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Rukh G, Akbar S, Rehman G, Alarfaj FK, Zou Q. StackedEnC-AOP: prediction of antioxidant proteins using transform evolutionary and sequential features based multi-scale vector with stacked ensemble learning. BMC Bioinformatics 2024; 25:256. [PMID: 39098908 PMCID: PMC11298090 DOI: 10.1186/s12859-024-05884-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 07/29/2024] [Indexed: 08/06/2024] Open
Abstract
BACKGROUND Antioxidant proteins are involved in several biological processes and can protect DNA and cells from the damage of free radicals. These proteins regulate the body's oxidative stress and perform a significant role in many antioxidant-based drugs. The current invitro-based medications are costly, time-consuming, and unable to efficiently screen and identify the targeted motif of antioxidant proteins. METHODS In this model, we proposed an accurate prediction method to discriminate antioxidant proteins namely StackedEnC-AOP. The training sequences are formulation encoded via incorporating a discrete wavelet transform (DWT) into the evolutionary matrix to decompose the PSSM-based images via two levels of DWT to form a Pseudo position-specific scoring matrix (PsePSSM-DWT) based embedded vector. Additionally, the Evolutionary difference formula and composite physiochemical properties methods are also employed to collect the structural and sequential descriptors. Then the combined vector of sequential features, evolutionary descriptors, and physiochemical properties is produced to cover the flaws of individual encoding schemes. To reduce the computational cost of the combined features vector, the optimal features are chosen using Minimum redundancy and maximum relevance (mRMR). The optimal feature vector is trained using a stacking-based ensemble meta-model. RESULTS Our developed StackedEnC-AOP method reported a prediction accuracy of 98.40% and an AUC of 0.99 via training sequences. To evaluate model validation, the StackedEnC-AOP training model using an independent set achieved an accuracy of 96.92% and an AUC of 0.98. CONCLUSION Our proposed StackedEnC-AOP strategy performed significantly better than current computational models with a ~ 5% and ~ 3% improved accuracy via training and independent sets, respectively. The efficacy and consistency of our proposed StackedEnC-AOP make it a valuable tool for data scientists and can execute a key role in research academia and drug design.
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Affiliation(s)
- Gul Rukh
- Department of Zoology, Abdul Wali Khan University Mardan, Mardan, 23200, KP, Pakistan
| | - Shahid Akbar
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
- Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, 23200, KP, Pakistan
| | - Gauhar Rehman
- Department of Zoology, Abdul Wali Khan University Mardan, Mardan, 23200, KP, Pakistan
| | - Fawaz Khaled Alarfaj
- Department of Management Information Systems (MIS), School of Business, King Faisal University (KFU), 31982, Al-Ahsa, Saudi Arabia
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, People's Republic of China.
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Ju H, Cui Y, Su Q, Juan L, Manavalan B. CODENET: A deep learning model for COVID-19 detection. Comput Biol Med 2024; 171:108229. [PMID: 38447500 DOI: 10.1016/j.compbiomed.2024.108229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 02/20/2024] [Accepted: 02/25/2024] [Indexed: 03/08/2024]
Abstract
Conventional COVID-19 testing methods have some flaws: they are expensive and time-consuming. Chest X-ray (CXR) diagnostic approaches can alleviate these flaws to some extent. However, there is no accurate and practical automatic diagnostic framework with good interpretability. The application of artificial intelligence (AI) technology to medical radiography can help to accurately detect the disease, reduce the burden on healthcare organizations, and provide good interpretability. Therefore, this study proposes a new deep neural network (CNN) based on CXR for COVID-19 diagnosis - CodeNet. This method uses contrastive learning to make full use of latent image data to enhance the model's ability to extract features and generalize across different data domains. On the evaluation dataset, the proposed method achieves an accuracy as high as 94.20%, outperforming several other existing methods used for comparison. Ablation studies validate the efficacy of the proposed method, while interpretability analysis shows that the method can effectively guide clinical professionals. This work demonstrates the superior detection performance of a CNN using contrastive learning techniques on CXR images, paving the way for computer vision and artificial intelligence technologies to leverage massive medical data for disease diagnosis.
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Affiliation(s)
- Hong Ju
- Heilongjiang Agricultural Engineering Vocational College, China
| | - Yanyan Cui
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Qiaosen Su
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea
| | - Liran Juan
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.
| | - Balachandran Manavalan
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea.
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Zhuang J, Gao W, Su R. EnAMP: A novel deep learning ensemble antibacterial peptide recognition algorithm based on multi-features. J Bioinform Comput Biol 2024; 22:2450001. [PMID: 38406833 DOI: 10.1142/s021972002450001x] [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] [Indexed: 02/27/2024]
Abstract
Antimicrobial peptides (AMPs), as the preferred alternatives to antibiotics, have wide application with good prospects. Identifying AMPs through wet lab experiments remains expensive, time-consuming and challenging. Many machine learning methods have been proposed to predict AMPs and achieved good results. In this work, we combine two kinds of word embedding features with the statistical features of peptide sequences to develop an ensemble classifier, named EnAMP, in which, two deep neural networks are trained based on Word2vec and Glove word embedding features of peptide sequences, respectively, meanwhile, we utilize statistical features of peptide sequences to train random forest and support vector machine classifiers. The average of four classifiers is the final prediction result. Compared with other state-of-the-art algorithms on six datasets, EnAMP outperforms most existing models with similar computational costs, even when compared with high computational cost algorithms based on Bidirectional Encoder Representation from Transformers (BERT), the performance of our model is comparable. EnAMP source code and the data are available at https://github.com/ruisue/EnAMP.
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Affiliation(s)
- Jujuan Zhuang
- School of Science, Dalian Maritime University, Dalian, Liaoning, P. R. China
| | - Wanquan Gao
- School of Science, Dalian Maritime University, Dalian, Liaoning, P. R. China
| | - Rui Su
- School of Science, Dalian Maritime University, Dalian, Liaoning, P. R. China
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Zulfiqar H, Guo Z, Ahmad RM, Ahmed Z, Cai P, Chen X, Zhang Y, Lin H, Shi Z. Deep-STP: a deep learning-based approach to predict snake toxin proteins by using word embeddings. Front Med (Lausanne) 2024; 10:1291352. [PMID: 38298505 PMCID: PMC10829051 DOI: 10.3389/fmed.2023.1291352] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 12/26/2023] [Indexed: 02/02/2024] Open
Abstract
Snake venom contains many toxic proteins that can destroy the circulatory system or nervous system of prey. Studies have found that these snake venom proteins have the potential to treat cardiovascular and nervous system diseases. Therefore, the study of snake venom protein is conducive to the development of related drugs. The research technologies based on traditional biochemistry can accurately identify these proteins, but the experimental cost is high and the time is long. Artificial intelligence technology provides a new means and strategy for large-scale screening of snake venom proteins from the perspective of computing. In this paper, we developed a sequence-based computational method to recognize snake toxin proteins. Specially, we utilized three different feature descriptors, namely g-gap, natural vector and word 2 vector, to encode snake toxin protein sequences. The analysis of variance (ANOVA), gradient-boost decision tree algorithm (GBDT) combined with incremental feature selection (IFS) were used to optimize the features, and then the optimized features were input into the deep learning model for model training. The results show that our model can achieve a prediction performance with an accuracy of 82.00% in 10-fold cross-validation. The model is further verified on independent data, and the accuracy rate reaches to 81.14%, which demonstrated that our model has excellent prediction performance and robustness.
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Affiliation(s)
- Hasan Zulfiqar
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China
| | - Zhiling Guo
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Ramala Masood Ahmad
- Department of Plant Breeding and Genetics, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Zahoor Ahmed
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China
| | - Peiling Cai
- School of Basic Medical Sciences, Chengdu University, Chengdu, China
| | - Xiang Chen
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Hao Lin
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China
| | - Zheng Shi
- Clinical Genetics Laboratory, Clinical Medical College & Affiliated Hospital, Chengdu University, Chengdu, China
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Li W, Li G, Sun Y, Zhang L, Cui X, Jia Y, Zhao T. Prediction of SARS-CoV-2 Infection Phosphorylation Sites and Associations of these Modifications with Lung Cancer Development. Curr Gene Ther 2024; 24:239-248. [PMID: 37957848 DOI: 10.2174/0115665232268074231026111634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 10/05/2023] [Accepted: 10/07/2023] [Indexed: 11/15/2023]
Abstract
INTRODUCTION Since the emergence of SARS-CoV-2 viruses, multiple mutant strains have been identified. Infection with SARS-CoV-2 virus leads to alterations in host cell phosphorylation signal, which systematically modulates the immune response. METHODS Identification and analysis of SARS-CoV-2 virus infection phosphorylation sites enable insight into the mechanisms of viral infection and effects on host cells, providing important fundamental data for the study and development of potent drugs for the treatment of immune inflammatory diseases. In this paper, we have analyzed the SARS-CoV-2 virus-infected phosphorylation region and developed a transformer-based deep learning-assisted identification method for the specific identification of phosphorylation sites in SARS-CoV-2 virus-infected host cells. RESULTS Furthermore, through association analysis with lung cancer, we found that SARS-CoV-2 infection may affect the regulatory role of the immune system, leading to an abnormal increase or decrease in the immune inflammatory response, which may be associated with the development and progression of cancer. CONCLUSION We anticipate that this study will provide an important reference for SARS-CoV-2 virus evolution as well as immune-related studies and provide a reliable complementary screening tool for anti-SARS-CoV-2 virus drug and vaccine design.
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Affiliation(s)
- Wei Li
- Institute of Bioinformatics, Harbin Institute of Technology, Harbin, China
| | - Gen Li
- Department of Radiation Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yuzhi Sun
- Institute for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Liyuan Zhang
- Institute for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xinran Cui
- Institute for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yuran Jia
- Institute for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Tianyi Zhao
- School of Medicine and Health, Harbin Institute of Technology, Harbin, China
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Zulfiqar H, Ahmad RM, Raza A, Shahzad S, Lin H. Promoter Prediction in Agrobacterium tumefaciens Strain C58 by Using Artificial Intelligence Strategies. Methods Mol Biol 2024; 2844:33-44. [PMID: 39068330 DOI: 10.1007/978-1-0716-4063-0_2] [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] [Indexed: 07/30/2024]
Abstract
Promoters are the genomic regions upstream of genes that RNA polymerase binds in order to initiate gene transcription. Understanding the regulation of gene expression depends on being able to identify promoters, because they are the most important component of gene expression. Agrobacterium tumefaciens (A. tumefaciens) strain C58 was the subject of this study with the goal of creating a machine learning-based model to predict promoters. In this study, nucleotide density (ND), k-mer, and one-hot were used to encode the promoter sequence. Support vector machine (SVM) on fivefold cross-validation with incremental feature selection (IFS) was used to optimize the generated features. These improved characteristics were then used to distinguish promoter sequences by feeding them into the random forest (RF) classifier. Tenfold cross-validation (CV) analysis revealed that the projected model has the ability to produce an accuracy of 84.22%.
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Affiliation(s)
- Hasan Zulfiqar
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China.
| | - Ramala Masood Ahmad
- Department of Plant Breeding and Genetics, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Ali Raza
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China
| | - Sana Shahzad
- Institute of Horticultural Sciences, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Hao Lin
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China.
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Poretsky E, Andorf CM, Sen TZ. PhosBoost: Improved phosphorylation prediction recall using gradient boosting and protein language models. PLANT DIRECT 2023; 7:e554. [PMID: 38124705 PMCID: PMC10732782 DOI: 10.1002/pld3.554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/20/2023] [Accepted: 11/26/2023] [Indexed: 12/23/2023]
Abstract
Protein phosphorylation is a dynamic and reversible post-translational modification that regulates a variety of essential biological processes. The regulatory role of phosphorylation in cellular signaling pathways, protein-protein interactions, and enzymatic activities has motivated extensive research efforts to understand its functional implications. Experimental protein phosphorylation data in plants remains limited to a few species, necessitating a scalable and accurate prediction method. Here, we present PhosBoost, a machine-learning approach that leverages protein language models and gradient-boosting trees to predict protein phosphorylation from experimentally derived data. Trained on data obtained from a comprehensive plant phosphorylation database, qPTMplants, we compared the performance of PhosBoost to existing protein phosphorylation prediction methods, PhosphoLingo and DeepPhos. For serine and threonine prediction, PhosBoost achieved higher recall than PhosphoLingo and DeepPhos (.78, .56, and .14, respectively) while maintaining a competitive area under the precision-recall curve (.54, .56, and .42, respectively). PhosphoLingo and DeepPhos failed to predict any tyrosine phosphorylation sites, while PhosBoost achieved a recall score of .6. Despite the precision-recall tradeoff, PhosBoost offers improved performance when recall is prioritized while consistently providing more confident probability scores. A sequence-based pairwise alignment step improved prediction results for all classifiers by effectively increasing the number of inferred positive phosphosites. We provide evidence to show that PhosBoost models are transferable across species and scalable for genome-wide protein phosphorylation predictions. PhosBoost is freely and publicly available on GitHub.
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Affiliation(s)
- Elly Poretsky
- Agricultural Research Service, Crop Improvement and Genetics Research UnitU.S. Department of AgricultureAlbanyCAUnited States
| | - Carson M. Andorf
- Agricultural Research Service, Corn Insects and Crop Genetics ResearchU.S. Department of AgricultureAmesIAUnited States
- Department of Computer ScienceIowa State UniversityAmesIAUnited States
| | - Taner Z. Sen
- Agricultural Research Service, Crop Improvement and Genetics Research UnitU.S. Department of AgricultureAlbanyCAUnited States
- Department of BioengineeringUniversity of CaliforniaBerkeleyCAUnited States
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8
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Su R, Zhuang J, Liu S, Liu D, Feng K. EnILs: A General Ensemble Computational Approach for Predicting Inducing Peptides of Multiple Interleukins. J Comput Biol 2023; 30:1289-1304. [PMID: 38010531 DOI: 10.1089/cmb.2023.0002] [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] [Indexed: 11/29/2023] Open
Abstract
Interleukins (ILs) are a group of multifunctional cytokines, which play important roles in immune regulations and inflammatory responses. Recently, IL-6 has been found to affect the development of COVID-19, and significantly elevated levels of IL-6 cytokines have been reported in patients with severe COVID-19. IL-10 and IL-17 are anti-inflammatory and proinflammatory cytokines, respectively, which play multiple protective roles in host defense against pathogens. At present, a number of machine learning methods have been proposed to predict ILs inducing peptides, but their predictive performance needs to be further improved, and the inducing peptides of different ILs are predicted separately, rather than using a general approach. In our work, we combine the statistical features of peptide sequence with word embedding to design a general ensemble model named EnILs to predict inducing peptides of different ILs, in which the predictive probabilities of random forest, eXtreme Gradient Boosting and neural network are integrated in an average way. Compared with the state-of-the-art machine learning methods, EnILs shows considerable performance in the prediction of IL-6, IL-10, and IL-17 inducing peptides. In addition, we predict the most promising IL-6 inducing peptides in Severe Acute Respiratory Syndrome Coronavirus 2 spike protein in the case study for further experimental verification.
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Affiliation(s)
- Rui Su
- Department of Statistics, School of Science, Dalian Maritime University, Dalian, Liaoning, China
| | - Jujuan Zhuang
- Department of Statistics, School of Science, Dalian Maritime University, Dalian, Liaoning, China
| | - Shuhan Liu
- Department of Statistics, School of Science, Dalian Maritime University, Dalian, Liaoning, China
| | - Di Liu
- Department of Computer Science and Technology, Information Science and Technology College, Dalian Maritime University, Dalian, Liaoning, China
| | - Kexin Feng
- Department of Statistics, School of Science, Dalian Maritime University, Dalian, Liaoning, China
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Esmaili F, Pourmirzaei M, Ramazi S, Shojaeilangari S, Yavari E. A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:1266-1285. [PMID: 37863385 PMCID: PMC11082408 DOI: 10.1016/j.gpb.2023.03.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 01/16/2023] [Accepted: 03/23/2023] [Indexed: 10/22/2023]
Abstract
Post-translational modifications (PTMs) have key roles in extending the functional diversity of proteins and, as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that occurs in most proteins and plays a significant role in many biological processes. Disorders in the phosphorylation process lead to multiple diseases, including neurological disorders and cancers. The purpose of this review is to organize this body of knowledge associated with phosphorylation site (p-site) prediction to facilitate future research in this field. At first, we comprehensively review all related databases and introduce all steps regarding dataset creation, data preprocessing, and method evaluation in p-site prediction. Next, we investigate p-site prediction methods, which are divided into two computational groups: algorithmic and machine learning (ML). Additionally, it is shown that there are basically two main approaches for p-site prediction by ML: conventional and end-to-end deep learning methods, both of which are given an overview. Moreover, this review introduces the most important feature extraction techniques, which have mostly been used in p-site prediction. Finally, we create three test sets from new proteins related to the released version of the database of protein post-translational modifications (dbPTM) in 2022 based on general and human species. Evaluating online p-site prediction tools on newly added proteins introduced in the dbPTM 2022 release, distinct from those in the dbPTM 2019 release, reveals their limitations. In other words, the actual performance of these online p-site prediction tools on unseen proteins is notably lower than the results reported in their respective research papers.
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Affiliation(s)
- Farzaneh Esmaili
- Department of Information Technology, Tarbiat Modares University, Tehran 14115-111, Iran
| | - Mahdi Pourmirzaei
- Department of Information Technology, Tarbiat Modares University, Tehran 14115-111, Iran
| | - Shahin Ramazi
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran 14115-111, Iran.
| | - Seyedehsamaneh Shojaeilangari
- Biomedical Engineering Group, Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology (IROST), Tehran 33535-111, Iran
| | - Elham Yavari
- Department of Information Technology, Tarbiat Modares University, Tehran 14115-111, Iran
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10
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Jiao S, Ye X, Ao C, Sakurai T, Zou Q, Xu L. Adaptive learning embedding features to improve the predictive performance of SARS-CoV-2 phosphorylation sites. Bioinformatics 2023; 39:btad627. [PMID: 37847658 PMCID: PMC10628388 DOI: 10.1093/bioinformatics/btad627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 09/11/2023] [Accepted: 10/16/2023] [Indexed: 10/19/2023] Open
Abstract
MOTIVATION The rapid and extensive transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an unprecedented global health emergency, affecting millions of people and causing an immense socioeconomic impact. The identification of SARS-CoV-2 phosphorylation sites plays an important role in unraveling the complex molecular mechanisms behind infection and the resulting alterations in host cell pathways. However, currently available prediction tools for identifying these sites lack accuracy and efficiency. RESULTS In this study, we presented a comprehensive biological function analysis of SARS-CoV-2 infection in a clonal human lung epithelial A549 cell, revealing dramatic changes in protein phosphorylation pathways in host cells. Moreover, a novel deep learning predictor called PSPred-ALE is specifically designed to identify phosphorylation sites in human host cells that are infected with SARS-CoV-2. The key idea of PSPred-ALE lies in the use of a self-adaptive learning embedding algorithm, which enables the automatic extraction of context sequential features from protein sequences. In addition, the tool uses multihead attention module that enables the capturing of global information, further improving the accuracy of predictions. Comparative analysis of features demonstrated that the self-adaptive learning embedding features are superior to hand-crafted statistical features in capturing discriminative sequence information. Benchmarking comparison shows that PSPred-ALE outperforms the state-of-the-art prediction tools and achieves robust performance. Therefore, the proposed model can effectively identify phosphorylation sites assistant the biomedical scientists in understanding the mechanism of phosphorylation in SARS-CoV-2 infection. AVAILABILITY AND IMPLEMENTATION PSPred-ALE is available at https://github.com/jiaoshihu/PSPred-ALE and Zenodo (https://doi.org/10.5281/zenodo.8330277).
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Affiliation(s)
- Shihu Jiao
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Xiucai Ye
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Chunyan Ao
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Tetsuya Sakurai
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, No. 4089 Shahexi Road, Shenzhen 518000, China
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Tan M, Xia J, Luo H, Meng G, Zhu Z. Applying the digital data and the bioinformatics tools in SARS-CoV-2 research. Comput Struct Biotechnol J 2023; 21:4697-4705. [PMID: 37841328 PMCID: PMC10568291 DOI: 10.1016/j.csbj.2023.09.044] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/29/2023] [Accepted: 09/29/2023] [Indexed: 10/17/2023] Open
Abstract
Bioinformatics has been playing a crucial role in the scientific progress to fight against the pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The advances in novel algorithms, mega data technology, artificial intelligence and deep learning assisted the development of novel bioinformatics tools to analyze daily increasing SARS-CoV-2 data in the past years. These tools were applied in genomic analyses, evolutionary tracking, epidemiological analyses, protein structure interpretation, studies in virus-host interaction and clinical performance. To promote the in-silico analysis in the future, we conducted a review which summarized the databases, web services and software applied in SARS-CoV-2 research. Those digital resources applied in SARS-CoV-2 research may also potentially contribute to the research in other coronavirus and non-coronavirus viruses.
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Affiliation(s)
- Meng Tan
- School of Life Sciences, Chongqing University, Chongqing, China
| | - Jiaxin Xia
- School of Life Sciences, Chongqing University, Chongqing, China
| | - Haitao Luo
- School of Life Sciences, Chongqing University, Chongqing, China
| | - Geng Meng
- College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Zhenglin Zhu
- School of Life Sciences, Chongqing University, Chongqing, China
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Li F, Guo X, Bi Y, Jia R, Pitt ME, Pan S, Li S, Gasser RB, Coin LJ, Song J. Digerati - A multipath parallel hybrid deep learning framework for the identification of mycobacterial PE/PPE proteins. Comput Biol Med 2023; 163:107155. [PMID: 37356289 DOI: 10.1016/j.compbiomed.2023.107155] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/05/2023] [Accepted: 06/07/2023] [Indexed: 06/27/2023]
Abstract
The genome of Mycobacterium tuberculosis contains a relatively high percentage (10%) of genes that are poorly characterised because of their highly repetitive nature and high GC content. Some of these genes encode proteins of the PE/PPE family, which are thought to be involved in host-pathogen interactions, virulence, and disease pathogenicity. Members of this family are genetically divergent and challenging to both identify and classify using conventional computational tools. Thus, advanced in silico methods are needed to identify proteins of this family for subsequent functional annotation efficiently. In this study, we developed the first deep learning-based approach, termed Digerati, for the rapid and accurate identification of PE and PPE family proteins. Digerati was built upon a multipath parallel hybrid deep learning framework, which equips multi-layer convolutional neural networks with bidirectional, long short-term memory, equipped with a self-attention module to effectively learn the higher-order feature representations of PE/PPE proteins. Empirical studies demonstrated that Digerati achieved a significantly better performance (∼18-20%) than alignment-based approaches, including BLASTP, PHMMER, and HHsuite, in both prediction accuracy and speed. Digerati is anticipated to facilitate community-wide efforts to conduct high-throughput identification and analysis of PE/PPE family members. The webserver and source codes of Digerati are publicly available at http://web.unimelb-bioinfortools.cloud.edu.au/Digerati/.
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Affiliation(s)
- Fuyi Li
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China; Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, Victoria, 3000, Australia.
| | - Xudong Guo
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
| | - Yue Bi
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, 3800, Australia
| | - Runchang Jia
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
| | - Miranda E Pitt
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, Victoria, 3000, Australia
| | - Shirui Pan
- School of Information and Communication Technology, Griffith University, QLD, 4222, Australia
| | - Shuqin Li
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
| | - Robin B Gasser
- Melbourne Veterinary School, Faculty of Science, The University of Melbourne, VIC, 3010, Australia
| | - Lachlan Jm Coin
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, Victoria, 3000, Australia.
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, 3800, Australia.
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13
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Pakhrin SC, Pokharel S, Pratyush P, Chaudhari M, Ismail HD, Kc DB. LMPhosSite: A Deep Learning-Based Approach for General Protein Phosphorylation Site Prediction Using Embeddings from the Local Window Sequence and Pretrained Protein Language Model. J Proteome Res 2023; 22:2548-2557. [PMID: 37459437 DOI: 10.1021/acs.jproteome.2c00667] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
Phosphorylation is one of the most important post-translational modifications and plays a pivotal role in various cellular processes. Although there exist several computational tools to predict phosphorylation sites, existing tools have not yet harnessed the knowledge distilled by pretrained protein language models. Herein, we present a novel deep learning-based approach called LMPhosSite for the general phosphorylation site prediction that integrates embeddings from the local window sequence and the contextualized embedding obtained using global (overall) protein sequence from a pretrained protein language model to improve the prediction performance. Thus, the LMPhosSite consists of two base-models: one for capturing effective local representation and the other for capturing global per-residue contextualized embedding from a pretrained protein language model. The output of these base-models is integrated using a score-level fusion approach. LMPhosSite achieves a precision, recall, Matthew's correlation coefficient, and F1-score of 38.78%, 67.12%, 0.390, and 49.15%, for the combined serine and threonine independent test data set and 34.90%, 62.03%, 0.298, and 44.67%, respectively, for the tyrosine independent test data set, which is better than the compared approaches. These results demonstrate that LMPhosSite is a robust computational tool for the prediction of the general phosphorylation sites in proteins.
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Affiliation(s)
- Subash C Pakhrin
- School of Computing, Wichita State University, 1845 Fairmount St., Wichita, Kansas 67260, United States
- Department of Computer Science & Engineering Technology, University of Houston-Downtown, 1 Main St., Houston, Texas 77002, United States
| | - Suresh Pokharel
- Department of Computer Science, Michigan Technological University, Houghton, Michigan 49931, United States
| | - Pawel Pratyush
- Department of Computer Science, Michigan Technological University, Houghton, Michigan 49931, United States
| | - Meenal Chaudhari
- Department of Biology, North Carolina A&T State University, Greensboro, North Carolina 27411, United States
| | - Hamid D Ismail
- Department of Computer Science, Michigan Technological University, Houghton, Michigan 49931, United States
| | - Dukka B Kc
- Department of Computer Science, Michigan Technological University, Houghton, Michigan 49931, United States
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14
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Charoenkwan P, Schaduangrat N, Shoombuatong W. StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens. BMC Bioinformatics 2023; 24:301. [PMID: 37507654 PMCID: PMC10386778 DOI: 10.1186/s12859-023-05421-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND The identification of tumor T cell antigens (TTCAs) is crucial for providing insights into their functional mechanisms and utilizing their potential in anticancer vaccines development. In this context, TTCAs are highly promising. Meanwhile, experimental technologies for discovering and characterizing new TTCAs are expensive and time-consuming. Although many machine learning (ML)-based models have been proposed for identifying new TTCAs, there is still a need to develop a robust model that can achieve higher rates of accuracy and precision. RESULTS In this study, we propose a new stacking ensemble learning-based framework, termed StackTTCA, for accurate and large-scale identification of TTCAs. Firstly, we constructed 156 different baseline models by using 12 different feature encoding schemes and 13 popular ML algorithms. Secondly, these baseline models were trained and employed to create a new probabilistic feature vector. Finally, the optimal probabilistic feature vector was determined based the feature selection strategy and then used for the construction of our stacked model. Comparative benchmarking experiments indicated that StackTTCA clearly outperformed several ML classifiers and the existing methods in terms of the independent test, with an accuracy of 0.932 and Matthew's correlation coefficient of 0.866. CONCLUSIONS In summary, the proposed stacking ensemble learning-based framework of StackTTCA could help to precisely and rapidly identify true TTCAs for follow-up experimental verification. In addition, we developed an online web server ( http://2pmlab.camt.cmu.ac.th/StackTTCA ) to maximize user convenience for high-throughput screening of novel TTCAs.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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15
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Zhang G, Tang Q, Feng P, Chen W. IPs-GRUAtt: An attention-based bidirectional gated recurrent unit network for predicting phosphorylation sites of SARS-CoV-2 infection. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 32:28-35. [PMID: 36908648 PMCID: PMC9968446 DOI: 10.1016/j.omtn.2023.02.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 02/22/2023] [Indexed: 02/27/2023]
Abstract
The global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has generated tremendous concern and poses a serious threat to international public health. Phosphorylation is a common post-translational modification affecting many essential cellular processes and is inextricably linked to SARS-CoV-2 infection. Hence, accurate identification of phosphorylation sites will be helpful to understand the mechanisms of SARS-CoV-2 infection and mitigate the ongoing COVID-19 pandemic. In the present study, an attention-based bidirectional gated recurrent unit network, called IPs-GRUAtt, was proposed to identify phosphorylation sites in SARS-CoV-2-infected host cells. Comparative results demonstrated that IPs-GRUAtt surpassed both state-of-the-art machine-learning methods and existing models for identifying phosphorylation sites. Moreover, the attention mechanism made IPs-GRUAtt able to extract the key features from protein sequences. These results demonstrated that the IPs-GRUAtt is a powerful tool for identifying phosphorylation sites. For facilitating its academic use, a freely available online web server for IPs-GRUAtt is provided at http://cbcb.cdutcm.edu.cn/phosphory/.
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Affiliation(s)
- Guiyang Zhang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Chengdu University of Traditional Chinese Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Qiang Tang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Chengdu University of Traditional Chinese Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Pengmian Feng
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Wei Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Chengdu University of Traditional Chinese Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,State Key Laboratory of Southwestern Chinese Medicine Resources, School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
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16
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Varshney N, Mishra AK. Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery. Proteomes 2023; 11:proteomes11020016. [PMID: 37218921 DOI: 10.3390/proteomes11020016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/24/2023] [Accepted: 04/25/2023] [Indexed: 05/24/2023] Open
Abstract
Protein phosphorylation is a key post-translational modification (PTM) that is a central regulatory mechanism of many cellular signaling pathways. Several protein kinases and phosphatases precisely control this biochemical process. Defects in the functions of these proteins have been implicated in many diseases, including cancer. Mass spectrometry (MS)-based analysis of biological samples provides in-depth coverage of phosphoproteome. A large amount of MS data available in public repositories has unveiled big data in the field of phosphoproteomics. To address the challenges associated with handling large data and expanding confidence in phosphorylation site prediction, the development of many computational algorithms and machine learning-based approaches have gained momentum in recent years. Together, the emergence of experimental methods with high resolution and sensitivity and data mining algorithms has provided robust analytical platforms for quantitative proteomics. In this review, we compile a comprehensive collection of bioinformatic resources used for the prediction of phosphorylation sites, and their potential therapeutic applications in the context of cancer.
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Affiliation(s)
- Neha Varshney
- Division of Biological Sciences, Department of Cellular and Molecular Medicine, University of California, San Diego, CA 93093, USA
- Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA
| | - Abhinava K Mishra
- Molecular, Cellular and Developmental Biology Department, University of California, Santa Barbara, CA 93106, USA
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17
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Wang M, Yan L, Jia J, Lai J, Zhou H, Yu B. DE-MHAIPs: Identification of SARS-CoV-2 phosphorylation sites based on differential evolution multi-feature learning and multi-head attention mechanism. Comput Biol Med 2023; 160:106935. [PMID: 37120990 PMCID: PMC10140648 DOI: 10.1016/j.compbiomed.2023.106935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/12/2023] [Accepted: 04/13/2023] [Indexed: 05/02/2023]
Abstract
The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) around the world affects the normal lives of people all over the world. The computational methods can be used to accurately identify SARS-CoV-2 phosphorylation sites. In this paper, a new prediction model of SARS-CoV-2 phosphorylation sites, called DE-MHAIPs, is proposed. First, we use six feature extraction methods to extract protein sequence information from different perspectives. For the first time, we use a differential evolution (DE) algorithm to learn individual feature weights and fuse multi-information in a weighted combination. Next, Group LASSO is used to select a subset of good features. Then, the important protein information is given higher weight through multi-head attention. After that, the processed data is fed into long short-term memory network (LSTM) to further enhance model's ability to learn features. Finally, the data from LSTM are input into fully connected neural network (FCN) to predict SARS-CoV-2 phosphorylation sites. The AUC values of the S/T and Y datasets under 5-fold cross-validation reach 91.98% and 98.32%, respectively. The AUC values of the two datasets on the independent test set reach 91.72% and 97.78%, respectively. The experimental results show that the DE-MHAIPs method exhibits excellent predictive ability compared with other methods.
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Affiliation(s)
- Minghui Wang
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Lu Yan
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Jihua Jia
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Jiali Lai
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Hongyan Zhou
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China.
| | - Bin Yu
- College of Information Science and Technology, School of Data Science, Qingdao University of Science and Technology, Qingdao, 266061, China; School of Data Science, University of Science and Technology of China, Hefei, 230027, China.
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18
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Zulfiqar H, Ahmed Z, Kissanga Grace-Mercure B, Hassan F, Zhang ZY, Liu F. Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique. Front Microbiol 2023; 14:1170785. [PMID: 37125199 PMCID: PMC10133480 DOI: 10.3389/fmicb.2023.1170785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 03/17/2023] [Indexed: 05/02/2023] Open
Abstract
Promotors are those genomic regions on the upstream of genes, which are bound by RNA polymerase for starting gene transcription. Because it is the most critical element of gene expression, the recognition of promoters is crucial to understand the regulation of gene expression. This study aimed to develop a machine learning-based model to predict promotors in Agrobacterium tumefaciens (A. tumefaciens) strain C58. In the model, promotor sequences were encoded by three different kinds of feature descriptors, namely, accumulated nucleotide frequency, k-mer nucleotide composition, and binary encodings. The obtained features were optimized by using correlation and the mRMR-based algorithm. These optimized features were inputted into a random forest (RF) classifier to discriminate promotor sequences from non-promotor sequences in A. tumefaciens strain C58. The examination of 10-fold cross-validation showed that the proposed model could yield an overall accuracy of 0.837. This model will provide help for the study of promoters in A. tumefaciens C58 strain.
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Affiliation(s)
- Hasan Zulfiqar
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zahoor Ahmed
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China
| | - Bakanina Kissanga Grace-Mercure
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Farwa Hassan
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhao-Yue Zhang
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fen Liu
- Department of Radiation Oncology, Peking University Cancer Hospital (Inner Mongolia Campus), Affiliated Cancer Hospital of Inner Mongolia Medical University, Inner Mongolia Cancer Hospital, Hohhot, China
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19
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Zulfiqar H, Guo Z, Grace-Mercure BK, Zhang ZY, Gao H, Lin H, Wu Y. Empirical comparison and recent advances of computational prediction of hormone binding proteins using machine learning methods. Comput Struct Biotechnol J 2023; 21:2253-2261. [PMID: 37035551 PMCID: PMC10073991 DOI: 10.1016/j.csbj.2023.03.024] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 03/15/2023] [Accepted: 03/16/2023] [Indexed: 03/19/2023] Open
Abstract
Hormone binding proteins (HBPs) belong to the group of soluble carrier proteins. These proteins selectively and non-covalently interact with hormones and promote growth hormone signaling in human and other animals. The HBPs are useful in many medical and commercial fields. Thus, the identification of HBPs is very important because it can help to discover more details about hormone binding proteins. Meanwhile, the experimental methods are time-consuming and expensive for hormone binding proteins recognition. Computational prediction methods have played significant roles in the correct recognition of hormone binding proteins with the use of sequence information and ML algorithms. In this review, we compared and assessed the implementation of ML-based tools in recognition of HBPs in a unique way. We hope that this study will give enough awareness and knowledge for research on HBPs.
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Affiliation(s)
- Hasan Zulfiqar
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang 313001, China
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
- School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhiling Guo
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Bakanina Kissanga Grace-Mercure
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhao-Yue Zhang
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Gao
- School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lin
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang 313001, China
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yun Wu
- College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
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20
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Su W, Xie XQ, Liu XW, Gao D, Ma CY, Zulfiqar H, Yang H, Lin H, Yu XL, Li YW. iRNA-ac4C: A novel computational method for effectively detecting N4-acetylcytidine sites in human mRNA. Int J Biol Macromol 2023; 227:1174-1181. [PMID: 36470433 DOI: 10.1016/j.ijbiomac.2022.11.299] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/10/2022] [Accepted: 11/25/2022] [Indexed: 12/07/2022]
Abstract
RNA N4-acetylcytidine (ac4C) is the acetylation of cytidine at the nitrogen-4 position, which is a highly conserved RNA modification and involves a variety of biological processes. Hence, accurate identification of genome-wide ac4C sites is vital for understanding regulation mechanism of gene expression. In this work, a novel predictor, named iRNA-ac4C, was established to identify ac4C sites in human mRNA based on three feature extraction methods, including nucleotide composition, nucleotide chemical property, and accumulated nucleotide frequency. Subsequently, minimum-Redundancy-Maximum-Relevance combined with incremental feature selection strategies was utilized to select the optimal feature subset. According to the optimal feature subset, the best ac4C classification model was trained by gradient boosting decision tree with 10-fold cross-validation. The results of independent testing set indicated that our proposed method could produce encouraging generalization capabilities. For the convenience of other researchers, we established a user-friendly web server which is freely available at http://lin-group.cn/server/iRNA-ac4C/. We hope that the tool could provide guide for wet-experimental scholars.
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Affiliation(s)
- Wei Su
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xue-Qin Xie
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xiao-Wei Liu
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Dong Gao
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Cai-Yi Ma
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Hasan Zulfiqar
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Hui Yang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Hao Lin
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Xiao-Long Yu
- School of Materials Science and Engineering, Hainan University, Haikou 570228, China.
| | - Yan-Wen Li
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China; Key Laboratory of Intelligent Information Processing of Jilin Province, Northeast Normal University, Changchun 130117, China; Institute of Computational Biology, Northeast Normal University, Changchun 130117, China.
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21
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Zhang L, Li H, Zhang Z, Wang J, Chen G, Chen D, Shi W, Jia G, Liu M. Hybrid gMLP model for interaction prediction of MHC-peptide and TCR. Front Genet 2023; 13:1092822. [PMID: 36685858 PMCID: PMC9845249 DOI: 10.3389/fgene.2022.1092822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 12/01/2022] [Indexed: 01/05/2023] Open
Abstract
Understanding the interaction of T-cell receptor (TCR) with major histocompatibility-peptide (MHC-peptide) complex is extremely important in human immunotherapy and vaccine development. However, due to the limited available data, the performance of existing models for predicting the interaction of T-cell receptors (TCR) with major histocompatibility-peptide complexes is still unsatisfactory. Deep learning models have been applied to prediction tasks in various fields and have achieved better results compared with other traditional models. In this study, we leverage the gMLP model combined with attention mechanism to predict the interaction of MHC-peptide and TCR. Experiments show that our model can predict TCR-peptide interactions accurately and can handle the problems caused by different TCR lengths. Moreover, we demonstrate that the models trained with paired CDR3β-chain and CDR3α-chain data are better than those trained with only CDR3β-chain or with CDR3α-chain data. We also demonstrate that the hybrid model has greater potential than the traditional convolutional neural network.
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Affiliation(s)
- Lichao Zhang
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China
| | - Haojin Li
- School of Software, Shandong University, Jinan, China
| | - Zhenjiu Zhang
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China
| | - Jinjin Wang
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China
| | | | | | - Wentao Shi
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China
| | - Gaozhi Jia
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China
| | - Mingjun Liu
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China
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22
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Gao W, Xu D, Li H, Du J, Wang G, Li D. Identification of adaptor proteins by incorporating deep learning and PSSM profiles. Methods 2023; 209:10-17. [PMID: 36427763 DOI: 10.1016/j.ymeth.2022.11.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/25/2022] [Accepted: 11/02/2022] [Indexed: 11/23/2022] Open
Abstract
Adaptor proteins, also known as signal transduction adaptor proteins, are important proteins in signal transduction pathways, and play a role in connecting signal proteins for signal transduction between cells. Studies have shown that adaptor proteins are closely related to some diseases, such as tumors and diabetes. Therefore, it is very meaningful to construct a relevant model to accurately identify adaptor proteins. In recent years, many studies have used a position-specific scoring matrix (PSSM) and neural network methods to identify adaptor proteins. However, ordinary neural network models cannot correlate the contextual information in PSSM profiles well, so these studies usually process 20×N (N > 20) PSSM into 20×20 dimensions, which results in the loss of a large amount of protein information; This research proposes an efficient method that combines one-dimensional convolution (1-D CNN) and a bidirectional long short-term memory network (biLSTM) to identify adaptor proteins. The complete PSSM profiles are the input of the model, and the complete information of the protein is retained during the training process. We perform cross-validation during model training and test the performance of the model on an independent test set; in the data set with 1224 adaptor proteins and 11,078 non-adaptor proteins, five indicators including specificity, sensitivity, accuracy, area under the receiver operating characteristic curve (AUC) metric and Matthews correlation coefficient (MCC), were employed to evaluate model performance. On the independent test set, the specificity, sensitivity, accuracy and MCC were 0.817, 0.865, 0.823 and 0.465, respectively. Those results show that our method is better than the state-of-the art methods. This study is committed to improve the accuracy of adaptor protein identification, and laid a foundation for further research on diseases related to adaptor protein. This research provided a new idea for the application of deep learning related models in bioinformatics and computational biology.
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Affiliation(s)
- Wentao Gao
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 150000, China
| | - Dali Xu
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 150000, China
| | - Hongfei Li
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 150000, China
| | - Junping Du
- Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Guohua Wang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 150000, China.
| | - Dan Li
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 150000, China.
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23
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A Novel Capsule Network with Attention Routing to Identify Prokaryote Phosphorylation Sites. Biomolecules 2022; 12:biom12121854. [PMID: 36551282 PMCID: PMC9775645 DOI: 10.3390/biom12121854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/07/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
By denaturing proteins and promoting the formation of multiprotein complexes, protein phosphorylation has important effects on the activity of protein functional molecules and cell signaling. The regulation of protein phosphorylation allows microbes to respond rapidly and reversibly to specific environmental stimuli or niches, which is closely related to the molecular mechanisms of bacterial drug resistance. Accurate prediction of phosphorylation sites (p-site) of prokaryotes can contribute to addressing bacterial resistance and providing new perspectives for developing novel antibacterial drugs. Most existing studies focus on human phosphorylation sites, while tools targeting phosphorylation site identification of prokaryotic proteins are still relatively scarce. This study designs a capsule network-based prediction technique for p-site in prokaryotes. To address the poor scalability and unreliability of dynamic routing processes in the output space of capsule networks, a more reliable way is introduced to learn the consistency between capsules. We incorporate a self-attention mechanism into the routing algorithm to capture the global information of the capsule, reducing the computational effort while enriching the representation capability of the capsule. Aiming at the weak robustness of the model, EcapsP improves the prediction accuracy and stability by introducing shortcuts and unconditional reconfiguration. In addition, the study compares and analyzes the prediction performance based on word vectors, physicochemical properties, and mixing characteristics in predicting serine (Ser/S), threonine (Thr/T), and tyrosine (Tyr/Y) p-site. The comprehensive experimental results show that the accuracy of the developed technique is close to 70% for the identification of the three phosphorylation sites in prokaryotes. Importantly, in side-by-side comparisons with other state-of-the-art predictors, our method improves the Matthews correlation coefficient (MCC) by approximately 7%. The results demonstrate the superiority of EcapsP in terms of high performance and reliability.
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24
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Zeng Y, Liu D, Wang Y. Identification of phosphorylation site using S-padding strategy based convolutional neural network. Health Inf Sci Syst 2022; 10:29. [PMID: 36124094 PMCID: PMC9481819 DOI: 10.1007/s13755-022-00196-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 08/25/2022] [Indexed: 10/14/2022] Open
Abstract
Purpose Abnormal phosphorylation has been proved to associate with a variety of human diseases, and the identification of phosphorylation sites is one of the research hotspots in healthcare. The study of phosphorylation site prediction in deep learning models often introduces a variety of information, and the utilization of complex models limits the usage scenarios of the models. Methods An enhanced deep learning method with S-padding strategy based on convolutional neural network is proposed in this paper. The S-padding strategy forms a three-dimensional matrix with extension information from original amino acid sequences, and a corresponding 2D-CNN model is designed to abstract the comprehensive features of phosphorylation site area in protein sequences. Results The fivefold cross-validation experiments are conducted, and the results show the performance of the proposed method on human dataset can achieve an accuracy of 89.68 % on serine/threonine sites and 88.16 % on tyrosine sites, respectively. Furthermore, phosphorylation site prediction on different organisms obtains the accuracy, sensitivity, and specificity of over 0.85, indicating a potential capability on phosphorylation site prediction task. Comparison result with existing models shows that the proposed method obtains better performance on both accuracy and AUC value, and the proposed method can further improve performance with sufficient training data. Conclusion This method enables proteome-wide predictions via models trained on a large amount of phosphorylation data, further exploiting the potential of protein phosphorylation site identification, and helping to provide insights into phosphorylation mechanisms.
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Affiliation(s)
- Yanjiao Zeng
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Dongning Liu
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Yang Wang
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
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Yu L, Ju B, Ren S. HLGNN-MDA: Heuristic Learning Based on Graph Neural Networks for miRNA-Disease Association Prediction. Int J Mol Sci 2022; 23:13155. [PMID: 36361945 PMCID: PMC9657597 DOI: 10.3390/ijms232113155] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 10/23/2022] [Accepted: 10/26/2022] [Indexed: 01/12/2024] Open
Abstract
Identifying disease-related miRNAs can improve the understanding of complex diseases. However, experimentally finding the association between miRNAs and diseases is expensive in terms of time and resources. The computational screening of reliable miRNA-disease associations has thus become a necessary tool to guide biological experiments. "Similar miRNAs will be associated with the same disease" is the assumption on which most current miRNA-disease association prediction methods rely; however, biased prior knowledge, and incomplete and inaccurate miRNA similarity data and disease similarity data limit the performance of the model. Here, we propose heuristic learning based on graph neural networks to predict microRNA-disease associations (HLGNN-MDA). We learn the local graph topology features of the predicted miRNA-disease node pairs using graph neural networks. In particular, our improvements to the graph convolution layer of the graph neural network enable it to learn information among homogeneous nodes and among heterogeneous nodes. We illustrate the performance of HLGNN-MDA by performing tenfold cross-validation against excellent baseline models. The results show that we have promising performance in multiple metrics. We also focus on the role of the improvements to the graph convolution layer in the model. The case studies are supported by evidence on breast cancer, hepatocellular carcinoma and renal cell carcinoma. Given the above, the experiments demonstrate that HLGNN-MDA can serve as a reliable method to identify novel miRNA-disease associations.
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Affiliation(s)
- Liang Yu
- School of Computer Science and Technology, Xidian University, Xi’an 710071, China
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26
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Liu S, Cui C, Chen H, Liu T. Ensemble learning-based feature selection for phosphorylation site detection. Front Genet 2022; 13:984068. [PMID: 36338976 PMCID: PMC9634105 DOI: 10.3389/fgene.2022.984068] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 10/05/2022] [Indexed: 11/18/2022] Open
Abstract
SARS-COV-2 is prevalent all over the world, causing more than six million deaths and seriously affecting human health. At present, there is no specific drug against SARS-COV-2. Protein phosphorylation is an important way to understand the mechanism of SARS -COV-2 infection. It is often expensive and time-consuming to identify phosphorylation sites with specific modified residues through experiments. A method that uses machine learning to make predictions about them is proposed. As all the methods of extracting protein sequence features are knowledge-driven, these features may not be effective for detecting phosphorylation sites without a complete understanding of the mechanism of protein. Moreover, redundant features also have a great impact on the fitting degree of the model. To solve these problems, we propose a feature selection method based on ensemble learning, which firstly extracts protein sequence features based on knowledge, then quantifies the importance score of each feature based on data, and finally uses the subset of important features as the final features to predict phosphorylation sites.
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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
| | - Tong Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
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DeeProPre: A promoter predictor based on deep learning. Comput Biol Chem 2022; 101:107770. [PMID: 36116322 DOI: 10.1016/j.compbiolchem.2022.107770] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 08/06/2022] [Accepted: 09/11/2022] [Indexed: 11/21/2022]
Abstract
The promoter is a DNA sequence recognized, bound and transcribed by RNA polymerase. It is usually located at the upstream or 5'end of the transcription start site (TSS). Studies have shown that the structure of the promoter affects its affinity for RNA polymerase, thus affecting the level of gene expression. Therefore, the correct identification of core promoter and common structural gene is of great significance in the field of biomedicine. At present, many methods have been proposed to improve the accuracy of promoter recognition, but the performances still need to be further improved. In this study, a deep learning algorithm (DeeProPre) based on bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN) was proposed. Firstly, the supervised embedding layer was applied to map the sequence to a high-dimensional space. Secondly, two 1D convolutional layers, BiLSTM and attentional mechanism layer were used for extracting features. Finally, the full connection layer activated by Sigmoid function was used to obtain the probability of classification into target categories. This model can identify the promoter region of eukaryotes with high accuracy, providing an analytical basis for further understanding of promoter physiological functions and studies of gene transcription mechanisms. The source code of DeeProPre is freely available at https://github.com/zzwwmmm/DeeProPre/tree/master.
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Mini-review: Recent advances in post-translational modification site prediction based on deep learning. Comput Struct Biotechnol J 2022; 20:3522-3532. [PMID: 35860402 PMCID: PMC9284371 DOI: 10.1016/j.csbj.2022.06.045] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/21/2022] [Accepted: 06/21/2022] [Indexed: 11/23/2022] Open
Abstract
Post-translational modifications (PTMs) are closely linked to numerous diseases, playing a significant role in regulating protein structures, activities, and functions. Therefore, the identification of PTMs is crucial for understanding the mechanisms of cell biology and diseases therapy. Compared to traditional machine learning methods, the deep learning approaches for PTM prediction provide accurate and rapid screening, guiding the downstream wet experiments to leverage the screened information for focused studies. In this paper, we reviewed the recent works in deep learning to identify phosphorylation, acetylation, ubiquitination, and other PTM types. In addition, we summarized PTM databases and discussed future directions with critical insights.
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Key Words
- AAindex, Amino acid index
- ATP, Adenosine triphosphate
- AUC, Area under curve
- Ac, Acetylation
- BE, Binary encoding
- BLOSUM, Blocks substitution matrix
- Bi-LSTM, Bidirectional LSTM
- CKSAAP, Composition of k-spaced amino acid Pairs
- CNN, Convolutional neural network
- CNNOH, CNN with the one-hot encoding
- CNNWE, CNN with the word-embedding encoding
- CNNrgb, CNN red green blue
- CV, Cross-validation
- DC-CNN, Densely connected convolutional neural network
- DL, Deep learning
- DNNs, Deep neural networks
- Deep learning
- E. coli, Escherichia coli
- EBGW, Encoding based on grouped weight
- EGAAC, Enhanced grouped amino acids content
- IG, Information gain
- K, Lysine
- KNN, k nearest neighbor
- LASSO, Least absolute shrinkage and selection operator
- LSTM, Long short-term memory
- LSTMWE, LSTM with the word-embedding encoding
- M.musculus, Mus musculus
- MDC, Modular densely connected convolutional networks
- MDCAN, Multilane dense convolutional attention network
- ML, Machine learning
- MLP, Multilayer perceptron
- MMI, Multivariate mutual information
- Machine learning
- Mass spectrometry
- NMBroto, Normalized Moreau-Broto autocorrelation
- P, Proline
- PSP, PhosphoSitePlus
- PSSM, Position-specific scoring matrix
- PTM, Post-translational modifications
- Ph, Phosphorylation
- Post-translational modification
- Prediction
- PseAAC, Pseudo-amino acid composition
- R, Arginine
- RF, Random forest
- RNN, Recurrent neural network
- ROC, Receiver operating characteristic
- S, Serine
- S. typhimurium, Salmonella typhimurium
- S.cerevisiae, Saccharomyces cerevisiae
- SE, Squeeze and excitation
- SEV, Split to Equal Validation
- ST, Source and target
- SUMO, Small ubiquitin-like modifier
- SVM, Support vector machines
- T, Threonine
- Ub, Ubiquitination
- Y, Tyrosine
- ZSL, Zero-shot learning
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Kurata H, Tsukiyama S, Manavalan B. iACVP: markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model. Brief Bioinform 2022; 23:6623727. [PMID: 35772910 DOI: 10.1093/bib/bbac265] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/23/2022] [Accepted: 06/06/2022] [Indexed: 01/22/2023] Open
Abstract
The COVID-19 pandemic caused several million deaths worldwide. Development of anti-coronavirus drugs is thus urgent. Unlike conventional non-peptide drugs, antiviral peptide drugs are highly specific, easy to synthesize and modify, and not highly susceptible to drug resistance. To reduce the time and expense involved in screening thousands of peptides and assaying their antiviral activity, computational predictors for identifying anti-coronavirus peptides (ACVPs) are needed. However, few experimentally verified ACVP samples are available, even though a relatively large number of antiviral peptides (AVPs) have been discovered. In this study, we attempted to predict ACVPs using an AVP dataset and a small collection of ACVPs. Using conventional features, a binary profile and a word-embedding word2vec (W2V), we systematically explored five different machine learning methods: Transformer, Convolutional Neural Network, bidirectional Long Short-Term Memory, Random Forest (RF) and Support Vector Machine. Via exhaustive searches, we found that the RF classifier with W2V consistently achieved better performance on different datasets. The two main controlling factors were: (i) the dataset-specific W2V dictionary was generated from the training and independent test datasets instead of the widely used general UniProt proteome and (ii) a systematic search was conducted and determined the optimal k-mer value in W2V, which provides greater discrimination between positive and negative samples. Therefore, our proposed method, named iACVP, consistently provides better prediction performance compared with existing state-of-the-art methods. To assist experimentalists in identifying putative ACVPs, we implemented our model as a web server accessible via the following link: http://kurata35.bio.kyutech.ac.jp/iACVP.
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Affiliation(s)
- Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Sho Tsukiyama
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Balachandran Manavalan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
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30
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Charoenkwan P, Schaduangrat N, Hasan MM, Moni MA, Lió P, Shoombuatong W. Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins. EXCLI JOURNAL 2022; 21:554-570. [PMID: 35651661 PMCID: PMC9150013 DOI: 10.17179/excli2022-4723] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 02/21/2022] [Indexed: 12/15/2022]
Abstract
Thermophilic proteins (TPPs) are critical for basic research and in the food industry due to their ability to maintain a thermodynamically stable fold at extremely high temperatures. Thus, the expeditious identification of novel TPPs through computational models from protein sequences is very desirable. Over the last few decades, a number of computational methods, especially machine learning (ML)-based methods, for in silico prediction of TPPs have been developed. Therefore, it is desirable to revisit these methods and summarize their advantages and disadvantages in order to further develop new computational approaches to achieve more accurate and improved prediction of TPPs. With this goal in mind, we comprehensively investigate a large collection of fourteen state-of-the-art TPP predictors in terms of their dataset size, feature encoding schemes, feature selection strategies, ML algorithms, evaluation strategies and web server/software usability. To the best of our knowledge, this article represents the first comprehensive review on the development of ML-based methods for in silico prediction of TPPs. Among these TPP predictors, they can be classified into two groups according to the interpretability of ML algorithms employed (i.e., computational black-box methods and computational white-box methods). In order to perform the comparative analysis, we conducted a comparative study on several currently available TPP predictors based on two benchmark datasets. Finally, we provide future perspectives for the design and development of new computational models for TPP prediction. We hope that this comprehensive review will facilitate researchers in selecting an appropriate TPP predictor that is the most suitable one to deal with their purposes and provide useful perspectives for the development of more effective and accurate TPP predictors.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, Thailand, 50200
| | - Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand, 10700
| | - Md Mehedi Hasan
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Mohammad Ali Moni
- School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, the University of Queensland, St Lucia, QLD 4072, Australia
| | - Pietro Lió
- Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD, UK
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand, 10700
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DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features. Appl Bionics Biomech 2022; 2022:5483115. [PMID: 35465187 PMCID: PMC9020926 DOI: 10.1155/2022/5483115] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/25/2021] [Accepted: 02/05/2022] [Indexed: 12/29/2022] Open
Abstract
In the domain of genome annotation, the identification of DNA-binding protein is one of the crucial challenges. DNA is considered a blueprint for the cell. It contained all necessary information for building and maintaining the trait of an organism. It is DNA, which makes a living thing, a living thing. Protein interaction with DNA performs an essential role in regulating DNA functions such as DNA repair, transcription, and regulation. Identification of these proteins is a crucial task for understanding the regulation of genes. Several methods have been developed to identify the binding sites of DNA and protein depending upon the structures and sequences, but they were costly and time-consuming. Therefore, we propose a methodology named “DNAPred_Prot”, which uses various position and frequency-dependent features from protein sequences for efficient and effective prediction of DNA-binding proteins. Using testing techniques like 10-fold cross-validation and jackknife testing an accuracy of 94.95% and 95.11% was yielded, respectively. The results of SVM and ANN were also compared with those of a random forest classifier. The robustness of the proposed model was evaluated by using the independent dataset PDB186, and an accuracy of 91.47% was achieved by it. From these results, it can be predicted that the suggested methodology performs better than other extant methods for the identification of DNA-binding proteins.
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32
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Wang H, Zhao H, Zhang J, Han J, Liu Z. A parallel model of DenseCNN and ordered-neuron LSTM for generic and species-specific succinylation site prediction. Biotechnol Bioeng 2022; 119:1755-1767. [PMID: 35320585 DOI: 10.1002/bit.28091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 03/12/2022] [Accepted: 03/19/2022] [Indexed: 11/07/2022]
Abstract
Lysine succinylation (Ksucc) regulates various metabolic processes, participates in vital life processes, ans is involved in the occurrence and development of numerous diseases. Accurate recognition of succinylation sites can reveal underlying functional mechanisms and pathogenesis. However, most remain undetected. Moreover, a deep learning architecture focusing on generic and species-specific predictions is still lacking. Thus, we proposed a deep learning-based framework named Deep-Ksucc, combining a dense convolutional network (DenseCNN) and ordered-neuron long short-term memory (OnLSTM) in parallel, which took the cascading characteristics of sequence information and physicochemical properties as the input. The results of the generic and species-specific predictions indicated that Deep-Ksucc can identify sequence patterns of different organisms and recognize plenty of succinylation sites. The case study showed that Deep-Ksucc can serve as a reliable tool for biology verification and computer-aided recognition of succinylation sites. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Huiqing Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Hong Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jing Zhang
- Engineering Training Center, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jiale Han
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Zhihao Liu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
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Lv H, Dao F, Lin H. DeepKla: An attention mechanism-based deep neural network for protein lysine lactylation site prediction. IMETA 2022; 1:e11. [PMID: 38867734 PMCID: PMC10989745 DOI: 10.1002/imt2.11] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 01/28/2022] [Accepted: 02/01/2022] [Indexed: 06/14/2024]
Abstract
As a newly discovered protein posttranslational modification, lysine lactylation (Kla) plays a pivotal role in various cellular processes. High throughput mass spectrometry is the primary approach for the detection of Kla sites. However, experimental approaches for identifying Kla sites are often time-consuming and labor-intensive when compared to computational methods. Therefore, it is desirable to develop a powerful tool for identifying Kla sites. For this purpose, we presented the first computational framework termed as DeepKla for Kla sites prediction in rice by combining supervised embedding layer, convolutional neural network, bidirectional gated recurrent units, and attention mechanism layer. Comprehensive experiment results demonstrated the excellent predictive power and robustness of DeepKla. Based on the proposed model, a web-server called DeepKla was established and is freely accessible at http://lin-group.cn/server/DeepKla. The source code of DeepKla is freely available at the repository https://github.com/linDing-group/DeepKla.
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Affiliation(s)
- Hao Lv
- Key Laboratory for Neuro‐Information of Ministry of Education, School of Life Science and Technology, Center for Informational BiologyUniversity of Electronic Science and Technology of ChinaChengduSichuanChina
- Department of Molecular Life SciencesUniversity of ZurichZurichSwitzerland
| | - Fu‐Ying Dao
- Key Laboratory for Neuro‐Information of Ministry of Education, School of Life Science and Technology, Center for Informational BiologyUniversity of Electronic Science and Technology of ChinaChengduSichuanChina
- School of Biological SciencesNanyang Technological UniversitySingaporeSingapore
| | - Hao Lin
- Key Laboratory for Neuro‐Information of Ministry of Education, School of Life Science and Technology, Center for Informational BiologyUniversity of Electronic Science and Technology of ChinaChengduSichuanChina
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34
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Sun W, Du D, Fu T, Han Y, Li P, Ju H. Alterations of the Gut Microbiota in Patients With Severe Chronic Heart Failure. Front Microbiol 2022; 12:813289. [PMID: 35173696 PMCID: PMC8843083 DOI: 10.3389/fmicb.2021.813289] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 12/03/2021] [Indexed: 12/12/2022] Open
Abstract
Chronic heart failure (CHF) is the final outcome of almost all forms of cardiovascular diseases, remaining the main cause of mortality worldwide. Accumulating evidence is focused on the roles of gut microbial community in cardiovascular disease, but few studies have unveiled the alterations and further directions of gut microbiota in severe CHF patients. Aimed to investigate this deficiency, fecal samples from 29 CHF patients diagnosed with NYHA Class III-IV and 30 healthy controls were collected and then analyzed using bacterial 16S rRNA gene sequencing. As a result, there were many significant differences between the two groups. Firstly, the phylum Firmicutes was found to be remarkably decreased in severe CHF patients, and the phylum Proteobacteria was the second most abundant phyla in severe CHF patients instead of phylum Bacteroides strangely. Secondly, the α diversity indices such as chao1, PD-whole-tree and Shannon indices were significantly decreased in the severe CHF versus the control group, as well as the notable difference in β-diversity between the two groups. Thirdly, our result revealed a remarkable decrease in the abundance of the short-chain fatty acids (SCFA)-producing bacteria including genera Ruminococcaceae UCG-004, Ruminococcaceae UCG-002, Lachnospiraceae FCS020 group, Dialister and the increased abundance of the genera in Enterococcus and Enterococcaceae with an increased production of lactic acid. Finally, the alternation of the gut microbiota was presumably associated with the function including Cell cycle control, cell division, chromosome partitioning, Amino acid transport and metabolism and Carbohydrate transport and metabolism through SCFA pathway. Our findings provide the direction and theoretical knowledge for the regulation of gut flora in the treatment of severe CHF.
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Affiliation(s)
- Weiju Sun
- Department of Cardiology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Debing Du
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Tongze Fu
- Harbin Medical University, Harbin, China
| | - Ying Han
- Department of Cardiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Peng Li
- National Center for Biomedical Analysis, Beijing, China
| | - Hong Ju
- Heilongjiang Vocational College of Biology Science and Technology, Harbin, China
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35
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Cai J, Xiao G, Su R. GC6mA-Pred: A deep learning approach to identify DNA N6-methyladenine sites in the rice genome. Methods 2022; 204:14-21. [PMID: 35149214 DOI: 10.1016/j.ymeth.2022.02.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 01/31/2022] [Accepted: 02/05/2022] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION DNA N6-methyladenine (6mA) is a pivotal DNA modification for various biological processes. More accurate prediction of 6mA methylation sites plays an irreplaceable part in grasping the internal rationale of related biological activities. However, the existing prediction methods only extract information from a single dimension, which has some limitations. Therefore, it is very necessary to obtain the information of 6mA sites from different dimensions, so as to establish a reliable prediction method. RESULTS In this study, a neural network based bioinformatics model named GC6mA-Pred is proposed to predict N6-methyladenine modifications in DNA sequences. GC6mA-Pred extracts significant information from both sequence level and graph level. In the sequence level, GC6mA-Pred uses a three-layer convolution neural network (CNN) model to represent the sequence. In the graph level, GC6mA-Pred employs graph neural network (GNN) method to integrate various information contained in the chemical molecular formula corresponding to DNA sequence. In our newly built dataset, GC6mA-Pred shows better performance than other existing models. The results of comparative experiments have illustrated that GC6mA-Pred is capable of producing a marked effect in accurately identifying DNA 6mA modifications.
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Affiliation(s)
- Jianhua Cai
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China; College of Mathematics and Computer Science, Fuzhou University, Fuzhou, PR China
| | - Guobao Xiao
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China.
| | - Ran Su
- College of Intelligence and Computing, Tianjin University, Tianjin, China.
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Zulfiqar H, Huang QL, Lv H, Sun ZJ, Dao FY, Lin H. Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites in Geobacter pickeringii by Using Correlation-Based Feature Selection Technique. Int J Mol Sci 2022; 23:1251. [PMID: 35163174 PMCID: PMC8836036 DOI: 10.3390/ijms23031251] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/19/2022] [Accepted: 01/20/2022] [Indexed: 12/15/2022] Open
Abstract
4mC is a type of DNA alteration that has the ability to synchronize multiple biological movements, for example, DNA replication, gene expressions, and transcriptional regulations. Accurate prediction of 4mC sites can provide exact information to their hereditary functions. The purpose of this study was to establish a robust deep learning model to recognize 4mC sites in Geobacter pickeringii. In the anticipated model, two kinds of feature descriptors, namely, binary and k-mer composition were used to encode the DNA sequences of Geobacter pickeringii. The obtained features from their fusion were optimized by using correlation and gradient-boosting decision tree (GBDT)-based algorithm with incremental feature selection (IFS) method. Then, these optimized features were inserted into 1D convolutional neural network (CNN) to classify 4mC sites from non-4mC sites in Geobacter pickeringii. The performance of the anticipated model on independent data exhibited an accuracy of 0.868, which was 4.2% higher than the existing model.
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Affiliation(s)
| | | | | | | | | | - Hao Lin
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; (H.Z.); (Q.-L.H.); (H.L.); (Z.-J.S.); (F.-Y.D.)
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Li F, Guo X, Xiang D, Pitt ME, Bainomugisa A, Coin LJ. Computational analysis and prediction of PE_PGRS proteins using machine learning. Comput Struct Biotechnol J 2022; 20:662-674. [PMID: 35140886 PMCID: PMC8804200 DOI: 10.1016/j.csbj.2022.01.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/09/2022] [Accepted: 01/18/2022] [Indexed: 12/18/2022] Open
Abstract
Mycobacterium tuberculosis genome comprises approximately 10% of two families of poorly characterised genes due to their high GC content and highly repetitive nature. The largest sub-group, the proline-glutamic acid polymorphic guanine-cytosine-rich sequence (PE_PGRS) family, is thought to be involved in host response and disease pathogenicity. Due to their high genetic variability and complexity of analysis, they are typically disregarded for further research in genomic studies. There are currently limited online resources and homology computational tools that can identify and analyse PE_PGRS proteins. In addition, they are computational-intensive and time-consuming, and lack sensitivity. Therefore, computational methods that can rapidly and accurately identify PE_PGRS proteins are valuable to facilitate the functional elucidation of the PE_PGRS family proteins. In this study, we developed the first machine learning-based bioinformatics approach, termed PEPPER, to allow users to identify PE_PGRS proteins rapidly and accurately. PEPPER was built upon a comprehensive evaluation of 13 popular machine learning algorithms with various sequence and physicochemical features. Empirical studies demonstrated that PEPPER achieved significantly better performance than alignment-based approaches, BLASTP and PHMMER, in both prediction accuracy and speed. PEPPER is anticipated to facilitate community-wide efforts to conduct high-throughput identification and analysis of PE_PGRS proteins.
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Affiliation(s)
- Fuyi Li
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, VIC 3000, Australia
| | - Xudong Guo
- School of Information Engineering, Ningxia University, Yinchuan, Ningxia 750021, China
| | - Dongxu Xiang
- Faculty of Engineering and Information Technology, The University of Melbourne, VIC 3000, Australia
| | - Miranda E. Pitt
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, VIC 3000, Australia
| | | | - Lachlan J.M. Coin
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, VIC 3000, Australia
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Zhai Y, Zhang J, Zhang T, Gong Y, Zhang Z, Zhang D, Zhao Y. AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs. Front Pharmacol 2022; 12:818115. [PMID: 35115948 PMCID: PMC8803896 DOI: 10.3389/fphar.2021.818115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 12/20/2021] [Indexed: 11/18/2022] Open
Abstract
Antioxidant proteins can not only balance the oxidative stress in the body, but are also an important component of antioxidant drugs. Accurate identification of antioxidant proteins is essential to help humans fight diseases and develop new drugs. In this paper, we developed a friendly method AOPM to identify antioxidant proteins. 188D and the Composition of k-spaced Amino Acid Pairs were adopted as the feature extraction method. In addition, the Max-Relevance-Max-Distance algorithm (MRMD) and random forest were the feature selection and classifier, respectively. We used 5-folds cross-validation and independent test dataset to evaluate our model. On the test dataset, AOPM presented a higher performance compared with the state-of-the-art methods. The sensitivity, specificity, accuracy, Matthew’s Correlation Coefficient and an Area Under the Curve reached 87.3, 94.2, 92.0%, 0.815 and 0.972, respectively. In addition, AOPM still has excellent performance in predicting the catalytic enzymes of antioxidant drugs. This work proved the feasibility of virtual drug screening based on sequence information and provided new ideas and solutions for drug development.
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Affiliation(s)
- Yixiao Zhai
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Jingyu Zhang
- Department of Neurology, the Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tianjiao Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Yue Gong
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Zixiao Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Dandan Zhang
- Department of Obstetrics and Gynecology, the First Affiliated Hospital of Harbin Medical University, Harbin, China
- *Correspondence: Dandan Zhang, ; Yuming Zhao,
| | - Yuming Zhao
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
- *Correspondence: Dandan Zhang, ; Yuming Zhao,
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Manavalan B, Basith S, Lee G. Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2. Brief Bioinform 2022; 23:bbab412. [PMID: 34595489 PMCID: PMC8500067 DOI: 10.1093/bib/bbab412] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/27/2021] [Accepted: 09/07/2021] [Indexed: 01/08/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) has impacted public health as well as societal and economic well-being. In the last two decades, various prediction algorithms and tools have been developed for predicting antiviral peptides (AVPs). The current COVID-19 pandemic has underscored the need to develop more efficient and accurate machine learning (ML)-based prediction algorithms for the rapid identification of therapeutic peptides against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Several peptide-based ML approaches, including anti-coronavirus peptides (ACVPs), IL-6 inducing epitopes and other epitopes targeting SARS-CoV-2, have been implemented in COVID-19 therapeutics. Owing to the growing interest in the COVID-19 field, it is crucial to systematically compare the existing ML algorithms based on their performances. Accordingly, we comprehensively evaluated the state-of-the-art IL-6 and AVP predictors against coronaviruses in terms of core algorithms, feature encoding schemes, performance evaluation metrics and software usability. A comprehensive performance assessment was then conducted to evaluate the robustness and scalability of the existing predictors using well-constructed independent validation datasets. Additionally, we discussed the advantages and disadvantages of the existing methods, providing useful insights into the development of novel computational tools for characterizing and identifying epitopes or ACVPs. The insights gained from this review are anticipated to provide critical guidance to the scientific community in the rapid design and development of accurate and efficient next-generation in silico tools against SARS-CoV-2.
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Affiliation(s)
| | - Shaherin Basith
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Korea
| | - Gwang Lee
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Korea
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40
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Liu M, Chen H, Gao D, Ma CY, Zhang ZY. Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:7493834. [PMID: 35069791 PMCID: PMC8769816 DOI: 10.1155/2022/7493834] [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: 11/07/2021] [Accepted: 12/16/2021] [Indexed: 11/28/2022]
Abstract
Helicobacter pylori (H. pylori) is the most common risk factor for gastric cancer worldwide. The membrane proteins of the H. pylori are involved in bacterial adherence and play a vital role in the field of drug discovery. Thus, an accurate and cost-effective computational model is needed to predict the uncharacterized membrane proteins of H. pylori. In this study, a reliable benchmark dataset consisted of 114 membrane and 219 nonmembrane proteins was constructed based on UniProt. A support vector machine- (SVM-) based model was developed for discriminating H. pylori membrane proteins from nonmembrane proteins by using sequence information. Cross-validation showed that our method achieved good performance with an accuracy of 91.29%. It is anticipated that the proposed model will be useful for the annotation of H. pylori membrane proteins and the development of new anti-H. pylori agents.
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Affiliation(s)
- Mujiexin Liu
- Ineye Hospital of Chengdu University of TCM, Chengdu University of TCM, Chengdu 610084, China
| | - Hui Chen
- School of Healthcare Technology, Chengdu Neusoft University, 611844 Chengdu, China
| | - Dong Gao
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Cai-Yi Ma
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhao-Yue Zhang
- School of Healthcare Technology, Chengdu Neusoft University, 611844 Chengdu, China
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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41
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Guo X, Zhou W, Yu Y, Cai Y, Zhang Y, Du A, Lu Q, Ding Y, Li C. Multiple Laplacian Regularized RBF Neural Network for Assessing Dry Weight of Patients With End-Stage Renal Disease. Front Physiol 2021; 12:790086. [PMID: 34966294 PMCID: PMC8711098 DOI: 10.3389/fphys.2021.790086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 11/17/2021] [Indexed: 11/28/2022] Open
Abstract
Dry weight (DW) is an important dialysis index for patients with end-stage renal disease. It can guide clinical hemodialysis. Brain natriuretic peptide, chest computed tomography image, ultrasound, and bioelectrical impedance analysis are key indicators (multisource information) for assessing DW. By these approaches, a trial-and-error method (traditional measurement method) is employed to assess DW. The assessment of clinician is time-consuming. In this study, we developed a method based on artificial intelligence technology to estimate patient DW. Based on the conventional radial basis function neural (RBFN) network, we propose a multiple Laplacian-regularized RBFN (MLapRBFN) model to predict DW of patient. Compared with other model and body composition monitor, our method achieves the lowest value (1.3226) of root mean square error. In Bland-Altman analysis of MLapRBFN, the number of out agreement interval is least (17 samples). MLapRBFN integrates multiple Laplace regularization terms, and employs an efficient iterative algorithm to solve the model. The ratio of out agreement interval is 3.57%, which is lower than 5%. Therefore, our method can be tentatively applied for clinical evaluation of DW in hemodialysis patients.
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Affiliation(s)
- Xiaoyi Guo
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China.,Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Zhou
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Yan Yu
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Yinghua Cai
- Department of Nursing, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Yuan Zhang
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Aiyan Du
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Qun Lu
- Department of Nursing, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Chao Li
- General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
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42
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Chen Y, Juan L, Lv X, Shi L. Bioinformatics Research on Drug Sensitivity Prediction. Front Pharmacol 2021; 12:799712. [PMID: 34955863 PMCID: PMC8696280 DOI: 10.3389/fphar.2021.799712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 11/18/2021] [Indexed: 11/28/2022] Open
Abstract
Modeling-based anti-cancer drug sensitivity prediction has been extensively studied in recent years. While most drug sensitivity prediction models only use gene expression data, the remarkable impacts of gene mutation, methylation, and copy number variation on drug sensitivity are neglected. Drug sensitivity prediction can both help protect patients from some adverse drug reactions and improve the efficacy of treatment. Genomics data are extremely useful for drug sensitivity prediction task. This article reviews the role of drug sensitivity prediction, describes a variety of methods for predicting drug sensitivity. Moreover, the research significance of drug sensitivity prediction, as well as existing problems are well discussed.
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Affiliation(s)
- Yaojia Chen
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Liran Juan
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xiao Lv
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Lei Shi
- Department of Spine Surgery Changzheng Hospital, Naval Medical University, Shanghai, China
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43
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Jia Y, Huang S, Zhang T. KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest. Front Genet 2021; 12:811158. [PMID: 34912382 PMCID: PMC8667860 DOI: 10.3389/fgene.2021.811158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 11/15/2021] [Indexed: 02/04/2023] Open
Abstract
DNA-binding protein (DBP) is a protein with a special DNA binding domain that is associated with many important molecular biological mechanisms. Rapid development of computational methods has made it possible to predict DBP on a large scale; however, existing methods do not fully integrate DBP-related features, resulting in rough prediction results. In this article, we develop a DNA-binding protein identification method called KK-DBP. To improve prediction accuracy, we propose a feature extraction method that fuses multiple PSSM features. The experimental results show a prediction accuracy on the independent test dataset PDB186 of 81.22%, which is the highest of all existing methods.
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Affiliation(s)
- Yuran Jia
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Shan Huang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tianjiao Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
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44
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Ao C, Zou Q, Yu L. NmRF: identification of multispecies RNA 2'-O-methylation modification sites from RNA sequences. Brief Bioinform 2021; 23:6446272. [PMID: 34850821 DOI: 10.1093/bib/bbab480] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/05/2021] [Accepted: 10/18/2021] [Indexed: 12/12/2022] Open
Abstract
2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2'-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety of RNA types (miRNA, tRNA, mRNA, etc.), plays an important role in biological processes and is associated with different diseases. There are few functional mechanisms developed at present, and traditional high-throughput experiments are time-consuming and expensive to explore functional mechanisms. For a deeper understanding of relevant biological mechanisms, it is necessary to develop efficient and accurate recognition tools based on machine learning. Based on this, we constructed a predictor called NmRF based on optimal mixed features and random forest classifier to identify 2'-O-methylation modification sites. The predictor can identify modification sites of multiple species at the same time. To obtain a better prediction model, a two-step strategy is adopted; that is, the optimal hybrid feature set is obtained by combining the light gradient boosting algorithm and incremental feature selection strategy. In 10-fold cross-validation, the accuracies of Homo sapiens and Saccharomyces cerevisiae were 89.069 and 93.885%, and the AUC were 0.9498 and 0.9832, respectively. The rigorous 10-fold cross-validation and independent tests confirm that the proposed method is significantly better than existing tools. A user-friendly web server is accessible at http://lab.malab.cn/∼acy/NmRF.
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Affiliation(s)
- Chunyan Ao
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.,Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, China
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45
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Li F, Dong S, Leier A, Han M, Guo X, Xu J, Wang X, Pan S, Jia C, Zhang Y, Webb GI, Coin LJM, Li C, Song J. Positive-unlabeled learning in bioinformatics and computational biology: a brief review. Brief Bioinform 2021; 23:6415313. [PMID: 34729589 DOI: 10.1093/bib/bbab461] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/27/2021] [Accepted: 10/07/2021] [Indexed: 12/14/2022] Open
Abstract
Conventional supervised binary classification algorithms have been widely applied to address significant research questions using biological and biomedical data. This classification scheme requires two fully labeled classes of data (e.g. positive and negative samples) to train a classification model. However, in many bioinformatics applications, labeling data is laborious, and the negative samples might be potentially mislabeled due to the limited sensitivity of the experimental equipment. The positive unlabeled (PU) learning scheme was therefore proposed to enable the classifier to learn directly from limited positive samples and a large number of unlabeled samples (i.e. a mixture of positive or negative samples). To date, several PU learning algorithms have been developed to address various biological questions, such as sequence identification, functional site characterization and interaction prediction. In this paper, we revisit a collection of 29 state-of-the-art PU learning bioinformatic applications to address various biological questions. Various important aspects are extensively discussed, including PU learning methodology, biological application, classifier design and evaluation strategy. We also comment on the existing issues of PU learning and offer our perspectives for the future development of PU learning applications. We anticipate that our work serves as an instrumental guideline for a better understanding of the PU learning framework in bioinformatics and further developing next-generation PU learning frameworks for critical biological applications.
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Affiliation(s)
- Fuyi Li
- Monash University, Australia
| | | | - André Leier
- Department of Genetics, UAB School of Medicine, USA
| | - Meiya Han
- Department of Biochemistry and Molecular Biology, Monash University, Australia
| | | | - Jing Xu
- Computer Science and Technology from Nankai University, China
| | - Xiaoyu Wang
- Department of Biochemistry and Molecular Biology and Biomedicine Discovery Institute, Monash University, Australia
| | - Shirui Pan
- University of Technology Sydney (UTS), Ultimo, NSW, Australia
| | - Cangzhi Jia
- College of Science, Dalian Maritime University, Australia
| | - Yang Zhang
- Northwestern Polytechnical University, China
| | - Geoffrey I Webb
- Faculty of Information Technology at Monash University, Australia
| | - Lachlan J M Coin
- Department of Clinical Pathology, University of Melbourne, Australia
| | - Chen Li
- Biomedicine Discovery Institute and Department of Biochemistry of Molecular Biology, Monash University, Australia
| | - Jiangning Song
- Monash Biomedicine Discovery Institute, Monash University, Melbourne, Australia
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Zhao YW, Zhang S, Ding H. Recent development of machine learning methods in sumoylation sites prediction. Curr Med Chem 2021; 29:894-907. [PMID: 34525906 DOI: 10.2174/0929867328666210915112030] [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: 03/10/2021] [Revised: 07/24/2021] [Accepted: 08/07/2021] [Indexed: 11/22/2022]
Abstract
Sumoylation of proteins is an important reversible post-translational modification of proteins and mediates a variety of cellular processes. Sumo-modified proteins can change their subcellular localization, activity and stability. In addition, it also plays an important role in various cellular processes such as transcriptional regulation and signal transduction. The abnormal sumoylation is involved in many diseases, including neurodegeneration and immune-related diseases, as well as the development of cancer. Therefore, identification of the sumoylation site (SUMO site) is fundamental to understanding their molecular mechanisms and regulatory roles. In contrast to labor-intensive and costly experimental approaches, computational prediction of sumoylation sites in silico also attracted much attention for its accuracy, convenience and speed. At present, many computational prediction models have been used to identify SUMO sites, but these contents have not been comprehensively summarized and reviewed. Therefore, the research progress of relevant models is summarized and discussed in this paper. We will briefly summarize the development of bioinformatics methods on sumoylation site prediction. We will mainly focus on the benchmark dataset construction, feature extraction, machine learning method, published results and online tools. We hope the review will provide more help for wet-experimental scholars.
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Affiliation(s)
- Yi-Wei Zhao
- School of Medicine, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Shihua Zhang
- College of Life Science and Health, Wuhan University of Science and Technology, Wuhan 430065. China
| | - Hui Ding
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
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47
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Yang YH, Wang JS, Yuan SS, Liu ML, Su W, Lin H, Zhang ZY. A Survey for Predicting ATP Binding Residues of Proteins Using Machine Learning Methods. Curr Med Chem 2021; 29:789-806. [PMID: 34514982 DOI: 10.2174/0929867328666210910125802] [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: 03/24/2021] [Revised: 06/29/2021] [Accepted: 07/04/2021] [Indexed: 11/22/2022]
Abstract
Protein-ligand interactions are necessary for majority protein functions. Adenosine-5'-triphosphate (ATP) is one such ligand that plays vital role as a coenzyme in providing energy for cellular activities, catalyzing biological reaction and signaling. Knowing ATP binding residues of proteins is helpful for annotation of protein function and drug design. However, due to the huge amounts of protein sequences influx into databases in the post-genome era, experimentally identifying ATP binding residues is cost-ineffective and time-consuming. To address this problem, computational methods have been developed to predict ATP binding residues. In this review, we briefly summarized the application of machine learning methods in detecting ATP binding residues of proteins. We expect this review will be helpful for further research.
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Affiliation(s)
- Yu-He Yang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Jia-Shu Wang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Shi-Shi Yuan
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Meng-Lu Liu
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Wei Su
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Zhao-Yue Zhang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
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48
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Zulfiqar H, Sun ZJ, Huang QL, Yuan SS, Lv H, Dao FY, Lin H, Li YW. Deep-4mCW2V: A sequence-based predictor to identify N4-methylcytosine sites in Escherichia coli. Methods 2021; 203:558-563. [PMID: 34352373 DOI: 10.1016/j.ymeth.2021.07.011] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 07/22/2021] [Accepted: 07/29/2021] [Indexed: 10/20/2022] Open
Abstract
N4-methylcytosine (4mC) is a type of DNA modification which could regulate several biological progressions such as transcription regulation, replication and gene expressions. Precisely recognizing 4mC sites in genomic sequences can provide specific knowledge about their genetic roles. This study aimed to develop a deep learning-based model to predict 4mC sites in the Escherichia coli. In the model, DNA sequences were encoded by word embedding technique 'word2vec'. The obtained features were inputted into 1-D convolutional neural network (CNN) to discriminate 4mC sites from non-4mC sites in Escherichia coli genome. The examination on independent dataset showed that our model could yield the overall accuracy of 0.861, which was about 4.3% higher than the existing model. To provide convenience to scholars, we provided the data and source code of the model which can be freely download from https://github.com/linDing-groups/Deep-4mCW2V.
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Affiliation(s)
- Hasan Zulfiqar
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zi-Jie Sun
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qin-Lai Huang
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shi-Shi Yuan
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lv
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fu-Ying Dao
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lin
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Yan-Wen Li
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China; Key Laboratory of Intelligent Information Processing of Jilin Province, Northeast Normal University, Changchun 130117, China; Institute of Computational Biology, Northeast Normal University, Changchun 130117, China.
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