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Ray P, Sedigh A, Confeld M, Alhalhooly L, Iduoku K, Casanola-Martin GM, Pham-The H, Rasulev B, Choi Y, Yang Z, Mallik S, Quadir M. Design and Evaluation of Nanoscale Materials with Programmed Responsivity towards Epigenetic Enzymes. bioRxiv 2024:2024.03.26.585429. [PMID: 38586020 PMCID: PMC10996597 DOI: 10.1101/2024.03.26.585429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Self-assembled materials capable of modulating their assembly properties in response to specific enzymes play a pivotal role in advancing 'intelligent' encapsulation platforms for biotechnological applications. Here, we introduce a previously unreported class of synthetic nanomaterials that programmatically interact with histone deacetylase (HDAC) as the triggering stimulus for disassembly. These nanomaterials consist of co-polypeptides comprising poly (acetyl L-lysine) and poly(ethylene glycol) blocks. Under neutral pH conditions, they self-assemble into particles. However, their stability is compromised upon exposure to HDACs, depending on enzyme concentration and exposure time. Our investigation, utilizing HDAC8 as the model enzyme, revealed that the primary mechanism behind disassembly involves a decrease in amphiphilicity within the block copolymer due to the deacetylation of lysine residues within the particles' hydrophobic domains. To elucidate the response mechanism, we encapsulated a fluorescent dye within these nanoparticles. Upon incubation with HDAC, the nanoparticle structure collapsed, leading to controlled release of the dye over time. Notably, this release was not triggered by denatured HDAC8, other proteolytic enzymes like trypsin, or the co-presence of HDAC8 and its inhibitor. We further demonstrated the biocompatibility and cellular effects of these materials and conducted a comprehensive computational study to unveil the possible interaction mechanism between enzymes and particles. By drawing parallels to the mechanism of naturally occurring histone proteins, this research represents a pioneering step toward developing functional materials capable of harnessing the activity of epigenetic enzymes such as HDACs.
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Karuth A, Casanola-Martin GM, Lystrom L, Sun W, Kilin D, Kilina S, Rasulev B. Combined Machine Learning, Computational, and Experimental Analysis of the Iridium(III) Complexes with Red to Near-Infrared Emission. J Phys Chem Lett 2024; 15:471-480. [PMID: 38190332 DOI: 10.1021/acs.jpclett.3c02533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Various coordination complexes have been the subject of experimental and theoretical studies in recent decades because of their fascinating photophysical properties. In this work, a combined experimental and computational approach was applied to investigate the optical properties of monocationic Ir(III) complexes. An interpretative machine learning-based quantitative structure-property relationship (ML/QSPR) model was successfully developed that could reliably predict the emission wavelength of the Ir(III) complexes and provide a foundation for the theoretical evaluation of the optical properties of Ir(III) complexes. A hypothesis was proposed to explain the differences in the emission wavelengths between structurally different individual Ir(III) complexes. The efficacy of the developed model was demonstrated by high R2 values of 0.84 and 0.87 for the training and test sets, respectively. It is worth noting that a relationship between the N-N distance in the diimine ligands of the Ir(III) complexes and emission wavelengths is detected. This effect is most probably associated with a degree of distortion in the octahedral geometry of the complexes, resulting in a perturbed ligand field. This combined experimental and computational approach shows great potential for the rational design of new Ir(III) complexes with the desired optical properties. Moreover, the developed methodology could be extended to other transition-metal complexes.
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Affiliation(s)
- Anas Karuth
- Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Gerardo M Casanola-Martin
- Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Levi Lystrom
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Wenfang Sun
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58108, United States
- Department of Chemistry and Biochemistry, The University of Alabama, Tuscaloosa, Alabama 35487, United States
| | - Dmitri Kilin
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Svetlana Kilina
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Bakhtiyor Rasulev
- Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58108, United States
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3
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Diem-Tran PT, Ho TT, Tuan NV, Bao LQ, Phuong HT, Chau TTG, Minh HTB, Nguyen CT, Smanova Z, Casanola-Martin GM, Rasulev B, Pham-The H, Cuong LCV. Stability Constant and Potentiometric Sensitivity of Heavy Metal-Organic Fluorescent Compound Complexes: QSPR Models for Prediction and Design of Novel Coumarin-like Ligands. Toxics 2023; 11:595. [PMID: 37505560 PMCID: PMC10383909 DOI: 10.3390/toxics11070595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/02/2023] [Accepted: 07/04/2023] [Indexed: 07/29/2023]
Abstract
Industrial wastewater often consists of toxic chemicals and pollutants, which are extremely harmful to the environment. Heavy metals are toxic chemicals and considered one of the major hazards to the aquatic ecosystem. Analytical techniques, such as potentiometric methods, are some of the methods to detect heavy metals in wastewaters. In this work, the quantitative structure-property relationship (QSPR) was applied using a range of machine learning techniques to predict the stability constant (logβML) and potentiometric sensitivity (PSML) of 200 ligands in complexes with the heavy metal ions Cu2+, Cd2+, and Pb2+. In result, the logβML models developed for four ions showed good performance with square correlation coefficients (R2) ranging from 0.80 to 1.00 for the training and 0.72 to 0.85 for the test sets. Likewise, the PSML displayed acceptable performance with an R2 of 0.87 to 1.00 for the training and 0.73 to 0.95 for the test sets. By screening a virtual database of coumarin-like structures, several new ligands bearing the coumarin moiety were identified. Three of them, namely NEW02, NEW03, and NEW07, showed very good sensitivity and stability in the metal complexes. Subsequent quantum-chemical calculations, as well as physicochemical/toxicological profiling were performed to investigate their metal-binding ability and developability of the designed sensors. Finally, synthesis schemes are proposed to obtain these three ligands with major efficiency from simple resources. The three coumarins designed clearly demonstrated capability to be suitable as good florescent chemosensors towards heavy metals. Overall, the computational methods applied in this study showed a very good performance as useful tools for designing novel fluorescent probes and assessing their sensing abilities.
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Affiliation(s)
- Phan Thi Diem-Tran
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
| | - Tue-Tam Ho
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Nguyen-Van Tuan
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Le-Quang Bao
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Ha Tran Phuong
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
| | - Trinh Thi Giao Chau
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
| | - Hoang Thi Binh Minh
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
| | - Cong-Truong Nguyen
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Zulayho Smanova
- Department of Chemistry, National University of Uzbekistan after Mirzo Ulugbek, Tashkent 100012, Uzbekistan
| | | | - Bakhtiyor Rasulev
- Department of Chemistry, National University of Uzbekistan after Mirzo Ulugbek, Tashkent 100012, Uzbekistan
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
| | - Hai Pham-The
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Le Canh Viet Cuong
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
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Bao LQ, Baecker D, Mai Dung DT, Phuong Nhung N, Thi Thuan N, Nguyen PL, Phuong Dung PT, Huong TTL, Rasulev B, Casanola-Martin GM, Nam NH, Pham-The H. Development of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer's Disease. Molecules 2023; 28:molecules28083588. [PMID: 37110831 PMCID: PMC10142303 DOI: 10.3390/molecules28083588] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 04/29/2023] Open
Abstract
Multi-target drug development has become an attractive strategy in the discovery of drugs to treat of Alzheimer's disease (AzD). In this study, for the first time, a rule-based machine learning (ML) approach with classification trees (CT) was applied for the rational design of novel dual-target acetylcholinesterase (AChE) and β-site amyloid-protein precursor cleaving enzyme 1 (BACE1) inhibitors. Updated data from 3524 compounds with AChE and BACE1 measurements were curated from the ChEMBL database. The best global accuracies of training/external validation for AChE and BACE1 were 0.85/0.80 and 0.83/0.81, respectively. The rules were then applied to screen dual inhibitors from the original databases. Based on the best rules obtained from each classification tree, a set of potential AChE and BACE1 inhibitors were identified, and active fragments were extracted using Murcko-type decomposition analysis. More than 250 novel inhibitors were designed in silico based on active fragments and predicted AChE and BACE1 inhibitory activity using consensus QSAR models and docking validations. The rule-based and ML approach applied in this study may be useful for the in silico design and screening of new AChE and BACE1 dual inhibitors against AzD.
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Affiliation(s)
- Le-Quang Bao
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Daniel Baecker
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, University of Greifswald, Friedrich-Ludwig-Jahn-Straße 17, 17489 Greifswald, Germany
| | - Do Thi Mai Dung
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Nguyen Phuong Nhung
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Nguyen Thi Thuan
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Phuong Linh Nguyen
- College of Computing & Informatics, Drexel University, 3141 Chestnut St., Philadelphia, PA 19104, USA
| | - Phan Thi Phuong Dung
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Tran Thi Lan Huong
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
| | | | - Nguyen-Hai Nam
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Hai Pham-The
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
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Martínez-López Y, Castillo-Garit JA, Casanola-Martin GM, Rasulev B, Rodríguez-Gonzalez AY, Martínez-Santiago O, Barigye SJ. Exploring proteasome inhibition using atomic weighted vector indices and machine learning approaches. Mol Divers 2023:10.1007/s11030-023-10638-2. [PMID: 37017875 DOI: 10.1007/s11030-023-10638-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 03/17/2023] [Indexed: 04/06/2023]
Abstract
Ubiquitin-proteasome system (UPS) is a highly regulated mechanism of intracellular protein degradation and turnover. The UPS is involved in different biological activities, such as the regulation of gene transcription and cell cycle. Several researchers have applied cheminformatics and artificial intelligence methods to study the inhibition of proteasomes, including the prediction of UPP inhibitors. Following this idea, we applied a new tool for obtaining molecular descriptors (MDs) for modeling proteasome Inhibition in terms of EC50 (µmol/L), in which a set of new MDs called atomic weighted vectors (AWV) and several prediction algorithms were used in cheminformatics studies. In the manuscript, a set of descriptors based on AWV are presented as datasets for training different machine learning techniques, such as linear regression, multiple linear regression (MLR), random forest (RF), K-nearest neighbors (IBK), multi-layer perceptron, best-first search, and genetic algorithm. The results suggest that these atomic descriptors allow adequate modeling of proteasome inhibitors despite artificial intelligence techniques, as a variant to build efficient models for the prediction of inhibitory activity.
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Affiliation(s)
- Yoan Martínez-López
- Department of Computer Sciences, Faculty of Informatics, Camagüey University, 74650, Camagüey City, Cuba.
| | | | - Gerardo M Casanola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, 58102, USA
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, 58102, USA
| | - Ansel Y Rodríguez-Gonzalez
- Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE-UT3), Unidad de Transferencia Tecnológica de Tepic, Tepic, México
| | - Oscar Martínez-Santiago
- Alfa Vitamins Laboratories, Miami, FL, 33166, USA
- Laboratorio de Bioinformática y Química Computacional, Universidad Católica del Maule, Talca, Chile
| | - Stephen J Barigye
- Departamento de Química Física Aplicada, Facultad de Ciencias, Universidad Autónoma de Madrid (UAM), 28049, Madrid, Spain
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Daghighi A, Casanola-Martin GM, Timmerman T, Milenković D, Lučić B, Rasulev B. In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach. Toxics 2022; 10:toxics10120746. [PMID: 36548579 PMCID: PMC9786026 DOI: 10.3390/toxics10120746] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/10/2022] [Accepted: 11/28/2022] [Indexed: 06/02/2023]
Abstract
In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure-Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD50). An initial set of 4885 molecular descriptors was generated and applied to build Support Vector Regression (SVR) models. The best two SVR models, SVR_A and SVR_B, were selected to build an Ensemble Model by means of Multiple Linear Regression (MLR). The obtained Ensemble Model showed improved performance over the base SVR models in the training set (R2 = 0.88), validation set (R2 = 0.95), and true external test set (R2 = 0.92). The models were also internally validated by 5-fold cross-validation and Y-scrambling experiments, showing that the models have high levels of goodness-of-fit, robustness and predictivity. The contribution of descriptors to the toxicity in the models was assessed using the Accumulated Local Effect (ALE) technique. The proposed approach provides an important tool to assess toxicity of nitroaromatic compounds, based on the ensemble QSAR model and the structural relationship to toxicity by analyzed contribution of the involved descriptors.
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Affiliation(s)
- Amirreza Daghighi
- Biomedical Engineering Program, North Dakota State University, Fargo, ND 58105, USA
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
| | | | - Troy Timmerman
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
- Department of Computer Science, North Dakota State University, Fargo, ND 58105, USA
| | - Dejan Milenković
- Department of Science, Institute for Information Technologies, University of Kragujevac, 34000 Kragujevac, Serbia
| | - Bono Lučić
- NMR Centre, Ruđer Bošković Institute, 10000 Zagreb, Croatia
| | - Bakhtiyor Rasulev
- Biomedical Engineering Program, North Dakota State University, Fargo, ND 58105, USA
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
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7
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Pham-The H, Nam NH, Nga DV, Hai DT, Dieguez-Santana K, Marrero-Poncee Y, Castillo-Garit JA, Casanola-Martin GM, Le-Thi-Thu H. Learning from Multiple Classifier Systems: Perspectives for Improving Decision Making of QSAR Models in Medicinal Chemistry. Curr Top Med Chem 2018; 17:3269-3288. [DOI: 10.2174/1568026618666171212111018] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Revised: 11/14/2017] [Accepted: 11/22/2017] [Indexed: 11/22/2022]
Affiliation(s)
- Hai Pham-The
- Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hanoi, Vietnam
| | - Nguyen-Hai Nam
- Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hanoi, Vietnam
| | - Doan-Viet Nga
- School of Medicine and Pharmacy, Vietnam National University (VNU), 144 Xuan Thuy, Hanoi, Vietnam
| | - Dang Thanh Hai
- University of Engineering and Technology, Vietnam National University, 144 Xuan Thuy, Hanoi, Vietnam
| | | | - Yovani Marrero-Poncee
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA),Escuela de Medicina, Edificio de Especialidades Medicas, Quito, Ecuador
| | - Juan A. Castillo-Garit
- Unidad de Toxicologia Experimental, Universidad de Ciencias Medicas, Dr. Serafín Ruiz de Zarate Ruiz, de Villa Clara, Cuba
| | | | - Huong Le-Thi-Thu
- School of Medicine and Pharmacy, Vietnam National University (VNU), 144 Xuan Thuy, Hanoi, Vietnam
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Castillo-Garit JA, Casanola-Martin GM, Le-Thi-Thu H, Pham-The H, Barigye SJ. A Simple Method to Predict Blood-Brain Barrier Permeability of Drug- Like Compounds Using Classification Trees. Med Chem 2017; 13:664-669. [DOI: 10.2174/1573406413666170209124302] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Revised: 12/15/2016] [Accepted: 02/03/2017] [Indexed: 11/22/2022]
Affiliation(s)
- Juan A. Castillo-Garit
- Unidad de Toxicologia Experimental, Universidad de Ciencias Medicas de Villa Clara, Carretera a acueducto y circunvalacion, Santa Clara, Villa Clara, Cuba
| | - Gerardo M. Casanola-Martin
- Unidad de Investigacion de Diseno de Farmacos y Conectividad Molecular, Departamento de /Quimica Fisica, Facultad de Farmacia, Universitat de Valencia, Spain
| | - Huong Le-Thi-Thu
- School of Medicine and Pharmacy, Vietnam National University, Hanoi (VNU) 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam
| | - Hai Pham-The
- Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi, Vietnam
| | - Stephen J. Barigye
- Department of Chemistry, McGill University, 801 Sherbrooke St. W., Montreal, Quebec H3A, Canada
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Dieguez-Santana K, Pham-The H, Rivera-Borroto OM, Puris A, Le-Thi-Thu H, Casanola-Martin GM. A Two QSAR Way for Antidiabetic Agents Targeting Using α-Amylase and α-Glucosidase Inhibitors: Model Parameters Settings in Artificial Intelligence Techniques. LETT DRUG DES DISCOV 2017. [DOI: 10.2174/1570180814666161128121142] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Karel Dieguez-Santana
- Faculty of Life Sciences, Amazonian State University, Paso Lateral km 2½ via Tena, Puyo, Pastaza, Ecuador
| | - Hai Pham-The
- Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hanoi, Vietnam
| | - Oscar M. Rivera-Borroto
- Departamento de Química Física Aplicada, Facultad de Ciencias, Universidad Autónoma de Madrid, 28049 Madrid, Spain
| | - Amilkar Puris
- Facultad de Ciencias de La Ingeniería, Universidad Técnica Estatal de Quevedo, Ecuador
| | - Huong Le-Thi-Thu
- School of Medicine and Pharmacy, Vietnam National University, Hanoi (VNU) 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam
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Casanola-Martin GM, Le-Thi-Thu H, Marrero-Ponce Y, Castillo-Garit JA, Torrens F, Rescigno A, Abad C, Khan MTH. Tyrosinase enzyme: 1. An overview on a pharmacological target. Curr Top Med Chem 2015; 14:1494-501. [PMID: 24853560 DOI: 10.2174/1568026614666140523121427] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2014] [Revised: 02/10/2014] [Accepted: 02/11/2014] [Indexed: 11/22/2022]
Abstract
The tyrosinase enzyme (EC 1.14.18.1) is an oxidoreductase inside the general enzyme classification and is involved in the oxidation and reduction process in the epidermis. These chemical reactions that the enzyme catalyzes are of principal importance in the melanogenesis process. This process of melanogenesis is related to the melanin formation, a heteropolymer of indolic nature that provides the different tonalities in the skin and helps to the protection from the ultraviolet radiation. However, a pigment overproduction, come up by the action of the tyrosinase, can cause different disorders in the skin related to the hyperpigmentation. Several studies mainly focused on the characteristics of the enzyme have been reported. In this work, an approximation to general aspects related to this enzyme is made. Besides, it is treated the researches that have been published in the part of the biochemical anatomy dealing with diseases associated with this protein (melanogenesis), its active place and its physiological states, the molecular mechanism, the methods carried out to detect the inhibitory activity, and the used substrates.
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Affiliation(s)
| | | | | | | | | | | | | | - Mahmud Tareq Hassan Khan
- Departament de Bioquímica i Biologia Molecular, Universitat de València, E-46100 Burjassot, Spain.
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Marrero-Ponce Y, M. Casanola-Martin G, Tareq Hassan Khan M, Torrens F, Rescigno A, Abad C. Ligand-Based Computer-Aided Discovery of Tyrosinase Inhibitors. Applications of the TOMOCOMD-CARDD Method to the Elucidation of New Compounds. Curr Pharm Des 2010; 16:2601-24. [DOI: 10.2174/138161210792389216] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2010] [Accepted: 06/30/2010] [Indexed: 11/22/2022]
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