1
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Perveen G, Alturise F, Alkhalifah T, Daanial Khan Y. Hemolytic-Pred: A machine learning-based predictor for hemolytic proteins using position and composition-based features. Digit Health 2023; 9:20552076231180739. [PMID: 37434723 PMCID: PMC10331097 DOI: 10.1177/20552076231180739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 05/22/2023] [Indexed: 07/13/2023] Open
Abstract
Objective The objective of this study is to propose a novel in-silico method called Hemolytic-Pred for identifying hemolytic proteins based on their sequences, using statistical moment-based features, along with position-relative and frequency-relative information. Methods Primary sequences were transformed into feature vectors using statistical and position-relative moment-based features. Varying machine learning algorithms were employed for classification. Computational models were rigorously evaluated using four different validation. The Hemolytic-Pred webserver is available for further analysis at http://ec2-54-160-229-10.compute-1.amazonaws.com/. Results XGBoost outperformed the other six classifiers with an accuracy value of 0.99, 0.98, 0.97, and 0.98 for self-consistency test, 10-fold cross-validation, Jackknife test, and independent set test, respectively. The proposed method with the XGBoost classifier is a workable and robust solution for predicting hemolytic proteins efficiently and accurately. Conclusions The proposed method of Hemolytic-Pred with XGBoost classifier is a reliable tool for the timely identification of hemolytic cells and diagnosis of various related severe disorders. The application of Hemolytic-Pred can yield profound benefits in the medical field.
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Affiliation(s)
- Gulnaz Perveen
- Department of Computer Science, School
of Systems and Technology, University of Management and Technology, Lahore, Punjab,
Pakistan
| | - Fahad Alturise
- Department of Computer, College of
Science and Arts in Ar Rass Qassim University, Buraidah, Qassim, Saudi Arabia
| | - Tamim Alkhalifah
- Department of Computer, College of
Science and Arts in Ar Rass Qassim University, Buraidah, Qassim, Saudi Arabia
| | - Yaser Daanial Khan
- Department of Computer Science, School
of Systems and Technology, University of Management and Technology, Lahore, Punjab,
Pakistan
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2
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Butt AH, Alkhalifah T, Alturise F, Khan YD. A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns. Sci Rep 2022; 12:15183. [PMID: 36071071 PMCID: PMC9452539 DOI: 10.1038/s41598-022-19099-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 08/24/2022] [Indexed: 11/26/2022] Open
Abstract
Enhancers regulate gene expression, by playing a crucial role in the synthesis of RNAs and proteins. They do not directly encode proteins or RNA molecules. In order to control gene expression, it is important to predict enhancers and their potency. Given their distance from the target gene, lack of common motifs, and tissue/cell specificity, enhancer regions are thought to be difficult to predict in DNA sequences. Recently, a number of bioinformatics tools were created to distinguish enhancers from other regulatory components and to pinpoint their advantages. However, because the quality of its prediction method needs to be improved, its practical application value must also be improved. Based on nucleotide composition and statistical moment-based features, the current study suggests a novel method for identifying enhancers and non-enhancers and evaluating their strength. The proposed study outperformed state-of-the-art techniques using fivefold and tenfold cross-validation in terms of accuracy. The accuracy from the current study results in 86.5% and 72.3% in enhancer site and its strength prediction respectively. The results of the suggested methodology point to the potential for more efficient and successful outcomes when statistical moment-based features are used. The current study's source code is available to the research community at https://github.com/csbioinfopk/enpred.
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Affiliation(s)
- Ahmad Hassan Butt
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Tamim Alkhalifah
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Saudi Arabia.
| | - Fahad Alturise
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Saudi Arabia
| | - Yaser Daanial Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
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3
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Qiu WR, Guan MY, Wang QK, Lou LL, Xiao X. Identifying Pupylation Proteins and Sites by Incorporating Multiple Methods. Front Endocrinol (Lausanne) 2022; 13:849549. [PMID: 35557849 PMCID: PMC9088680 DOI: 10.3389/fendo.2022.849549] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 03/07/2022] [Indexed: 11/20/2022] Open
Abstract
Pupylation is an important posttranslational modification in proteins and plays a key role in the cell function of microorganisms; an accurate prediction of pupylation proteins and specified sites is of great significance for the study of basic biological processes and development of related drugs since it would greatly save experimental costs and improve work efficiency. In this work, we first constructed a model for identifying pupylation proteins. To improve the pupylation protein prediction model, the KNN scoring matrix model based on functional domain GO annotation and the Word Embedding model were used to extract the features and Random Under-sampling (RUS) and Synthetic Minority Over-sampling Technique (SMOTE) were applied to balance the dataset. Finally, the balanced data sets were input into Extreme Gradient Boosting (XGBoost). The performance of 10-fold cross-validation shows that accuracy (ACC), Matthew's correlation coefficient (MCC), and area under the ROC curve (AUC) are 95.23%, 0.8100, and 0.9864, respectively. For the pupylation site prediction model, six feature extraction codes (i.e., TPC, AAI, One-hot, PseAAC, CKSAAP, and Word Embedding) served to extract protein sequence features, and the chi-square test was employed for feature selection. Rigorous 10-fold cross-validations indicated that the accuracies are very high and outperformed its existing counterparts. Finally, for the convenience of researchers, PUP-PS-Fuse has been established at https://bioinfo.jcu.edu.cn/PUP-PS-Fuse and http://121.36.221.79/PUP-PS-Fuse/as a backup.
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Affiliation(s)
| | | | | | | | - Xuan Xiao
- *Correspondence: Wang-Ren Qiu, ; Xuan Xiao,
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4
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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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5
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Khan YD, Khan NS, Naseer S, Butt AH. iSUMOK-PseAAC: prediction of lysine sumoylation sites using statistical moments and Chou's PseAAC. PeerJ 2021; 9:e11581. [PMID: 34430072 PMCID: PMC8349168 DOI: 10.7717/peerj.11581] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 05/19/2021] [Indexed: 01/25/2023] Open
Abstract
Sumoylation is the post-translational modification that is involved in the adaption of the cells and the functional properties of a large number of proteins. Sumoylation has key importance in subcellular concentration, transcriptional synchronization, chromatin remodeling, response to stress, and regulation of mitosis. Sumoylation is associated with developmental defects in many human diseases such as cancer, Huntington's, Alzheimer's, Parkinson's, Spin cerebellar ataxia 1, and amyotrophic lateral sclerosis. The covalent bonding of Sumoylation is essential to inheriting part of the operative characteristics of some other proteins. For that reason, the prediction of the Sumoylation site has significance in the scientific community. A novel and efficient technique is proposed to predict the Sumoylation sites in proteins by incorporating Chou's Pseudo Amino Acid Composition (PseAAC) with statistical moments-based features. The outcomes from the proposed system using 10 fold cross-validation testing are 94.51%, 94.24%, 94.79% and 0.8903% accuracy, sensitivity, specificity and MCC, respectively. The performance of the proposed system is so far the best in comparison to the other state-of-the-art methods. The codes for the current study are available on the GitHub repository using the link: https://github.com/csbioinfopk/iSumoK-PseAAC.
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Affiliation(s)
- Yaser Daanial Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Punjab, Pakistan
| | - Nabeel Sabir Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Punjab, Pakistan
| | - Sheraz Naseer
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Punjab, Pakistan
| | - Ahmad Hassan Butt
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Punjab, Pakistan
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6
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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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7
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Khan YD, Alzahrani E, Alghamdi W, Ullah MZ. Sequence-based Identification of Allergen Proteins Developed by Integration of PseAAC and Statistical Moments via 5-Step Rule. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200424085947] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Background:
Allergens are antigens that can stimulate an atopic type I human
hypersensitivity reaction by an immunoglobulin E (IgE) reaction. Some proteins are naturally
allergenic than others. The challenge for toxicologists is to identify properties that allow proteins
to cause allergic sensitization and allergic diseases. The identification of allergen proteins is a very
critical and pivotal task. The experimental identification of protein functions is a hectic, laborious
and costly task; therefore, computer scientists have proposed various methods in the field of
computational biology and bioinformatics using various data science approaches. Objectives:
Herein, we report a novel predictor for the identification of allergen proteins.
Methods:
For feature extraction, statistical moments and various position-based features have been
incorporated into Chou’s pseudo amino acid composition (PseAAC), and are used for training of a
neural network.
Results:
The predictor is validated through 10-fold cross-validation and Jackknife testing, which
gave 99.43% and 99.87% accurate results.
Conclusions:
Thus, the proposed predictor can help in predicting the Allergen proteins in an
efficient and accurate way and can provide baseline data for the discovery of new drugs and
biomarkers.
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Affiliation(s)
- Yaser Daanial Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, C II Johar Town, Lahore 54770, Pakistan
| | - Ebraheem Alzahrani
- Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia
| | - Wajdi Alghamdi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box 80221, Jeddah, Saudi Arabia
| | - Malik Zaka Ullah
- Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia
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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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9
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Amanat S, Ashraf A, Hussain W, Rasool N, Khan YD. Identification of Lysine Carboxylation Sites in Proteins by Integrating Statistical Moments and Position Relative Features via General PseAAC. Curr Bioinform 2020. [DOI: 10.2174/1574893614666190723114923] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Background:
Carboxylation is one of the most biologically important post-translational
modifications and occurs on lysine, arginine, and glutamine residues of a protein. Among all these
three, the covalent attachment of the carboxyl group with the lysine side chain is the most frequent
and biologically important type of carboxylation. For studying such biological functions, it is essential
to correctly determine the lysine sites sensitive to carboxylation.
Objective:
Herein, we present a computational model for the prediction of the carboxylysine site
which is based on machine learning.
Methods:
Various position and composition relative features have been incorporated into the Pse-
AAC for construction of feature vectors and a neural network is employed as a classifier. The
model is validated by jackknife, cross-validation, self-consistency, and independent testing.
Results:
The results of the self-consistency test elaborated that model has 99.76% Acc, 99.76% Sp,
99.76% Sp, and 0.99 MCC. Using the jackknife method, prediction model validation gave 97.07%
Acc, while for 10-fold cross-validation, prediction model validation gave 95.16% Acc.
Conclusion:
The results of independent dataset testing were 94.3% which illustrated that the proposed
model has better performance as compared to the existing model PreLysCar; however, the
accuracy can be improved further, in the future, due to the increasing number of carboxylysine
sites in proteins.
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Affiliation(s)
- Saba Amanat
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Adeel Ashraf
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Waqar Hussain
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Nouman Rasool
- Department of Life Sciences, School of Science University of Management and Technology, Lahore, Pakistan
| | - Yaser D. Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
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10
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Gachpazan M, Kashani H, Khazaei M, Hassanian SM, Rezayi M, Asgharzadeh F, Ghayour-Mobarhan M, Ferns GA, Avan A. The Impact of Statin Therapy on the Survival of Patients with Gastrointestinal Cancer. Curr Drug Targets 2020; 20:738-747. [PMID: 30539694 DOI: 10.2174/1389450120666181211165449] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 10/25/2018] [Accepted: 12/05/2018] [Indexed: 12/13/2022]
Abstract
Statins are 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors that may play an important role in the evolution of cancers, due to their effects on cancer cell metabolism. Statins affect several potential pathways, including cell proliferation, angiogenesis, apoptosis and metastasis. The number of trials assessing the putative clinical benefits of statins in cancer is increasing. Currently, there are several trials listed on the global trial identifier website clinicaltrials.gov. Given the compelling evidence from these trials in a variety of clinical settings, there have been calls for a clinical trial of statins in the adjuvant gastrointestinal cancer setting. However, randomized controlled trials on specific cancer types in relation to statin use, as well as studies on populations without a clinical indication for using statins, have elucidated some potential underlying biological mechanisms, and the investigation of different statins is probably warranted. It would be useful for these trials to incorporate the assessment of tumour biomarkers predictive of statin response in their design. This review summarizes the recent preclinical and clinical studies that assess the application of statins in the treatment of gastrointestinal cancers with particular emphasize on their association with cancer risk.
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Affiliation(s)
- Meysam Gachpazan
- Metabolic syndrome Research center, Mashhad University of Medical Sciences, Mashhad, Iran.,Department of Modern Sciences and Technologies; Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hoda Kashani
- Department of Modern Sciences and Technologies; Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Khazaei
- Metabolic syndrome Research center, Mashhad University of Medical Sciences, Mashhad, Iran.,Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Seyed Mahdi Hassanian
- Metabolic syndrome Research center, Mashhad University of Medical Sciences, Mashhad, Iran.,Department of Medical Biochemistry; Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Rezayi
- Metabolic syndrome Research center, Mashhad University of Medical Sciences, Mashhad, Iran.,Department of Modern Sciences and Technologies; Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fereshteh Asgharzadeh
- Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Ghayour-Mobarhan
- Metabolic syndrome Research center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon A Ferns
- Brighton & Sussex Medical School, Division of Medical Education, Falmer, Brighton, Sussex BN1 9PH, United Kingdom
| | - Amir Avan
- Metabolic syndrome Research center, Mashhad University of Medical Sciences, Mashhad, Iran.,Department of Modern Sciences and Technologies; Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.,Cancer Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
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11
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Hu J, Zhou XG, Zhu YH, Yu DJ, Zhang GJ. TargetDBP: Accurate DNA-Binding Protein Prediction Via Sequence-Based Multi-View Feature Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1419-1429. [PMID: 30668479 DOI: 10.1109/tcbb.2019.2893634] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Accurately identifying DNA-binding proteins (DBPs) from protein sequence information is an important but challenging task for protein function annotations. In this paper, we establish a novel computational method, named TargetDBP, for accurately targeting DBPs from primary sequences. In TargetDBP, four single-view features, i.e., AAC (Amino Acid Composition), PsePSSM (Pseudo Position-Specific Scoring Matrix), PsePRSA (Pseudo Predicted Relative Solvent Accessibility), and PsePPDBS (Pseudo Predicted Probabilities of DNA-Binding Sites), are first extracted to represent different base features, respectively. Second, differential evolution algorithm is employed to learn the weights of four base features. Using the learned weights, we weightedly combine these base features to form the original super feature. An excellent subset of the super feature is then selected by using a suitable feature selection algorithm SVM-REF+CBR (Support Vector Machine Recursive Feature Elimination with Correlation Bias Reduction). Finally, the prediction model is learned via using support vector machine on the selected feature subset. We also construct a new gold-standard and non-redundant benchmark dataset from PDB database to evaluate and compare the proposed TargetDBP with other existing predictors. On this new dataset, TargetDBP can achieve higher performance than other state-of-the-art predictors. The TargetDBP web server and datasets are freely available at http://csbio.njust.edu.cn/bioinf/targetdbp/ for academic use.
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12
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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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13
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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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14
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15
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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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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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Liu XX, Chou KC. pLoc_Deep-mGneg: Predict Subcellular Localization of Gram Negative Bacterial Proteins by Deep Learning. ACTA ACUST UNITED AC 2020. [DOI: 10.4236/abb.2020.115011] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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21
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Ju Z, Wang SY. Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou's 5-steps rule and general pseudo components. Genomics 2020; 112:859-866. [DOI: 10.1016/j.ygeno.2019.05.027] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2019] [Revised: 05/13/2019] [Accepted: 05/30/2019] [Indexed: 11/30/2022]
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22
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Lu Z, Chou KC. pLoc_Deep-mGpos: Predict Subcellular Localization of Gram Positive Bacteria Proteins by Deep Learning. ACTA ACUST UNITED AC 2020. [DOI: 10.4236/jbise.2020.135005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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23
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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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24
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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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25
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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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26
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Chou KC. Advances in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs. Curr Med Chem 2019; 26:4918-4943. [PMID: 31060481 DOI: 10.2174/0929867326666190507082559] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 01/29/2019] [Accepted: 01/31/2019] [Indexed: 12/16/2022]
Abstract
The smallest unit of life is a cell, which contains numerous protein molecules. Most
of the functions critical to the cell’s survival are performed by these proteins located in its different
organelles, usually called ‘‘subcellular locations”. Information of subcellular localization
for a protein can provide useful clues about its function. To reveal the intricate pathways at the
cellular level, knowledge of the subcellular localization of proteins in a cell is prerequisite.
Therefore, one of the fundamental goals in molecular cell biology and proteomics is to determine
the subcellular locations of proteins in an entire cell. It is also indispensable for prioritizing
and selecting the right targets for drug development. Unfortunately, it is both timeconsuming
and costly to determine the subcellular locations of proteins purely based on experiments.
With the avalanche of protein sequences generated in the post-genomic age, it is highly
desired to develop computational methods for rapidly and effectively identifying the subcellular
locations of uncharacterized proteins based on their sequences information alone. Actually,
considerable progresses have been achieved in this regard. This review is focused on those
methods, which have the capacity to deal with multi-label proteins that may simultaneously
exist in two or more subcellular location sites. Protein molecules with this kind of characteristic
are vitally important for finding multi-target drugs, a current hot trend in drug development.
Focused in this review are also those methods that have use-friendly web-servers established so
that the majority of experimental scientists can use them to get the desired results without the
need to go through the detailed mathematics involved.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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27
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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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28
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Abstract
The smallest unit of life is a cell, which contains numerous protein molecules. Most
of the functions critical to the cell’s survival are performed by these proteins located in its different
organelles, usually called ‘‘subcellular locations”. Information of subcellular localization
for a protein can provide useful clues about its function. To reveal the intricate pathways at the
cellular level, knowledge of the subcellular localization of proteins in a cell is prerequisite.
Therefore, one of the fundamental goals in molecular cell biology and proteomics is to determine
the subcellular locations of proteins in an entire cell. It is also indispensable for prioritizing
and selecting the right targets for drug development. Unfortunately, it is both timeconsuming
and costly to determine the subcellular locations of proteins purely based on experiments.
With the avalanche of protein sequences generated in the post-genomic age, it is highly
desired to develop computational methods for rapidly and effectively identifying the subcellular
locations of uncharacterized proteins based on their sequences information alone. Actually,
considerable progresses have been achieved in this regard. This review is focused on those
methods, which have the capacity to deal with multi-label proteins that may simultaneously
exist in two or more subcellular location sites. Protein molecules with this kind of characteristic
are vitally important for finding multi-target drugs, a current hot trend in drug development.
Focused in this review are also those methods that have use-friendly web-servers established so
that the majority of experimental scientists can use them to get the desired results without the
need to go through the detailed mathematics involved.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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29
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Lan J, Liu Z, Liao C, Merkler DJ, Han Q, Li J. A Study for Therapeutic Treatment against Parkinson's Disease via Chou's 5-steps Rule. Curr Top Med Chem 2019; 19:2318-2333. [PMID: 31629395 DOI: 10.2174/1568026619666191019111528] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 08/05/2019] [Accepted: 08/22/2019] [Indexed: 11/22/2022]
Abstract
The enzyme L-DOPA decarboxylase (DDC), also called aromatic-L-amino-acid decarboxylase, catalyzes the biosynthesis of dopamine, serotonin, and trace amines. Its deficiency or perturbations in expression result in severe motor dysfunction or a range of neurodegenerative and psychiatric disorders. A DDC substrate, L-DOPA, combined with an inhibitor of the enzyme is still the most effective treatment for symptoms of Parkinson's disease. In this review, we provide an update regarding the structures, functions, and inhibitors of DDC, particularly with regards to the treatment of Parkinson's disease. This information will provide insight into the pharmacological treatment of Parkinson's disease.
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Affiliation(s)
- Jianqiang Lan
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Life and Pharmaceutical Sciences, Hainan University, Haikou, Hainan 570228, China
| | - Zhongqiang Liu
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Life and Pharmaceutical Sciences, Hainan University, Haikou, Hainan 570228, China
| | - Chenghong Liao
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Life and Pharmaceutical Sciences, Hainan University, Haikou, Hainan 570228, China
| | - David J Merkler
- Department of Chemistry, University of South Florida, Tampa, FL, 33620, United States
| | - Qian Han
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Life and Pharmaceutical Sciences, Hainan University, Haikou, Hainan 570228, China
| | - Jianyong Li
- Department of Biochemistry, Virginia Tech, Blacksburg, VA 24061, United States
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30
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Identifying DNase I hypersensitive sites using multi-features fusion and F-score features selection via Chou's 5-steps rule. Biophys Chem 2019; 253:106227. [DOI: 10.1016/j.bpc.2019.106227] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 07/04/2019] [Accepted: 07/10/2019] [Indexed: 01/12/2023]
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31
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Zhang M, Li F, Marquez-Lago TT, Leier A, Fan C, Kwoh CK, Chou KC, Song J, Jia C. MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters. Bioinformatics 2019; 35:2957-2965. [PMID: 30649179 PMCID: PMC6736106 DOI: 10.1093/bioinformatics/btz016] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 12/09/2018] [Accepted: 01/05/2019] [Indexed: 12/22/2022] Open
Abstract
MOTIVATION Promoters are short DNA consensus sequences that are localized proximal to the transcription start sites of genes, allowing transcription initiation of particular genes. However, the precise prediction of promoters remains a challenging task because individual promoters often differ from the consensus at one or more positions. RESULTS In this study, we present a new multi-layer computational approach, called MULTiPly, for recognizing promoters and their specific types. MULTiPly took into account the sequences themselves, including both local information such as k-tuple nucleotide composition, dinucleotide-based auto covariance and global information of the entire samples based on bi-profile Bayes and k-nearest neighbour feature encodings. Specifically, the F-score feature selection method was applied to identify the best unique type of feature prediction results, in combination with other types of features that were subsequently added to further improve the prediction performance of MULTiPly. Benchmarking experiments on the benchmark dataset and comparisons with five state-of-the-art tools show that MULTiPly can achieve a better prediction performance on 5-fold cross-validation and jackknife tests. Moreover, the superiority of MULTiPly was also validated on a newly constructed independent test dataset. MULTiPly is expected to be used as a useful tool that will facilitate the discovery of both general and specific types of promoters in the post-genomic era. AVAILABILITY AND IMPLEMENTATION The MULTiPly webserver and curated datasets are freely available at http://flagshipnt.erc.monash.edu/MULTiPly/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Meng Zhang
- School of Science, Dalian Maritime University, Dalian, China
| | - Fuyi Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
| | - Tatiana T Marquez-Lago
- Department of Genetics, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - André Leier
- Department of Genetics, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Cunshuo Fan
- College of Information Engineering, Northwest A&F University, Yangling, China
| | - Chee Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | | | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
- ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC, Australia
| | - Cangzhi Jia
- School of Science, Dalian Maritime University, Dalian, China
- College of Information Engineering, Northwest A&F University, Yangling, China
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32
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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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33
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34
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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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35
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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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36
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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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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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Han Q, Yang C, Lu J, Zhang Y, Li J. Metabolism of Oxalate in Humans: A Potential Role Kynurenine Aminotransferase/Glutamine Transaminase/Cysteine Conjugate Beta-lyase Plays in Hyperoxaluria. Curr Med Chem 2019; 26:4944-4963. [PMID: 30907303 DOI: 10.2174/0929867326666190325095223] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 02/17/2019] [Accepted: 02/22/2019] [Indexed: 11/22/2022]
Abstract
Hyperoxaluria, excessive urinary oxalate excretion, is a significant health problem worldwide. Disrupted oxalate metabolism has been implicated in hyperoxaluria and accordingly, an enzymatic disturbance in oxalate biosynthesis can result in the primary hyperoxaluria. Alanine glyoxylate aminotransferase-1 and glyoxylate reductase, the enzymes involving glyoxylate (precursor for oxalate) metabolism, have been related to primary hyperoxalurias. Some studies suggest that other enzymes such as glycolate oxidase and alanine glyoxylate aminotransferase-2 might be associated with primary hyperoxaluria as well, but evidence of a definitive link is not strong between the clinical cases and gene mutations. There are still some idiopathic hyperoxalurias, which require a further study for the etiologies. Some aminotransferases, particularly kynurenine aminotransferases, can convert glyoxylate to glycine. Based on biochemical and structural characteristics, expression level, subcellular localization of some aminotransferases, a number of them appear able to catalyze the transamination of glyoxylate to glycine more efficiently than alanine glyoxylate aminotransferase-1. The aim of this minireview is to explore other undermining causes of primary hyperoxaluria and stimulate research toward achieving a comprehensive understanding of underlying mechanisms leading to the disease. Herein, we reviewed all aminotransferases in the liver for their functions in glyoxylate metabolism. Particularly, kynurenine aminotransferase-I and III were carefully discussed regarding their biochemical and structural characteristics, cellular localization, and enzyme inhibition. Kynurenine aminotransferase-III is, so far, the most efficient putative mitochondrial enzyme to transaminate glyoxylate to glycine in mammalian livers, might be an interesting enzyme to look over in hyperoxaluria etiology of primary hyperoxaluria and should be carefully investigated for its involvement in oxalate metabolism.
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Affiliation(s)
- Qian Han
- Key Laboratory of Tropical Biological Resources of Ministry of Education, Hainan University, Haikou, Hainan 570228. China
| | - Cihan Yang
- Key Laboratory of Tropical Biological Resources of Ministry of Education, Hainan University, Haikou, Hainan 570228. China
| | - Jun Lu
- Central South University Xiangya School of Medicine Affiliated Haikou People's Hospital, Haikou, Hainan 570208. China
| | - Yinai Zhang
- Central South University Xiangya School of Medicine Affiliated Haikou People's Hospital, Haikou, Hainan 570208. China
| | - Jianyong Li
- Department of Biochemistry, Virginia Tech, Blacksburg, VA 24061. United States
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41
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Taju SW, Ou Y. DeepIon: Deep learning approach for classifying ion transporters and ion channels from membrane proteins. J Comput Chem 2019; 40:1521-1529. [DOI: 10.1002/jcc.25805] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 01/19/2019] [Accepted: 01/30/2019] [Indexed: 01/20/2023]
Affiliation(s)
- Semmy Wellem Taju
- Department of Computer Science and EngineeringYuan Ze University Chung‐Li 32003 Taiwan
| | - Yu‐Yen Ou
- Department of Computer Science and EngineeringYuan Ze University Chung‐Li 32003 Taiwan
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42
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Karasev V. A model of molecular vector machine of proteins. Biosystems 2019; 180:7-18. [PMID: 30878497 DOI: 10.1016/j.biosystems.2019.02.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 01/15/2019] [Accepted: 02/02/2019] [Indexed: 01/10/2023]
Abstract
Based on the analysis of NiH⋯Oi-4C bond region of the proteins pentafragments using the quantum chemistry representations, a group of 20 vectors of action has been isolated. These vectors of action are reconstructed by a group of 20 side chains of amino acids being the irreducible representations of vectors. As a result, a model of molecular vector machine of proteins (MVM) has been proposed. This machine includes proteins pentafragments, a system of 20 vectors inside dodecahedron coming out of the Oi-4 atom as from the center and tetrahedral αi-carbon atom with the side chains of amino acids that change as the polypeptide chain grows. A model with the structured canonical set of amino acids based on the principles of antisymmetry was transferred onto the dodecahedron as an MVM element (Karasev et al., 2005; Karasev et al., 2007). The perspective of MVM application for predicting and designing the secondary structure of proteins has been discussed.
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Affiliation(s)
- Vladimir Karasev
- St. Petersburg State Electrotechnical University, Prof. Popov str. 5, 197376, St. Petersburg, Russia.
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43
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SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins. Anal Biochem 2019; 568:14-23. [DOI: 10.1016/j.ab.2018.12.019] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 12/19/2018] [Accepted: 12/22/2018] [Indexed: 02/06/2023]
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Ahmad A, Shatabda S. EPAI-NC: Enhanced prediction of adenosine to inosine RNA editing sites using nucleotide compositions. Anal Biochem 2019; 569:16-21. [DOI: 10.1016/j.ab.2019.01.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 01/03/2019] [Accepted: 01/11/2019] [Indexed: 01/24/2023]
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45
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Romero-Molina S, Ruiz-Blanco YB, Harms M, Münch J, Sanchez-Garcia E. PPI-Detect: A support vector machine model for sequence-based prediction of protein-protein interactions. J Comput Chem 2019; 40:1233-1242. [PMID: 30768790 DOI: 10.1002/jcc.25780] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 11/29/2018] [Accepted: 12/29/2018] [Indexed: 12/18/2022]
Abstract
The prediction of peptide-protein or protein-protein interactions (PPI) is a challenging task, especially if amino acid sequences are the only information available. Machine learning methods allow us to exploit the information content in PPI datasets. However, the numerical codification of these datasets often influences the performance of data mining approaches. Here, we introduce a procedure for the general-purpose numerical codification of polypeptides. This procedure transforms pairs of amino acid sequences into a machine learning-friendly vector, whose elements represent numerical descriptors of residues in proteins. We used this numerical encoding procedure for the development of a support vector machine model (PPI-Detect), which allows predicting whether two proteins will interact or not. PPI-Detect (https://ppi-detect.zmb.uni-due.de/) outperforms state of the art sequence-based predictors of PPI. We employed PPI-Detect for the analysis of derivatives of EPI-X4, an endogenous peptide inhibitor of CXCR4, a G-protein-coupled receptor. There, we identified with high accuracy those peptides which bind better than EPI-X4 to the receptor. Also using PPI-Detect, we designed a novel peptide and then experimentally established its anti-CXCR4 activity. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- Sandra Romero-Molina
- Center of Medical Biotechnology, University of Duisburg-Essen, Duisburg, Germany
| | - Yasser B Ruiz-Blanco
- Center of Medical Biotechnology, University of Duisburg-Essen, Duisburg, Germany
| | - Mirja Harms
- Institute of Molecular Virology, Ulm University Medical Center, Ulm, Germany
| | - Jan Münch
- Institute of Molecular Virology, Ulm University Medical Center, Ulm, Germany.,Core Facility Functional Peptidomics, Ulm University Medical Center, Ulm, Germany
| | - Elsa Sanchez-Garcia
- Center of Medical Biotechnology, University of Duisburg-Essen, Duisburg, Germany
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Tian B, Wu X, Chen C, Qiu W, Ma Q, Yu B. Predicting protein–protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach. J Theor Biol 2019; 462:329-346. [DOI: 10.1016/j.jtbi.2018.11.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 11/08/2018] [Accepted: 11/15/2018] [Indexed: 12/26/2022]
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47
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Khan YD, Jamil M, Hussain W, Rasool N, Khan SA, Chou KC. pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments. J Theor Biol 2019; 463:47-55. [DOI: 10.1016/j.jtbi.2018.12.015] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Revised: 12/05/2018] [Accepted: 12/11/2018] [Indexed: 02/08/2023]
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MFSC: Multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components. J Theor Biol 2019; 463:99-109. [DOI: 10.1016/j.jtbi.2018.12.017] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 12/02/2018] [Accepted: 12/14/2018] [Indexed: 12/29/2022]
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49
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Jia J, Li X, Qiu W, Xiao X, Chou KC. iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC. J Theor Biol 2019; 460:195-203. [DOI: 10.1016/j.jtbi.2018.10.021] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2018] [Revised: 09/16/2018] [Accepted: 10/08/2018] [Indexed: 01/11/2023]
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50
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Xiao X, Xu ZC, Qiu WR, Wang P, Ge HT, Chou KC. iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition. Genomics 2018; 111:1785-1793. [PMID: 30529532 DOI: 10.1016/j.ygeno.2018.12.001] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 11/20/2018] [Accepted: 12/04/2018] [Indexed: 12/20/2022]
Abstract
The promoter is a regulatory DNA region about 81-1000 base pairs long, usually located near the transcription start site (TSS) along upstream of a given gene. By combining a certain protein called transcription factor, the promoter provides the starting point for regulated gene transcription, and hence plays a vitally important role in gene transcriptional regulation. With explosive growth of DNA sequences in the post-genomic age, it has become an urgent challenge to develop computational method for effectively identifying promoters because the information thus obtained is very useful for both basic research and drug development. Although some prediction methods were developed in this regard, most of them were limited at merely identifying whether a query DNA sequence being of a promoter or not. However, based on their strength-distinct levels for transcriptional activation and expression, promoter should be divided into two categories: strong and weak types. Here a new two-layer predictor, called "iPSW(2L)-PseKNC", was developed by fusing the physicochemical properties of nucleotides and their nucleotide density into PseKNC (pseudo K-tuple nucleotide composition). Its 1st-layer serves to predict whether a query DNA sequence sample is of promoter or not, while its 2nd-layer is able to predict the strength of promoters. It has been observed through rigorous cross-validations that the 1st-layer sub-predictor is remarkably superior to the existing state-of-the-art predictors in identifying the promoters and non-promoters, and that the 2nd-layer sub-predictor can do what is beyond the reach of the existing predictors. Moreover, the web-server for iPSW(2L)-PseKNC has been established at http://www.jci-bioinfo.cn/iPSW(2L)-PseKNC, by which the majority of experimental scientists can easily get the results they need.
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Affiliation(s)
- Xuan Xiao
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China; The Gordon Life Science Institute, Boston, MA 02478, USA.
| | - Zhao-Chun Xu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.
| | - Wang-Ren Qiu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China; The Gordon Life Science Institute, Boston, MA 02478, USA
| | - Peng Wang
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Hui-Ting Ge
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Kuo-Chen Chou
- The Gordon Life Science Institute, Boston, MA 02478, USA; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
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