yun Wu
- Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
| | - Nai-Yang Deng
- College of Science, China Agricultural University, Beijing, China
| | - Kuo-Chen Chou
- Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
- Gordon Life Science Institute, Boston, Massachusetts, United States of America
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iCTX-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels. BIOMED RESEARCH INTERNATIONAL 2014; 2014:286419. [PMID: 24991545 PMCID: PMC4058692 DOI: 10.1155/2014/286419] [Citation(s) in RCA: 137] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2014] [Revised: 04/22/2014] [Accepted: 05/07/2014] [Indexed: 11/30/2022]
Abstract
Conotoxins are small disulfide-rich neurotoxic peptides, which can bind to ion channels with very high specificity and modulate their activities. Over the last few decades, conotoxins have been the drug candidates for treating chronic pain, epilepsy, spasticity, and cardiovascular diseases. According to their functions and targets, conotoxins are generally categorized into three types: potassium-channel type, sodium-channel type, and calcium-channel types. With the avalanche of peptide sequences generated in the postgenomic age, it is urgent and challenging to develop an automated method for rapidly and accurately identifying the types of conotoxins based on their sequence information alone. To address this challenge, a new predictor, called iCTX-Type, was developed by incorporating the dipeptide occurrence frequencies of a conotoxin sequence into a 400-D (dimensional) general pseudoamino acid composition, followed by the feature optimization procedure to reduce the sample representation from 400-D to 50-D vector. The overall success rate achieved by iCTX-Type via a rigorous cross-validation was over 91%, outperforming its counterpart (RBF network). Besides, iCTX-Type is so far the only predictor in this area with its web-server available, and hence is particularly useful for most experimental scientists to get their desired results without the need to follow the complicated mathematics involved.
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35
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iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. BIOMED RESEARCH INTERNATIONAL 2014; 2014:623149. [PMID: 24967386 PMCID: PMC4055483 DOI: 10.1155/2014/623149] [Citation(s) in RCA: 97] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2014] [Revised: 04/22/2014] [Accepted: 04/23/2014] [Indexed: 11/17/2022]
Abstract
In eukaryotic genes, exons are generally interrupted by introns. Accurately removing introns and joining exons together are essential processes in eukaryotic gene expression. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapid and effective detection of splice sites that play important roles in gene structure annotation and even in RNA splicing. Although a series of computational methods were proposed for splice site identification, most of them neglected the intrinsic local structural properties. In the present study, a predictor called “iSS-PseDNC” was developed for identifying splice sites. In the new predictor, the sequences were formulated by a novel feature-vector called “pseudo dinucleotide composition” (PseDNC) into which six DNA local structural properties were incorporated. It was observed by the rigorous cross-validation tests on two benchmark datasets that the overall success rates achieved by iSS-PseDNC in identifying splice donor site and splice acceptor site were 85.45% and 87.73%, respectively. It is anticipated that iSS-PseDNC may become a useful tool for identifying splice sites and that the six DNA local structural properties described in this paper may provide novel insights for in-depth investigations into the mechanism of RNA splicing.
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36
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Fan YN, Xiao X, Min JL, Chou KC. iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking. Int J Mol Sci 2014; 15:4915-37. [PMID: 24651462 PMCID: PMC3975431 DOI: 10.3390/ijms15034915] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2014] [Revised: 02/12/2014] [Accepted: 02/16/2014] [Indexed: 12/20/2022] Open
Abstract
Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.
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Affiliation(s)
- Yue-Nong Fan
- Computer Department, Jing-De-Zhen Ceramic Institute, Jingdezhen 333046, Jiangxi, China.
| | - Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jingdezhen 333046, Jiangxi, China.
| | - Jian-Liang Min
- Computer Department, Jing-De-Zhen Ceramic Institute, Jingdezhen 333046, Jiangxi, China.
| | - Kuo-Chen Chou
- Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.
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Du P, Gu S, Jiao Y. PseAAC-General: fast building various modes of general form of Chou's pseudo-amino acid composition for large-scale protein datasets. Int J Mol Sci 2014; 15:3495-506. [PMID: 24577312 PMCID: PMC3975349 DOI: 10.3390/ijms15033495] [Citation(s) in RCA: 242] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2014] [Revised: 02/13/2014] [Accepted: 02/14/2014] [Indexed: 11/16/2022] Open
Abstract
The general form pseudo-amino acid composition (PseAAC) has been widely used to represent protein sequences in predicting protein structural and functional attributes. We developed the program PseAAC-General to generate various different modes of Chou’s general PseAAC, such as the gene ontology mode, the functional domain mode, and the sequential evolution mode. This program allows the users to define their own desired modes. In every mode, 544 physicochemical properties of the amino acids are available for choosing. The computing efficiency is at least 100 times that of existing programs, which makes it able to facilitate the extensive studies on proteins and peptides. The PseAAC-General is freely available via SourceForge. It runs on both Linux and Windows.
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Affiliation(s)
- Pufeng Du
- School of Computer Science and Technology, Tianjin University, Tianjin 300072, China.
| | - Shuwang Gu
- School of Computer Science and Technology, Tianjin University, Tianjin 300072, China.
| | - Yasen Jiao
- School of Computer Science and Technology, Tianjin University, Tianjin 300072, China.
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38
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iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components. Int J Mol Sci 2014; 15:1746-66. [PMID: 24469313 PMCID: PMC3958819 DOI: 10.3390/ijms15021746] [Citation(s) in RCA: 211] [Impact Index Per Article: 21.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2014] [Revised: 01/14/2014] [Accepted: 01/16/2014] [Indexed: 01/22/2023] Open
Abstract
Meiosis and recombination are the two opposite aspects that coexist in a DNA system. As a driving force for evolution by generating natural genetic variations, meiotic recombination plays a very important role in the formation of eggs and sperm. Interestingly, the recombination does not occur randomly across a genome, but with higher probability in some genomic regions called “hotspots”, while with lower probability in so-called “coldspots”. With the ever-increasing amount of genome sequence data in the postgenomic era, computational methods for effectively identifying the hotspots and coldspots have become urgent as they can timely provide us with useful insights into the mechanism of meiotic recombination and the process of genome evolution as well. To meet the need, we developed a new predictor called “iRSpot-TNCPseAAC”, in which a DNA sample was formulated by combining its trinucleotide composition (TNC) and the pseudo amino acid components (PseAAC) of the protein translated from the DNA sample according to its genetic codes. The former was used to incorporate its local or short-rage sequence order information; while the latter, its global and long-range one. Compared with the best existing predictor in this area, iRSpot-TNCPseAAC achieved higher rates in accuracy, Mathew’s correlation coefficient, and sensitivity, indicating that the new predictor may become a useful tool for identifying the recombination hotspots and coldspots, or, at least, become a complementary tool to the existing methods. It has not escaped our notice that the aforementioned novel approach to incorporate the DNA sequence order information into a discrete model may also be used for many other genome analysis problems. The web-server for iRSpot-TNCPseAAC is available at http://www.jci-bioinfo.cn/iRSpot-TNCPseAAC. Furthermore, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the current web server to obtain their desired result without the need to follow the complicated mathematical equations.
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Li X, Wu X, Wu G. Robust feature generation for protein subchloroplast location prediction with a weighted GO transfer model. J Theor Biol 2014; 347:84-94. [PMID: 24423409 DOI: 10.1016/j.jtbi.2014.01.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2013] [Revised: 10/17/2013] [Accepted: 01/03/2014] [Indexed: 10/25/2022]
Abstract
Chloroplasts are crucial organelles of green plants and eukaryotic algae since they conduct photosynthesis. Predicting the subchloroplast location of a protein can provide important insights for understanding its biological functions. The performance of subchloroplast location prediction algorithms often depends on deriving predictive and succinct features from genomic and proteomic data. In this work, a novel weighted Gene Ontology (GO) transfer model is proposed to generate discriminating features from sequence data and GO Categories. This model contains two components. First, we transfer the GO terms of the homologous protein, and then assign the bit-score as weights to GO features. Second, we employ term-selection methods to determine weights for GO terms. This model is capable of improving prediction accuracy due to the tolerance of the noise derived from homolog knowledge transfer. The proposed weighted GO transfer method based on bit-score and a logarithmic transformation of CHI-square (WS-LCHI) performs better than the baseline models, and also outperforms the four off-the-shelf subchloroplast prediction methods.
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Affiliation(s)
- Xiaomei Li
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, PR China.
| | - Xindong Wu
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, PR China; Department of Computer Science, University of Vermont, Burlington, VT 50405, USA.
| | - Gongqing Wu
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, PR China.
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40
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Protein subcellular localization in human and hamster cell lines: Employing local ternary patterns of fluorescence microscopy images. J Theor Biol 2014; 340:85-95. [DOI: 10.1016/j.jtbi.2013.08.017] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Revised: 07/09/2013] [Accepted: 08/15/2013] [Indexed: 11/21/2022]
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41
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Probability weighted ensemble transfer learning for predicting interactions between HIV-1 and human proteins. PLoS One 2013; 8:e79606. [PMID: 24260261 PMCID: PMC3832534 DOI: 10.1371/journal.pone.0079606] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Accepted: 09/24/2013] [Indexed: 11/20/2022] Open
Abstract
Reconstruction of host-pathogen protein interaction networks is of great significance to reveal the underlying microbic pathogenesis. However, the current experimentally-derived networks are generally small and should be augmented by computational methods for less-biased biological inference. From the point of view of computational modelling, data scarcity, data unavailability and negative data sampling are the three major problems for host-pathogen protein interaction networks reconstruction. In this work, we are motivated to address the three concerns and propose a probability weighted ensemble transfer learning model for HIV-human protein interaction prediction (PWEN-TLM), where support vector machine (SVM) is adopted as the individual classifier of the ensemble model. In the model, data scarcity and data unavailability are tackled by homolog knowledge transfer. The importance of homolog knowledge is measured by the ROC-AUC metric of the individual classifiers, whose outputs are probability weighted to yield the final decision. In addition, we further validate the assumption that only the homolog knowledge is sufficient to train a satisfactory model for host-pathogen protein interaction prediction. Thus the model is more robust against data unavailability with less demanding data constraint. As regards with negative data construction, experiments show that exclusiveness of subcellular co-localized proteins is unbiased and more reliable than random sampling. Last, we conduct analysis of overlapped predictions between our model and the existing models, and apply the model to novel host-pathogen PPIs recognition for further biological research.
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42
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Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou's pseudo amino acid compositions. J Theor Biol 2013; 335:205-12. [DOI: 10.1016/j.jtbi.2013.06.034] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2013] [Revised: 05/26/2013] [Accepted: 06/29/2013] [Indexed: 12/19/2022]
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43
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Mei S. SVM ensemble based transfer learning for large-scale membrane proteins discrimination. J Theor Biol 2013; 340:105-10. [PMID: 24050851 DOI: 10.1016/j.jtbi.2013.09.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2013] [Revised: 09/04/2013] [Accepted: 09/06/2013] [Indexed: 11/16/2022]
Abstract
Membrane proteins play important roles in molecular trans-membrane transport, ligand-receptor recognition, cell-cell interaction, enzyme catalysis, host immune defense response and infectious disease pathways. Up to present, discriminating membrane proteins remains a challenging problem from the viewpoints of biological experimental determination and computational modeling. This work presents SVM ensemble based transfer learning model for membrane proteins discrimination (SVM-TLM). To reduce the data constraints on computational modeling, this method investigates the effectiveness of transferring the homolog knowledge to the target membrane proteins under the framework of probability weighted ensemble learning. As compared to multiple kernel learning based transfer learning model, the method takes the advantages of sparseness based SVM optimization on large data, thus more computationally efficient for large protein data analysis. The experiments on large membrane protein benchmark dataset show that SVM-TLM achieves significantly better cross validation performance than the baseline model.
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Affiliation(s)
- Suyu Mei
- Software College, Shenyang Normal University, Shenyang, China.
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44
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Xiaohui N, Nana L, Jingbo X, Dingyan C, Yuehua P, Yang X, Weiquan W, Dongming W, Zengzhen W. Using the concept of Chou's pseudo amino acid composition to predict protein solubility: An approach with entropies in information theory. J Theor Biol 2013; 332:211-7. [DOI: 10.1016/j.jtbi.2013.03.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2012] [Revised: 03/10/2013] [Accepted: 03/11/2013] [Indexed: 11/15/2022]
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45
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SubMito-PSPCP: predicting protein submitochondrial locations by hybridizing positional specific physicochemical properties with pseudoamino acid compositions. BIOMED RESEARCH INTERNATIONAL 2013; 2013:263829. [PMID: 24027753 PMCID: PMC3763570 DOI: 10.1155/2013/263829] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/12/2013] [Revised: 07/10/2013] [Accepted: 07/20/2013] [Indexed: 11/17/2022]
Abstract
Knowing the submitochondrial location of a mitochondrial protein is an important step in understanding its function. We developed a new method for predicting protein submitochondrial locations by introducing a new concept: positional specific physicochemical properties. With the framework of general form pseudoamino acid compositions, our method used only about 100 features to represent protein sequences, which is much simpler than the existing methods. On the dataset of SubMito, our method achieved over 93% overall accuracy, with 98.60% for inner membrane, 93.90% for matrix, and 70.70% for outer membrane, which are comparable to all state-of-the-art methods. As our method can be used as a general method to upgrade all pseudoamino-acid-composition-based methods, it should be very useful in future studies. We implement our method as an online service: SubMito-PSPCP.
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46
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Using over-represented tetrapeptides to predict protein submitochondria locations. Acta Biotheor 2013; 61:259-68. [PMID: 23475502 DOI: 10.1007/s10441-013-9181-9] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2012] [Accepted: 02/23/2013] [Indexed: 01/25/2023]
Abstract
The mitochondrion is a key organelle of eukaryotic cell that provides the energy for cellular activities. Correctly identifying submitochondria locations of proteins can provide plentiful information for understanding their functions. However, using web-experimental methods to recognize submitochondria locations of proteins are time-consuming and costly. Thus, it is highly desired to develop a bioinformatics method to predict the submitochondria locations of mitochondrion proteins. In this work, a novel method based on support vector machine was developed to predict the submitochondria locations of mitochondrion proteins by using over-represented tetrapeptides selected by using binomial distribution. A reliable and rigorous benchmark dataset including 495 mitochondrion proteins with sequence identity ≤25% was constructed for testing and evaluating the proposed model. Jackknife cross-validated results showed that the 91.1% of the 495 mitochondrion proteins can be correctly predicted. Subsequently, our model was estimated by three existing benchmark datasets. The overall accuracies are 94.0, 94.7 and 93.4%, respectively, suggesting that the proposed model is potentially useful in the realm of mitochondrion proteome research. Based on this model, we built a predictor called TetraMito which is freely available at http://lin.uestc.edu.cn/server/TetraMito.
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47
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Sun XY, Shi SP, Qiu JD, Suo SB, Huang SY, Liang RP. Identifying protein quaternary structural attributes by incorporating physicochemical properties into the general form of Chou's PseAAC via discrete wavelet transform. MOLECULAR BIOSYSTEMS 2013; 8:3178-84. [PMID: 22990717 DOI: 10.1039/c2mb25280e] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
In vivo, some proteins exist as monomers and others as oligomers. Oligomers can be further classified into homo-oligomers (formed by identical subunits) and hetero-oligomers (formed by different subunits), and they form the structural components of various biological functions, including cooperative effects, allosteric mechanism and ion-channel gating. Therefore, with the avalanche of protein sequences generated in the post-genomic era, it is very important for both basic research and the pharmaceutical industry to acquire the possible knowledge about quaternary structural attributes of their proteins of interest. In view of this, a high throughput method (DWT_DT), a 2-layer approach by fusing discrete wavelet transform (DWT) and decision-tree algorithm (DT) with physicochemical features, has been developed to predict protein quaternary structures. The 1st layer is to assign a query protein to one of the 10 main quaternary structural attributes. The 2nd layer is to evaluate whether the protein in question is composed of homo- or hetero-oligomers. The overall accuracy by jackknife test for the 1st layer identification was 89.60%. The overall accuracy of the 2nd layer varies from 88.23 to 100%. The results suggest that this newly developed protocol (DWT_DT) is very promising in predicting quaternary structures with complicated composition.
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Affiliation(s)
- Xing-Yu Sun
- Department of Chemistry, Nanchang University, Nanchang 330031, P.R. China.
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48
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LIN HAO, DING CHEN, YUAN LUFENG, CHEN WEI, DING HUI, LI ZIQIANG, GUO FENGBIAO, HUANG JIAN, RAO NINI. PREDICTING SUBCHLOROPLAST LOCATIONS OF PROTEINS BASED ON THE GENERAL FORM OF CHOU'S PSEUDO AMINO ACID COMPOSITION: APPROACHED FROM OPTIMAL TRIPEPTIDE COMPOSITION. INT J BIOMATH 2013. [DOI: 10.1142/s1793524513500034] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Chloroplasts are organelles found in plant cells that conduct photosynthesis. The subchloroplast locations of proteins are correlated with their functions. With the availability of a great number of protein data, it is highly desired to develop a computational method to predict the subchloroplast locations of chloroplast proteins. In this study, we proposed a novel method to predict subchloroplast locations of proteins using tripeptide compositions. It first used the binomial distribution to optimize the feature sets. Then the support vector machine was selected to perform the prediction of subchloroplast locations of proteins. The proposed method was tested on a reliable and rigorous dataset including 259 chloroplast proteins with sequence identity ≤ 25%. In the jack-knife cross-validation, 92.21% envelope proteins, 93.20% thylakoid membrane, 52.63% thylakoid lumen and 85.00% stroma can be correctly identified. The overall accuracy achieves 88.03% which is higher than that of other models. Based on this method, a predictor called ChloPred has been built and can be freely available from http://cobi.uestc.edu.cn/people/hlin/tools/ChloPred/ . The predictor will provide important information for theoretical and experimental research of chloroplast proteins.
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Affiliation(s)
- HAO LIN
- Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - CHEN DING
- Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - LU-FENG YUAN
- Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - WEI CHEN
- Center for Genomics and Computational Biology, Department of Physics, College of Sciences, Hebei United University, Tangshan 063000, P. R. China
| | - HUI DING
- Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - ZI-QIANG LI
- School of Information and Engineering, Sichuan Agricultural University, Yaan 625014, P. R. China
| | - FENG-BIAO GUO
- Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - JIAN HUANG
- Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - NI-NI RAO
- Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
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Huang C, Yuan JQ. A Multilabel Model Based on Chou’s Pseudo–Amino Acid Composition for Identifying Membrane Proteins with Both Single and Multiple Functional Types. J Membr Biol 2013; 246:327-34. [DOI: 10.1007/s00232-013-9536-9] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2012] [Accepted: 03/11/2013] [Indexed: 11/24/2022]
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50
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Cantoni V, Ferone A, Ozbudak O, Petrosino A. Protein motifs retrieval by SS terns occurrences. Pattern Recognit Lett 2013. [DOI: 10.1016/j.patrec.2012.12.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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