1
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The Graphical Studies of the Major Molecular Interactions for Neural Cell Adhesion Molecule (NCAM) Polysialylation by Incorporating Wenxiang Diagram into NMR Spectroscopy. Int J Mol Sci 2022; 23:ijms232315128. [PMID: 36499451 PMCID: PMC9736422 DOI: 10.3390/ijms232315128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/24/2022] [Accepted: 11/28/2022] [Indexed: 12/05/2022] Open
Abstract
Polysialylation is a process of polysialic acid (polySia) addition to neural cell adhesion molecule (NCAM), which is associated with tumor cell migration and progression in many metastatic cancers and neurocognition. Polysialylation can be catalyzed by two highly homologous mammalian polysialyltransferases (polySTs), ST8Sia II (STX) and ST8Sia IV (PST). It has been proposed that two polybasic domains, polybasic region (PBR) and polysialyltransferase domain (PSTD) in polySTs, are possible binding sites for the intermolecular interactions of polyST-NCAM and polyST-polySia, respectively, as well as the intramolecular interaction of PSTD-PBR. In this study, Chou's wenxiang diagrams of the PSTD and PBR are used to determine the key amino acids of these intermolecular and intramolecular interactions, and thus it may be helpful for the identification of the crucial amino acids in the polyST and for the understanding of the molecular mechanism of NCAM polysialylation by incorporating the wenxiang diagram and molecular modeling into NMR spectroscopy.
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2
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Nguyen TTD, Ho QT, Le NQK, Phan VD, Ou YY. Use Chou's 5-Steps Rule With Different Word Embedding Types to Boost Performance of Electron Transport Protein Prediction Model. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1235-1244. [PMID: 32750894 DOI: 10.1109/tcbb.2020.3010975] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Living organisms receive necessary energy substances directly from cellular respiration. The completion of electron storage and transportation requires the process of cellular respiration with the aid of electron transport chains. Therefore, the work of deciphering electron transport proteins is inevitably needed. The identification of these proteins with high performance has a prompt dependence on the choice of methods for feature extraction and machine learning algorithm. In this study, protein sequences served as natural language sentences comprising words. The nominated word embedding-based feature sets, hinged on the word embedding modulation and protein motif frequencies, were useful for feature choosing. Five word embedding types and a variety of conjoint features were examined for such feature selection. The support vector machine algorithm consequentially was employed to perform classification. The performance statistics within the 5-fold cross-validation including average accuracy, specificity, sensitivity, as well as MCC rates surpass 0.95. Such metrics in the independent test are 96.82, 97.16, 95.76 percent, and 0.9, respectively. Compared to state-of-the-art predictors, the proposed method can generate more preferable performance above all metrics indicating the effectiveness of the proposed method in determining electron transport proteins. Furthermore, this study reveals insights about the applicability of various word embeddings for understanding surveyed sequences.
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3
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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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4
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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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5
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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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6
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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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7
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Wiktorowicz A, Wit A, Dziewierz A, Rzeszutko L, Dudek D, Kleczynski P. Calcium Pattern Assessment in Patients with Severe Aortic Stenosis Via the Chou's 5-Steps Rule. Curr Pharm Des 2020; 25:3769-3775. [PMID: 31566130 DOI: 10.2174/1381612825666190930101258] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 09/26/2019] [Indexed: 02/07/2023]
Abstract
BACKGROUND Progression of aortic valve calcifications (AVC) leads to aortic valve stenosis (AS). Importantly, the AVC degree has a great impact on AS progression, treatment selection and outcomes. Methods of AVC assessment do not provide accurate quantitative evaluation and analysis of calcium distribution and deposition in a repetitive manner. OBJECTIVE We aim to prepare a reliable tool for detailed AVC pattern analysis with quantitative parameters. METHODS We analyzed computed tomography (CT) scans of fifty patients with severe AS using a dedicated software based on MATLAB version R2017a (MathWorks, Natick, MA, USA) and ImageJ version 1.51 (NIH, USA) with the BoneJ plugin version 1.4.2 with a self-developed algorithm. RESULTS We listed unique parameters describing AVC and prepared 3D AVC models with color pointed calcium layer thickness in the stenotic aortic valve. These parameters were derived from CT-images in a semi-automated and repeatable manner. They were divided into morphometric, topological and textural parameters and may yield crucial information about the anatomy of the stenotic aortic valve. CONCLUSION In our study, we were able to obtain and define quantitative parameters for calcium assessment of the degenerated aortic valves. Whether the defined parameters are able to predict potential long-term outcomes after treatment, requires further investigation.
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Affiliation(s)
- Agata Wiktorowicz
- 2nd Department of Cardiology, Institute of Cardiology, Jagiellonian University Medical College, 31-501 Kopernika St. 17, Krakow, Poland
| | - Adrian Wit
- Faculty of Physics and Applied Computer Science, University of Science and Technology, Mickiewicza Ave. 30, 30-059 Krakow, Poland
| | - Artur Dziewierz
- 2nd Department of Cardiology, Institute of Cardiology, Jagiellonian University Medical College, 31-501 Kopernika St. 17, Krakow, Poland
| | - Lukasz Rzeszutko
- 2nd Department of Cardiology, Institute of Cardiology, Jagiellonian University Medical College, 31-501 Kopernika St. 17, Krakow, Poland
| | - Dariusz Dudek
- 2nd Department of Cardiology, Institute of Cardiology, Jagiellonian University Medical College, 31-501 Kopernika St. 17, Krakow, Poland
| | - Pawel Kleczynski
- 2nd Department of Cardiology, Institute of Cardiology, Jagiellonian University Medical College, 31-501 Kopernika St. 17, Krakow, Poland
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Saikia S, Bordoloi M, Sarmah R. Established and In-trial GPCR Families in Clinical Trials: A Review for Target Selection. Curr Drug Targets 2020; 20:522-539. [PMID: 30394207 DOI: 10.2174/1389450120666181105152439] [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: 06/18/2018] [Revised: 08/28/2018] [Accepted: 10/22/2018] [Indexed: 12/14/2022]
Abstract
The largest family of drug targets in clinical trials constitute of GPCRs (G-protein coupled receptors) which accounts for about 34% of FDA (Food and Drug Administration) approved drugs acting on 108 unique GPCRs. Factors such as readily identifiable conserved motif in structures, 127 orphan GPCRs despite various de-orphaning techniques, directed functional antibodies for validation as drug targets, etc. has widened their therapeutic windows. The availability of 44 crystal structures of unique receptors, unexplored non-olfactory GPCRs (encoded by 50% of the human genome) and 205 ligand receptor complexes now present a strong foundation for structure-based drug discovery and design. The growing impact of polypharmacology for complex diseases like schizophrenia, cancer etc. warrants the need for novel targets and considering the undiscriminating and selectivity of GPCRs, they can fulfill this purpose. Again, natural genetic variations within the human genome sometimes delude the therapeutic expectations of some drugs, resulting in medication response differences and ADRs (adverse drug reactions). Around ~30 billion US dollars are dumped annually for poor accounting of ADRs in the US alone. To curb such undesirable reactions, the knowledge of established and currently in clinical trials GPCRs families can offer huge understanding towards the drug designing prospects including "off-target" effects reducing economical resource and time. The druggability of GPCR protein families and critical roles played by them in complex diseases are explained. Class A, class B1, class C and class F are generally established family and GPCRs in phase I (19%), phase II(29%), phase III(52%) studies are also reviewed. From the phase I studies, frizzled receptors accounted for the highest in trial targets, neuropeptides in phase II and melanocortin in phase III studies. Also, the bioapplications for nanoparticles along with future prospects for both nanomedicine and GPCR drug industry are discussed. Further, the use of computational techniques and methods employed for different target validations are also reviewed along with their future potential for the GPCR based drug discovery.
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Affiliation(s)
- Surovi Saikia
- Natural Products Chemistry Group, CSIR North East Institute of Science & Technology, Jorhat-785006, Assam, India
| | - Manobjyoti Bordoloi
- Natural Products Chemistry Group, CSIR North East Institute of Science & Technology, Jorhat-785006, Assam, India
| | - Rajeev Sarmah
- Allied Health Sciences, Assam Down Town University, Panikhaiti, Guwahati 781026, Assam, India
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9
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Malik N, Dhiman P, Khatkar A. In Silico Design and Synthesis of Targeted Curcumin Derivatives as Xanthine Oxidase Inhibitors. Curr Drug Targets 2020; 20:593-603. [PMID: 30465499 DOI: 10.2174/1389450120666181122100511] [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: 08/21/2018] [Revised: 09/24/2018] [Accepted: 11/02/2018] [Indexed: 12/11/2022]
Abstract
BACKGROUND Curcumin is a well-known pharmacophore and some of its derivatives are shown to target xanthine oxidase (XO) to alleviate disorders caused by the excess production of uric acid. OBJECTIVE Curcumin based derivatives were designed, synthesized and evaluated for their antioxidant and xanthine oxidase inhibitory potential. METHOD In this report, we designed and synthesized two series of curcumin derivatives modified by inserting pyrazole and pyrimidine ring to central keto group. The synthesized compounds were evaluated for their antioxidant and xanthine oxidase inhibitory potential. RESULTS Results showed that pyrazole analogues of curcumin produced excellent XO inhibitory potency with the IC50 values varying from 06.255 µM to 10.503 µM. Among pyrimidine derivatives compound CU3a1 having ortho nitro substitution exhibited more potent xanthine oxidase inhibitory activity than any other curcumin derivative of this series. CONCLUSION Curcumin derivatives CU5b1, CU5b2, CU5b3, and CU3a1 showed a potent inhibitory activity against xanthine oxidase along with good antioxidant potential.
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Affiliation(s)
- Neelam Malik
- Laboratory for Preservation Technology and Enzyme Inhibition Studies, Department of Pharmaceutical Sciences, M.D.University, Rohtak, Haryana, India
| | - Priyanka Dhiman
- Laboratory for Preservation Technology and Enzyme Inhibition Studies, Department of Pharmaceutical Sciences, M.D.University, Rohtak, Haryana, India
| | - Anurag Khatkar
- Laboratory for Preservation Technology and Enzyme Inhibition Studies, Department of Pharmaceutical Sciences, M.D.University, Rohtak, Haryana, India
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10
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11
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Studying Calcium Ion-Dependent Effect on the Inter-subunit Interaction Between the cTnC N-terminal Domain and cTnI C-terminal Switch Peptide of Human Cardiac Troponin via Chou’s 5-Steps Rule. Int J Pept Res Ther 2020. [DOI: 10.1007/s10989-019-09875-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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12
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13
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Humbert N, Kovalenko L, Saladini F, Giannini A, Pires M, Botzanowski T, Cherenok S, Boudier C, Sharma KK, Real E, Zaporozhets OA, Cianférani S, Seguin-Devaux C, Poggialini F, Botta M, Zazzi M, Kalchenko VI, Mori M, Mély Y. (Thia)calixarenephosphonic Acids as Potent Inhibitors of the Nucleic Acid Chaperone Activity of the HIV-1 Nucleocapsid Protein with a New Binding Mode and Multitarget Antiviral Activity. ACS Infect Dis 2020; 6:687-702. [PMID: 32045204 DOI: 10.1021/acsinfecdis.9b00290] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The nucleocapsid protein (NC) is a highly conserved protein that plays key roles in HIV-1 replication through its nucleic acid chaperone properties mediated by its two zinc fingers and basic residues. NC is a promising target for antiviral therapy, particularly to control viral strains resistant to currently available drugs. Since calixarenes with antiviral properties have been described, we explored the ability of calixarene hydroxymethylphosphonic or sulfonic acids to inhibit NC chaperone properties and exhibit antiviral activity. By using fluorescence-based assays, we selected four calixarenes inhibiting NC chaperone activity with submicromolar IC50 values. These compounds were further shown by mass spectrometry, isothermal titration calorimetry, and fluorescence anisotropy to bind NC with no zinc ejection and to compete with nucleic acids for the binding to NC. Molecular dynamic simulations further indicated that these compounds interact via their phosphonate or sulfonate groups with the basic surface of NC but not with the hydrophobic plateau at the top of the folded fingers. Cellular studies showed that the most soluble compound CIP201 inhibited the infectivity of wild-type and drug-resistant HIV-1 strains at low micromolar concentrations, primarily targeting the early steps of HIV-1 replication. Moreover, CIP201 was also found to inhibit the flipping and polymerization activity of reverse transcriptase. Calixarenes thus form a class of noncovalent NC inhibitors, endowed with a new binding mode and multitarget antiviral activity.
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Affiliation(s)
- Nicolas Humbert
- Laboratoire de Bioimagerie et Pathologies, UMR 7021 CNRS, Université de Strasbourg, Faculté de Pharmacie, 74 route du Rhin, 67401 Illkirch, France
| | - Lesia Kovalenko
- Laboratoire de Bioimagerie et Pathologies, UMR 7021 CNRS, Université de Strasbourg, Faculté de Pharmacie, 74 route du Rhin, 67401 Illkirch, France
- Department of Chemistry, Taras Shevchenko National University of Kyiv, 01601 Kyiv, Ukraine
| | - Francesco Saladini
- Department of Medical Biotechnologies, University of Siena, viale Mario Bracci no. 16, 53100 Siena, Italy
| | - Alessia Giannini
- Department of Medical Biotechnologies, University of Siena, viale Mario Bracci no. 16, 53100 Siena, Italy
| | - Manuel Pires
- Laboratoire de Bioimagerie et Pathologies, UMR 7021 CNRS, Université de Strasbourg, Faculté de Pharmacie, 74 route du Rhin, 67401 Illkirch, France
| | - Thomas Botzanowski
- Laboratoire de Spectrométrie de Masse BioOrganique, IPHC UMR 7178 CNRS, Université de Strasbourg, 67000 Strasbourg, France
| | - Sergiy Cherenok
- Institute of Organic Chemistry, National Academy of Science of Ukraine, Murmanska str. 5, Kyiv 02660, Ukraine
| | - Christian Boudier
- Laboratoire de Bioimagerie et Pathologies, UMR 7021 CNRS, Université de Strasbourg, Faculté de Pharmacie, 74 route du Rhin, 67401 Illkirch, France
| | - Kamal K. Sharma
- Laboratoire de Bioimagerie et Pathologies, UMR 7021 CNRS, Université de Strasbourg, Faculté de Pharmacie, 74 route du Rhin, 67401 Illkirch, France
| | - Eleonore Real
- Laboratoire de Bioimagerie et Pathologies, UMR 7021 CNRS, Université de Strasbourg, Faculté de Pharmacie, 74 route du Rhin, 67401 Illkirch, France
| | - Olga A. Zaporozhets
- Department of Chemistry, Taras Shevchenko National University of Kyiv, 01601 Kyiv, Ukraine
| | - Sarah Cianférani
- Laboratoire de Spectrométrie de Masse BioOrganique, IPHC UMR 7178 CNRS, Université de Strasbourg, 67000 Strasbourg, France
| | - Carole Seguin-Devaux
- Department of Infection and Immunity, Luxembourg Institute of Health, 29 rue Henri Koch, L-4354 Esch-sur-Alzette, Luxembourg
| | - Federica Poggialini
- Department of Biotechnology, Chemistry and Pharmacy, Department of Excellence 2018-2022 Università degli Studi di Siena, via Aldo Moro 2, I-53019 Siena, Italy
| | - Maurizio Botta
- Department of Biotechnology, Chemistry and Pharmacy, Department of Excellence 2018-2022 Università degli Studi di Siena, via Aldo Moro 2, I-53019 Siena, Italy
| | - Maurizio Zazzi
- Department of Medical Biotechnologies, University of Siena, viale Mario Bracci no. 16, 53100 Siena, Italy
| | - Vitaly I. Kalchenko
- Institute of Organic Chemistry, National Academy of Science of Ukraine, Murmanska str. 5, Kyiv 02660, Ukraine
| | - Mattia Mori
- Department of Biotechnology, Chemistry and Pharmacy, Department of Excellence 2018-2022 Università degli Studi di Siena, via Aldo Moro 2, I-53019 Siena, Italy
| | - Yves Mély
- Laboratoire de Bioimagerie et Pathologies, UMR 7021 CNRS, Université de Strasbourg, Faculté de Pharmacie, 74 route du Rhin, 67401 Illkirch, France
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14
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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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15
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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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16
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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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17
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iRSpot-DTS: Predict recombination spots by incorporating the dinucleotide-based spare-cross covariance information into Chou's pseudo components. Genomics 2019; 111:1760-1770. [DOI: 10.1016/j.ygeno.2018.11.031] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 11/29/2018] [Accepted: 11/30/2018] [Indexed: 12/16/2022]
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18
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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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19
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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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20
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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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21
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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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22
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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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23
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24
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Khan S, Khan M, Iqbal N, Hussain T, Khan SA, Chou KC. A Two-Level Computation Model Based on Deep Learning Algorithm for Identification of piRNA and Their Functions via Chou’s 5-Steps Rule. Int J Pept Res Ther 2019. [DOI: 10.1007/s10989-019-09887-3] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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25
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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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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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Messerli MA, Sarkar A. Advances in Electrochemistry for Monitoring Cellular Chemical Flux. Curr Med Chem 2019; 26:4984-5002. [PMID: 31057100 DOI: 10.2174/0929867326666190506111629] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 03/06/2019] [Accepted: 03/12/2019] [Indexed: 11/22/2022]
Abstract
The transport of organic and inorganic molecules, along with inorganic ions across the plasma membrane results in chemical fluxes that reflect the cellular function in healthy and diseased states. Measurement of these chemical fluxes enables the characterization of protein function and transporter stoichiometry, characterization of a single cell and embryo viability prior to implantation, and screening of pharmaceutical agents. Electrochemical sensors emerge as sensitive and non-invasive tools for measuring chemical fluxes immediately outside the cells in the boundary layer, that are capable of monitoring a diverse range of transported analytes including inorganic ions, gases, neurotransmitters, hormones, and pharmaceutical agents. Used on their own or in combination with other methods, these sensors continue to expand our understanding of the function of rare cells and small tissues. Advances in sensor construction and detection strategies continue to improve sensitivity under physiological conditions, diversify analyte detection, and increase throughput. These advances will be discussed in the context of addressing technical challenges to measuring chemical flux in the boundary layer of cells and measuring the resultant changes to the chemical concentration in the bulk media.
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Affiliation(s)
- Mark A Messerli
- Department of Biology and Microbiology, South Dakota State University, Brookings, SD. United States
| | - Anyesha Sarkar
- Department of Biology and Microbiology, South Dakota State University, Brookings, SD. United States
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iN6-methylat (5-step): identifying DNA N6-methyladenine sites in rice genome using continuous bag of nucleobases via Chou’s 5-step rule. Mol Genet Genomics 2019; 294:1173-1182. [DOI: 10.1007/s00438-019-01570-y] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Accepted: 04/25/2019] [Indexed: 12/21/2022]
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29
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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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30
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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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31
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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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32
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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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33
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Jiang QX. Structural Variability in the RLR-MAVS Pathway and Sensitive Detection of Viral RNAs. Med Chem 2019; 15:443-458. [PMID: 30569868 PMCID: PMC6858087 DOI: 10.2174/1573406415666181219101613] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 10/23/2018] [Accepted: 12/12/2018] [Indexed: 12/25/2022]
Abstract
Cells need high-sensitivity detection of non-self molecules in order to fight against pathogens. These cellular sensors are thus of significant importance to medicinal purposes, especially for treating novel emerging pathogens. RIG-I-like receptors (RLRs) are intracellular sensors for viral RNAs (vRNAs). Their active forms activate mitochondrial antiviral signaling protein (MAVS) and trigger downstream immune responses against viral infection. Functional and structural studies of the RLR-MAVS signaling pathway have revealed significant supramolecular variability in the past few years, which revealed different aspects of the functional signaling pathway. Here I will discuss the molecular events of RLR-MAVS pathway from the angle of detecting single copy or a very low copy number of vRNAs in the presence of non-specific competition from cytosolic RNAs, and review key structural variability in the RLR / vRNA complexes, the MAVS helical polymers, and the adapter-mediated interactions between the active RLR / vRNA complex and the inactive MAVS in triggering the initiation of the MAVS filaments. These structural variations may not be exclusive to each other, but instead may reflect the adaptation of the signaling pathways to different conditions or reach different levels of sensitivity in its response to exogenous vRNAs.
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Affiliation(s)
- Qiu-Xing Jiang
- Department of Microbiology and Cell Science, University of Florida, Gainesville, FL 32611, United States
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34
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Chiu JKH, Dillon TS, Chen YPP. Large-scale frequent stem pattern mining in RNA families. J Theor Biol 2018; 455:131-139. [PMID: 30036526 DOI: 10.1016/j.jtbi.2018.07.015] [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: 03/05/2018] [Revised: 07/09/2018] [Accepted: 07/11/2018] [Indexed: 11/19/2022]
Abstract
Functionally similar non-coding RNAs are expected to be similar in certain regions of their secondary structures. These similar regions are called common structure motifs, and are structurally conserved throughout evolution to maintain their functional roles. Common structure motif identification is one of the critical tasks in RNA secondary structure analysis. Nevertheless, current approaches suffer several limitations, and/or do not scale with both structure size and the number of input secondary structures. In this work, we present a method to transform the conserved base pair stems into transaction items and apply frequent itemset mining to identify common structure motifs existing in a majority of input structures. Our experimental results on telomerase and ribosomal RNA secondary structures report frequent stem patterns that are of biological significance. Moreover, the algorithms utilized in our method are scalable and frequent stem patterns can be identified efficiently among many large structures.
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Affiliation(s)
- Jimmy Ka Ho Chiu
- Department of Computer Science and Information, Technology, La Trobe University, Melbourne VIC 3086, Australia.
| | - Tharam S Dillon
- Department of Computer Science and Information, Technology, La Trobe University, Melbourne VIC 3086, Australia.
| | - Yi-Ping Phoebe Chen
- Department of Computer Science and Information, Technology, La Trobe University, Melbourne VIC 3086, Australia.
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35
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Qiu WR, Sun BQ, Xiao X, Xu ZC, Chou KC. iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC. Oncotarget 2018; 7:44310-44321. [PMID: 27322424 PMCID: PMC5190098 DOI: 10.18632/oncotarget.10027] [Citation(s) in RCA: 141] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 05/29/2016] [Indexed: 12/30/2022] Open
Abstract
Protein hydroxylation is a posttranslational modification (PTM), in which a CH group in Pro (P) or Lys (K) residue has been converted into a COH group, or a hydroxyl group (−OH) is converted into an organic compound. Closely associated with cellular signaling activities, this type of PTM is also involved in some major diseases, such as stomach cancer and lung cancer. Therefore, from the angles of both basic research and drug development, we are facing a challenging problem: for an uncharacterized protein sequence containing many residues of P or K, which ones can be hydroxylated, and which ones cannot? With the explosive growth of protein sequences in the post-genomic age, the problem has become even more urgent. To address such a problem, we have developed a predictor called iHyd-PseCp by incorporating the sequence-coupled information into the general pseudo amino acid composition (PseAAC) and introducing the “Random Forest” algorithm to operate the calculation. Rigorous jackknife tests indicated that the new predictor remarkably outperformed the existing state-of-the-art prediction method for the same purpose. For the convenience of most experimental scientists, a user-friendly web-server for iHyd-PseCp has been established at http://www.jci-bioinfo.cn/iHyd-PseCp, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.
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Affiliation(s)
- Wang-Ren Qiu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.,Department of Computer Science and Bond Life Science Center, University of Missouri, Columbia, MO, USA
| | - Bi-Qian Sun
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Xuan Xiao
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.,Gordon Life Science Institute, Boston, MA, USA
| | - Zhao-Chun Xu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA, USA.,Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia.,Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
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iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition. Oncotarget 2018; 7:69783-69793. [PMID: 27626500 PMCID: PMC5342515 DOI: 10.18632/oncotarget.11975] [Citation(s) in RCA: 157] [Impact Index Per Article: 26.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Accepted: 09/06/2016] [Indexed: 02/07/2023] Open
Abstract
The initiation of replication is an extremely important process in DNA life cycle. Given an uncharacterized DNA sequence, can we identify where its origin of replication (ORI) is located? It is no doubt a fundamental problem in genome analysis. Particularly, with the rapid development of genome sequencing technology that results in a huge amount of sequence data, it is highly desired to develop computational methods for rapidly and effectively identifying the ORIs in these genomes. Unfortunately, by means of the existing computational methods, such as sequence alignment or kmer strategies, it could hardly achieve decent success rates. To address this problem, we developed a predictor called “iOri-Human”. Rigorous jackknife tests have shown that its overall accuracy and stability in identifying human ORIs are over 75% and 50%, respectively. In the predictor, it is through the pseudo nucleotide composition (an extension of pseudo amino acid composition) that 96 physicochemical properties for the 16 possible constituent dinucleotides have been incorporated to reflect the global sequence patterns in DNA as well as its local sequence patterns. Moreover, a user-friendly web-server for iOri-Human has been established at http://lin.uestc.edu.cn/server/iOri-Human.html, by which users can easily get their desired results without the need to through the complicated mathematics involved.
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Prediction of HIV-1 and HIV-2 proteins by using Chou's pseudo amino acid compositions and different classifiers. Sci Rep 2018; 8:2359. [PMID: 29402983 PMCID: PMC5799304 DOI: 10.1038/s41598-018-20819-x] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 01/24/2018] [Indexed: 01/02/2023] Open
Abstract
Human immunodeficiency virus (HIV) is the retroviral agent that causes acquired immune deficiency syndrome (AIDS). The number of HIV caused deaths was about 4 million in 2016 alone; it was estimated that about 33 million to 46 million people worldwide living with HIV. The HIV disease is especially harmful because the progressive destruction of the immune system prevents the ability of forming specific antibodies and to maintain an efficacious killer T cell activity. Successful prediction of HIV protein has important significance for the biological and pharmacological functions. In this study, based on the concept of Chou’s pseudo amino acid (PseAA) composition and increment of diversity (ID), support vector machine (SVM), logisitic regression (LR), and multilayer perceptron (MP) were presented to predict HIV-1 proteins and HIV-2 proteins. The results of the jackknife test indicated that the highest prediction accuracy and CC values were obtained by the SVM and MP were 0.9909 and 0.9763, respectively, indicating that the classifiers presented in this study were suitable for predicting two groups of HIV proteins.
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Chamorro C, Camarasa MJ, Pérez-Pérez MJ, de Clercq E, Balzarini J, Félix AS. An Approach towards the Synthesis of Potential Metal-Chelating TSAO-T Derivatives as Bidentate Inhibitors of Human Immunodeficiency virus Type 1 Reverse Transcriptase. ACTA ACUST UNITED AC 2016. [DOI: 10.1177/095632029800900505] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Novel derivatives of the potent human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) inhibitor TSAO-T have been designed, synthesized and tested for their in vitro antiretro-viral activity against HIV. These TSAO-T derivatives have been designed as potential bidentate inhibitors of HIV-1 RT, which combine in their structure the functionality of a non-nucleoside RT inhibitor (TSAO-T) and a bivalent ion-chelating moiety (a β-diketone moiety) linked through an appropriate spacer to the N-3 of thymine of TSAO-T . Some of the new compounds have an anti-HIV-1 activity comparable to that of the parent compound TSAO-T, but display a markedly increased antiviral selectivity. There was a clear relationship between antiviral activity and the length of the spacer group that links the TSAO molecule with the chelating moiety. A shorter spacer invariably resulted in increased antiviral potency. None of the TSAO-T derivatives were endowed with anti-HIV-2 activity.
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Affiliation(s)
- C Chamorro
- Instituto de Química Médica (CSIC), Juan de la Cierva 3, 28006 Madrid, Spain
| | - M-J Camarasa
- Instituto de Química Médica (CSIC), Juan de la Cierva 3, 28006 Madrid, Spain
| | - M-J Pérez-Pérez
- Instituto de Química Médica (CSIC), Juan de la Cierva 3, 28006 Madrid, Spain
| | - E de Clercq
- Rega Institute for Medical Research, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium
| | - J Balzarini
- Rega Institute for Medical Research, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium
| | - A San Félix
- Instituto de Química Médica (CSIC), Juan de la Cierva 3, 28006 Madrid, Spain
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39
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Yang L, Wang S, Zhou M, Chen X, Zuo Y, Lv Y. Characterization of BioPlex network by topological properties. J Theor Biol 2016; 409:148-154. [PMID: 27552850 DOI: 10.1016/j.jtbi.2016.08.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Revised: 07/28/2016] [Accepted: 08/20/2016] [Indexed: 11/16/2022]
Abstract
Protein-protein interaction (PPI) networks are emerging as valuable prototypes to study important problems in molecular cellular biology and systems biomedicine. An analysis of the topological properties of a PPI network is very helpful for understanding the function and structure of networks. In this study, we analyzed the topological patterns in the BioPlex network containing interactions among 10,961 proteins; most interactions were previously undocumented. The BioPlex network is a comprehensive map of human protein interactions and represents the first phase of a long-term effort to profile the entire human ORFEOME collection. Similar to other biological networks, we observed that the BioPlex network has several topological properties. We also quantified correlations profiles for the BioPlex network and compared them to randomized versions of the same network. We found that for the BioPlex network, edges between proteins with intermediate degrees were strongly suppressed, whereas edges between low-connected proteins were favored. Finally, the degrees of essential genes were compared with the degrees of non-essential genes and randomly selected proteins. There were no significant differences between the groups.
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Affiliation(s)
- Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shiyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Meng Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xiaowen Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yongchun Zuo
- The National Research Center for Animal Transgenic Biotechnology, Inner Mongolia University, Hohhot 010021, China
| | - Yingli Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
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40
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Xu C, Sun D, Liu S, Zhang Y. Protein sequence analysis by incorporating modified chaos game and physicochemical properties into Chou's general pseudo amino acid composition. J Theor Biol 2016; 406:105-15. [PMID: 27375218 DOI: 10.1016/j.jtbi.2016.06.034] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Revised: 06/17/2016] [Accepted: 06/25/2016] [Indexed: 11/27/2022]
Abstract
In this contribution we introduced a novel graphical method to compare protein sequences. By mapping a protein sequence into 3D space based on codons and physicochemical properties of 20 amino acids, we are able to get a unique P-vector from the 3D curve. This approach is consistent with wobble theory of amino acids. We compute the distance between sequences by their P-vectors to measure similarities/dissimilarities among protein sequences. Finally, we use our method to analyze four datasets and get better results compared with previous approaches.
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Affiliation(s)
- Chunrui Xu
- School of Mathematics and Statistics, Shandong University at Weihai, Weihai 264209, China
| | - Dandan Sun
- School of Mathematics and Statistics, Shandong University at Weihai, Weihai 264209, China
| | - Shenghui Liu
- School of Mathematics and Statistics, Shandong University at Weihai, Weihai 264209, China
| | - Yusen Zhang
- School of Mathematics and Statistics, Shandong University at Weihai, Weihai 264209, China.
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41
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Sharma KK, Przybilla F, Restle T, Godet J, Mély Y. FRET-based assay to screen inhibitors of HIV-1 reverse transcriptase and nucleocapsid protein. Nucleic Acids Res 2016; 44:e74. [PMID: 26762982 PMCID: PMC4856972 DOI: 10.1093/nar/gkv1532] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2015] [Accepted: 12/23/2015] [Indexed: 12/15/2022] Open
Abstract
During HIV-1 reverse transcription, the single-stranded RNA genome is converted into proviral double stranded DNA by Reverse Transcriptase (RT) within a reverse transcription complex composed of the genomic RNA and a number of HIV-1 encoded proteins, including the nucleocapsid protein NCp7. Here, we developed a one-step and one-pot RT polymerization assay. In this in vitro assay, RT polymerization is monitored in real-time by Förster resonance energy transfer (FRET) using a commercially available doubly-labeled primer/template DNA. The assay can monitor and quantify RT polymerization activity as well as its promotion by NCp7. Z-factor values as high as 0.89 were obtained, indicating that the assay is suitable for high-throughput drug screening. Using Nevirapine and AZT as prototypical RT inhibitors, reliable IC50 values were obtained from the changes in the RT polymerization kinetics. Interestingly, the assay can also detect NCp7 inhibitors, making it suitable for high-throughput screening of drugs targeting RT, NCp7 or simultaneously, both proteins.
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Affiliation(s)
- Kamal K Sharma
- Laboratoire de Biophotonique et Pharmacologie, UMR 7213 CNRS, Université de Strasbourg, Faculté de pharmacie, 74 route du Rhin, 67401 Illkirch, France
| | - Frédéric Przybilla
- Laboratoire de Biophotonique et Pharmacologie, UMR 7213 CNRS, Université de Strasbourg, Faculté de pharmacie, 74 route du Rhin, 67401 Illkirch, France
| | - Tobias Restle
- Institute für Molekulare Medizin, Universitätsklinikum Schleswig-Holstein, Universität zu Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Julien Godet
- Laboratoire de Biophotonique et Pharmacologie, UMR 7213 CNRS, Université de Strasbourg, Faculté de pharmacie, 74 route du Rhin, 67401 Illkirch, France Département d'Information Médicale et de Biostatistiques, Hôpitaux Universitaires de Strasbourg, 1, pl de l'Hôpital, 67400 Strasbourg, France
| | - Yves Mély
- Laboratoire de Biophotonique et Pharmacologie, UMR 7213 CNRS, Université de Strasbourg, Faculté de pharmacie, 74 route du Rhin, 67401 Illkirch, France
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42
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Liu B, Fang L, Liu F, Wang X, Chen J, Chou KC. Identification of real microRNA precursors with a pseudo structure status composition approach. PLoS One 2015; 10:e0121501. [PMID: 25821974 PMCID: PMC4378912 DOI: 10.1371/journal.pone.0121501] [Citation(s) in RCA: 165] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2014] [Accepted: 01/31/2015] [Indexed: 01/08/2023] Open
Abstract
Containing about 22 nucleotides, a micro RNA (abbreviated miRNA) is a small non-coding RNA molecule, functioning in transcriptional and post-transcriptional regulation of gene expression. The human genome may encode over 1000 miRNAs. Albeit poorly characterized, miRNAs are widely deemed as important regulators of biological processes. Aberrant expression of miRNAs has been observed in many cancers and other disease states, indicating they are deeply implicated with these diseases, particularly in carcinogenesis. Therefore, it is important for both basic research and miRNA-based therapy to discriminate the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops). Particularly, with the avalanche of RNA sequences generated in the postgenomic age, it is highly desired to develop computational sequence-based methods in this regard. Here two new predictors, called “iMcRNA-PseSSC” and “iMcRNA-ExPseSSC”, were proposed for identifying the human pre-microRNAs by incorporating the global or long-range structure-order information using a way quite similar to the pseudo amino acid composition approach. Rigorous cross-validations on a much larger and more stringent newly constructed benchmark dataset showed that the two new predictors (accessible at http://bioinformatics.hitsz.edu.cn/iMcRNA/) outperformed or were highly comparable with the best existing predictors in this area.
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Affiliation(s)
- Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- Gordon Life Science Institute, Belmont, Massachusetts, United States of America
- * E-mail:
| | - Longyun Fang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Fule Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Xiaolong Wang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Junjie Chen
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Belmont, Massachusetts, United States of America
- Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
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43
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Fatemi MH, Heidari A, Gharaghani S. QSAR prediction of HIV-1 protease inhibitory activities using docking derived molecular descriptors. J Theor Biol 2015; 369:13-22. [PMID: 25600056 DOI: 10.1016/j.jtbi.2015.01.008] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Revised: 01/10/2015] [Accepted: 01/12/2015] [Indexed: 01/30/2023]
Abstract
In this study, application of a new hybrid docking-quantitative structure activity relationship (QSAR) methodology to model and predict the HIV-1 protease inhibitory activities of a series of newly synthesized chemicals is reported. This hybrid docking-QSAR approach can provide valuable information about the most important chemical and structural features of the ligands that affect their inhibitory activities. Docking studies were used to find the actual conformations of chemicals in active site of HIV-1 protease. Then the molecular descriptors were calculated from these conformations. Multiple linear regression (MLR) and least square support vector machine (LS-SVM) were used as QSAR models, respectively. The obtained results reveal that statistical parameters of the LS-SVM model are better than the MLR model, which indicate that there are some non-linear relations between selected molecular descriptors and anti-HIV activities of interested chemicals. The correlation coefficient (R), root mean square error (RMSE) and average absolute error (AAE) for LS-SVM are: R=0.988, RMSE=0.207 and AAE=0.145 for the training set, and R=0.965, RMSE=0.403 and AAE=0.338 for the test set. Leave one out cross validation test was used for assessment of the predictive power and validity of models which led to cross-validation correlation coefficient QUOTE of 0.864 and 0.850 and standardized predicted relative error sum of squares (SPRESS) of 0.553 and 0.581 for LS-SVM and MLR models, respectively.
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Affiliation(s)
- Mohammad H Fatemi
- Chemometrics Laboratory, Faculty of Chemistry, University of Mazandaran, Babolsar 47416-95447, Iran.
| | - Afsane Heidari
- Chemometrics Laboratory, Faculty of Chemistry, University of Mazandaran, Babolsar 47416-95447, Iran
| | - Sajjad Gharaghani
- Department of Bioinformatics, Laboratory of Chemoinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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44
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An effective haplotype assembly algorithm based on hypergraph partitioning. J Theor Biol 2014; 358:85-92. [DOI: 10.1016/j.jtbi.2014.05.034] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2014] [Revised: 05/08/2014] [Accepted: 05/25/2014] [Indexed: 11/20/2022]
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45
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iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. BIOMED RESEARCH INTERNATIONAL 2014; 2014:623149. [PMID: 24967386 PMCID: PMC4055483 DOI: 10.1155/2014/623149] [Citation(s) in RCA: 97] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2014] [Revised: 04/22/2014] [Accepted: 04/23/2014] [Indexed: 11/17/2022]
Abstract
In eukaryotic genes, exons are generally interrupted by introns. Accurately removing introns and joining exons together are essential processes in eukaryotic gene expression. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapid and effective detection of splice sites that play important roles in gene structure annotation and even in RNA splicing. Although a series of computational methods were proposed for splice site identification, most of them neglected the intrinsic local structural properties. In the present study, a predictor called “iSS-PseDNC” was developed for identifying splice sites. In the new predictor, the sequences were formulated by a novel feature-vector called “pseudo dinucleotide composition” (PseDNC) into which six DNA local structural properties were incorporated. It was observed by the rigorous cross-validation tests on two benchmark datasets that the overall success rates achieved by iSS-PseDNC in identifying splice donor site and splice acceptor site were 85.45% and 87.73%, respectively. It is anticipated that iSS-PseDNC may become a useful tool for identifying splice sites and that the six DNA local structural properties described in this paper may provide novel insights for in-depth investigations into the mechanism of RNA splicing.
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46
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A QSPR-like model for multilocus genotype networks of Fasciola hepatica in Northwest Spain. J Theor Biol 2014; 343:16-24. [DOI: 10.1016/j.jtbi.2013.11.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2013] [Revised: 11/08/2013] [Accepted: 11/11/2013] [Indexed: 11/23/2022]
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47
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Liu Q, Chen YPP, Li J. k-Partite cliques of protein interactions: A novel subgraph topology for functional coherence analysis on PPI networks. J Theor Biol 2014; 340:146-54. [PMID: 24056214 DOI: 10.1016/j.jtbi.2013.09.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2013] [Revised: 08/09/2013] [Accepted: 09/10/2013] [Indexed: 01/02/2023]
Abstract
Many studies are aimed at identifying dense clusters/subgraphs from protein-protein interaction (PPI) networks for protein function prediction. However, the prediction performance based on the dense clusters is actually worse than a simple guilt-by-association method using neighbor counting ideas. This indicates that the local topological structures and properties of PPI networks are still open to new theoretical investigation and empirical exploration. We introduce a novel topological structure called k-partite cliques of protein interactions-a functionally coherent but not-necessarily dense subgraph topology in PPI networks-to study PPI networks. A k-partite protein clique is a maximal k-partite clique comprising two or more nonoverlapping protein subsets between any two of which full interactions are exhibited. In the detection of PPI's maximal k-partite cliques, we propose to transform PPI networks into induced K-partite graphs where edges exist only between the partites. Then, we present a maximal k-partite clique mining (MaCMik) algorithm to enumerate maximal k-partite cliques from K-partite graphs. Our MaCMik algorithm is then applied to a yeast PPI network. We observed interesting and unusually high functional coherence in k-partite protein cliques-the majority of the proteins in k-partite protein cliques, especially those in the same partites, share the same functions, although k-partite protein cliques are not restricted to be dense compared with dense subgraph patterns or (quasi-)cliques. The idea of k-partite protein cliques provides a novel approach of characterizing PPI networks, and so it will help function prediction for unknown proteins.
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Affiliation(s)
- Qian Liu
- Advanced Analytics Institute, University of Technology Sydney, Sydney, Australia
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48
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Liu B, Zhang D, Xu R, Xu J, Wang X, Chen Q, Dong Q, Chou KC. Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection. ACTA ACUST UNITED AC 2013; 30:472-9. [PMID: 24318998 PMCID: PMC7537947 DOI: 10.1093/bioinformatics/btt709] [Citation(s) in RCA: 250] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Motivation: Owing to its importance in both basic research (such as molecular evolution and protein attribute prediction) and practical application (such as timely modeling the 3D structures of proteins targeted for drug development), protein remote homology detection has attracted a great deal of interest. It is intriguing to note that the profile-based approach is promising and holds high potential in this regard. To further improve protein remote homology detection, a key step is how to find an optimal means to extract the evolutionary information into the profiles. Results: Here, we propose a novel approach, the so-called profile-based protein representation, to extract the evolutionary information via the frequency profiles. The latter can be calculated from the multiple sequence alignments generated by PSI-BLAST. Three top performing sequence-based kernels (SVM-Ngram, SVM-pairwise and SVM-LA) were combined with the profile-based protein representation. Various tests were conducted on a SCOP benchmark dataset that contains 54 families and 23 superfamilies. The results showed that the new approach is promising, and can obviously improve the performance of the three kernels. Furthermore, our approach can also provide useful insights for studying the features of proteins in various families. It has not escaped our notice that the current approach can be easily combined with the existing sequence-based methods so as to improve their performance as well. Availability and implementation: For users’ convenience, the source code of generating the profile-based proteins and the multiple kernel learning was also provided at http://bioinformatics.hitsz.edu.cn/main/∼binliu/remote/ Contact:bliu@insun.hit.edu.cn or bliu@gordonlifescience.org Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Bin Liu
- School of Computer Science and Technology and Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China, Shanghai Key Laboratory of Intelligent Information Processing, Shanghai 200433, China, Gordon Life Science Institute, Belmont, MA 02478, USA, School of Computer, Shenyang Aerospace University, Shenyang, Liaoning, China, School of Computer Science, Fudan University, Shanghai 200433, China and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia
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49
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Wu X, Liu H, Liu H, Su J, Lv J, Cui Y, Wang F, Zhang Y. Z curve theory-based analysis of the dynamic nature of nucleosome positioning in Saccharomyces cerevisiae. Gene 2013; 530:8-18. [DOI: 10.1016/j.gene.2013.08.018] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2013] [Revised: 07/30/2013] [Accepted: 08/03/2013] [Indexed: 01/01/2023]
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50
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Al-Mamun M, Brown L, Hossain M, Fall C, Wagstaff L, Bass R. A hybrid computational model for the effects of maspin on cancer cell dynamics. J Theor Biol 2013; 337:150-60. [DOI: 10.1016/j.jtbi.2013.08.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2013] [Revised: 08/07/2013] [Accepted: 08/15/2013] [Indexed: 01/01/2023]
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