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Bhattacharjee A, Kar S, Ojha PK. Unveiling G-protein coupled receptor kinase-5 inhibitors for chronic degenerative diseases: Multilayered prioritization employing explainable machine learning-driven multi-class QSAR, ligand-based pharmacophore and free energy-inspired molecular simulation. Int J Biol Macromol 2024; 269:131784. [PMID: 38697440 DOI: 10.1016/j.ijbiomac.2024.131784] [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: 01/24/2024] [Revised: 04/02/2024] [Accepted: 04/21/2024] [Indexed: 05/05/2024]
Abstract
GRK5 holds a pivotal role in cellular signaling pathways, with its overexpression in cardiomyocytes, neuronal cells, and tumor cells strongly associated with various chronic degenerative diseases, which highlights the urgent need for potential inhibitors. In this study, multiclass classification-based QSAR models were developed using diverse machine learning algorithms. These models were built from curated compounds with experimentally derived GRK5 inhibitory activity. Additionally, a pharmacophore model was constructed using active compounds from the dataset. Among the models, the SVM-based approach proved most effective and was initially used to screen DrugBank compounds within the applicability domain. Compounds showing significant GRK5 inhibitory potential underwent evaluation for key pharmacophoric features. Prospective compounds were subjected to molecular docking to assess binding affinity towards GRK5's key active site amino acid residues. Stability at the binding site was analyzed through 200 ns molecular dynamics simulations. MM-GBSA analysis quantified individual free energy components contributing to the total binding energy with respect to binding site residues. Metadynamics analysis, including PCA, FEL, and PDF, provided crucial insights into conformational changes of both apo and holo forms of GRK5 at defined energy states. The study identifies DB02844 (S-Adenosyl-1,8-Diamino-3-Thiooctane) and DB13155 (Esculin) as promising GRK5 inhibitors, warranting further in vitro and in vivo validation studies.
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Affiliation(s)
- Arnab Bhattacharjee
- Drug Discovery and Development Laboratory (DDD Lab), Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Supratik Kar
- Chemometrics and Molecular Modeling Laboratory, Department of Chemistry and Physics, Kean University, 1000 Morris Avenue, Union, NJ, 07083, USA
| | - Probir Kumar Ojha
- Drug Discovery and Development Laboratory (DDD Lab), Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
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2
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Neal WM, Pandey P, Khan SI, Khan IA, Chittiboyina AG. Machine learning and traditional QSAR modeling methods: a case study of known PXR activators. J Biomol Struct Dyn 2024; 42:903-917. [PMID: 37059719 DOI: 10.1080/07391102.2023.2196701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 03/22/2023] [Indexed: 04/16/2023]
Abstract
Pregnane X receptor (PXR), extensively expressed in human tissues related to digestion and metabolism, is responsible for recognizing and detoxifying diverse xenobiotics encountered by humans. To comprehend the promiscuous nature of PXR and its ability to bind a variety of ligands, computational approaches, viz., quantitative structure-activity relationship (QSAR) models, aid in the rapid dereplication of potential toxicological agents and mitigate the number of animals used to establish a meaningful regulatory decision. Recent advancements in machine learning techniques accommodating larger datasets are expected to aid in developing effective predictive models for complex mixtures (viz., dietary supplements) before undertaking in-depth experiments. Five hundred structurally diverse PXR ligands were used to develop traditional two-dimensional (2D) QSAR, machine-learning-based 2D-QSAR, field-based three-dimensional (3D) QSAR, and machine-learning-based 3D-QSAR models to establish the utility of predictive machine learning methods. Additionally, the applicability domain of the agonists was established to ensure the generation of robust QSAR models. A prediction set of dietary PXR agonists was used to externally-validate generated QSAR models. QSAR data analysis revealed that machine-learning 3D-QSAR techniques were more accurate in predicting the activity of external terpenes with an external validation squared correlation coefficient (R2) of 0.70 versus an R2 of 0.52 in machine-learning 2D-QSAR. Additionally, a visual summary of the binding pocket of PXR was assembled from the field 3D-QSAR models. By developing multiple QSAR models in this study, a robust groundwork for assessing PXR agonism from various chemical backbones has been established in anticipation of the identification of potential causative agents in complex mixtures.
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Affiliation(s)
- William M Neal
- Division of Pharmacognosy, Department of BioMolecular Sciences, School of Pharmacy, The University of Mississippi, University, MS, USA
| | - Pankaj Pandey
- National Center for Natural Products Research, Research Institute of Pharmaceutical Sciences, School of Pharmacy, The University of Mississippi, University, MS, USA
| | - Shabana I Khan
- Division of Pharmacognosy, Department of BioMolecular Sciences, School of Pharmacy, The University of Mississippi, University, MS, USA
- National Center for Natural Products Research, Research Institute of Pharmaceutical Sciences, School of Pharmacy, The University of Mississippi, University, MS, USA
| | - Ikhlas A Khan
- Division of Pharmacognosy, Department of BioMolecular Sciences, School of Pharmacy, The University of Mississippi, University, MS, USA
- National Center for Natural Products Research, Research Institute of Pharmaceutical Sciences, School of Pharmacy, The University of Mississippi, University, MS, USA
| | - Amar G Chittiboyina
- National Center for Natural Products Research, Research Institute of Pharmaceutical Sciences, School of Pharmacy, The University of Mississippi, University, MS, USA
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3
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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] [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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4
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Tang T, Huang H, Hu J, Huang S, Liu M, Yu S, Xiao X. Discovery of novel anti-cyanobacterial allelochemicals by multi-conformational QSAR approach. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2023; 256:106420. [PMID: 36774780 DOI: 10.1016/j.aquatox.2023.106420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 02/02/2023] [Accepted: 02/03/2023] [Indexed: 06/18/2023]
Abstract
Microcystis aeruginosa causes cyanobacterial harmful algal blooms (cHABs) in various freshwater environments. Due to global climate change, the cHABs have even spread to estuaries and coasts. Plant-derived flavones have been reported as allelochemicals that efficiently inhibit the growth of M. aeruginosa. Quantitative structure-activity relationship (QSAR) was applied to investigate the factors affecting the M. aeruginosa inhibitory activity of flavones, and to discover novel allelochemicals against M. aeruginosa. We constructed 2D and 3D-QSAR models based on the half maximum inhibitory concentration (IC50) of 22 flavones against M. aeruginosa, using molecular descriptors from multiple stable conformations. Both models showed satisfactory performances (2D-QSAR: r2=0.899, q2=0.596, rtest2=0.801; 3D-QSAR: r2=0.810, q2=0.516, rtest2=0.897). The 2D-QSAR model indicates that the anti-cyanobacterial activity is positively correlated with minimum and maximum surface electrostatic potential, and negatively correlated with polarity index and polar surface area. Through the 3D-QSAR approach, electronegative hydroxyl groups in 5- and 4'-position were favorable for the anti-cyanobacterial activity. In addition, we selected six untested flavones that fit the "activity-favorable" pattern of the visualized 3D-QSAR model. Five of the external flavones exhibited significant cyanobacterial inhibitory ability at their predicted IC50 by the 3D-QSAR model. In particular, diosmetin achieved an inhibition rate of 70.50±4.74%, which was much higher than expected. The flavones screened by the 3D-QSAR model are novel cyanobacterial inhibitors and should be fully exploited to mitigate cHABs.
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Affiliation(s)
- Tao Tang
- Department of Marine Science, Ocean College, Zhejiang University, Zhoushan 316021, PR China; Key Laboratory of Marine Ecological Monitoring and Restoration Technologies of the Ministry of Natural Resources of China, Shanghai 201206, PR China
| | - Haomin Huang
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, PR China
| | - Jing Hu
- Department of Marine Science, Ocean College, Zhejiang University, Zhoushan 316021, PR China
| | - Shitao Huang
- Department of Marine Science, Ocean College, Zhejiang University, Zhoushan 316021, PR China
| | - Muyuan Liu
- Department of Marine Science, Ocean College, Zhejiang University, Zhoushan 316021, PR China
| | - Shumiao Yu
- Department of Marine Science, Ocean College, Zhejiang University, Zhoushan 316021, PR China
| | - Xi Xiao
- Department of Marine Science, Ocean College, Zhejiang University, Zhoushan 316021, PR China; Key Laboratory of Marine Ecological Monitoring and Restoration Technologies of the Ministry of Natural Resources of China, Shanghai 201206, PR China; Donghai Laboratory, Zhoushan 316000, PR China; Key Laboratory of Watershed Non-point Source Pollution Control and Water Eco-security of Ministry of Water Resources, College of Environmental and Resources Sciences, Zhejiang University, Hangzhou 310058, PR China. https://person.zju.edu.cn/en/xixiao/844893.html
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Zothantluanga JH, Chetia D, Rajkhowa S, Umar AK. Unsupervised machine learning, QSAR modelling and web tool development for streamlining the lead identification process of antimalarial flavonoids. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2023; 34:117-146. [PMID: 36744427 DOI: 10.1080/1062936x.2023.2169347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/10/2023] [Indexed: 06/18/2023]
Abstract
Identification of lead compounds with the traditional laboratory approach is expensive and time-consuming. Nowadays, in silico techniques have emerged as a promising approach for lead identification. In this study, we aim to develop robust and predictive 2D-QSAR models to identify lead flavonoids by predicting the IC50 against Plasmodium falciparum. We applied machine learning algorithms (Principal component analysis followed by K-means clustering) and Pearson correlation analysis to select 9 molecular descriptors (MDs) for model building. We selected and validated the three best QSAR models after execution of multiple linear regression (MLR) 100 times with different combinations of MDs. The developed models have fulfilled the five principles for QSAR models as specified by the Organization for Economic Co-operation and Development. The outcome of the study is a reliable and sustainable in silico method of IC50 (Mean ± SD) prediction that will positively impact the antimalarial drug development process by reducing the money and time required to identify potential antimalarial lead compounds from the class of flavonoids. We also developed a web tool (JazQSAR, https://etflin.com/news/4) to offer an easily accessible platform for the developed QSAR models.
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Affiliation(s)
- J H Zothantluanga
- Department of Pharmaceutical Sciences, Faculty of Science and Engineering, Dibrugarh University, Dibrugarh, India
| | - D Chetia
- Department of Pharmaceutical Sciences, Faculty of Science and Engineering, Dibrugarh University, Dibrugarh, India
| | - S Rajkhowa
- Centre for Biotechnology and Bioinformatics, Faculty of Biological Sciences, Dibrugarh University, Dibrugarh, India
| | - A K Umar
- Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, Indonesia
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Li J, Wang C, Yue L, Chen F, Cao X, Wang Z. Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 243:113955. [PMID: 35961199 DOI: 10.1016/j.ecoenv.2022.113955] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 07/11/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
Given the rapid development of nanotechnology, it is crucial to understand the effects of nanoparticles on living organisms. However, it is laborious to perform toxicological tests on a case-by-case basis. Quantitative structure-activity relationship (QSAR) is an effective computational technique because it saves time, costs, and animal sacrifice. Therefore, this review presents general procedures for the construction and application of nano-QSAR models of metal-based and metal-oxide nanoparticles (MBNPs and MONPs). We also provide an overview of available databases and common algorithms. The molecular descriptors and their roles in the toxicological interpretation of MBNPs and MONPs are systematically reviewed and the future of nano-QSAR is discussed. Finally, we address the growing demand for novel nano-specific descriptors, new computational strategies to address the data shortage, in situ data for regulatory concerns, a better understanding of the physicochemical properties of NPs with bioactivity, and, most importantly, the design of nano-QSAR for real-life environmental predictions rather than laboratory simulations.
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Affiliation(s)
- Jing Li
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Chuanxi Wang
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Le Yue
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Feiran Chen
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Xuesong Cao
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Zhenyu Wang
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China.
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7
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Zarei O, Raeppel SL, Hamzeh-Mivehroud M. An alignment-independent three-dimensional quantitative structure-activity relationship study on ron receptor tyrosine kinase inhibitors. J Bioinform Comput Biol 2022; 20:2250015. [PMID: 35880255 DOI: 10.1142/s0219720022500159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Recepteur d'Origine Nantais known as RON is a member of the receptor tyrosine kinase (RTK) superfamily which has recently gained increasing attention as cancer target for therapeutic intervention. The aim of this work was to perform an alignment-independent three-dimensional quantitative structure-activity relationship (3D QSAR) study for a series of RON inhibitors. A 3D QSAR model based on GRid-INdependent Descriptors (GRIND) methodology was generated using a set of 19 compounds with RON inhibitory activities. The generated 3D QSAR model revealed the main structural features important in the potency of RON inhibitors. The results obtained from the presented study can be used in lead optimization projects for designing of novel compounds where inhibition of RON is needed.
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Affiliation(s)
- Omid Zarei
- Cellular and Molecular Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Stéphane L Raeppel
- ChemRF Laboratories Inc., 3194, rue Claude-Jodoin, Montréal, QC, Canada H1Y 3M2, Canada
| | - Maryam Hamzeh-Mivehroud
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
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de Oliveira PIC, de Santana Miranda PH, Lourenço EMG, de Santana Nogueira Silverio PS, Barbosa EG. Planning new Trypanosoma cruzi CYP51 inhibitors using QSAR studies. Mol Divers 2021; 25:2219-2235. [PMID: 32557280 DOI: 10.1007/s11030-020-10113-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 05/30/2020] [Indexed: 11/30/2022]
Abstract
Chagas disease kills over 10,000 people per year, and approximately 8 million people are infected by Trypanosoma cruzi. The reference drug for treatment of the disease, benznidazole, is the same since the 70s. In recent years, many CYP51 inhibitors were tested against this parasite's target. One of them, posaconazole, was even tested in clinical trials that unfortunately were not successful. Nevertheless, there are still many evidences that CYP51 is a great potential target to treat T. cruzi infection. The research for new effective molecules that can cure the chronic phase of the disease is essential. 2D and 3D-quantitative structure activity relationship (QSAR) studies were conducted in this work to create three QSAR models using the chemical structures of 197 published compounds that already went through either in vivo or in vitro tests. After the analysis of the models, new analogues not yet synthesized were suggested here and had their biological activity and synthetic availability assessed.
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Affiliation(s)
- Pedro Igor Camara de Oliveira
- Programa de Pós-Graduação em Bioinformática, Faculdade de Farmácia, Universidade Federal do Rio Grande do Norte, UFRN, Rua Gen. Gustavo Cordeiro de Faria, S/N - Petrópolis, Natal, RN, 59012-570, Brazil
| | - Paulo Henrique de Santana Miranda
- Programa de Pós-Graduação em Ciências Farmacêuticas, Faculdade de Farmácia, Universidade Federal do Rio Grande do Norte, UFRN, Rua Gen. Gustavo Cordeiro de Faria, S/N - Petrópolis, Natal, RN, 59012-570, Brazil
| | - Estela Mariana Guimaraes Lourenço
- Programa de Pós-Graduação em Ciências Farmacêuticas, Faculdade de Farmácia, Universidade Federal do Rio Grande do Norte, UFRN, Rua Gen. Gustavo Cordeiro de Faria, S/N - Petrópolis, Natal, RN, 59012-570, Brazil
| | - Priscilla Suene de Santana Nogueira Silverio
- Programa de Pós-Graduação em Ciências Farmacêuticas, Faculdade de Farmácia, Universidade Federal do Rio Grande do Norte, UFRN, Rua Gen. Gustavo Cordeiro de Faria, S/N - Petrópolis, Natal, RN, 59012-570, Brazil
| | - Euzebio Guimaraes Barbosa
- Programa de Pós-Graduação em Bioinformática, Faculdade de Farmácia, Universidade Federal do Rio Grande do Norte, UFRN, Rua Gen. Gustavo Cordeiro de Faria, S/N - Petrópolis, Natal, RN, 59012-570, Brazil.
- Programa de Pós-Graduação em Ciências Farmacêuticas, Faculdade de Farmácia, Universidade Federal do Rio Grande do Norte, UFRN, Rua Gen. Gustavo Cordeiro de Faria, S/N - Petrópolis, Natal, RN, 59012-570, Brazil.
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Appell M, Tu YS, Compton DL, Evans KO, Wang LC. Quantitative structure-activity relationship study for prediction of antifungal properties of phenolic compounds. Struct Chem 2020. [DOI: 10.1007/s11224-020-01549-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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10
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Miranda PHDS, Lourenço EMG, Morais AMS, de Oliveira PIC, Silverio PSDSN, Jordão AK, Barbosa EG. Molecular modeling of a series of dehydroquinate dehydratase type II inhibitors of Mycobacterium tuberculosis and design of new binders. Mol Divers 2019; 25:1-12. [PMID: 31820222 DOI: 10.1007/s11030-019-10020-1] [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: 08/28/2019] [Accepted: 11/22/2019] [Indexed: 11/24/2022]
Abstract
Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), is still responsible for a large number of fatal cases, especially in developing countries with alarming rates of incidence and prevalence worldwide. Mycobacterium tuberculosis has a remarkable ability to develop new resistance mechanisms to the conventional antimicrobials treatment. Because of this, there is an urgent need for novel bioactive compounds for its treatment. The dehydroquinate dehydratase II (DHQase II) is considered a key enzyme of shikimate pathway, and it can be used as a promising target for the design of new bioactive compounds with antibacterial action. The aim of this work was the construction of QSAR models to aid the design of new potential DHQase II inhibitors. For that purpose, various molecular modeling approaches, such as activity cliff, QSAR models and computer-aided ligand design were utilized. A predictive in silico 4D-QSAR model was built using a database comprising 86 inhibitors of DHQase II, and the model was used to predict the activity of the designed ligands. The obtained model proved to predict well the DHQase II inhibition for an external validation dataset ([Formula: see text] = 0.72). Also, the Activity Cliff analysis shed light on important structural features applied to the ligand design.
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Affiliation(s)
- Paulo H de S Miranda
- Departamento de Farmácia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil
| | - Estela M G Lourenço
- Departamento de Farmácia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil
| | - Alexander M S Morais
- Departamento de Farmácia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil
| | - Pedro I C de Oliveira
- Programa de Pós-Graduação em Bioinformática, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil
| | | | - Alessandro K Jordão
- Departamento de Farmácia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil
| | - Euzébio G Barbosa
- Departamento de Farmácia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil. .,Programa de Pós-Graduação em Bioinformática, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil.
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Abstract
A number of anti-retroviral drugs are being used for treating Human Immunodeficiency Virus (HIV) infection. Due to emergence of drug resistant strains, there is a constant quest to discover more effective anti-HIV compounds. In this endeavor, computational tools have proven useful in accelerating drug discovery. Although methods were published to design a class of compounds against a specific HIV protein, but an integrated web server for the same is lacking. Therefore, we have developed support vector machine based regression models using experimentally validated data from ChEMBL repository. Quantitative structure activity relationship based features were selected for predicting inhibition activity of a compound against HIV proteins namely protease (PR), reverse transcriptase (RT) and integrase (IN). The models presented a maximum Pearson correlation coefficient of 0.78, 0.76, 0.74 and 0.76, 0.68, 0.72 during tenfold cross-validation on IC50 and percent inhibition datasets of PR, RT, IN respectively.
These models performed equally well on the independent datasets. Chemical space mapping, applicability domain analyses and other statistical tests further support robustness of the predictive models. Currently, we have identified a number of chemical descriptors that are imperative in predicting the compound inhibition potential. HIVprotI platform (http://bioinfo.imtech.res.in/manojk/hivproti) would be useful in virtual screening of inhibitors as well as designing of new molecules against the important HIV proteins for therapeutics development.![]()
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Sheridan RP. The Relative Importance of Domain Applicability Metrics for Estimating Prediction Errors in QSAR Varies with Training Set Diversity. J Chem Inf Model 2015; 55:1098-107. [DOI: 10.1021/acs.jcim.5b00110] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Robert P. Sheridan
- Cheminformatics Department, RY800B-305, Merck Research Laboratories, Rahway, New Jersey 07065, United States
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13
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Yu KQ, Zhao YR, Li XL, Shao YN, Liu F, He Y. Hyperspectral imaging for mapping of total nitrogen spatial distribution in pepper plant. PLoS One 2014; 9:e116205. [PMID: 25549353 PMCID: PMC4280196 DOI: 10.1371/journal.pone.0116205] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2014] [Accepted: 12/06/2014] [Indexed: 11/19/2022] Open
Abstract
Visible/near-infrared (Vis/NIR) hyperspectral imaging was employed to determine the spatial distribution of total nitrogen in pepper plant. Hyperspectral images of samples (leaves, stems, and roots of pepper plants) were acquired and their total nitrogen contents (TNCs) were measured using Dumas combustion method. Mean spectra of all samples were extracted from regions of interest (ROIs) in hyperspectral images. Random frog (RF) algorithm was implemented to select important wavelengths which carried effective information for predicting the TNCs in leaf, stem, root, and whole-plant (leaf-stem-root), respectively. Based on full spectra and the selected important wavelengths, the quantitative relationships between spectral data and the corresponding TNCs in organs (leaf, stem, and root) and whole-plant (leaf-stem-root) were separately developed using partial least-squares regression (PLSR). As a result, the PLSR model built by the important wavelengths for predicting TNCs in whole-plant (leaf-stem-root) offered a promising result of correlation coefficient (R) for prediction (RP = 0.876) and root mean square error (RMSE) for prediction (RMSEP = 0.426%). Finally, the TNC of each pixel within ROI of the sample was estimated to generate the spatial distribution map of TNC in pepper plant. The achievements of the research indicated that hyperspectral imaging is promising and presents a powerful potential to determine nitrogen contents spatial distribution in pepper plant.
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Affiliation(s)
- Ke-Qiang Yu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Yan-Ru Zhao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Xiao-Li Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture, Beijing, China
| | - Yong-Ni Shao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture, Beijing, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture, Beijing, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture, Beijing, China
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