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Chen Z, Liu X, Zhao P, Li C, Wang Y, Li F, Akutsu T, Bain C, Gasser RB, Li J, Yang Z, Gao X, Kurgan L, Song J. iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets. Nucleic Acids Res 2022; 50:W434-W447. [PMID: 35524557 PMCID: PMC9252729 DOI: 10.1093/nar/gkac351] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/22/2022] [Accepted: 04/25/2022] [Indexed: 01/07/2023] Open
Abstract
The rapid accumulation of molecular data motivates development of innovative approaches to computationally characterize sequences, structures and functions of biological and chemical molecules in an efficient, accessible and accurate manner. Notwithstanding several computational tools that characterize protein or nucleic acids data, there are no one-stop computational toolkits that comprehensively characterize a wide range of biomolecules. We address this vital need by developing a holistic platform that generates features from sequence and structural data for a diverse collection of molecule types. Our freely available and easy-to-use iFeatureOmega platform generates, analyzes and visualizes 189 representations for biological sequences, structures and ligands. To the best of our knowledge, iFeatureOmega provides the largest scope when directly compared to the current solutions, in terms of the number of feature extraction and analysis approaches and coverage of different molecules. We release three versions of iFeatureOmega including a webserver, command line interface and graphical interface to satisfy needs of experienced bioinformaticians and less computer-savvy biologists and biochemists. With the assistance of iFeatureOmega, users can encode their molecular data into representations that facilitate construction of predictive models and analytical studies. We highlight benefits of iFeatureOmega based on three research applications, demonstrating how it can be used to accelerate and streamline research in bioinformatics, computational biology, and cheminformatics areas. The iFeatureOmega webserver is freely available at http://ifeatureomega.erc.monash.edu and the standalone versions can be downloaded from https://github.com/Superzchen/iFeatureOmega-GUI/ and https://github.com/Superzchen/iFeatureOmega-CLI/.
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Affiliation(s)
- Zhen Chen
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450046, China
- Center for Crop Genome Engineering, Henan Agricultural University, Zhengzhou 450046, China
| | - Xuhan Liu
- Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Einsteinweg 55, Leiden 2333 CC, The Netherlands
| | - Pei Zhao
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang 455000, China
| | - Chen Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Yanan Wang
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Fuyi Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan
| | - Chris Bain
- Monash Data Future Institutes, Monash University, Melbourne, Victoria 3800, Australia
| | - Robin B Gasser
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Junzhou Li
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450046, China
| | - Zuoren Yang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang 455000, China
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
- Monash Data Future Institutes, Monash University, Melbourne, Victoria 3800, Australia
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Comparative Analysis on Alignment-Based and Pretrained Feature Representations for the Identification of DNA-Binding Proteins. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:5847242. [PMID: 35799660 PMCID: PMC9256349 DOI: 10.1155/2022/5847242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 06/07/2022] [Indexed: 11/17/2022]
Abstract
The interaction between DNA and protein is vital for the development of a living body. Previous numerous studies on in silico identification of DNA-binding proteins (DBPs) usually include features extracted from the alignment-based (pseudo) position-specific scoring matrix (PSSM), leading to limited application due to its time-consuming generation. Few researchers have paid attention to the application of pretrained language models at the scale of evolution to the identification of DBPs. To this end, we present comprehensive insights into a comparison study on alignment-based PSSM and pretrained evolutionary scale modeling (ESM) representations in the field of DBP classification. The comparison is conducted by extracting information from PSSM and ESM representations using four unified averaging operations and by performing various feature selection (FS) methods. Experimental results demonstrate that the pretrained ESM representation outperforms the PSSM-derived features in a fair comparison perspective. The pretrained feature presentation deserves wide application to the area of in silico DBP identification as well as other function annotation issues. Finally, it is also confirmed that an ensemble scheme by aggregating various trained FS models can significantly improve the classification performance of DBPs.
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Lu W, Zhou N, Ding Y, Wu H, Zhang Y, Fu Q, Li H. Application of DNA-Binding Protein Prediction Based on Graph Convolutional Network and Contact Map. BIOMED RESEARCH INTERNATIONAL 2022; 2022:9044793. [PMID: 35083336 PMCID: PMC8786515 DOI: 10.1155/2022/9044793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 12/24/2021] [Indexed: 11/24/2022]
Abstract
DNA contains the genetic information for the synthesis of proteins and RNA, and it is an indispensable substance in living organisms. DNA-binding proteins are an enzyme, which can bind with DNA to produce complex proteins, and play an important role in the functions of a variety of biological molecules. With the continuous development of deep learning, the introduction of deep learning into DNA-binding proteins for prediction is conducive to improving the speed and accuracy of DNA-binding protein recognition. In this study, the features and structures of proteins were used to obtain their representations through graph convolutional networks. A protein prediction model based on graph convolutional network and contact map was proposed. The method had some advantages by testing various indexes of PDB14189 and PDB2272 on the benchmark dataset.
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Affiliation(s)
- Weizhong Lu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China
- Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, China
| | - Nan Zhou
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China
| | - Yijie Ding
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China
| | - Hongjie Wu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China
| | - Yu Zhang
- Suzhou Industrial Park Institute of Services Outsourcing, Suzhou, China
| | - Qiming Fu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China
| | - Haiou Li
- Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, China
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Chen Z, Zhao P, Li C, Li F, Xiang D, Chen YZ, Akutsu T, Daly RJ, Webb GI, Zhao Q, Kurgan L, Song J. iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization. Nucleic Acids Res 2021; 49:e60. [PMID: 33660783 PMCID: PMC8191785 DOI: 10.1093/nar/gkab122] [Citation(s) in RCA: 107] [Impact Index Per Article: 35.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 02/05/2021] [Accepted: 02/25/2021] [Indexed: 12/14/2022] Open
Abstract
Sequence-based analysis and prediction are fundamental bioinformatic tasks that facilitate understanding of the sequence(-structure)-function paradigm for DNAs, RNAs and proteins. Rapid accumulation of sequences requires equally pervasive development of new predictive models, which depends on the availability of effective tools that support these efforts. We introduce iLearnPlus, the first machine-learning platform with graphical- and web-based interfaces for the construction of machine-learning pipelines for analysis and predictions using nucleic acid and protein sequences. iLearnPlus provides a comprehensive set of algorithms and automates sequence-based feature extraction and analysis, construction and deployment of models, assessment of predictive performance, statistical analysis, and data visualization; all without programming. iLearnPlus includes a wide range of feature sets which encode information from the input sequences and over twenty machine-learning algorithms that cover several deep-learning approaches, outnumbering the current solutions by a wide margin. Our solution caters to experienced bioinformaticians, given the broad range of options, and biologists with no programming background, given the point-and-click interface and easy-to-follow design process. We showcase iLearnPlus with two case studies concerning prediction of long noncoding RNAs (lncRNAs) from RNA transcripts and prediction of crotonylation sites in protein chains. iLearnPlus is an open-source platform available at https://github.com/Superzchen/iLearnPlus/ with the webserver at http://ilearnplus.erc.monash.edu/.
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Affiliation(s)
- Zhen Chen
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450046, China
| | - Pei Zhao
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang 455000, China
| | - Chen Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Fuyi Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.,Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia.,Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria 3000, Australia
| | - Dongxu Xiang
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.,Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Yong-Zi Chen
- Laboratory of Tumor Cell Biology, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan
| | - Roger J Daly
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Geoffrey I Webb
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Quanzhi Zhao
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450046, China.,Key Laboratory of Rice Biology in Henan Province, Henan Agricultural University, Zhengzhou 450046, China
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.,Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
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Zhang Q, Liu P, Wang X, Zhang Y, Han Y, Yu B. StackPDB: Predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2020.106921] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Zhang Y, Chen P, Gao Y, Ni J, Wang X. DBP-PSSM: Combination of Evolutionary Profiles with the XGBoost Algorithm to Improve the Identification of DNA-binding Proteins. Comb Chem High Throughput Screen 2020; 25:3-12. [PMID: 33238837 DOI: 10.2174/1386207323999201124203531] [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: 05/27/2020] [Revised: 10/16/2020] [Accepted: 10/29/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND AND OBJECTIVE DNA-binding proteins play important roles in a variety of biological processes, such as gene transcription and regulation, DNA replication and repair, DNA recombination and packaging, and the formation of chromatin and ribosomes. Therefore, it is urgent to develop a computational method to improve the recognition efficiency of DNA-binding proteins. METHODS We proposed a novel method, DBP-PSSM, which constructed the features from amino acid composition and evolutionary information of protein sequences. The maximum relevance, minimum redundancy (mRMR) was employed to select the optimal features for establishing the XGBoost classifier, therefore, the novel model of prediction DNA-binding proteins, DBP-PSSM, was established with 5-fold cross-validation on the training dataset. RESULTS DBP-PSSM achieved an accuracy of 81.18% and MCC of 0.657 in a test dataset, which outperformed the many existing methods. These results demonstrated that our method can effectively predict DNA-binding proteins. CONCLUSION The data and source code are provided at https://github.com/784221489/DNA-binding.
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Affiliation(s)
- Yanping Zhang
- School of Mathematics and Physics Science and Engineering, Hebei University of Engineering, Handan 056038,China
| | - Pengcheng Chen
- School of Mathematics and Physics Science and Engineering, Hebei University of Engineering, Handan 056038,China
| | - Ya Gao
- School of Mathematics and Physics Science and Engineering, Hebei University of Engineering, Handan 056038,China
| | - Jianwei Ni
- School of Mathematics and Physics Science and Engineering, Hebei University of Engineering, Handan 056038,China
| | - Xiaosheng Wang
- School of Mathematics and Physics Science and Engineering, Hebei University of Engineering, Handan 056038,China
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