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Zhu L, Wang L, Yang Z, Xu P, Yang S. PPSNO: A Feature-Rich SNO Sites Predictor by Stacking Ensemble Strategy from Protein Sequence-Derived Information. Interdiscip Sci 2024; 16:192-217. [PMID: 38206557 DOI: 10.1007/s12539-023-00595-7] [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: 07/01/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 01/12/2024]
Abstract
The protein S-nitrosylation (SNO) is a significant post-translational modification that affects the stability, activity, cellular localization, and function of proteins. Therefore, highly accurate prediction of SNO sites aids in grasping biological function mechanisms. In this document, we have constructed a predictor, named PPSNO, forecasting protein SNO sites using stacked integrated learning. PPSNO integrates multiple machine learning techniques into an ensemble model, enhancing its predictive accuracy. First, we established benchmark datasets by collecting SNO sites from various sources, including literature, databases, and other predictors. Second, various techniques for feature extraction are applied to derive characteristics from protein sequences, which are subsequently amalgamated into the PPSNO predictor for training. Five-fold cross-validation experiments show that PPSNO outperformed existing predictors, such as PSNO, PreSNO, pCysMod, DeepNitro, RecSNO, and Mul-SNO. The PPSNO predictor achieved an impressive accuracy of 92.8%, an area under the curve (AUC) of 96.1%, a Matthews correlation coefficient (MCC) of 81.3%, an F1-score of 85.6%, an SN of 79.3%, an SP of 97.7%, and an average precision (AP) of 92.2%. We also employed ROC curves, PR curves, and radar plots to show the superior performance of PPSNO. Our study shows that fused protein sequence features and two-layer stacked ensemble models can improve the accuracy of predicting SNO sites, which can aid in comprehending cellular processes and disease mechanisms. The codes and data are available at https://github.com/serendipity-wly/PPSNO .
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Affiliation(s)
- Lun Zhu
- School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software, Changzhou University, Changzhou, 213164, China
| | - Liuyang Wang
- School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software, Changzhou University, Changzhou, 213164, China
| | - Zexi Yang
- School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software, Changzhou University, Changzhou, 213164, China
| | - Piao Xu
- College of Economics and Management, Nanjing Forestry University, Nanjing, 210037, China
| | - Sen Yang
- School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software, Changzhou University, Changzhou, 213164, China.
- The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, 213164, China.
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2
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Ramazi S, Tabatabaei SAH, Khalili E, Nia AG, Motarjem K. Analysis and review of techniques and tools based on machine learning and deep learning for prediction of lysine malonylation sites in protein sequences. Database (Oxford) 2024; 2024:baad094. [PMID: 38245002 PMCID: PMC10799748 DOI: 10.1093/database/baad094] [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: 09/17/2023] [Revised: 11/30/2023] [Accepted: 12/20/2023] [Indexed: 01/22/2024]
Abstract
The post-translational modifications occur as crucial molecular regulatory mechanisms utilized to regulate diverse cellular processes. Malonylation of proteins, a reversible post-translational modification of lysine/k residues, is linked to a variety of biological functions, such as cellular regulation and pathogenesis. This modification plays a crucial role in metabolic pathways, mitochondrial functions, fatty acid oxidation and other life processes. However, accurately identifying malonylation sites is crucial to understand the molecular mechanism of malonylation, and the experimental identification can be a challenging and costly task. Recently, approaches based on machine learning (ML) have been suggested to address this issue. It has been demonstrated that these procedures improve accuracy while lowering costs and time constraints. However, these approaches also have specific shortcomings, including inappropriate feature extraction out of protein sequences, high-dimensional features and inefficient underlying classifiers. As a result, there is an urgent need for effective predictors and calculation methods. In this study, we provide a comprehensive analysis and review of existing prediction models, tools and benchmark datasets for predicting malonylation sites in protein sequences followed by a comparison study. The review consists of the specifications of benchmark datasets, explanation of features and encoding methods, descriptions of the predictions approaches and their embedding ML or deep learning models and the description and comparison of the existing tools in this domain. To evaluate and compare the prediction capability of the tools, a new bunch of data has been extracted based on the most updated database and the tools have been assessed based on the extracted data. Finally, a hybrid architecture consisting of several classifiers including classical ML models and a deep learning model has been proposed to ensemble the prediction results. This approach demonstrates the better performance in comparison with all prediction tools included in this study (the source codes of the models presented in this manuscript are available in https://github.com/Malonylation). Database URL: https://github.com/A-Golshan/Malonylation.
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Affiliation(s)
| | - Seyed Amir Hossein Tabatabaei
- Department of Computer Science, Faculty of Mathematical Sciences, University of Guilan, Namjoo St. Postal, Rasht 41938-33697, Iran
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Jalal AleAhmad, Tehran 14117-13116, Iran
| | - Elham Khalili
- Department of Plant Sciences, Faculty of Science, Tarbiat Modares University, Jalal AleAhmad, Tehran 14117-13116, Iran
| | - Amirhossein Golshan Nia
- Department of Mathematics and Computer Science, Amirkabir University of Technology, No. 350, Hafez Ave, Tehran 15916-34311, Iran
| | - Kiomars Motarjem
- Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University, Jalal AleAhmad, Tehran 14117-13116, Iran
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3
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Ma X, Liang Y, Zhang S. iAVPs-ResBi: Identifying antiviral peptides by using deep residual network and bidirectional gated recurrent unit. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:21563-21587. [PMID: 38124610 DOI: 10.3934/mbe.2023954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Human history is also the history of the fight against viral diseases. From the eradication of viruses to coexistence, advances in biomedicine have led to a more objective understanding of viruses and a corresponding increase in the tools and methods to combat them. More recently, antiviral peptides (AVPs) have been discovered, which due to their superior advantages, have achieved great impact as antiviral drugs. Therefore, it is very necessary to develop a prediction model to accurately identify AVPs. In this paper, we develop the iAVPs-ResBi model using k-spaced amino acid pairs (KSAAP), encoding based on grouped weight (EBGW), enhanced grouped amino acid composition (EGAAC) based on the N5C5 sequence, composition, transition and distribution (CTD) based on physicochemical properties for multi-feature extraction. Then we adopt bidirectional long short-term memory (BiLSTM) to fuse features for obtaining the most differentiated information from multiple original feature sets. Finally, the deep model is built by combining improved residual network and bidirectional gated recurrent unit (BiGRU) to perform classification. The results obtained are better than those of the existing methods, and the accuracies are 95.07, 98.07, 94.29 and 97.50% on the four datasets, which show that iAVPs-ResBi can be used as an effective tool for the identification of antiviral peptides. The datasets and codes are freely available at https://github.com/yunyunliang88/iAVPs-ResBi.
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Affiliation(s)
- Xinyan Ma
- School of Science, Xi'an Polytechnic University, Xi'an 710048, China
| | - Yunyun Liang
- School of Science, Xi'an Polytechnic University, Xi'an 710048, China
| | - Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, China
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4
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Liu B, Yang Z, Liu Q, Zhang Y, Ding H, Lai H, Li Q. Computational prediction of allergenic proteins based on multi-feature fusion. Front Genet 2023; 14:1294159. [PMID: 37928245 PMCID: PMC10622758 DOI: 10.3389/fgene.2023.1294159] [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: 09/15/2023] [Accepted: 10/11/2023] [Indexed: 11/07/2023] Open
Abstract
Allergy is an autoimmune disorder described as an undesirable response of the immune system to typically innocuous substance in the environment. Studies have shown that the ability of proteins to trigger allergic reactions in susceptible individuals can be evaluated by bioinformatics tools. However, developing computational methods to accurately identify new allergenic proteins remains a vital challenge. This work aims to propose a machine learning model based on multi-feature fusion for predicting allergenic proteins efficiently. Firstly, we prepared a benchmark dataset of allergenic and non-allergenic protein sequences and pretested on it with a machine-learning platform. Then, three preferable feature extraction methods, including amino acid composition (AAC), dipeptide composition (DPC) and composition of k-spaced amino acid pairs (CKSAAP) were chosen to extract protein sequence features. Subsequently, these features were fused and optimized by Pearson correlation coefficient (PCC) and principal component analysis (PCA). Finally, the most representative features were picked out to build the optimal predictor based on random forest (RF) algorithm. Performance evaluation results via 5-fold cross-validation showed that the final model, called iAller (https://github.com/laihongyan/iAller), could precisely distinguish allergenic proteins from non-allergenic proteins. The prediction accuracy and AUC value for validation dataset achieved 91.4% and 0.97%, respectively. This model will provide guide for users to identify more allergenic proteins.
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Affiliation(s)
- Bin Liu
- Department of Anesthesiology, The Fourth People's Hospital of Sichuan Province, Chengdu, Sichuan, China
| | - Ziman Yang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Qing Liu
- Department of Pain, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Ying Zhang
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Hui Ding
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongyan Lai
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Qun Li
- Department of Pain, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan, China
- Research Center of Integrated Traditional Chinese and Western Medicine, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan, China
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5
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Tanveerul Hassan M, Tayara H, To Chong K. Meta-IL4: An Ensemble Learning Approach for IL-4-Inducing Peptide Prediction. Methods 2023:S1046-2023(23)00113-5. [PMID: 37454743 DOI: 10.1016/j.ymeth.2023.07.002] [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: 10/18/2022] [Revised: 03/25/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023] Open
Abstract
The cytokine interleukin-4 (IL-4) plays an important role in our immune system. IL-4 leads the way in the differentiation of naïve T-helper 0 cells (Th0) to T-helper 2 cells (Th2). The Th2 responses are characterized by the release of IL-4. CD4+ T cells produce the cytokine IL-4 in response to exogenous parasites. IL-4 has a critical role in the growth of CD8+ cells, inflammation, and responses of T-cells. We propose an ensemble model for the prediction of IL-4 inducing peptides. Four feature encodings were extracted to build an efficient predictor: pseudo-amino acid composition, amphiphilic pseudo-amino acid composition, quasi-sequence-order, and Shannon entropy. We developed an ensemble learning model fusion of random forest, extreme gradient boost, light gradient boosting machine, and extra tree classifier in the first layer, and a Gaussian process classifier as a meta classifier in the second layer. The outcome of the benchmarking testing dataset, with a Matthews correlation coefficient of 0.793, showed that the meta-model (Meta-IL4) outperformed individual classifiers. The highest accuracy achieved by the Meta-IL4 model is 90.70%. These findings suggest that peptides that induce IL-4 can be predicted with reasonable accuracy. These models could aid in the development of peptides that trigger the appropriate Th2 response.
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Affiliation(s)
- Mir Tanveerul Hassan
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, South Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju, South Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, South Korea; Advances Electronics and Information Research Centre, Jeonbuk National University, Jeonju, South Korea.
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6
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Zenin V, Tsedilin A, Yurkova M, Siniavin A, Fedorov A. Thermostable chaperone-based polypeptide biosynthesis: Enfuvirtide model product quality and protocol-related impurities. PLoS One 2023; 18:e0286752. [PMID: 37289764 PMCID: PMC10249821 DOI: 10.1371/journal.pone.0286752] [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: 11/03/2022] [Accepted: 05/23/2023] [Indexed: 06/10/2023] Open
Abstract
Large peptide biosynthesis is a valuable alternative to conventional chemical synthesis. Enfuvirtide, the largest therapeutic peptide used in HIV infection treatment, was synthesized in our thermostable chaperone-based peptide biosynthesis system and evaluated for peptide quality as well as the profile of process-related impurities. Host cell proteins (HCPs) and BrCN cleavage-modified peptides were evaluated by LC-MS in intermediate. Cleavage modifications during the reaction were assessed after LC-MS maps were aligned by simple in-house algorithm and formylation/oxidation levels were estimated. Circular dichroism spectra of the obtained enfuvirtide were compared to the those of the chemically- synthesized standard product. Final-product endotoxin and HCPs content were assessed resulting 1.06 EU/mg and 5.58 ppm respectively. Peptide therapeutic activity was measured using the MT-4 cells HIV infection-inhibition model. The biosynthetic peptide IC50 was 0.0453 μM while the standard one had 0.0180 μM. Non-acylated C-terminus was proposed as a cause of IC50 and CD spectra difference. Otherwise, the peptide has met all the requirements of the original chemically synthesized enfuvirtide in the cell-culture and in vivo experiments.
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Affiliation(s)
- Vladimir Zenin
- Bach Institute of Biochemistry, Research Center of Biotechnology of the Russian Academy of Sciences, Moscow, Russian Federation
| | - Andrey Tsedilin
- Bach Institute of Biochemistry, Research Center of Biotechnology of the Russian Academy of Sciences, Moscow, Russian Federation
| | - Maria Yurkova
- Bach Institute of Biochemistry, Research Center of Biotechnology of the Russian Academy of Sciences, Moscow, Russian Federation
| | - Andrey Siniavin
- Ivanovsky Institute of Virology, N.F. Gamaleya National Research Center for Epidemiology and Microbiology, Ministry of Health of the Russian Federation, Moscow, Russian Federation
| | - Alexey Fedorov
- Bach Institute of Biochemistry, Research Center of Biotechnology of the Russian Academy of Sciences, Moscow, Russian Federation
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7
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Wang X, Ding Z, Wang R, Lin X. Deepro-Glu: combination of convolutional neural network and Bi-LSTM models using ProtBert and handcrafted features to identify lysine glutarylation sites. Brief Bioinform 2023; 24:6991122. [PMID: 36653898 DOI: 10.1093/bib/bbac631] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 12/11/2022] [Accepted: 12/28/2022] [Indexed: 01/20/2023] Open
Abstract
Lysine glutarylation (Kglu) is a newly discovered post-translational modification of proteins with important roles in mitochondrial functions, oxidative damage, etc. The established biological experimental methods to identify glutarylation sites are often time-consuming and costly. Therefore, there is an urgent need to develop computational methods for efficient and accurate identification of glutarylation sites. Most of the existing computational methods only utilize handcrafted features to construct the prediction model and do not consider the positive impact of the pre-trained protein language model on the prediction performance. Based on this, we develop an ensemble deep-learning predictor Deepro-Glu that combines convolutional neural network and bidirectional long short-term memory network using the deep learning features and traditional handcrafted features to predict lysine glutaryation sites. The deep learning features are generated from the pre-trained protein language model called ProtBert, and the handcrafted features consist of sequence-based features, physicochemical property-based features and evolution information-based features. Furthermore, the attention mechanism is used to efficiently integrate the deep learning features and the handcrafted features by learning the appropriate attention weights. 10-fold cross-validation and independent tests demonstrate that Deepro-Glu achieves competitive or superior performance than the state-of-the-art methods. The source codes and data are publicly available at https://github.com/xwanggroup/Deepro-Glu.
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Affiliation(s)
- Xiao Wang
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, No. 136, Science Avenue, 450002, Zhengzhou, China
| | - Zhaoyuan Ding
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, No. 136, Science Avenue, 450002, Zhengzhou, China
| | - Rong Wang
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, No. 136, Science Avenue, 450002, Zhengzhou, China
| | - Xi Lin
- Instiute of Artificial Intelligence, Xiamen University, No.4221, Xiang'an South Road, 361000, Xiamen, China
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8
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Yang S, Yang Z, Yang J. 4mCBERT: A computing tool for the identification of DNA N4-methylcytosine sites by sequence- and chemical-derived information based on ensemble learning strategies. Int J Biol Macromol 2023; 231:123180. [PMID: 36646347 DOI: 10.1016/j.ijbiomac.2023.123180] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/26/2022] [Accepted: 12/30/2022] [Indexed: 01/15/2023]
Abstract
N4-methylcytosine (4mC) is an important DNA chemical modification pattern which is a new methylation modification discovered in recent years and plays critical roles in gene expression regulation, defense against invading genetic elements, genomic imprinting, and so on. Identifying 4mC site from DNA sequence segment contributes to discovering more novel modification patterns. In this paper, we present a model called 4mCBERT that encodes DNA sequence segments by sequence characteristics including one-hot, electron-ion interaction pseudopotential, nucleotide chemical property, word2vec and chemical information containing physicochemical properties (PCP), chemical bidirectional encoder representations from transformers (chemical BERT) and employs ensemble learning framework to develop a prediction model. PCP and chemical BERT features are firstly constructed and applied to predict 4mC sites and show positive contributions to identifying 4mC. For the Matthew's Correlation Coefficient, 4mCBERT significantly outperformed other state-of-the-art models on six independent benchmark datasets including A. thaliana, C. elegans, D. melanogaster, E. coli, G. Pickering, and G. subterraneous by 4.32 % to 24.39 %, 2.52 % to 31.65 %, 2 % to 16.49 %, 6.63 % to 35.15, 8.59 % to 61.85 %, and 8.45 % to 34.45 %. Moreover, 4mCBERT is designed to allow users to predict 4mC sites and retrain 4mC prediction models. In brief, 4mCBERT shows higher performance on six benchmark datasets by incorporating sequence- and chemical-driven information and is available at http://cczubio.top/4mCBERT and https://github.com/abcair/4mCBERT.
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Affiliation(s)
- Sen Yang
- School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou 213164, China; The Affiliated Changzhou No 2 People's Hospital of Nanjing Medical University, Changzhou 213164, China.
| | - Zexi Yang
- School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou 213164, China
| | - Jun Yang
- School of Educational Sciences, Yili Normal University, Yining 835000, China
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9
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Genç M, Özkale MR. Lasso regression under stochastic restrictions in linear regression: An application to genomic data. COMMUN STAT-THEOR M 2022. [DOI: 10.1080/03610926.2022.2149243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
- Murat Genç
- Department of Management Information Systems, Faculty of Economics and Administrative Sciences, Tarsus University, Mersin, Turkey
| | - M. Revan Özkale
- Department of Statistics, Faculty of Science and Letters, Çukurova University, Adana, Turkey
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10
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Ahmed S, Rahman A, Hasan MAM, Rahman J, Islam MKB, Ahmad S. predML-Site: Predicting Multiple Lysine PTM Sites With Optimal Feature Representation and Data Imbalance Minimization. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3624-3634. [PMID: 34546927 DOI: 10.1109/tcbb.2021.3114349] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Identifying of post-translational modifications (PTM) is crucial in the study of computational proteomics, cell biology, pathogenesis, and drug development due to its role in many bio-molecular mechanisms. Computational methods for predicting multiple PTM at the same lysine residues, often referred to as K-PTM, is still evolving. This paper presents a novel computational tool, abbreviated as predML-Site, for predicting KPTM, such as acetylation, crotonylation, methylation, succinylation from an uncategorized peptide sample involving single, multiple, or no modification. For informative feature representation, multiple sequence encoding schemes, such as the sequence-coupling, binary encoding, k-spaced amino acid pairs, amino acid factor have been used with ANOVA and incremental feature selection. As a core predictor, a cost-sensitive SVM classifier has been adopted which effectively mitigates the effect of class-label imbalance in the dataset. predML-Site predicts multi-label PTM sites with 84.18% accuracy using the top 91 features. It has also achieved 85.34% aiming and 86.58% coverage rate which are much better than the existing state-of-the-art predictors on the same rigorous validation test. This performance indicates that predML-Site can be used as a supportive tool for further K-PTM study. For the convenience of the experimental scientists, predML-Site has been deployed as a user-friendly web-server at http://103.99.176.239/predML-Site.
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11
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Rahman A, Ahmed S, Al Mehedi Hasan M, Ahmad S, Dehzangi I. Accurately predicting nitrosylated tyrosine sites using probabilistic sequence information. Gene 2022; 826:146445. [PMID: 35358650 DOI: 10.1016/j.gene.2022.146445] [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: 09/08/2021] [Revised: 02/16/2022] [Accepted: 03/18/2022] [Indexed: 11/04/2022]
Abstract
Post-translational modification (PTM) is defined as the enzymatic changes of proteins after the translation process in protein biosynthesis. Nitrotyrosine, which is one of the most important modifications of proteins, is interceded by the active nitrogen molecule. It is known to be associated with different diseases including autoimmune diseases characterized by chronic inflammation and cell damage. Currently, nitrotyrosine sites are identified using experimental approaches which are laborious and costly. In this study, we propose a new machine learning method called PredNitro to accurately predict nitrotyrosine sites. To build PredNitro, we use sequence coupling information from the neighboring amino acids of tyrosine residues along with a support vector machine as our classification technique.Our results demonstrates that PredNitro achieves 98.0% accuracy with more than 0.96 MCC and 0.99 AUC in both 5-fold cross-validation and jackknife cross-validation tests which are significantly better than those reported in previous studies. PredNitro is publicly available as an online predictor at: http://103.99.176.239/PredNitro.
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Affiliation(s)
- Afrida Rahman
- Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Sabit Ahmed
- Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Md Al Mehedi Hasan
- Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Shamim Ahmad
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Iman Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ 08102, USA; Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA.
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12
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Sorkhi AG, Pirgazi J, Ghasemi V. A hybrid feature extraction scheme for efficient malonylation site prediction. Sci Rep 2022; 12:5756. [PMID: 35388017 PMCID: PMC8987080 DOI: 10.1038/s41598-022-08555-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 03/07/2022] [Indexed: 11/09/2022] Open
Abstract
Lysine malonylation is one of the most important post-translational modifications (PTMs). It affects the functionality of cells. Malonylation site prediction in proteins can unfold the mechanisms of cellular functionalities. Experimental methods are one of the due prediction approaches. But they are typically costly and time-consuming to implement. Recently, methods based on machine-learning solutions have been proposed to tackle this problem. Such practices have been shown to reduce costs and time complexities and increase accuracy. However, these approaches also have specific shortcomings, including inappropriate feature extraction out of protein sequences, high-dimensional features, and inefficient underlying classifiers. A machine learning-based method is proposed in this paper to cope with these problems. In the proposed approach, seven different features are extracted. Then, the extracted features are combined, ranked based on the Fisher's score (F-score), and the most efficient ones are selected. Afterward, malonylation sites are predicted using various classifiers. Simulation results show that the proposed method has acceptable performance compared with some state-of-the-art approaches. In addition, the XGBOOST classifier, founded on extracted features such as TFCRF, has a higher prediction rate than the other methods. The codes are publicly available at: https://github.com/jimy2020/Malonylation-site-prediction.
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Affiliation(s)
- Ali Ghanbari Sorkhi
- Department of Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran
| | - Jamshid Pirgazi
- Department of Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran.
| | - Vahid Ghasemi
- Department of Computer Engineering, Faculty of Information Technology, Kermanshah University of Technology, Kermanshah, Iran
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13
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Liu Y, Shen Y, Wang H, Zhang Y, Zhu X. m5Cpred-XS: A New Method for Predicting RNA m5C Sites Based on XGBoost and SHAP. Front Genet 2022; 13:853258. [PMID: 35432446 PMCID: PMC9005994 DOI: 10.3389/fgene.2022.853258] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 02/16/2022] [Indexed: 11/13/2022] Open
Abstract
As one of the most important post-transcriptional modifications of RNA, 5-cytosine-methylation (m5C) is reported to closely relate to many chemical reactions and biological functions in cells. Recently, several computational methods have been proposed for identifying m5C sites. However, the accuracy and efficiency are still not satisfactory. In this study, we proposed a new method, m5Cpred-XS, for predicting m5C sites of H. sapiens, M. musculus, and A. thaliana. First, the powerful SHAP method was used to select the optimal feature subset from seven different kinds of sequence-based features. Second, different machine learning algorithms were used to train the models. The results of five-fold cross-validation indicate that the model based on XGBoost achieved the highest prediction accuracy. Finally, our model was compared with other state-of-the-art models, which indicates that m5Cpred-XS is superior to other methods. Moreover, we deployed the model on a web server that can be accessed through http://m5cpred-xs.zhulab.org.cn/, and m5Cpred-XS is expected to be a useful tool for studying m5C sites.
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Affiliation(s)
| | | | | | - Yong Zhang
- *Correspondence: Xiaolei Zhu, ; Yong Zhang,
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14
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Ahmed Z, Zulfiqar H, Khan AA, Gul I, Dao FY, Zhang ZY, Yu XL, Tang L. iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy. Front Microbiol 2022; 13:790063. [PMID: 35273581 PMCID: PMC8902591 DOI: 10.3389/fmicb.2022.790063] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 01/10/2022] [Indexed: 01/20/2023] Open
Abstract
Thermophilic proteins have important application value in biotechnology and industrial processes. The correct identification of thermophilic proteins provides important information for the application of these proteins in engineering. The identification method of thermophilic proteins based on biochemistry is laborious, time-consuming, and high cost. Therefore, there is an urgent need for a fast and accurate method to identify thermophilic proteins. Considering this urgency, we constructed a reliable benchmark dataset containing 1,368 thermophilic and 1,443 non-thermophilic proteins. A multi-layer perceptron (MLP) model based on a multi-feature fusion strategy was proposed to discriminate thermophilic proteins from non-thermophilic proteins. On independent data set, the proposed model could achieve an accuracy of 96.26%, which demonstrates that the model has a good application prospect. In order to use the model conveniently, a user-friendly software package called iThermo was established and can be freely accessed at http://lin-group.cn/server/iThermo/index.html. The high accuracy of the model and the practicability of the developed software package indicate that this study can accelerate the discovery and engineering application of thermally stable proteins.
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Affiliation(s)
- Zahoor Ahmed
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hasan Zulfiqar
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Abdullah Aman Khan
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.,Sichuan Artificial Intelligence Research Institute, Yibin, China
| | - Ijaz Gul
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Tsinghua Shenzhen International Graduate School, Institute of Biopharmaceutical and Health Engineering, Tsinghua University, Shenzhen, China
| | - Fu-Ying Dao
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhao-Yue Zhang
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiao-Long Yu
- School of Materials Science and Engineering, Hainan University, Haikou, China
| | - Lixia Tang
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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15
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Nguyen TTD, Ho QT, Le NQK, Phan VD, Ou YY. Use Chou's 5-Steps Rule With Different Word Embedding Types to Boost Performance of Electron Transport Protein Prediction Model. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1235-1244. [PMID: 32750894 DOI: 10.1109/tcbb.2020.3010975] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Living organisms receive necessary energy substances directly from cellular respiration. The completion of electron storage and transportation requires the process of cellular respiration with the aid of electron transport chains. Therefore, the work of deciphering electron transport proteins is inevitably needed. The identification of these proteins with high performance has a prompt dependence on the choice of methods for feature extraction and machine learning algorithm. In this study, protein sequences served as natural language sentences comprising words. The nominated word embedding-based feature sets, hinged on the word embedding modulation and protein motif frequencies, were useful for feature choosing. Five word embedding types and a variety of conjoint features were examined for such feature selection. The support vector machine algorithm consequentially was employed to perform classification. The performance statistics within the 5-fold cross-validation including average accuracy, specificity, sensitivity, as well as MCC rates surpass 0.95. Such metrics in the independent test are 96.82, 97.16, 95.76 percent, and 0.9, respectively. Compared to state-of-the-art predictors, the proposed method can generate more preferable performance above all metrics indicating the effectiveness of the proposed method in determining electron transport proteins. Furthermore, this study reveals insights about the applicability of various word embeddings for understanding surveyed sequences.
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16
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Naseer S, Ali RF, Fati SM, Muneer A. Computational identification of 4-carboxyglutamate sites to supplement physiological studies using deep learning. Sci Rep 2022; 12:128. [PMID: 34996975 PMCID: PMC8741832 DOI: 10.1038/s41598-021-03895-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/03/2021] [Indexed: 01/23/2023] Open
Abstract
In biological systems, Glutamic acid is a crucial amino acid which is used in protein biosynthesis. Carboxylation of glutamic acid is a significant post-translational modification which plays important role in blood coagulation by activating prothrombin to thrombin. Contrariwise, 4-carboxy-glutamate is also found to be involved in diseases including plaque atherosclerosis, osteoporosis, mineralized heart valves, bone resorption and serves as biomarker for onset of these diseases. Owing to the pathophysiological significance of 4-carboxyglutamate, its identification is important to better understand pathophysiological systems. The wet lab identification of prospective 4-carboxyglutamate sites is costly, laborious and time consuming due to inherent difficulties of in-vivo, ex-vivo and in vitro experiments. To supplement these experiments, we proposed, implemented, and evaluated a different approach to develop 4-carboxyglutamate site predictors using pseudo amino acid compositions (PseAAC) and deep neural networks (DNNs). Our approach does not require any feature extraction and employs deep neural networks to learn feature representation of peptide sequences and performing classification thereof. Proposed approach is validated using standard performance evaluation metrics. Among different deep neural networks, convolutional neural network-based predictor achieved best scores on independent dataset with accuracy of 94.7%, AuC score of 0.91 and F1-score of 0.874 which shows the promise of proposed approach. The iCarboxE-Deep server is deployed at https://share.streamlit.io/sheraz-n/carboxyglutamate/app.py .
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Affiliation(s)
- Sheraz Naseer
- Department of Computer Science, University of Management and Technology, Lahore, 54770, Pakistan
| | - Rao Faizan Ali
- Department of Computer Science, University of Management and Technology, Lahore, 54770, Pakistan.
- Computer and Information Sciences Department, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Malaysia.
| | - Suliman Mohamed Fati
- College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia
| | - Amgad Muneer
- Computer and Information Sciences Department, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Malaysia
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17
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Al-Saggaf UM, Usman M, Naseem I, Moinuddin M, Jiman AA, Alsaggaf MU, Alshoubaki HK, Khan S. ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs. Front Bioeng Biotechnol 2021; 9:752658. [PMID: 34722479 PMCID: PMC8552119 DOI: 10.3389/fbioe.2021.752658] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 09/13/2021] [Indexed: 12/26/2022] Open
Abstract
Extracelluar matrix (ECM) proteins create complex networks of macromolecules which fill-in the extracellular spaces of living tissues. They provide structural support and play an important role in maintaining cellular functions. Identification of ECM proteins can play a vital role in studying various types of diseases. Conventional wet lab-based methods are reliable; however, they are expensive and time consuming and are, therefore, not scalable. In this research, we propose a sequence-based novel machine learning approach for the prediction of ECM proteins. In the proposed method, composition of k-spaced amino acid pair (CKSAAP) features are encoded into a classifiable latent space (LS) with the help of deep latent space encoding (LSE). A comprehensive ablation analysis is conducted for performance evaluation of the proposed method. Results are compared with other state-of-the-art methods on the benchmark dataset, and the proposed ECM-LSE approach has shown to comprehensively outperform the contemporary methods.
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Affiliation(s)
- Ubaid M. Al-Saggaf
- Center of Excellence in Intelligent Engineering Systems, King Abdulaziz University, Jeddah, Saudi Arabia
- Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Muhammad Usman
- Department of Computer Engineering, Chosun University, Gwangju, South Korea
| | - Imran Naseem
- Research and Development, Love For Data, Karachi, Pakistan
- School of Electrical, Electronic and Computer Engineering, The University of Western Australia, Perth, WA, Australia
- College of Engineering, Karachi Institute of Economics and Technology, Korangi Creek, Karachi, Pakistan
| | - Muhammad Moinuddin
- Center of Excellence in Intelligent Engineering Systems, King Abdulaziz University, Jeddah, Saudi Arabia
- Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmad A. Jiman
- Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mohammed U. Alsaggaf
- Center of Excellence in Intelligent Engineering Systems, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Radiology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Hitham K. Alshoubaki
- Center of Excellence in Intelligent Engineering Systems, King Abdulaziz University, Jeddah, Saudi Arabia
- Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Shujaat Khan
- Department of Bio and Brain Engineering, Daejeon, South Korea
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18
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Sohrawordi M, Hossain MA. Prediction of lysine formylation sites using support vector machine based on the sample selection from majority classes and synthetic minority over-sampling techniques. Biochimie 2021; 192:125-135. [PMID: 34627982 DOI: 10.1016/j.biochi.2021.10.001] [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: 05/15/2021] [Revised: 10/03/2021] [Accepted: 10/05/2021] [Indexed: 12/22/2022]
Abstract
Lysine formylation is a newly discovered and mostly interested type of post-translational modification (PTM) that is generally found on core and linker histone proteins of prokaryote and eukaryote and plays various important roles on the regulation of various cellular mechanisms. Hence, it is very urgent to properly identify formylation site in protein for understanding the molecular mechanism of formylation deeply and defining drug for relevant diseases. As experimentally identification of formylation site using traditional processes are expensive and time consuming, a simple and high speedy mathematical model for predicting accurately lysine formylation sites is highly desired. A useful computational model named PLF_SVM is deigned and proposed in this study by using binary encoding (BE), amino acid composition (AAC), reverse position relative incidence matrix (RPRIM), position relative incidence matrix (PRIM), and position specific amino acid propensity (PSAAP) feature generation methods for predicting formylated and non-formylated lysine sites. Besides, the Synthetic Minority Oversampling Technique (SMOTE) and a proposed sample selection strategy named EnSVM are applied to handle the imbalance training dataset problem. Thereafter, the optimal number of features are selected by F-score method to train the model. Finally, it has been seen that PLF_SVM outperforms the state-of-the-art approaches in validation and independent test with an accuracy of 98.61% and 98.77% respectively. At https://plf-svm.herokuapp.com/, a user-friendly web tool is also created for identifying formylation sites. Therefore, the proposed method may be helpful guideline for the analysis and prediction of formylated lysine and knowing the process of cellular regulation.
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Affiliation(s)
- Md Sohrawordi
- Dept. of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh; Dept. of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh.
| | - Md Ali Hossain
- Dept. of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
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19
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AoP-LSE: Antioxidant Proteins Classification Using Deep Latent Space Encoding of Sequence Features. Curr Issues Mol Biol 2021; 43:1489-1501. [PMID: 34698113 PMCID: PMC8928959 DOI: 10.3390/cimb43030105] [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: 09/09/2021] [Revised: 09/28/2021] [Accepted: 09/29/2021] [Indexed: 11/16/2022] Open
Abstract
It is of utmost importance to develop a computational method for accurate prediction of antioxidants, as they play a vital role in the prevention of several diseases caused by oxidative stress. In this correspondence, we present an effective computational methodology based on the notion of deep latent space encoding. A deep neural network classifier fused with an auto-encoder learns class labels in a pruned latent space. This strategy has eliminated the need to separately develop classifier and the feature selection model, allowing the standalone model to effectively harness discriminating feature space and perform improved predictions. A thorough analytical study has been presented alongwith the PCA/tSNE visualization and PCA-GCNR scores to show the discriminating power of the proposed method. The proposed method showed a high MCC value of 0.43 and a balanced accuracy of 76.2%, which is superior to the existing models. The model has been evaluated on an independent dataset during which it outperformed the contemporary methods by correctly identifying the novel proteins with an accuracy of 95%.
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20
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Ahmed S, Rahman A, Hasan MAM, Ahmad S, Shovan SM. Computational identification of multiple lysine PTM sites by analyzing the instance hardness and feature importance. Sci Rep 2021; 11:18882. [PMID: 34556767 PMCID: PMC8460736 DOI: 10.1038/s41598-021-98458-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/08/2021] [Indexed: 02/08/2023] Open
Abstract
Identification of post-translational modifications (PTM) is significant in the study of computational proteomics, cell biology, pathogenesis, and drug development due to its role in many bio-molecular mechanisms. Though there are several computational tools to identify individual PTMs, only three predictors have been established to predict multiple PTMs at the same lysine residue. Furthermore, detailed analysis and assessment on dataset balancing and the significance of different feature encoding techniques for a suitable multi-PTM prediction model are still lacking. This study introduces a computational method named 'iMul-kSite' for predicting acetylation, crotonylation, methylation, succinylation, and glutarylation, from an unrecognized peptide sample with one, multiple, or no modifications. After successfully eliminating the redundant data samples from the majority class by analyzing the hardness of the sequence-coupling information, feature representation has been optimized by adopting the combination of ANOVA F-Test and incremental feature selection approach. The proposed predictor predicts multi-label PTM sites with 92.83% accuracy using the top 100 features. It has also achieved a 93.36% aiming rate and 96.23% coverage rate, which are much better than the existing state-of-the-art predictors on the validation test. This performance indicates that 'iMul-kSite' can be used as a supportive tool for further K-PTM study. For the convenience of the experimental scientists, 'iMul-kSite' has been deployed as a user-friendly web-server at http://103.99.176.239/iMul-kSite .
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Affiliation(s)
- Sabit Ahmed
- grid.443086.d0000 0004 1755 355XComputer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, 6204 Bangladesh
| | - Afrida Rahman
- grid.443086.d0000 0004 1755 355XComputer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, 6204 Bangladesh
| | - Md. Al Mehedi Hasan
- grid.443086.d0000 0004 1755 355XComputer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, 6204 Bangladesh
| | - Shamim Ahmad
- grid.412656.20000 0004 0451 7306Computer Science and Engineering, University of Rajshahi, Rajshahi, 6205 Bangladesh
| | - S. M. Shovan
- grid.443086.d0000 0004 1755 355XComputer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, 6204 Bangladesh
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21
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Islam MKB, Rahman J, Hasan MAM, Ahmad S. predForm-Site: Formylation site prediction by incorporating multiple features and resolving data imbalance. Comput Biol Chem 2021; 94:107553. [PMID: 34384997 DOI: 10.1016/j.compbiolchem.2021.107553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 06/22/2021] [Accepted: 07/28/2021] [Indexed: 10/20/2022]
Abstract
Formylation is one of the newly discovered post-translational modifications in lysine residue which is responsible for different kinds of diseases. In this work, a novel predictor, named predForm-Site, has been developed to predict formylation sites with higher accuracy. We have integrated multiple sequence features for developing a more informative representation of formylation sites. Moreover, decision function of the underlying classifier have been optimized on skewed formylation dataset during prediction model training for prediction quality improvement. On the dataset used by LFPred and Formator predictor, predForm-Site achieved 99.5% sensitivity, 99.8% specificity and 99.8% overall accuracy with AUC of 0.999 in the jackknife test. In the independent test, it has also achieved more than 97% sensitivity and 99% specificity. Similarly, in benchmarking with recent method CKSAAP_FormSite, the proposed predictor significantly outperformed in all the measures, particularly sensitivity by around 20%, specificity by nearly 30% and overall accuracy by more than 22%. These experimental results show that the proposed predForm-Site can be used as a complementary tool for the fast exploration of formylation sites. For convenience of the scientific community, predForm-Site has been deployed as an online tool, accessible at http://103.99.176.239:8080/predForm-Site.
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Affiliation(s)
- Md Khaled Ben Islam
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia; Department of Computer Science & Engineering, Pabna University of Science and Technology, Pabna, Bangladesh.
| | - Julia Rahman
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia; Department of Computer Science & Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh.
| | - Md Al Mehedi Hasan
- Department of Computer Science & Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Shamim Ahmad
- Department of Computer Science & Engineering, Rajshahi University, Rajshahi, Bangladesh
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22
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Pang Y, Yao L, Jhong JH, Wang Z, Lee TY. AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches. Brief Bioinform 2021; 22:6323205. [PMID: 34279599 DOI: 10.1093/bib/bbab263] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 06/07/2021] [Accepted: 06/21/2021] [Indexed: 02/06/2023] Open
Abstract
Antiviral peptide (AVP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against virus infection. Machine learning-based prediction with a computational biology approach can facilitate the development of the novel therapeutic agents. In this study, we proposed a double-stage classification scheme, named AVPIden, for predicting the AVPs and their functional activities against different viruses. The first stage is to distinguish the AVP from a broad-spectrum peptide collection, including not only the regular peptides (non-AMP) but also the AMPs without antiviral functions (non-AVP). The second stage is responsible for characterizing one or more virus families or species that the AVP targets. Imbalanced learning is utilized to improve the performance of prediction. The AVPIden uses multiple descriptors to precisely demonstrate the peptide properties and adopts explainable machine learning strategies based on Shapley value to exploit how the descriptors impact the antiviral activities. Finally, the evaluation performance of the proposed model suggests its ability to predict the antivirus activities and their potential functions against six virus families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight kinds of virus (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). The AVPIden gives an option for reinforcing the development of AVPs with the computer-aided method and has been deployed at http://awi.cuhk.edu.cn/AVPIden/.
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Affiliation(s)
- Yuxuan Pang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, PR China
| | - Lantian Yao
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, PR China
| | - Jhih-Hua Jhong
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, PR China
| | - Zhuo Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, PR China
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, PR China
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23
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Behbahani M, Moradi M, Mohabatkar H. In silico design of a multi-epitope peptide construct as a potential vaccine candidate for Influenza A based on neuraminidase protein. In Silico Pharmacol 2021; 9:36. [PMID: 33987075 PMCID: PMC8112742 DOI: 10.1007/s40203-021-00095-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 04/30/2021] [Indexed: 01/25/2023] Open
Abstract
Designing an effective vaccine against different subtypes of Influenza A virus is a critical issue in the field of medical biotechnology. At the current study, a novel potential multi-epitope vaccine candidate based on the neuraminidase proteins for seven subtypes of Influenza virus was designed, using the in silico approach. Potential linear B-cell and T-cell binding epitopes from each neuraminidase protein (N1, N2, N3, N4, N6, N7, N8) were predicted by in silico tools of epitope prediction. The selected epitopes were joined by three different linkers, and physicochemical properties, toxicity, and allergenecity were investigated. The final multi-epitope construct was modeled using GalaxyWEB server, and the molecular interactions with immune receptors were investigated and the immune response simulation assay was performed. A multi-epitope construct with GPGPGPG linker with the lowest allergenicity and highest stability was selected. The molecular docking assay indicated the interactions with immune system receptors, including HLA1, HLA2, and TLR-3. Immune response simulation detected both humoral and cellular response, including the elevated count of B-cells, T-cell, and Nk-cells.
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Affiliation(s)
- Mandana Behbahani
- Department of Biotechnology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran
| | - Mohammad Moradi
- Department of Biotechnology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran
| | - Hassan Mohabatkar
- Department of Biotechnology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran
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24
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Dou L, Yang F, Xu L, Zou Q. A comprehensive review of the imbalance classification of protein post-translational modifications. Brief Bioinform 2021; 22:6217722. [PMID: 33834199 DOI: 10.1093/bib/bbab089] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 02/17/2021] [Accepted: 02/24/2021] [Indexed: 12/13/2022] Open
Abstract
Post-translational modifications (PTMs) play significant roles in regulating protein structure, activity and function, and they are closely involved in various pathologies. Therefore, the identification of associated PTMs is the foundation of in-depth research on related biological mechanisms, disease treatments and drug design. Due to the high cost and time consumption of high-throughput sequencing techniques, developing machine learning-based predictors has been considered an effective approach to rapidly recognize potential modified sites. However, the imbalanced distribution of true and false PTM sites, namely, the data imbalance problem, largely effects the reliability and application of prediction tools. In this article, we conduct a systematic survey of the research progress in the imbalanced PTMs classification. First, we describe the modeling process in detail and outline useful data imbalance solutions. Then, we summarize the recently proposed bioinformatics tools based on imbalanced PTM data and simultaneously build a convenient website, ImClassi_PTMs (available at lab.malab.cn/∼dlj/ImbClassi_PTMs/), to facilitate the researchers to view. Moreover, we analyze the challenges of current computational predictors and propose some suggestions to improve the efficiency of imbalance learning. We hope that this work will provide comprehensive knowledge of imbalanced PTM recognition and contribute to advanced predictors in the future.
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Affiliation(s)
- Lijun Dou
- University of Electronic Science and Technology of China and the Shenzhen Polytechnic, China
| | - Fenglong Yang
- University of Electronic Science and Technology of China and the Shenzhen Polytechnic, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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25
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Ahmed S, Rahman A, Hasan MAM, Islam MKB, Rahman J, Ahmad S. predPhogly-Site: Predicting phosphoglycerylation sites by incorporating probabilistic sequence-coupling information into PseAAC and addressing data imbalance. PLoS One 2021; 16:e0249396. [PMID: 33793659 PMCID: PMC8016359 DOI: 10.1371/journal.pone.0249396] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 03/18/2021] [Indexed: 12/14/2022] Open
Abstract
Post-translational modification (PTM) involves covalent modification after the biosynthesis process and plays an essential role in the study of cell biology. Lysine phosphoglycerylation, a newly discovered reversible type of PTM that affects glycolytic enzyme activities, and is responsible for a wide variety of diseases, such as heart failure, arthritis, and degeneration of the nervous system. Our goal is to computationally characterize potential phosphoglycerylation sites to understand the functionality and causality more accurately. In this study, a novel computational tool, referred to as predPhogly-Site, has been developed to predict phosphoglycerylation sites in the protein. It has effectively utilized the probabilistic sequence-coupling information among the nearby amino acid residues of phosphoglycerylation sites along with a variable cost adjustment for the skewed training dataset to enhance the prediction characteristics. It has achieved around 99% accuracy with more than 0.96 MCC and 0.97 AUC in both 10-fold cross-validation and independent test. Even, the standard deviation in 10-fold cross-validation is almost negligible. This performance indicates that predPhogly-Site remarkably outperformed the existing prediction tools and can be used as a promising predictor, preferably with its web interface at http://103.99.176.239/predPhogly-Site.
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Affiliation(s)
- Sabit Ahmed
- Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
- * E-mail:
| | - Afrida Rahman
- Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Md. Al Mehedi Hasan
- Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Md Khaled Ben Islam
- Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh
| | - Julia Rahman
- Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Shamim Ahmad
- Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
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26
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Du X, Hu J, Li S. Using Chou's 5-Step Rule to Predict DNA-Protein Binding with Multi-scale Complementary Feature. J Proteome Res 2021; 20:1639-1656. [PMID: 33522829 DOI: 10.1021/acs.jproteome.0c00864] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
It is well known that DNA-protein binding (DPB) prediction is not only beneficial to understand the regulation mechanism of gene expression but also a challenging task in the field of computational biology. Traditional methods for DPB prediction that depend on manually extracted features may lead to classification errors. Recently, deep learning such as convolutional neural network (CNN) has been successfully applied to classification tasks and improved DPB prediction performance significantly. Yet, these methods are based on the original DNA sequence modeling, ignoring the hidden complex dependency and complementarity between multiple sequence features. In consideration of this problem, we propose a method to fuse different sequence features and analyze them systematically through multi-scale CNN. First, sliding windows of specified lengths are set on distinct DNA sequences to generate multiple sequence features with unequal lengths. Second, multiple feature sequences are fused and encoded for feature representation. Third, multi-scale CNN with different binding motif lengths is used to automatically learn and mine the influence of internal attributes and hidden complex relations between the fusion sequence features and make full use of the complementary advantages of extracted CNN features to predict DPB. When our model is applied to 690 ChIP-seq datasets, it achieves an average AUC of 0.9112, which is significantly better than the latest methods. The results show that our method is effective for DPB prediction and is freely available at http://121.5.71.120/mscDPB/.
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Affiliation(s)
- Xiuquan Du
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, Anhui, China.,School of Computer Science and Technology, Anhui University, Hefei 230601, Anhui, China
| | - Jiajia Hu
- School of Computer Science and Technology, Anhui University, Hefei 230601, Anhui, China
| | - Shuo Li
- Department of Medical Imaging, Western University, London, ON N6A 3K7, Canada
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Liu T, Chen JM, Zhang D, Zhang Q, Peng B, Xu L, Tang H. ApoPred: Identification of Apolipoproteins and Their Subfamilies With Multifarious Features. Front Cell Dev Biol 2021; 8:621144. [PMID: 33490085 PMCID: PMC7820372 DOI: 10.3389/fcell.2020.621144] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 11/24/2020] [Indexed: 01/24/2023] Open
Abstract
Apolipoprotein is a group of plasma proteins that are associated with a variety of diseases, such as hyperlipidemia, atherosclerosis, Alzheimer’s disease, and diabetes. In order to investigate the function of apolipoproteins and to develop effective targets for related diseases, it is necessary to accurately identify and classify apolipoproteins. Although it is possible to identify apolipoproteins accurately through biochemical experiments, they are expensive and time-consuming. This work aims to establish a high-efficiency and high-accuracy prediction model for recognition of apolipoproteins and their subfamilies. We firstly constructed a high-quality benchmark dataset including 270 apolipoproteins and 535 non-apolipoproteins. Based on the dataset, pseudo-amino acid composition (PseAAC) and composition of k-spaced amino acid pairs (CKSAAP) were used as input vectors. To improve the prediction accuracy and eliminate redundant information, analysis of variance (ANOVA) was used to rank the features. And the incremental feature selection was utilized to obtain the best feature subset. Support vector machine (SVM) was proposed to construct the classification model, which could produce the accuracy of 97.27%, sensitivity of 96.30%, and specificity of 97.76% for discriminating apolipoprotein from non-apolipoprotein in 10-fold cross-validation. In addition, the same process was repeated to generate a new model for predicting apolipoprotein subfamilies. The new model could achieve an overall accuracy of 95.93% in 10-fold cross-validation. According to our proposed model, a convenient webserver called ApoPred was established, which can be freely accessed at http://tang-biolab.com/server/ApoPred/service.html. We expect that this work will contribute to apolipoprotein function research and drug development in relevant diseases.
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Affiliation(s)
- Ting Liu
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, China
| | - Jia-Mao Chen
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, China
| | - Dan Zhang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Qian Zhang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, China
| | - Bowen Peng
- Division of international Cooperation, Health Commission of Sichuan Province, Chengdu, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Hua Tang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, China.,Central Nervous System Drug Key Laboratory of Sichuan Province, Luzhou, China
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Mahmood MK, Ehsan A, Khan YD, Chou KC. iHyd-LysSite (EPSV): Identifying Hydroxylysine Sites in Protein Using Statistical Formulation by Extracting Enhanced Position and Sequence Variant Feature Technique. Curr Genomics 2020; 21:536-545. [PMID: 33214770 PMCID: PMC7604750 DOI: 10.2174/1389202921999200831142629] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 05/14/2020] [Accepted: 05/15/2020] [Indexed: 11/22/2022] Open
Abstract
Introduction Hydroxylation is one of the most important post-translational modifications (PTM) in cellular functions and is linked to various diseases. The addition of one of the hydroxyl groups (OH) to the lysine sites produces hydroxylysine when undergoes chemical modification. Methods The method which is used in this study for identifying hydroxylysine sites based on powerful mathematical and statistical methodology incorporating the sequence-order effect and composition of each object within protein sequences. This predictor is called “iHyd-LysSite (EPSV)” (identifying hydroxylysine sites by extracting enhanced position and sequence variant technique). The prediction of hydroxylysine sites by experimental methods is difficult, laborious and highly expensive. In silico technique is an alternative approach to identify hydroxylysine sites in proteins. Results The experimental results require that the predictive model should have high sensitivity and specificity values and must be more accurate. The self-consistency, independent, 10-fold cross-validation and jackknife tests are performed for validation purposes. These tests are resulted by using three renowned classifiers, Neural Networks (NN), Random Forest (RF) and Support Vector Machine (SVM) with the demanding prediction rate. The overall predictive outcomes are extraordinarily superior to the results obtained by previous predictors. The proposed model contributed an excellent prediction rate in the system for NN, RF, and SVM classifiers. The sensitivity and specificity results using all these classifiers for jackknife test are 96.08%, 94.99%, 98.16% and 97.52%, 98.52%, 80.95%. Conclusion The results obtained by the proposed tool show that this method may meet the future demand of hydroxylysine sites with a better prediction rate over the existing methods.
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Affiliation(s)
- Muhammad Khalid Mahmood
- 1Department of Mathematics, University of the Punjab, Lahore, Pakistan; 2Faculty of Information Technology, University of Management and Tecnology, Lahore, Pakistan; 3Gordon Life Science Institute, Boston, MA02478, USA
| | - Asma Ehsan
- 1Department of Mathematics, University of the Punjab, Lahore, Pakistan; 2Faculty of Information Technology, University of Management and Tecnology, Lahore, Pakistan; 3Gordon Life Science Institute, Boston, MA02478, USA
| | - Yaser Daanial Khan
- 1Department of Mathematics, University of the Punjab, Lahore, Pakistan; 2Faculty of Information Technology, University of Management and Tecnology, Lahore, Pakistan; 3Gordon Life Science Institute, Boston, MA02478, USA
| | - Kuo-Chen Chou
- 1Department of Mathematics, University of the Punjab, Lahore, Pakistan; 2Faculty of Information Technology, University of Management and Tecnology, Lahore, Pakistan; 3Gordon Life Science Institute, Boston, MA02478, USA
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Abstract
During the last three decades or so, many efforts have been made to study the protein cleavage
sites by some disease-causing enzyme, such as HIV (Human Immunodeficiency Virus) protease
and SARS (Severe Acute Respiratory Syndrome) coronavirus main proteinase. It has become increasingly
clear <i>via</i> this mini-review that the motivation driving the aforementioned studies is quite wise,
and that the results acquired through these studies are very rewarding, particularly for developing peptide
drugs.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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30
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Bouziane H, Chouarfia A. Use of Chou's 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment. J Integr Bioinform 2020; 18:51-79. [PMID: 32598314 PMCID: PMC8035964 DOI: 10.1515/jib-2019-0091] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 04/08/2020] [Indexed: 12/31/2022] Open
Abstract
To date, many proteins generated by large-scale genome sequencing projects are still uncharacterized and subject to intensive investigations by both experimental and computational means. Knowledge of protein subcellular localization (SCL) is of key importance for protein function elucidation. However, it remains a challenging task, especially for multiple sites proteins known to shuttle between cell compartments to perform their proper biological functions and proteins which do not have significant homology to proteins of known subcellular locations. Due to their low-cost and reasonable accuracy, machine learning-based methods have gained much attention in this context with the availability of a plethora of biological databases and annotated proteins for analysis and benchmarking. Various predictive models have been proposed to tackle the SCL problem, using different protein sequence features pertaining to the subcellular localization, however, the overwhelming majority of them focuses on single localization and cover very limited cellular locations. The prediction was basically established on sorting signals, amino acids compositions, and homology. To improve the prediction quality, focus is actually on knowledge information extracted from annotation databases, such as protein-protein interactions and Gene Ontology (GO) functional domains annotation which has been recently a widely adopted and essential information for learning systems. To deal with such problem, in the present study, we considered SCL prediction task as a multi-label learning problem and tried to label both single site and multiple sites unannotated bacterial protein sequences by mining proteins homology relationships using both GO terms of protein homologs and PSI-BLAST profiles. The experiments using 5-fold cross-validation tests on the benchmark datasets showed a significant improvement on the results obtained by the proposed consensus multi-label prediction model which discriminates six compartments for Gram-negative and five compartments for Gram-positive bacterial proteins.
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Affiliation(s)
- Hafida Bouziane
- Département d’Informatique, Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf, USTO-MB BP 1505, El M’Naouer, 31000, Oran, Algeria
| | - Abdallah Chouarfia
- Département d’Informatique, Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf, USTO-MB BP 1505, El M’Naouer, 31000, Oran, Algeria
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Wiktorowicz A, Wit A, Dziewierz A, Rzeszutko L, Dudek D, Kleczynski P. Calcium Pattern Assessment in Patients with Severe Aortic Stenosis Via the Chou's 5-Steps Rule. Curr Pharm Des 2020; 25:3769-3775. [PMID: 31566130 DOI: 10.2174/1381612825666190930101258] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 09/26/2019] [Indexed: 02/07/2023]
Abstract
BACKGROUND Progression of aortic valve calcifications (AVC) leads to aortic valve stenosis (AS). Importantly, the AVC degree has a great impact on AS progression, treatment selection and outcomes. Methods of AVC assessment do not provide accurate quantitative evaluation and analysis of calcium distribution and deposition in a repetitive manner. OBJECTIVE We aim to prepare a reliable tool for detailed AVC pattern analysis with quantitative parameters. METHODS We analyzed computed tomography (CT) scans of fifty patients with severe AS using a dedicated software based on MATLAB version R2017a (MathWorks, Natick, MA, USA) and ImageJ version 1.51 (NIH, USA) with the BoneJ plugin version 1.4.2 with a self-developed algorithm. RESULTS We listed unique parameters describing AVC and prepared 3D AVC models with color pointed calcium layer thickness in the stenotic aortic valve. These parameters were derived from CT-images in a semi-automated and repeatable manner. They were divided into morphometric, topological and textural parameters and may yield crucial information about the anatomy of the stenotic aortic valve. CONCLUSION In our study, we were able to obtain and define quantitative parameters for calcium assessment of the degenerated aortic valves. Whether the defined parameters are able to predict potential long-term outcomes after treatment, requires further investigation.
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Affiliation(s)
- Agata Wiktorowicz
- 2nd Department of Cardiology, Institute of Cardiology, Jagiellonian University Medical College, 31-501 Kopernika St. 17, Krakow, Poland
| | - Adrian Wit
- Faculty of Physics and Applied Computer Science, University of Science and Technology, Mickiewicza Ave. 30, 30-059 Krakow, Poland
| | - Artur Dziewierz
- 2nd Department of Cardiology, Institute of Cardiology, Jagiellonian University Medical College, 31-501 Kopernika St. 17, Krakow, Poland
| | - Lukasz Rzeszutko
- 2nd Department of Cardiology, Institute of Cardiology, Jagiellonian University Medical College, 31-501 Kopernika St. 17, Krakow, Poland
| | - Dariusz Dudek
- 2nd Department of Cardiology, Institute of Cardiology, Jagiellonian University Medical College, 31-501 Kopernika St. 17, Krakow, Poland
| | - Pawel Kleczynski
- 2nd Department of Cardiology, Institute of Cardiology, Jagiellonian University Medical College, 31-501 Kopernika St. 17, Krakow, Poland
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Usman M, Khan S, Lee JA. AFP-LSE: Antifreeze Proteins Prediction Using Latent Space Encoding of Composition of k-Spaced Amino Acid Pairs. Sci Rep 2020; 10:7197. [PMID: 32345989 PMCID: PMC7188683 DOI: 10.1038/s41598-020-63259-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 03/26/2020] [Indexed: 02/06/2023] Open
Abstract
Species living in extremely cold environments resist the freezing conditions through antifreeze proteins (AFPs). Apart from being essential proteins for various organisms living in sub-zero temperatures, AFPs have numerous applications in different industries. They possess very small resemblance to each other and cannot be easily identified using simple search algorithms such as BLAST and PSI-BLAST. Diverse AFPs found in fishes (Type I, II, III, IV and antifreeze glycoproteins (AFGPs)), are sub-types and show low sequence and structural similarity, making their accurate prediction challenging. Although several machine-learning methods have been proposed for the classification of AFPs, prediction methods that have greater reliability are required. In this paper, we propose a novel machine-learning-based approach for the prediction of AFP sequences using latent space learning through a deep auto-encoder method. For latent space pruning, we use the output of the auto-encoder with a deep neural network classifier to learn the non-linear mapping of the protein sequence descriptor and class label. The proposed method outperformed the existing methods, yielding excellent results in comparison. A comprehensive ablation study is performed, and the proposed method is evaluated in terms of widely used performance measures. In particular, the proposed method demonstrated a high Matthews correlation coefficient of 0.52, F-score of 0.49, and Youden’s index of 0.81 on an independent test dataset, thereby outperforming the existing methods for AFP prediction.
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Affiliation(s)
- Muhammad Usman
- Department of Computer Engineering, Chosun University, Gwangju, 61452, Republic of Korea
| | - Shujaat Khan
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Jeong-A Lee
- Department of Computer Engineering, Chosun University, Gwangju, 61452, Republic of Korea.
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AHMAD WAKIL, ARAFAT EASIN, TAHERZADEH GHAZALEH, SHARMA ALOK, DIPTA SHUBHASHISROY, DEHZANGI ABDOLLAH, SHATABDA SWAKKHAR. Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:77888-77902. [PMID: 33354488 PMCID: PMC7751949 DOI: 10.1109/access.2020.2989713] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Post Translational Modification (PTM) is considered an important biological process with a tremendous impact on the function of proteins in both eukaryotes, and prokaryotes cells. During the past decades, a wide range of PTMs has been identified. Among them, malonylation is a recently identified PTM which plays a vital role in a wide range of biological interactions. Notwithstanding, this modification plays a potential role in energy metabolism in different species including Homo Sapiens. The identification of PTM sites using experimental methods is time-consuming and costly. Hence, there is a demand for introducing fast and cost-effective computational methods. In this study, we propose a new machine learning method, called Mal-Light, to address this problem. To build this model, we extract local evolutionary-based information according to the interaction of neighboring amino acids using a bi-peptide based method. We then use Light Gradient Boosting (LightGBM) as our classifier to predict malonylation sites. Our results demonstrate that Mal-Light is able to significantly improve malonylation site prediction performance compared to previous studies found in the literature. Using Mal-Light we achieve Matthew's correlation coefficient (MCC) of 0.74 and 0.60, Accuracy of 86.66% and 79.51%, Sensitivity of 78.26% and 67.27%, and Specificity of 95.05% and 91.75%, for Homo Sapiens and Mus Musculus proteins, respectively. Mal-Light is implemented as an online predictor which is publicly available at: (http://brl.uiu.ac.bd/MalLight/).
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Affiliation(s)
- WAKIL AHMAD
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Dhaka 1212, Bangladesh
| | - EASIN ARAFAT
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Dhaka 1212, Bangladesh
| | - GHAZALEH TAHERZADEH
- Institute for Bioscience and Biotechnology Research, University of Maryland, College Park, MD, 20742, USA
| | - ALOK SHARMA
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD-4111, Australia
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, 113-8510, Japan
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Kanagawa, Japan
- School of Engineering and Physics, Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji
- CREST, JST, Tokyo, 102-8666, Japan
| | - SHUBHASHIS ROY DIPTA
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Dhaka 1212, Bangladesh
| | - ABDOLLAH DEHZANGI
- Department of Computer Science, Morgan State University, Baltimore, MD, 21251, USA
| | - SWAKKHAR SHATABDA
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Dhaka 1212, Bangladesh
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Wang S, Wang Y, Yu C, Cao Y, Yu Y, Pan Y, Su D, Lu Q, Yang W, Zuo Y, Yang L. Characterization of the relationship between FLI1 and immune infiltrate level in tumour immune microenvironment for breast cancer. J Cell Mol Med 2020; 24:5501-5514. [PMID: 32249526 PMCID: PMC7214163 DOI: 10.1111/jcmm.15205] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 01/31/2020] [Accepted: 03/06/2020] [Indexed: 12/24/2022] Open
Abstract
Breast cancer is the most common cancer and the leading cause of cancer death among women in the world. Tumour‐infiltrating lymphocytes were defined as the white blood cells left in the vasculature and localized in tumours. Recently, tumour‐infiltrating lymphocytes were found to be associated with good prognosis and response to immunotherapy in tumours. In this study, to examine the influence of FLI1 in immune system in breast cancer, we interrogated the relationship between the FLI1 expression levels with infiltration levels of 28 immune cell types. By splitting the breast cancer samples into high and low expression FLI1 subtypes, we found that the high expression FLI1 subtype was enriched in many immune cell types, and the up‐regulated differentially expressed genes between them were enriched in immune system processes, immune‐related KEGG pathways and biological processes. In addition, many important immune‐related features were found to be positively correlated with the FLI1 expression level. Furthermore, we found that the FLI1 was correlated with the immune‐related genes. Our findings may provide useful help for recognizing the relationship between tumour immune microenvironment and FLI1, and may unravel clinical outcomes and immunotherapy utility for FLI1 in breast cancer.
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Affiliation(s)
- Shiyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yakun Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chunlu Yu
- Public Health College, Harbin Medical University, Harbin, China
| | - Yiyin Cao
- Public Health College, Harbin Medical University, Harbin, China
| | - Yao Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yi Pan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Dongqing Su
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Qianzi Lu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wuritu Yang
- The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Yongchun Zuo
- The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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Some illuminating remarks on molecular genetics and genomics as well as drug development. Mol Genet Genomics 2020; 295:261-274. [PMID: 31894399 DOI: 10.1007/s00438-019-01634-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 12/05/2019] [Indexed: 02/07/2023]
Abstract
Facing the explosive growth of biological sequences unearthed in the post-genomic age, one of the most important but also most difficult problems in computational biology is how to express a biological sequence with a discrete model or a vector, but still keep it with considerable sequence-order information or its special pattern. To deal with such a challenging problem, the ideas of "pseudo amino acid components" and "pseudo K-tuple nucleotide composition" have been proposed. The ideas and their approaches have further stimulated the birth for "distorted key theory", "wenxing diagram", and substantially strengthening the power in treating the multi-label systems, as well as the establishment of the famous "5-steps rule". All these logic developments are quite natural that are very useful not only for theoretical scientists but also for experimental scientists in conducting genetics/genomics analysis and drug development. Presented in this review paper are also their future perspectives; i.e., their impacts will become even more significant and propounding.
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Shao YT, Liu XX, Lu Z, Chou KC. pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning. ACTA ACUST UNITED AC 2020. [DOI: 10.4236/ns.2020.127042] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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37
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Shao Y, Chou KC. pLoc_Deep-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by Deep Learning. ACTA ACUST UNITED AC 2020. [DOI: 10.4236/ns.2020.126034] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou's 5-Steps Rule and Informative Physicochemical Properties. Int J Mol Sci 2019; 21:ijms21010075. [PMID: 31861928 PMCID: PMC6981611 DOI: 10.3390/ijms21010075] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 12/13/2019] [Accepted: 12/18/2019] [Indexed: 01/18/2023] Open
Abstract
Understanding of quorum-sensing peptides (QSPs) in their functional mechanism plays an essential role in finding new opportunities to combat bacterial infections by designing drugs. With the avalanche of the newly available peptide sequences in the post-genomic age, it is highly desirable to develop a computational model for efficient, rapid and high-throughput QSP identification purely based on the peptide sequence information alone. Although, few methods have been developed for predicting QSPs, their prediction accuracy and interpretability still requires further improvements. Thus, in this work, we proposed an accurate sequence-based predictor (called iQSP) and a set of interpretable rules (called IR-QSP) for predicting and analyzing QSPs. In iQSP, we utilized a powerful support vector machine (SVM) cooperating with 18 informative features from physicochemical properties (PCPs). Rigorous independent validation test showed that iQSP achieved maximum accuracy and MCC of 93.00% and 0.86, respectively. Furthermore, a set of interpretable rules IR-QSP was extracted by using random forest model and the 18 informative PCPs. Finally, for the convenience of experimental scientists, the iQSP web server was established and made freely available online. It is anticipated that iQSP will become a useful tool or at least as a complementary existing method for predicting and analyzing QSPs.
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