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Varghese DM, Nussinov R, Ahmad S. Predictive modeling of moonlighting DNA-binding proteins. NAR Genom Bioinform 2022; 4:lqac091. [DOI: 10.1093/nargab/lqac091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 10/25/2022] [Accepted: 11/11/2022] [Indexed: 12/03/2022] Open
Abstract
Abstract
Moonlighting proteins are multifunctional, single-polypeptide chains capable of performing multiple autonomous functions. Most moonlighting proteins have been discovered through work unrelated to their multifunctionality. We believe that prediction of moonlighting proteins from first principles, that is, using sequence, predicted structure, evolutionary profiles, and global gene expression profiles, for only one functional class of proteins in a single organism at a time will significantly advance our understanding of multifunctional proteins. In this work, we investigated human moonlighting DNA-binding proteins (mDBPs) in terms of properties that distinguish them from other (non-moonlighting) proteins with the same DNA-binding protein (DBP) function. Following a careful and comprehensive analysis of discriminatory features, a machine learning model was developed to assess the predictability of mDBPs from other DBPs (oDBPs). We observed that mDBPs can be discriminated from oDBPs with high accuracy of 74% AUC of ROC using these first principles features. A number of novel predicted mDBPs were found to have literature support for their being moonlighting and others are proposed as candidates, for which the moonlighting function is currently unknown. We believe that this work will help in deciphering and annotating novel moonlighting DBPs and scale up other functions. The source codes and data sets used for this work are freely available at https://zenodo.org/record/7299265#.Y2pO3ctBxPY
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Affiliation(s)
- Dana Mary Varghese
- School of Computational and Integrative Sciences, Jawaharlal Nehru University , New Delhi- 110067 , India
| | - Ruth Nussinov
- Computational Structural Biology Section, Cancer Innovation Laboratory, Frederick National Laboratory for Cancer Research , Frederick , MD 21702 , USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University , Israel
| | - Shandar Ahmad
- School of Computational and Integrative Sciences, Jawaharlal Nehru University , New Delhi- 110067 , India
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Chen Y, Li S, Guo J. A method for identifying moonlighting proteins based on linear discriminant analysis and bagging-SVM. Front Genet 2022; 13:963349. [PMID: 36046247 PMCID: PMC9420859 DOI: 10.3389/fgene.2022.963349] [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: 06/07/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Moonlighting proteins have at least two independent functions and are widely found in animals, plants and microorganisms. Moonlighting proteins play important roles in signal transduction, cell growth and movement, tumor inhibition, DNA synthesis and repair, and metabolism of biological macromolecules. Moonlighting proteins are difficult to find through biological experiments, so many researchers identify moonlighting proteins through bioinformatics methods, but their accuracies are relatively low. Therefore, we propose a new method. In this study, we select SVMProt-188D as the feature input, and apply a model combining linear discriminant analysis and basic classifiers in machine learning to study moonlighting proteins, and perform bagging ensemble on the best-performing support vector machine. They are identified accurately and efficiently. The model achieves an accuracy of 93.26% and an F-sorce of 0.946 on the MPFit dataset, which is better than the existing MEL-MP model. Meanwhile, it also achieves good results on the other two moonlighting protein datasets.
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Khalid W, Arshad MS, Aslam N, Majid Noor M, Siddeeg A, Abdul Rahim M, Zubair Khalid M, Ali A, Maqbool Z. Meat myofibril: Chemical composition, sources and its potential for cardiac layers and strong skeleton muscle. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2022. [DOI: 10.1080/10942912.2022.2044847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Waseem Khalid
- Department of Food Science, Government College University, Faisalabad, Pakistan
| | | | - Noman Aslam
- Department of Food Science, Government College University, Faisalabad, Pakistan
| | - Muhammad Majid Noor
- Department of Food Science, Government College University, Faisalabad, Pakistan
| | - Azhari Siddeeg
- Department of Food Engineering and Technology, Faculty of Engineering and Technology, University of Gezira, Wad Medani, Sudan
| | | | | | - Anwar Ali
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, HN, China
| | - Zahra Maqbool
- Department of Food Science, Government College University, Faisalabad, Pakistan
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