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Yue ZX, Yan TC, Xu HQ, Liu YH, Hong YF, Chen GX, Xie T, Tao L. A systematic review on the state-of-the-art strategies for protein representation. Comput Biol Med 2023; 152:106440. [PMID: 36543002 DOI: 10.1016/j.compbiomed.2022.106440] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/08/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research. With the progress of machine learning, protein representation methods have gradually attracted attention and have consequently developed rapidly. Therefore, in this review, we systematically classify current protein representation methods, comprehensively review them, and discuss the latest advances of interest. According to the information extraction methods and information sources, these representation methods are generally divided into structure and sequence-based representation methods. Each primary class can be further divided into specific subcategories. As for the particular representation methods involve both traditional and the latest approaches. This review contains a comprehensive assessment of the various methods which researchers can use as a reference for their specific protein-related research requirements, including drug research.
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Affiliation(s)
- Zi-Xuan Yue
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Tian-Ci Yan
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Hong-Quan Xu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Yu-Hong Liu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Yan-Feng Hong
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Gong-Xing Chen
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Tian Xie
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
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2
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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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3
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Hayat M, Tahir M, Alarfaj FK, Alturki R, Gazzawe F. NLP-BCH-Ens: NLP-based intelligent computational model for discrimination of malaria parasite. Comput Biol Med 2022; 149:105962. [DOI: 10.1016/j.compbiomed.2022.105962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 07/29/2022] [Accepted: 08/13/2022] [Indexed: 11/03/2022]
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4
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Ning Q, Zhao X, Ma Z. A Novel Method for Identification of Glutarylation Sites Combining Borderline-SMOTE With Tomek Links Technique in Imbalanced Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2632-2641. [PMID: 34236968 DOI: 10.1109/tcbb.2021.3095482] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Glutarylation is a type of post-translational modification that occurs on lysine residues. It plays an irreplaceable role in various cellular functions. Therefore, identification of glutarylation sites is significant for understanding the molecular mechanism of glutarylation. In this study, we proposed a method named DEXGB_Glu to identify lysine glutarylation sites using XGBoost as classifier which was optimized by differential evolution algorithm. Aiming at the imbalance between positive samples and negative samples, Borderline-SMOTE method was employed to synthesize positive samples, increasing their amount equal to negative samples. Then, Tomek links technique was applied to filter out noise data. Analysis of this method and its results showed that differential evolution algorithm obviously improved the performance and the combination of Borderline-SMOTE and Tomek links effectively solved the imbalance between positive samples and negative samples. Finally, the performance of this method was much better than other methods in prediction of glutarylation sites. The data and code are available on https://github.com/ningq669/DEXGB_Glu.
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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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Zhang Z, Wang L. Using Chou's 5-steps rule to identify N 6-methyladenine sites by ensemble learning combined with multiple feature extraction methods. J Biomol Struct Dyn 2022; 40:796-806. [PMID: 32948102 DOI: 10.1080/07391102.2020.1821778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
N6-methyladenine (m6A), a type of modification mostly affecting the downstream biological functions and determining the levels of gene expression, is mediated by the methylation of adenine in nucleic acids. It is also a key factor for influencing biological processes and has attracted attention as a target for treating diseases. Here, an ensemble predictor named as TL-Methy, was developed to identify m6A sites across the genome. TL-Methy is a 2-level machine learning method developed by combining the support vector machine model and multiple features extraction methods, including nucleic acid composition, di-nucleotide composition, tri-nucleotide composition, position-specific trinucleotide propensity, Bi-profile Bayes, binary encoding, and accumulated nucleotide frequency. For Homo sapiens, TL-Methy method reached the accuracy of 91.68% on jackknife test and of 92.23% on 10-fold cross validation test; For Mus musculus, TL-Methy method achieved the accuracy of 93.66% on jackknife test and of 97.07% on 10-fold cross validation test; For Saccharomyces cerevisiae, TL-Methy method obtained the accuracy of 81.57% on jackknife test and of 82.54% on 10-fold cross validation test; For rice genome, TL-Methy method achieved the accuracy of 91.87% on jackknife test and of 93.04% on 10-fold cross validation test. The results via these two test approaches demonstrated the robustness and practicality of our TL-Methy model. The TL-Methy model may be as a potential method for m6A site identification.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Zhongwang Zhang
- College of Science, Dalian Maritime University, Dalian, P.R. China
| | - Lidong Wang
- College of Science, Dalian Maritime University, Dalian, P.R. China
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Zhang ZM, Guan ZX, Wang F, Zhang D, Ding H. Application of Machine Learning Methods in Predicting Nuclear Receptors and their Families. Med Chem 2021; 16:594-604. [PMID: 31584374 DOI: 10.2174/1573406415666191004125551] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 06/18/2019] [Accepted: 08/23/2019] [Indexed: 11/22/2022]
Abstract
Nuclear receptors (NRs) are a superfamily of ligand-dependent transcription factors that are closely related to cell development, differentiation, reproduction, homeostasis, and metabolism. According to the alignments of the conserved domains, NRs are classified and assigned the following seven subfamilies or eight subfamilies: (1) NR1: thyroid hormone like (thyroid hormone, retinoic acid, RAR-related orphan receptor, peroxisome proliferator activated, vitamin D3- like), (2) NR2: HNF4-like (hepatocyte nuclear factor 4, retinoic acid X, tailless-like, COUP-TFlike, USP), (3) NR3: estrogen-like (estrogen, estrogen-related, glucocorticoid-like), (4) NR4: nerve growth factor IB-like (NGFI-B-like), (5) NR5: fushi tarazu-F1 like (fushi tarazu-F1 like), (6) NR6: germ cell nuclear factor like (germ cell nuclear factor), and (7) NR0: knirps like (knirps, knirpsrelated, embryonic gonad protein, ODR7, trithorax) and DAX like (DAX, SHP), or dividing NR0 into (7) NR7: knirps like and (8) NR8: DAX like. Different NRs families have different structural features and functions. Since the function of a NR is closely correlated with which subfamily it belongs to, it is highly desirable to identify NRs and their subfamilies rapidly and effectively. The knowledge acquired is essential for a proper understanding of normal and abnormal cellular mechanisms. With the advent of the post-genomics era, huge amounts of sequence-known proteins have increased explosively. Conventional methods for accurately classifying the family of NRs are experimental means with high cost and low efficiency. Therefore, it has created a greater need for bioinformatics tools to effectively recognize NRs and their subfamilies for the purpose of understanding their biological function. In this review, we summarized the application of machine learning methods in the prediction of NRs from different aspects. We hope that this review will provide a reference for further research on the classification of NRs and their families.
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Affiliation(s)
- Zi-Mei Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zheng-Xing Guan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fang Wang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Dan Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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8
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Jing XY, Li FM. Predicting Cell Wall Lytic Enzymes Using Combined Features. Front Bioeng Biotechnol 2021; 8:627335. [PMID: 33585423 PMCID: PMC7874139 DOI: 10.3389/fbioe.2020.627335] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 12/04/2020] [Indexed: 11/13/2022] Open
Abstract
Due to the overuse of antibiotics, people are worried that existing antibiotics will become ineffective against pathogens with the rapid rise of antibiotic-resistant strains. The use of cell wall lytic enzymes to destroy bacteria has become a viable alternative to avoid the crisis of antimicrobial resistance. In this paper, an improved method for cell wall lytic enzymes prediction was proposed and the amino acid composition (AAC), the dipeptide composition (DC), the position-specific score matrix auto-covariance (PSSM-AC), and the auto-covariance average chemical shift (acACS) were selected to predict the cell wall lytic enzymes with support vector machine (SVM). In order to overcome the imbalanced data classification problems and remove redundant or irrelevant features, the synthetic minority over-sampling technique (SMOTE) was used to balance the dataset. The F-score was used to select features. The Sn, Sp, MCC, and Acc were 99.35%, 99.02%, 0.98, and 99.19% with jackknife test using the optimized combination feature AAC+DC+acACS+PSSM-AC. The Sn, Sp, MCC, and Acc of cell wall lytic enzymes in our predictive model were higher than those in existing methods. This improved method may be helpful for protein function prediction.
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Affiliation(s)
- Xiao-Yang Jing
- College of Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Feng-Min Li
- College of Science, Inner Mongolia Agricultural University, Hohhot, China
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9
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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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10
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Pundir H, Joshi T, Joshi T, Sharma P, Mathpal S, Chandra S, Tamta S. Using Chou's 5-steps rule to study pharmacophore-based virtual screening of SARS-CoV-2 Mpro inhibitors. Mol Divers 2020; 25:1731-1744. [PMID: 33079314 PMCID: PMC7573527 DOI: 10.1007/s11030-020-10148-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 10/08/2020] [Indexed: 11/29/2022]
Abstract
Abstract Recently emerged SARS-CoV-2 is the cause of the ongoing outbreak of COVID-19. It is responsible for the deaths of millions of people and has caused global economic and social disruption. The numbers of COVID-19 cases are increasing exponentially across the world. Control of this pandemic disease is challenging because there is no effective drug or vaccine available against this virus and this situation demands an urgent need for the development of anti-SARS-CoV-2 potential medicines. In this regard, the main protease (Mpro) has emerged as an essential drug target as it plays a vital role in virus replication and transcription. In this research, we have identified two novel potent inhibitors of the Mpro (PubChem3408741 and PubChem4167619) from PubChem database by pharmacophore-based high-throughput virtual screening. The molecular docking, toxicity, and pharmacophore analysis indicate that these compounds may act as potential anti-viral candidates. The molecular dynamic simulation along with the binding free energy calculation by MMPBSA showed that these compounds bind to Mpro enzyme with high stability over 50 ns. Our results showed that two compounds: PubChem3408741 and PubChem4167619 had the binding free energy of − 94.02 kJ mol−1 and − 122.75 kJ mol−1, respectively, as compared to reference X77 (− 76.48 kJ mol−1). Based on our work’s findings, we propose that these compounds can be considered as lead molecules for targeting Mpro enzyme and they can be potential SARS-CoV-2 inhibitors. These inhibitors could be tested in vitro and explored for effective drug development against COVID-19. Graphic abstract ![]()
Electronic supplementary material The online version of this article (10.1007/s11030-020-10148-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hemlata Pundir
- Department of Botany, D.S.B Campus, Kumaun University, Nainital, Uttarakhand, 263002, India
| | - Tanuja Joshi
- Computational Biology and Biotechnology Laboratory, Department of Botany, Soban Singh Jeena University, Almora, Uttarakhand, India
| | - Tushar Joshi
- Department of Biotechnology, Kumaun University, Bhimtal Campus, Bhimtal, Uttarakhand, 263136, India
| | - Priyanka Sharma
- Department of Botany, D.S.B Campus, Kumaun University, Nainital, Uttarakhand, 263002, India
| | - Shalini Mathpal
- Department of Biotechnology, Kumaun University, Bhimtal Campus, Bhimtal, Uttarakhand, 263136, India
| | - Subhash Chandra
- Computational Biology and Biotechnology Laboratory, Department of Botany, Soban Singh Jeena University, Almora, Uttarakhand, India. .,Department of Botany, Kumaun University, S. S. J. Campus, Almora, Uttarakhand, India.
| | - Sushma Tamta
- Department of Botany, D.S.B Campus, Kumaun University, Nainital, Uttarakhand, 263002, India
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Use Chou’s 5-steps rule to identify DNase I hypersensitive sites via dinucleotide property matrix and extreme gradient boosting. Mol Genet Genomics 2020; 295:1431-1442. [DOI: 10.1007/s00438-020-01711-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 07/11/2020] [Indexed: 01/08/2023]
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12
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Ju Z, Wang SY. Computational Identification of Lysine Glutarylation Sites Using Positive-Unlabeled Learning. Curr Genomics 2020; 21:204-211. [PMID: 33071614 PMCID: PMC7521029 DOI: 10.2174/1389202921666200511072327] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 04/12/2020] [Accepted: 04/13/2020] [Indexed: 12/27/2022] Open
Abstract
Background
As a new type of protein acylation modification, lysine glutarylation has been found to play a crucial role in metabolic processes and mitochondrial functions. To further explore the biological mechanisms and functions of glutarylation, it is significant to predict the potential glutarylation sites. In the existing glutarylation site predictors, experimentally verified glutarylation sites are treated as positive samples and non-verified lysine sites as the negative samples to train predictors. However, the non-verified lysine sites may contain some glutarylation sites which have not been experimentally identified yet. Methods
In this study, experimentally verified glutarylation sites are treated as the positive samples, whereas the remaining non-verified lysine sites are treated as unlabeled samples. A bioinformatics tool named PUL-GLU was developed to identify glutarylation sites using a positive-unlabeled learning algorithm. Results
Experimental results show that PUL-GLU significantly outperforms the current glutarylation site predictors. Therefore, PUL-GLU can be a powerful tool for accurate identification of protein glutarylation sites. Conclusion
A user-friendly web-server for PUL-GLU is available at http://bioinform.cn/pul_glu/.
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Affiliation(s)
- Zhe Ju
- College of Science, Shenyang Aerospace University, Shenyang110136, P.R. China
| | - Shi-Yun Wang
- College of Science, Shenyang Aerospace University, Shenyang110136, P.R. China
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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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14
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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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15
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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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16
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Prediction of N6-methyladenosine sites using convolution neural network model based on distributed feature representations. Neural Netw 2020; 129:385-391. [PMID: 32593932 DOI: 10.1016/j.neunet.2020.05.027] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Revised: 05/21/2020] [Accepted: 05/24/2020] [Indexed: 01/24/2023]
Abstract
N6-methyladenosine (m6A) is a well-studied and most common interior messenger RNA (mRNA) modification that plays an important function in cell development. N6A is found in all kingdoms of life and many other cellular processes such as RNA splicing, immune tolerance, regulatory functions, RNA processing, and cancer. Despite the crucial role of m6A in cells, it was targeted computationally, but unfortunately, the obtained results were unsatisfactory. It is imperative to develop an efficient computational model that can truly represent m6A sites. In this regard, an intelligent and highly discriminative computational model namely: m6A-word2vec is introduced for the discrimination of m6A sites. Here, a concept of natural language processing in the form of word2vec is used to represent the motif of the target class automatically. These motifs (numerical descriptors) are automatically targeted from the human genome without any clear definition. Further, the extracted feature space is then forwarded to the convolution neural network model as input for prediction. The developed computational model obtained 83.17%, 92.69%, and 90.50% accuracy for benchmark datasets S1, S2, and S3, respectively, using a 10-fold cross-validation test. The predictive outcomes validate that the developed intelligent computational model showed better performance compared to existing computational models. It is thus greatly estimated that the introduced computational model "m6A-word2vec" may be a supportive and practical tool for elementary and pharmaceutical research such as in drug design along with academia.
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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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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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Chou KC. Impacts of Pseudo Amino Acid Components and 5-steps Rule to Proteomics and Proteome Analysis. Curr Top Med Chem 2019; 19:2283-2300. [DOI: 10.2174/1568026619666191018100141] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 08/18/2019] [Accepted: 08/26/2019] [Indexed: 01/27/2023]
Abstract
Stimulated by the 5-steps rule during the last decade or so, computational proteomics has achieved remarkable progresses in the following three areas: (1) protein structural class prediction; (2) protein subcellular location prediction; (3) post-translational modification (PTM) site prediction. The results obtained by these predictions are very useful not only for an in-depth study of the functions of proteins and their biological processes in a cell, but also for developing novel drugs against major diseases such as cancers, Alzheimer’s, and Parkinson’s. Moreover, since the targets to be predicted may have the multi-label feature, two sets of metrics are introduced: one is for inspecting the global prediction quality, while the other for the local prediction quality. All the predictors covered in this review have a userfriendly web-server, through which the majority of experimental scientists can easily obtain their desired data without the need to go through the complicated mathematics.
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Affiliation(s)
- Kuo-Chen Chou
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
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Malebary SJ, Rehman MSU, Khan YD. iCrotoK-PseAAC: Identify lysine crotonylation sites by blending position relative statistical features according to the Chou's 5-step rule. PLoS One 2019; 14:e0223993. [PMID: 31751380 PMCID: PMC6874067 DOI: 10.1371/journal.pone.0223993] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 10/02/2019] [Indexed: 01/22/2023] Open
Abstract
Among different post-translational modifications (PTMs), one of the most important one is the lysine crotonylation in proteins. Its importance cannot be undermined related to different diseases and essential biological practice. The key step for finding the hidden mechanisms of crotonylation along with their occurrence sites is to completely apprehend the mechanism behind this biological process. In previously reported studies, researchers have used different techniques, like position weighted matrix (PWM), support vector machine (SVM), k nearest neighbors (KNN), and many others. However, the maximum prediction accuracy achieved was not such high. To address this, herein, we propose an improved predictor for lysine crotonylation sites named iCrotoK-PseAAC, in which we have incorporated various position and composition relative features along with statistical moments into PseAAC. The results of self-consistency testing were 100% accurate, while the 10-fold cross validation gave 99.0% accuracy. Based on the validation and comparison of model, it is concluded that the iCrotoK-PseAAC is more accurate than the previously proposed models.
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Affiliation(s)
- Sharaf Jameel Malebary
- Department of Information Technology, King Abdul Aziz University, Rabigh, Kingdom of Saudi Arabia
| | - Muhammad Safi ur Rehman
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Yaser Daanial Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
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Chou KC. Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses. Int J Pept Res Ther 2019. [DOI: 10.1007/s10989-019-09910-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Lai HY, Zhang ZY, Su ZD, Su W, Ding H, Chen W, Lin H. iProEP: A Computational Predictor for Predicting Promoter. MOLECULAR THERAPY. NUCLEIC ACIDS 2019; 17:337-346. [PMID: 31299595 PMCID: PMC6616480 DOI: 10.1016/j.omtn.2019.05.028] [Citation(s) in RCA: 103] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Revised: 05/18/2019] [Accepted: 05/19/2019] [Indexed: 11/29/2022]
Abstract
Promoter is a fundamental DNA element located around the transcription start site (TSS) and could regulate gene transcription. Promoter recognition is of great significance in determining transcription units, studying gene structure, analyzing gene regulation mechanisms, and annotating gene functional information. Many models have already been proposed to predict promoters. However, the performances of these methods still need to be improved. In this work, we combined pseudo k-tuple nucleotide composition (PseKNC) with position-correlation scoring function (PCSF) to formulate promoter sequences of Homo sapiens (H. sapiens), Drosophila melanogaster (D. melanogaster), Caenorhabditis elegans (C. elegans), Bacillus subtilis (B. subtilis), and Escherichia coli (E. coli). Minimum Redundancy Maximum Relevance (mRMR) algorithm and increment feature selection strategy were then adopted to find out optimal feature subsets. Support vector machine (SVM) was used to distinguish between promoters and non-promoters. In the 10-fold cross-validation test, accuracies of 93.3%, 93.9%, 95.7%, 95.2%, and 93.1% were obtained for H. sapiens, D. melanogaster, C. elegans, B. subtilis, and E. coli, with the areas under receiver operating curves (AUCs) of 0.974, 0.975, 0.981, 0.988, and 0.976, respectively. Comparative results demonstrated that our method outperforms existing methods for identifying promoters. An online web server was established that can be freely accessed (http://lin-group.cn/server/iProEP/).
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Affiliation(s)
- Hong-Yan Lai
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhao-Yue Zhang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhen-Dong Su
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Su
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Ding
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Chen
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China; Center for Genomics and Computational Biology, School of Life Sciences, North China University of Science and Technology, Tangshan 063000, China.
| | - Hao Lin
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
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