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Le NQK, Nguyen BP. Prediction of FMN Binding Sites in Electron Transport Chains Based on 2-D CNN and PSSM Profiles. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2189-2197. [PMID: 31380767 DOI: 10.1109/tcbb.2019.2932416] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Flavin mono-nucleotides (FMNs) are cofactors that hold responsibility for carrying and transferring electrons in the electron transport chain stage of cellular respiration. Without being facilitated by FMNs, energy production is stagnant due to the interruption in most of the cellular processes. Investigation on FMN's functions, therefore, can gain holistic understanding about human diseases and molecular information on drug targets. We proposed a deep learning model using a two-dimensional convolutional neural network and position specific scoring matrices that could identify FMN interacting residues with the sensitivity of 83.7 percent, specificity of 99.2 percent, accuracy of 98.2 percent, and Matthews correlation coefficients of 0.85 for an independent dataset containing 141 FMN binding sites and 1,920 non-FMN binding sites. The proposed method outperformed other previous studies using similar evaluation metrics. Our positive outcome can also promote the utilization of deep learning in dealing with various problems in bioinformatics and computational biology.
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Liu T, Tang H. A Brief Survey of Machine Learning Methods in Identification of Mitochondria Proteins in Malaria Parasite. Curr Pharm Des 2020; 26:3049-3058. [PMID: 32156226 DOI: 10.2174/1381612826666200310122324] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Accepted: 02/10/2020] [Indexed: 11/22/2022]
Abstract
The number of human deaths caused by malaria is increasing day-by-day. In fact, the mitochondrial proteins of the malaria parasite play vital roles in the organism. For developing effective drugs and vaccines against infection, it is necessary to accurately identify mitochondrial proteins of the malaria parasite. Although precise details for the mitochondrial proteins can be provided by biochemical experiments, they are expensive and time-consuming. In this review, we summarized the machine learning-based methods for mitochondrial proteins identification in the malaria parasite and compared the construction strategies of these computational methods. Finally, we also discussed the future development of mitochondrial proteins recognition with algorithms.
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Affiliation(s)
- Ting Liu
- Department of Pathophysiology, Key Laboratory of Medical Electrophysiology, Ministry of Education, Southwest Medical University, Luzhou 646000, China
| | - Hua Tang
- Department of Pathophysiology, Key Laboratory of Medical Electrophysiology, Ministry of Education, Southwest Medical University, Luzhou 646000, China
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Kumari B, Kumar R, Kumar M. Prediction of Rare Palmitoylation Events in Proteins. J Comput Biol 2018; 25:997-1008. [PMID: 29963911 DOI: 10.1089/cmb.2017.0069] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Palmitoylation directs many cellular processes such as protein trafficking, sorting, signaling, interactions with other biomolecules, to name a few. Palmitoylation commonly occurs on cysteine; however, occasional palmitoylation of few other amino acids has also been reported. To date, comprehensive analysis on occasional palmitoylation is unavailable. In the present study, we reported a computational method to predict palmitoylation of glycine and serine residues in a protein. The method is based on support vector machine (SVM). It was trained on position-specific scoring matrix of amino acids that surrounds palmitoylated glycine and serine. During training, SVM models were evaluated on leave-one-out cross validation, and the maximum prediction accuracies achieved during training were 100% glycine palmitoylation and 99.94% for serine palmitoylation. Similar prediction for performance was also shown on independent data sets. The two SVM models were used to develop a prediction method called RAREPalm. We provide web-server and standalone of RAREPalm, using the user that can predict the potential glycine and serine palmitoylation site(s) in a protein. Comparative analysis of glycine, serine, and cysteine palmitoylation was also done to analyze pathways and classes to which different forms of palmitoylation belong. We hope that our attempt will be useful in finding more glycine and serine that may undergo palmitoylation and expanding the information on these lesser known sites of palmitoylation.
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Affiliation(s)
- Bandana Kumari
- Department of Biophysics, University of Delhi South Campus , New Delhi, India
| | - Ravindra Kumar
- Department of Biophysics, University of Delhi South Campus , New Delhi, India
| | - Manish Kumar
- Department of Biophysics, University of Delhi South Campus , New Delhi, India
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Zhang L, Zhang C, Gao R, Yang R, Song Q. Sequence Based Prediction of Antioxidant Proteins Using a Classifier Selection Strategy. PLoS One 2016; 11:e0163274. [PMID: 27662651 PMCID: PMC5035026 DOI: 10.1371/journal.pone.0163274] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2016] [Accepted: 09/05/2016] [Indexed: 11/28/2022] Open
Abstract
Antioxidant proteins perform significant functions in maintaining oxidation/antioxidation balance and have potential therapies for some diseases. Accurate identification of antioxidant proteins could contribute to revealing physiological processes of oxidation/antioxidation balance and developing novel antioxidation-based drugs. In this study, an ensemble method is presented to predict antioxidant proteins with hybrid features, incorporating SSI (Secondary Structure Information), PSSM (Position Specific Scoring Matrix), RSA (Relative Solvent Accessibility), and CTD (Composition, Transition, Distribution). The prediction results of the ensemble predictor are determined by an average of prediction results of multiple base classifiers. Based on a classifier selection strategy, we obtain an optimal ensemble classifier composed of RF (Random Forest), SMO (Sequential Minimal Optimization), NNA (Nearest Neighbor Algorithm), and J48 with an accuracy of 0.925. A Relief combined with IFS (Incremental Feature Selection) method is adopted to obtain optimal features from hybrid features. With the optimal features, the ensemble method achieves improved performance with a sensitivity of 0.95, a specificity of 0.93, an accuracy of 0.94, and an MCC (Matthew’s Correlation Coefficient) of 0.880, far better than the existing method. To evaluate the prediction performance objectively, the proposed method is compared with existing methods on the same independent testing dataset. Encouragingly, our method performs better than previous studies. In addition, our method achieves more balanced performance with a sensitivity of 0.878 and a specificity of 0.860. These results suggest that the proposed ensemble method can be a potential candidate for antioxidant protein prediction. For public access, we develop a user-friendly web server for antioxidant protein identification that is freely accessible at http://antioxidant.weka.cc.
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Affiliation(s)
- Lina Zhang
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Chengjin Zhang
- School of Control Science and Engineering, Shandong University, Jinan, China
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, China
- * E-mail:
| | - Rui Gao
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Runtao Yang
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Qing Song
- School of Electrical Engineering, University of Jinan, Jinan, China
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A Gram-Negative Bacterial Secreted Protein Types Prediction Method Based on PSI-BLAST Profile. BIOMED RESEARCH INTERNATIONAL 2016; 2016:3206741. [PMID: 27563663 PMCID: PMC4985605 DOI: 10.1155/2016/3206741] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Revised: 07/04/2016] [Accepted: 07/05/2016] [Indexed: 11/29/2022]
Abstract
Prediction of secreted protein types based solely on sequence data remains to be a challenging problem. In this study, we extract the long-range correlation information and linear correlation information from position-specific score matrix (PSSM). A total of 6800 features are extracted at 17 different gaps; then, 309 features are selected by a filter feature selection method based on the training set. To verify the performance of our method, jackknife and independent dataset tests are performed on the test set and the reported overall accuracies are 93.60% and 100%, respectively. Comparison of our results with the existing method shows that our method provides the favorable performance for secreted protein type prediction.
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Mahalingam R, Peng HP, Yang AS. Prediction of FMN-binding residues with three-dimensional probability distributions of interacting atoms on protein surfaces. J Theor Biol 2013; 343:154-61. [PMID: 24211525 DOI: 10.1016/j.jtbi.2013.10.020] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2013] [Revised: 10/29/2013] [Accepted: 10/30/2013] [Indexed: 12/12/2022]
Abstract
Flavin mono-nucleotide (FMN) is a cofactor which is involved in many biological reactions. The insights on protein-FMN interactions aid the protein functional annotation and also facilitate in drug design. In this study, we have established a new method, making use of an encoding scheme of the three-dimensional probability density maps that describe the distributions of 40 non-covalent interacting atom types around protein surfaces, to predict FMN-binding sites on protein surfaces. One machine learning model was trained for each of the 30 protein atom types to predict tentative FMN-binding sites on protein structures. The method's capability was evaluated by five-fold cross-validation on a dataset containing 81 non-redundant FMN-binding protein structures and further tested on independent datasets of 30 and 15 non-redundant protein structures respectively. These predictions achieved an accuracy of 0.94, 0.94 and 0.96 with the Matthews correlation coefficient (MCC) of 0.53, 0.53 and 0.65 respectively for the three protein structure sets. The prediction capability is superior to the existing method. This is the first structure-based approach that does not rely on evolutionary information for predicting FMN-interacting residues. The webserver for the prediction is available at http://ismblab.genomics.sinica.edu.tw/.
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Affiliation(s)
- Rajasekaran Mahalingam
- Genomics Research Center, Academia Sinica, 128 Academia Rd., Sec. 2, Nankang Dist., Taipei 115, Taiwan.
| | - Hung-Pin Peng
- Genomics Research Center, Academia Sinica, 128 Academia Rd., Sec. 2, Nankang Dist., Taipei 115, Taiwan; Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan; Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei 115, Taiwan
| | - An-Suei Yang
- Genomics Research Center, Academia Sinica, 128 Academia Rd., Sec. 2, Nankang Dist., Taipei 115, Taiwan.
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Niu B, Zhang Y, Ding J, Lu Y, Wang M, Lu W, Yuan X, Yin J. Predicting network of drug-enzyme interaction based on machine learning method. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2013; 1844:214-23. [PMID: 23907006 DOI: 10.1016/j.bbapap.2013.07.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2012] [Revised: 07/16/2013] [Accepted: 07/18/2013] [Indexed: 12/11/2022]
Abstract
It is important to correctly and efficiently map drugs and enzymes to their possible interaction network in modern drug research. In this work, a novel approach was introduced to encode drug and enzyme molecules with physicochemical molecular descriptors and pseudo amino acid composition, respectively. Based on this encoding method, Random Forest was adopted to build the drug-enzyme interaction network. After selecting the optimal features that are able to represent the main factors of drug-enzyme interaction in our prediction, a total of 129 features were attained which can be clustered into nine categories: Elemental Analysis, Geometry, Chemistry, Amino Acid Composition, Secondary Structure, Polarity, Molecular Volume, Codon Diversity and Electrostatic Charge. It is further found that Geometry features were the most important of all the features. As a result, our predicting model achieved an MCC of 0.915 and a sensitivity of 87.9% at the specificity level of 99.8% for 10-fold cross-validation test, and achieved an MCC of 0.895 and a sensitivity of 95.7% at the specificity level of 95.4% for independent set test. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.
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Affiliation(s)
- Bing Niu
- College of Life Science, Shanghai University, 99 Shang-Da Road, Shanghai 200072, China
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Gao YF, Li BQ, Cai YD, Feng KY, Li ZD, Jiang Y. Prediction of active sites of enzymes by maximum relevance minimum redundancy (mRMR) feature selection. ACTA ACUST UNITED AC 2013; 9:61-9. [DOI: 10.1039/c2mb25327e] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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