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Emami N, Ferdousi R. HormoNet: a deep learning approach for hormone-drug interaction prediction. BMC Bioinformatics 2024; 25:87. [PMID: 38418979 PMCID: PMC10903040 DOI: 10.1186/s12859-024-05708-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 02/16/2024] [Indexed: 03/02/2024] Open
Abstract
Several experimental evidences have shown that the human endogenous hormones can interact with drugs in many ways and affect drug efficacy. The hormone drug interactions (HDI) are essential for drug treatment and precision medicine; therefore, it is essential to understand the hormone-drug associations. Here, we present HormoNet to predict the HDI pairs and their risk level by integrating features derived from hormone and drug target proteins. To the best of our knowledge, this is one of the first attempts to employ deep learning approach for prediction of HDI prediction. Amino acid composition and pseudo amino acid composition were applied to represent target information using 30 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied synthetic minority over-sampling technique technique. Additionally, we constructed novel datasets for HDI prediction and the risk level of their interaction. HormoNet achieved high performance on our constructed hormone-drug benchmark datasets. The results provide insights into the understanding of the relationship between hormone and a drug, and indicate the potential benefit of reducing risk levels of interactions in designing more effective therapies for patients in drug treatments. Our benchmark datasets and the source codes for HormoNet are available in: https://github.com/EmamiNeda/HormoNet .
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Affiliation(s)
- Neda Emami
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
| | - Reza Ferdousi
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
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2
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Li J, Zou Q, Yuan L. A review from biological mapping to computation-based subcellular localization. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 32:507-521. [PMID: 37215152 PMCID: PMC10192651 DOI: 10.1016/j.omtn.2023.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Subcellular localization is crucial to the study of virus and diseases. Specifically, research on protein subcellular localization can help identify clues between virus and host cells that can aid in the design of targeted drugs. Research on RNA subcellular localization is significant for human diseases (such as Alzheimer's disease, colon cancer, etc.). To date, only reviews addressing subcellular localization of proteins have been published, which are outdated for reference, and reviews of RNA subcellular localization are not comprehensive. Therefore, we collated (the most up-to-date) literature on protein and RNA subcellular localization to help researchers understand changes in the field of protein and RNA subcellular localization. Extensive and complete methods for constructing subcellular localization models have also been summarized, which can help readers understand the changes in application of biotechnology and computer science in subcellular localization research and explore how to use biological data to construct improved subcellular localization models. This paper is the first review to cover both protein subcellular localization and RNA subcellular localization. We urge researchers from biology and computational biology to jointly pay attention to transformation patterns, interrelationships, differences, and causality of protein subcellular localization and RNA subcellular localization.
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Affiliation(s)
- Jing Li
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, 1 Chengdian Road, Quzhou, Zhejiang 324000, China
- School of Biomedical Sciences, University of Hong Kong, Hong Kong, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, 1 Chengdian Road, Quzhou, Zhejiang 324000, China
| | - Lei Yuan
- Department of Hepatobiliary Surgery, Quzhou People's Hospital, 100 Minjiang Main Road, Quzhou, Zhejiang 324000, China
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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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Akmal MA, Hussain W, Rasool N, Khan YD, Khan SA, Chou KC. Using CHOU'S 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2045-2056. [PMID: 31985438 DOI: 10.1109/tcbb.2020.2968441] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Glycosylation of proteins in eukaryote cells is an important and complicated post-translation modification due to its pivotal role and association with crucial physiological functions within most of the proteins. Identification of glycosylation sites in a polypeptide chain is not an easy task due to multiple impediments. Analytical identification of these sites is expensive and laborious. There is a dire need to develop a reliable computational method for precise determination of such sites which can help researchers to save time and effort. Herein, we propose a novel predictor namely iGlycoS-PseAAC by integrating the Chou's Pseudo Amino Acid Composition (PseAAC) and relative/absolute position-based features. The self-consistency results show that the accuracy revealed by the model using the benchmark dataset for prediction of O-linked glycosylation having serine sites is 98.8 percent. The overall accuracy of predictor achieved through 10-fold cross validation by combining the positive and negative results is 97.2 percent. The overall accuracy achieved through Jackknife test is 96.195 percent by aggregating of all the prediction results. Thus the proposed predictor can help in predicting the O-linked glycosylated serine sites in an efficient and accurate way. The overall results show that the accuracy of the iGlycoS-PseAAC is higher than the existing tools.
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Tabassum H, Ahmad IZ. Molecular Docking and Dynamics Simulation Analysis of Thymoquinone and Thymol Compounds from Nigella sativa L. that Inhibit Cag A and Vac A Oncoprotein of Helicobacter pylori: Probable Treatment of H. pylori Infections. Med Chem 2021; 17:146-157. [PMID: 32116195 DOI: 10.2174/1573406416666200302113729] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 10/24/2019] [Accepted: 12/04/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Helicobacter pylori infection is accountable for most of the peptic ulcer and intestinal cancers. Due to the uprising resistance towards H. pylori infection through the present and common proton pump inhibitors regimens, the investigation of novel candidates is the inevitable issue. Medicinal plants have always been a source of lead compounds for drug discovery. The research of the related effective enzymes linked with this gram-negative bacterium is critical for the discovery of novel drug targets. OBJECTIVE The aim of the study is to identify the best candidate to evaluate the inhibitory effect of thymoquinone and thymol against H. pylori oncoproteins, Cag A and Vac A in comparison to the standard drug, metronidazole by using a computational approach. MATERIALS AND METHODS The targeted oncoproteins, Cag A and Vac A were retrieved from RCSB PDB. Lipinski's rule and ADMET toxicity profiling were carried out on the phytoconstituents of the N. sativa. The two compounds of N. sativa were further analyzed by molecular docking and MD simulation studies. The reported phytoconstituents, thymoquinone and thymol present in N. sativa were docked with H. pylori Cag A and Vac A oncoproteins. Structures of ligands were prepared using ChemDraw Ultra 10 software and then changed into their 3D PDB structures using Molinspiration followed by energy minimization by using software Discovery Studio client 2.5. RESULTS The docking results revealed the promising inhibitory potential of thymoquinone against Cag A and Vac A with docking energy of -5.81 kcal/mole and -3.61kcal/mole, respectively. On the contrary, the inhibitory potential of thymol against Cag A and Vac A in terms of docking energy was -5.37 kcal/mole and -3.94kcal/mole as compared to the standard drug, metronidazole having docking energy of -4.87 kcal/mole and -3.20 kcal/mole, respectively. Further, molecular dynamic simulations were conducted for 5ns for optimization, flexibility prediction, and determination of folded Cag A and Vac A oncoproteins stability. The Cag A and Vac A oncoproteins-TQ complexes were found to be quite stable with the root mean square deviation value of 0.2nm. CONCLUSION The computational approaches suggested that thymoquinone and thymol may play an effective pharmacological role to treat H. pylori infection. Hence, it could be summarized that the ligands thymoquinone and thymol bound and interacted well with the proteins Cag A and Vac A as compared to the ligand MTZ. Our study showed that all lead compounds had good interaction with Cag A and Vac A proteins and suggested them to be a useful target to inhibit H. pylori infection.
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Affiliation(s)
- Heena Tabassum
- Natural Products Laboratory, Department of Bioengineering, Integral University, Dasauli, Kursi Road, Lucknow- 226026, Uttar Pradesh, India
| | - Iffat Zareen Ahmad
- Natural Products Laboratory, Department of Bioengineering, Integral University, Dasauli, Kursi Road, Lucknow- 226026, Uttar Pradesh, India
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Emami N, Ferdousi R. AptaNet as a deep learning approach for aptamer-protein interaction prediction. Sci Rep 2021; 11:6074. [PMID: 33727685 PMCID: PMC7971039 DOI: 10.1038/s41598-021-85629-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 03/03/2021] [Indexed: 02/08/2023] Open
Abstract
Aptamers are short oligonucleotides (DNA/RNA) or peptide molecules that can selectively bind to their specific targets with high specificity and affinity. As a powerful new class of amino acid ligands, aptamers have high potentials in biosensing, therapeutic, and diagnostic fields. Here, we present AptaNet-a new deep neural network-to predict the aptamer-protein interaction pairs by integrating features derived from both aptamers and the target proteins. Aptamers were encoded by using two different strategies, including k-mer and reverse complement k-mer frequency. Amino acid composition (AAC) and pseudo amino acid composition (PseAAC) were applied to represent target information using 24 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied a neighborhood cleaning algorithm. The predictor was constructed based on a deep neural network, and optimal features were selected using the random forest algorithm. As a result, 99.79% accuracy was achieved for the training dataset, and 91.38% accuracy was obtained for the testing dataset. AptaNet achieved high performance on our constructed aptamer-protein benchmark dataset. The results indicate that AptaNet can help identify novel aptamer-protein interacting pairs and build more-efficient insights into the relationship between aptamers and proteins. Our benchmark dataset and the source codes for AptaNet are available in: https://github.com/nedaemami/AptaNet .
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Affiliation(s)
- Neda Emami
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Reza Ferdousi
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran.
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7
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Awais M, Hussain W, Khan YD, Rasool N, Khan SA, Chou KC. iPhosH-PseAAC: Identify Phosphohistidine Sites in Proteins by Blending Statistical Moments and Position Relative Features According to the Chou's 5-Step Rule and General Pseudo Amino Acid Composition. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:596-610. [PMID: 31144645 DOI: 10.1109/tcbb.2019.2919025] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Protein phosphorylation is one of the key mechanism in prokaryotes and eukaryotes and is responsible for various biological functions such as protein degradation, intracellular localization, the multitude of cellular processes, molecular association, cytoskeletal dynamics, and enzymatic inhibition/activation. Phosphohistidine (PhosH) has a key role in a number of biological processes, including central metabolism to signalling in eukaryotes and bacteria. Thus, identification of phosphohistidine sites in a protein sequence is crucial, and experimental identification can be expensive, time-taking, and laborious. To address this problem, here, we propose a novel computational model namely iPhosH-PseAAC for prediction of phosphohistidine sites in a given protein sequence using pseudo amino acid composition (PseAAC), statistical moments, and position relative features. The results of the proposed predictor are validated through self-consistency testing, 10-fold cross-validation, and jackknife testing. The self-consistency validation gave the 100 percent accuracy, whereas, for cross-validation, the accuracy achieved is 94.26 percent. Moreover, jackknife testing gave 97.07 percent accuracy for the proposed model. Thus, the proposed model iPhosH-PseAAC for prediction of iPhosH site has the great ability to predict the PhosH sites in given proteins.
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8
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Liu GH, Zhang BW, Qian G, Wang B, Mao B, Bichindaritz I. Bioimage-Based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1966-1980. [PMID: 31107658 DOI: 10.1109/tcbb.2019.2917429] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Prediction of protein subcellular location has currently become a hot topic because it has been proven to be useful for understanding both the disease mechanisms and novel drug design. With the rapid development of automated microscopic imaging technology in recent years, classification methods of bioimage-based protein subcellular location have attracted considerable attention for images can describe the protein distribution intuitively and in detail. In the current study, a prediction method of protein subcellular location was proposed based on multi-view image features that are extracted from three different views, including the four texture features of the original image, the global and local features of the protein extracted from the protein channel images after color segmentation, and the global features of DNA extracted from the DNA channel image. Finally, the extracted features were combined together to improve the performance of subcellular localization prediction. From the performance comparison of different combination features under the same classifier, the best ensemble features could be obtained. In this work, a classifier based on Stacked Auto-encoders and the random forest was also put forward. To improve the prediction results, the deep network was combined with the traditional statistical classification methods. Stringent cross-validation and independent validation tests on the benchmark dataset demonstrated the efficacy of the proposed method.
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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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10
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Chou KC. An Insightful 10-year Recollection Since the Emergence of the 5-steps Rule. Curr Pharm Des 2020; 25:4223-4234. [PMID: 31782354 DOI: 10.2174/1381612825666191129164042] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 11/25/2019] [Indexed: 11/22/2022]
Abstract
OBJECTIVE One of the most challenging and also the most difficult problems is how to formulate a biological sequence with a vector but considerably keep its sequence order information. METHODS To address such a problem, the approach of Pseudo Amino Acid Components or PseAAC has been developed. RESULTS AND CONCLUSION It has become increasingly clear via the 10-year recollection that the aforementioned proposal has been indeed very powerful.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, Massachusetts 02478, United States.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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11
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Mohabatkar H, Ebrahimi S, Moradi M. Using Chou’s Five-steps Rule to Classify and Predict Glutathione S-transferases with Different Machine Learning Algorithms and Pseudo Amino Acid Composition. Int J Pept Res Ther 2020. [DOI: 10.1007/s10989-020-10087-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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12
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13
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Du L, Meng Q, Chen Y, Wu P. Subcellular location prediction of apoptosis proteins using two novel feature extraction methods based on evolutionary information and LDA. BMC Bioinformatics 2020; 21:212. [PMID: 32448129 PMCID: PMC7245797 DOI: 10.1186/s12859-020-3539-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 05/06/2020] [Indexed: 11/13/2022] Open
Abstract
Background Apoptosis, also called programmed cell death, refers to the spontaneous and orderly death of cells controlled by genes in order to maintain a stable internal environment. Identifying the subcellular location of apoptosis proteins is very helpful in understanding the mechanism of apoptosis and designing drugs. Therefore, the subcellular localization of apoptosis proteins has attracted increased attention in computational biology. Effective feature extraction methods play a critical role in predicting the subcellular location of proteins. Results In this paper, we proposed two novel feature extraction methods based on evolutionary information. One of the features obtained the evolutionary information via the transition matrix of the consensus sequence (CTM). And the other utilized the evolutionary information from PSSM based on absolute entropy correlation analysis (AECA-PSSM). After fusing the two kinds of features, linear discriminant analysis (LDA) was used to reduce the dimension of the proposed features. Finally, the support vector machine (SVM) was adopted to predict the protein subcellular locations. The proposed CTM-AECA-PSSM-LDA subcellular location prediction method was evaluated using the CL317 dataset and ZW225 dataset. By jackknife test, the overall accuracy was 99.7% (CL317) and 95.6% (ZW225) respectively. Conclusions The experimental results show that the proposed method which is hopefully to be a complementary tool for the existing methods of subcellular localization, can effectively extract more abundant features of protein sequence and is feasible in predicting the subcellular location of apoptosis proteins.
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Affiliation(s)
- Lei Du
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, China.,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China
| | - Qingfang Meng
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, China. .,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China.
| | - Yuehui Chen
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, China.,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China
| | - Peng Wu
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, China.,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China
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Zheng L, Huang S, Mu N, Zhang H, Zhang J, Chang Y, Yang L, Zuo Y. RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2019:5650975. [PMID: 31802128 PMCID: PMC6893003 DOI: 10.1093/database/baz131] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 10/16/2019] [Accepted: 10/17/2019] [Indexed: 12/12/2022]
Abstract
By reducing amino acid alphabet, the protein complexity can be significantly simplified, which could improve computational efficiency, decrease information redundancy and reduce chance of overfitting. Although some reduced alphabets have been proposed, different classification rules could produce distinctive results for protein sequence analysis. Thus, it is urgent to construct a systematical frame for reduced alphabets. In this work, we constructed a comprehensive web server called RAACBook for protein sequence analysis and machine learning application by integrating reduction alphabets. The web server contains three parts: (i) 74 types of reduced amino acid alphabet were manually extracted to generate 673 reduced amino acid clusters (RAACs) for dealing with unique protein problems. It is easy for users to select desired RAACs from a multilayer browser tool. (ii) An online tool was developed to analyze primary sequence of protein. The tool could produce K-tuple reduced amino acid composition by defining three correlation parameters (K-tuple, g-gap, λ-correlation). The results are visualized as sequence alignment, mergence of RAA composition, feature distribution and logo of reduced sequence. (iii) The machine learning server is provided to train the model of protein classification based on K-tuple RAAC. The optimal model could be selected according to the evaluation indexes (ROC, AUC, MCC, etc.). In conclusion, RAACBook presents a powerful and user-friendly service in protein sequence analysis and computational proteomics. RAACBook can be freely available at http://bioinfor.imu.edu.cn/raacbook. Database URL: http://bioinfor.imu.edu.cn/raacbook
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Affiliation(s)
- Lei Zheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Zhaojun Road No.24, Hohhot, 010070, China
| | - Shenghui Huang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Zhaojun Road No.24, Hohhot, 010070, China
| | - Nengjiang Mu
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Zhaojun Road No.24, Hohhot, 010070, China
| | - Haoyue Zhang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Zhaojun Road No.24, Hohhot, 010070, China
| | - Jiayu Zhang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Zhaojun Road No.24, Hohhot, 010070, China
| | - Yu Chang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Zhaojun Road No.24, Hohhot, 010070, China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Baojian Road No.157, Harbin 150081, China
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Zhaojun Road No.24, Hohhot, 010070, China
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15
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Rehman AU, Olof Olsson P, Khan N, Khan K. Identification of Human Secretome and Membrane Proteome-Based Cancer Biomarkers Utilizing Bioinformatics. J Membr Biol 2020; 253:257-270. [PMID: 32415382 DOI: 10.1007/s00232-020-00122-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 05/02/2020] [Indexed: 12/12/2022]
Abstract
Cellular secreted proteins (secretome), together with cellular membrane proteins, collectively referred to as secretory and membrane proteins (SMPs) are a large potential source of biomarkers as they can be used to indicate cell types and conditions. SMPs have been shown to be ideal candidates for several clinically approved drug regimens including for cancer. This study aimed at performing a functional analysis of SMPs within different cancer subtypes to provide great clinical targets for potential prognostic, diagnostic and the therapeutics use. Using an innovative majority decision-based algorithm and transcriptomic data spanning 5 cancer types and over 3000 samples, we quantified the relative difference in SMPs gene expression compared to normal adjacent tissue. A detailed deep data mining analysis revealed a consistent group of downregulated SMP isoforms, enriched in hematopoietic cell lineages (HCL), in multiple cancer types. HCL-associated genes were frequently downregulated in successive cancer stages and high expression was associated with good patient prognosis. In addition, we suggest a potential mechanism by which cancer cells suppress HCL signaling by reducing the expression of immune-related genes. Our data identified potential biomarkers for the cancer immunotherapy. We conclude that our approach may be applicable for the delineation of other types of cancer and illuminate specific targets for therapeutics and diagnostics.
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Affiliation(s)
- Adeel Ur Rehman
- Hefei National Laboratory for Physical Sciences at Microscale, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, School of Life Sciences, University of Science and Technology of China, Hefei, 230027, China.
| | | | - Naveed Khan
- Max Plank Partner Institute of Computational Biology, Shanghai Institute of Biological Sciences, Shanghai, 200032, China
| | - Khalid Khan
- Department of Respiratory and Critical Care Medicine, The Second Clinical Medical College (Shenzhen People's Hospital) of Jinan University, Shenzhen Institute of Respiratory Diseases, Shenzhen, China.,Integrated Chinese and Western Medicine Postdoctoral Research Station, Jinan University, Guangzhou, China
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16
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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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17
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Identifying FL11 subtype by characterizing tumor immune microenvironment in prostate adenocarcinoma via Chou's 5-steps rule. Genomics 2020; 112:1500-1515. [DOI: 10.1016/j.ygeno.2019.08.021] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 08/03/2019] [Accepted: 08/26/2019] [Indexed: 12/14/2022]
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18
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Zheng H, Yang H, Gong D, Mai L, Qiu X, Chen L, Su X, Wei R, Zeng Z. Progress in the Mechanism and Clinical Application of Cilostazol. Curr Top Med Chem 2020; 19:2919-2936. [PMID: 31763974 DOI: 10.2174/1568026619666191122123855] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 07/27/2019] [Accepted: 08/02/2019] [Indexed: 12/20/2022]
Abstract
Cilostazol is a unique platelet inhibitor that has been used clinically for more than 20 years. As a phosphodiesterase type III inhibitor, cilostazol is capable of reversible inhibition of platelet aggregation and vasodilation, has antiproliferative effects, and is widely used in the treatment of peripheral arterial disease, cerebrovascular disease, percutaneous coronary intervention, etc. This article briefly reviews the pharmacological mechanisms and clinical application of cilostazol.
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Affiliation(s)
- Huilei Zheng
- Department of Medical Examination & Health Management, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.,Guangxi Key Laboratory of Precision Medicine in Cardio-cerebrovascular Diseases Control and Prevention,Nanning, Guangxi, China.,Guangxi Clinical Research Center for Cardio-cerebrovascular Diseases, Nanning, Guangxi, China
| | - Hua Yang
- Guangxi Key Laboratory of Precision Medicine in Cardio-cerebrovascular Diseases Control and Prevention,Nanning, Guangxi, China.,Guangxi Clinical Research Center for Cardio-cerebrovascular Diseases, Nanning, Guangxi, China.,Department of Critical Care Medicine, Second People's Hospital of Nanning, Nanning, Guangxi, China
| | - Danping Gong
- Guangxi Key Laboratory of Precision Medicine in Cardio-cerebrovascular Diseases Control and Prevention,Nanning, Guangxi, China.,Guangxi Clinical Research Center for Cardio-cerebrovascular Diseases, Nanning, Guangxi, China.,Elderly Cardiology Ward, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Lanxian Mai
- Guangxi Key Laboratory of Precision Medicine in Cardio-cerebrovascular Diseases Control and Prevention,Nanning, Guangxi, China.,Guangxi Clinical Research Center for Cardio-cerebrovascular Diseases, Nanning, Guangxi, China.,Disciplinary Construction Office, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Xiaoling Qiu
- Guangxi Key Laboratory of Precision Medicine in Cardio-cerebrovascular Diseases Control and Prevention,Nanning, Guangxi, China.,Guangxi Clinical Research Center for Cardio-cerebrovascular Diseases, Nanning, Guangxi, China
| | - Lidai Chen
- Guangxi Key Laboratory of Precision Medicine in Cardio-cerebrovascular Diseases Control and Prevention,Nanning, Guangxi, China.,Guangxi Clinical Research Center for Cardio-cerebrovascular Diseases, Nanning, Guangxi, China
| | - Xiaozhou Su
- Guangxi Key Laboratory of Precision Medicine in Cardio-cerebrovascular Diseases Control and Prevention,Nanning, Guangxi, China.,Guangxi Clinical Research Center for Cardio-cerebrovascular Diseases, Nanning, Guangxi, China
| | - Ruoqi Wei
- Department of Computer Science and Engineering, University of Bridgeport,126 Park Ave, BRIDGEPORT, CT 06604, United States
| | - Zhiyu Zeng
- Guangxi Key Laboratory of Precision Medicine in Cardio-cerebrovascular Diseases Control and Prevention,Nanning, Guangxi, China.,Guangxi Clinical Research Center for Cardio-cerebrovascular Diseases, Nanning, Guangxi, China.,Elderly Cardiology Ward, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
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19
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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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20
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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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21
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Shao Y, Chou KC. pLoc_Deep-mVirus: A CNN Model for Predicting Subcellular Localization of Virus Proteins by Deep Learning. ACTA ACUST UNITED AC 2020. [DOI: 10.4236/ns.2020.126033] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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22
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Affiliation(s)
- Guo-Ping Zhou
- Adjunct Professor Guangxi Academy of Sciences Nanning, Guangxi 530004, China
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23
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Ju Z, Wang SY. Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature Extraction via the Chou's 5-steps Rule and General Pseudo Components. Curr Genomics 2019; 20:592-601. [PMID: 32581647 PMCID: PMC7290059 DOI: 10.2174/1389202921666191223154629] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 10/19/2019] [Accepted: 11/07/2019] [Indexed: 01/06/2023] Open
Abstract
Introduction Neddylation is a highly dynamic and reversible post-translational modification. The abnormality of neddylation has previously been shown to be closely related to some human diseases. The detection of neddylation sites is essential for elucidating the regulation mechanisms of protein neddylation. Objective As the detection of the lysine neddylation sites by the traditional experimental method is often expensive and time-consuming, it is imperative to design computational methods to identify neddylation sites. Methods In this study, a bioinformatics tool named NeddPred is developed to identify underlying protein neddylation sites. A bi-profile bayes feature extraction is used to encode neddylation sites and a fuzzy support vector machine model is utilized to overcome the problem of noise and class imbalance in the prediction. Results Matthew's correlation coefficient of NeddPred achieved 0.7082 and an area under the receiver operating characteristic curve of 0.9769. Independent tests show that NeddPred significantly outperforms existing lysine neddylation sites predictor NeddyPreddy. Conclusion Therefore, NeddPred can be a complement to the existing tools for the prediction of neddylation sites. A user-friendly webserver for NeddPred is accessible at 123.206.31.171/NeddPred/.
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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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24
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Behbahani M, Nosrati M, Moradi M, Mohabatkar H. Using Chou's General Pseudo Amino Acid Composition to Classify Laccases from Bacterial and Fungal Sources via Chou's Five-Step Rule. Appl Biochem Biotechnol 2019; 190:1035-1048. [PMID: 31659712 DOI: 10.1007/s12010-019-03141-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 09/12/2019] [Indexed: 01/28/2023]
Abstract
Laccases are a group of enzymes with a critical activity in the degradation process of both phenolic and non-phenolic compounds. These enzymes present in a diverse array of species, including fungi and bacteria. Since this enzyme is in the market for different usages from industry to medicine, having a better knowledge of its structures and properties from diverse sources will be useful to select the most appropriate candidate for different purposes. In the current study, sequence- and structure-based characteristics of these enzymes from fungi and bacteria, including pseudo amino acid composition (PseAAC), physicochemical characteristics, and their secondary structures, are being compared and classified. Autodock 4 software was used for docking analysis between these laccases and some phenolic and non-phenolic compounds. The results indicated that features including molecular weight, aliphatic, extinction coefficient, and random coil percentage of these protein groups present high degrees of diversity in most cases. Categorization of these enzymes by the notion of PseAAC, showed over 96% accuracy. The binding free energy between fungal laccases and their substrates showed to be considerably higher than those of bacterial ones. According to the outcomes of the current study, data mining methods by using different machine learning algorithms, especially neural networks, could provide valuable information for a fair comparison between fungal and bacterial laccases. These results also suggested an association between efficacy and physicochemical features of laccase enzymes from different sources.
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Affiliation(s)
- Mandana Behbahani
- Department of Biotechnology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran
| | - Mokhtar Nosrati
- 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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25
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Kang C. 19F-NMR in Target-based Drug Discovery. Curr Med Chem 2019; 26:4964-4983. [PMID: 31187703 DOI: 10.2174/0929867326666190610160534] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Revised: 08/14/2018] [Accepted: 03/13/2019] [Indexed: 02/06/2023]
Abstract
Solution NMR spectroscopy plays important roles in understanding protein structures, dynamics and protein-protein/ligand interactions. In a target-based drug discovery project, NMR can serve an important function in hit identification and lead optimization. Fluorine is a valuable probe for evaluating protein conformational changes and protein-ligand interactions. Accumulated studies demonstrate that 19F-NMR can play important roles in fragment- based drug discovery (FBDD) and probing protein-ligand interactions. This review summarizes the application of 19F-NMR in understanding protein-ligand interactions and drug discovery. Several examples are included to show the roles of 19F-NMR in confirming identified hits/leads in the drug discovery process. In addition to identifying hits from fluorinecontaining compound libraries, 19F-NMR will play an important role in drug discovery by providing a fast and robust way in novel hit identification. This technique can be used for ranking compounds with different binding affinities and is particularly useful for screening competitive compounds when a reference ligand is available.
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Affiliation(s)
- CongBao Kang
- Experimental Drug Development Centre (EDDC), Agency for Science, Technology and Research (A*STAR), 10 Biopolis Road, #05-01, Singapore, 138670, Singapore
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26
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Liang R, Xie J, Zhang C, Zhang M, Huang H, Huo H, Cao X, Niu B. Identifying Cancer Targets Based on Machine Learning Methods via Chou's 5-steps Rule and General Pseudo Components. Curr Top Med Chem 2019; 19:2301-2317. [PMID: 31622219 DOI: 10.2174/1568026619666191016155543] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2019] [Revised: 07/19/2019] [Accepted: 08/26/2019] [Indexed: 01/09/2023]
Abstract
In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of 'big data' derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.
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Affiliation(s)
- Ruirui Liang
- School of Life Sciences, Shanghai University, Shanghai, 200444, China
| | - Jiayang Xie
- School of Life Sciences, Shanghai University, Shanghai, 200444, China
| | - Chi Zhang
- Foshan Huaxia Eye Hospital, Huaxia Eye Hospital Group, Foshan 528000, China
| | - Mengying Zhang
- School of Life Sciences, Shanghai University, Shanghai, 200444, China
| | - Hai Huang
- School of Life Sciences, Shanghai University, Shanghai, 200444, China
| | - Haizhong Huo
- Department of General Surgery, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Xin Cao
- Zhongshan Hospital, Institute of Clinical Science, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Bing Niu
- School of Life Sciences, Shanghai University, Shanghai, 200444, China
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27
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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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28
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29
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Du X, Diao Y, Liu H, Li S. MsDBP: Exploring DNA-Binding Proteins by Integrating Multiscale Sequence Information via Chou’s Five-Step Rule. J Proteome Res 2019; 18:3119-3132. [DOI: 10.1021/acs.jproteome.9b00226] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Xiuquan Du
- The School of Computer Science and Technology, Anhui University, Hefei, Anhui, China
| | - Yanyu Diao
- The School of Computer Science and Technology, Anhui University, Hefei, Anhui, China
| | - Heng Liu
- Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Shuo Li
- Department of Medical Imaging, Western University, London, ON N6A 3K7, Canada
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30
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Zhou GP, Li J. Medicinal Chemistry Driven by the Development of System Biology & Cheminformatics. Med Chem 2019; 15:441-442. [DOI: 10.2174/157340641505190506125340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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31
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The preliminary efficacy evaluation of the CTLA-4-Ig treatment against Lupus nephritis through in-silico analyses. J Theor Biol 2019; 471:74-81. [DOI: 10.1016/j.jtbi.2019.03.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 03/22/2019] [Indexed: 01/04/2023]
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32
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Niu B, Liang C, Lu Y, Zhao M, Chen Q, Zhang Y, Zheng L, Chou KC. Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks. Genomics 2019; 112:837-847. [PMID: 31150762 DOI: 10.1016/j.ygeno.2019.05.024] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 05/25/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND Glioma is the most lethal nervous system cancer. Recent studies have made great efforts to study the occurrence and development of glioma, but the molecular mechanisms are still unclear. This study was designed to reveal the molecular mechanisms of glioma based on protein-protein interaction network combined with machine learning methods. Key differentially expressed genes (DEGs) were screened and selected by using the protein-protein interaction (PPI) networks. RESULTS As a result, 19 genes between grade I and grade II, 21 genes between grade II and grade III, and 20 genes between grade III and grade IV. Then, five machine learning methods were employed to predict the gliomas stages based on the selected key genes. After comparison, Complement Naive Bayes classifier was employed to build the prediction model for grade II-III with accuracy 72.8%. And Random forest was employed to build the prediction model for grade I-II and grade III-VI with accuracy 97.1% and 83.2%, respectively. Finally, the selected genes were analyzed by PPI networks, Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and the results improve our understanding of the biological functions of select DEGs involved in glioma growth. We expect that the key genes expressed have a guiding significance for the occurrence of gliomas or, at the very least, that they are useful for tumor researchers. CONCLUSION Machine learning combined with PPI networks, GO and KEGG analyses of selected DEGs improve our understanding of the biological functions involved in glioma growth.
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Affiliation(s)
- Bing Niu
- School of Life Sciences, Shanghai University, Shanghai 200444, China; Gordon Life Science Institute, Boston, MA 02478, USA.
| | - Chaofeng Liang
- Department of Neurosurgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yi Lu
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Manman Zhao
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Qin Chen
- School of Life Sciences, Shanghai University, Shanghai 200444, China.
| | - Yuhui Zhang
- Renji Hospital, Medical School, Shanghai Jiaotong University, 160 Pujian Rd, New Pudong District, Shanghai 200127, China; Changhai Hospital, Second Military Medical University, Shanghai 200433, China.
| | - Linfeng Zheng
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China; Department of Radiology, Shanghai First People's Hospital, Baoshan Branch, Shanghai 200940, China.
| | - Kuo-Chen Chou
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; Gordon Life Science Institute, Boston, MA 02478, USA.
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Barukab O, Khan YD, Khan SA, Chou KC. iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou's 5-steps Rule and Pseudo Components. Curr Genomics 2019; 20:306-320. [PMID: 32030089 PMCID: PMC6983959 DOI: 10.2174/1389202920666190819091609] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 08/04/2019] [Accepted: 08/06/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND The amino acid residues, in protein, undergo post-translation modification (PTM) during protein synthesis, a process of chemical and physical change in an amino acid that in turn alters behavioral properties of proteins. Tyrosine sulfation is a ubiquitous posttranslational modification which is known to be associated with regulation of various biological functions and pathological pro-cesses. Thus its identification is necessary to understand its mechanism. Experimental determination through site-directed mutagenesis and high throughput mass spectrometry is a costly and time taking process, thus, the reliable computational model is required for identification of sulfotyrosine sites. METHODOLOGY In this paper, we present a computational model for the prediction of the sulfotyrosine sites named iSulfoTyr-PseAAC in which feature vectors are constructed using statistical moments of protein amino acid sequences and various position/composition relative features. These features are in-corporated into PseAAC. The model is validated by jackknife, cross-validation, self-consistency and in-dependent testing. RESULTS Accuracy determined through validation was 93.93% for jackknife test, 95.16% for cross-validation, 94.3% for self-consistency and 94.3% for independent testing. CONCLUSION The proposed model has better performance as compared to the existing predictors, how-ever, the accuracy can be improved further, in future, due to increasing number of sulfotyrosine sites in proteins.
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Affiliation(s)
| | | | - Sher Afzal Khan
- Address correspondence to this author at the Department of Information Technology, Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, P.O. Box 344, Rabigh, 21911, Saudi Arabia; and Department of Computer Sciences, Abdul Wali Khan University, Mardan, Pakistan; E-mail:
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Ilyas S, Hussain W, Ashraf A, Khan YD, Khan SA, Chou KC. iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC via Chou's 5-steps Rule. Curr Genomics 2019; 20:275-292. [PMID: 32030087 PMCID: PMC6983956 DOI: 10.2174/1389202920666190809095206] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 07/02/2019] [Accepted: 07/26/2019] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Methylation is one of the most important post-translational modifications in the human body which usually arises on lysine among the most intensely modified residues. It performs a dynamic role in numerous biological procedures, such as regulation of gene expression, regulation of protein function and RNA processing. Therefore, to identify lysine methylation sites is an important challenge as some experimental procedures are time-consuming. OBJECTIVE Herein, we propose a computational predictor named iMethylK_pseAAC to identify lysine methylation sites. METHODS Firstly, we constructed feature vectors based on PseAAC using position and composition rel-ative features and statistical moments. A neural network is trained based on the extracted features. The performance of the proposed method is then validated using cross-validation and jackknife testing. RESULTS The objective evaluation of the predictor showed accuracy of 96.7% for self-consistency, 91.61% for 10-fold cross-validation and 93.42% for jackknife testing. CONCLUSION It is concluded that iMethylK_pseAAC outperforms the counterparts to identify lysine methylation sites such as iMethyl_pseACC, BPB_pPMS and PMeS.
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Affiliation(s)
| | | | | | - Yaser Daanial Khan
- Address correspondence to this author at the Department of Computer Science, School of Systems and Technology, University of Management and Technology, P.O. Box 10033, C-II, Johar Town, Lahore, Pakistan; Tel: +923054440271; E-mail:
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35
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Pan Q, Guo Y, Guo L, Liao S, Zhao C, Wang S, Liu HF. Mechanistic Insights of Chemicals and Drugs as Risk Factors for Systemic Lupus Erythematosus. Curr Med Chem 2019; 27:5175-5188. [PMID: 30947650 DOI: 10.2174/0929867326666190404140658] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 03/25/2019] [Accepted: 03/27/2019] [Indexed: 12/21/2022]
Abstract
Systemic Lupus Erythematosus (SLE) is a chronic and relapsing heterogenous autoimmune disease that primarily affects women of reproductive age. Genetic and environmental risk factors are involved in the pathogenesis of SLE, and susceptibility genes have recently been identified. However, as gene therapy is far from clinical application, further investigation of environmental risk factors could reveal important therapeutic approaches. We systematically explored two groups of environmental risk factors: chemicals (including silica, solvents, pesticides, hydrocarbons, heavy metals, and particulate matter) and drugs (including procainamide, hydralazine, quinidine, Dpenicillamine, isoniazid, and methyldopa). Furthermore, the mechanisms underlying risk factors, such as genetic factors, epigenetic change, and disrupted immune tolerance, were explored. This review identifies novel risk factors and their underlying mechanisms. Practicable measures for the management of these risk factors will benefit SLE patients and provide potential therapeutic strategies.
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Affiliation(s)
- Qingjun Pan
- Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, 57th South Renmin Road, Zhanjiang 524001, Guangdong, China
| | - Yun Guo
- Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, 57th South Renmin Road, Zhanjiang 524001, Guangdong, China
| | - Linjie Guo
- Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, 57th South Renmin Road, Zhanjiang 524001, Guangdong, China
| | - Shuzhen Liao
- Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, 57th South Renmin Road, Zhanjiang 524001, Guangdong, China
| | - Chunfei Zhao
- Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, 57th South Renmin Road, Zhanjiang 524001, Guangdong, China
| | - Sijie Wang
- Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, 57th South Renmin Road, Zhanjiang 524001, Guangdong, China
| | - Hua-Feng Liu
- Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, 57th South Renmin Road, Zhanjiang 524001, Guangdong, China
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