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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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2
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Zhuravskyi Y, Iduoku K, Erickson ME, Karuth A, Usmanov D, Casanola-Martin G, Sayfiyev MN, Ziyaev DA, Smanova Z, Mikolajczyk A, Rasulev B. Quantitative Structure-Permittivity Relationship Study of a Series of Polymers. ACS Mater Au 2024; 4:195-203. [PMID: 38496050 PMCID: PMC10941280 DOI: 10.1021/acsmaterialsau.3c00079] [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] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/01/2023] [Accepted: 12/13/2023] [Indexed: 03/19/2024]
Abstract
Dielectric constant is an important property which is widely utilized in many scientific fields and characterizes the degree of polarization of substances under the external electric field. In this work, a structure-property relationship of the dielectric constants (ε) for a diverse set of polymers was investigated. A transparent mechanistic model was developed with the application of a machine learning approach that combines genetic algorithm and multiple linear regression analysis, to obtain a mechanistically explainable and transparent model. Based on the evaluation conducted using various validation criteria, four- and eight-variable models were proposed. The best model showed a high predictive performance for training and test sets, with R2 values of 0.905 and 0.812, respectively. Obtained statistical performance results and selected descriptors in the best models were analyzed and discussed. With the validation procedures applied, the models were proven to have a good predictive ability and robustness for further applications in polymer permittivity prediction.
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Affiliation(s)
- Yevhenii Zhuravskyi
- Department of Technology of Organic Products, Lviv Polytechnic National University, Lviv 79013, Ukraine
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Kweeni Iduoku
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Meade E Erickson
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Anas Karuth
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Durbek Usmanov
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Institute of the Chemistry of Plant Substances AS RUz, Tashkent 100170, Uzbekistan
| | - Gerardo Casanola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Maqsud N Sayfiyev
- Department of Chemistry, National University of Uzbekistan, Tashkent 100174, Uzbekistan
| | - Dilshod A Ziyaev
- Department of Chemistry, National University of Uzbekistan, Tashkent 100174, Uzbekistan
| | - Zulayho Smanova
- Department of Chemistry, National University of Uzbekistan, Tashkent 100174, Uzbekistan
| | - Alicja Mikolajczyk
- Laboratory of Environmental Chemometrics, Institute for Environmental and Human Health Protection, Faculty of Chemistry, University of Gdansk, Gdansk 80-308, Poland
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Department of Chemistry, National University of Uzbekistan, Tashkent 100174, Uzbekistan
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3
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Erickson M, Casañola-Martin G, Han Y, Rasulev B, Kilin D. Relationships between the Photodegradation Reaction Rate and Structural Properties of Polymer Systems. J Phys Chem B 2024; 128:2190-2200. [PMID: 38386478 DOI: 10.1021/acs.jpcb.3c06854] [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: 02/24/2024]
Abstract
The development of reusable polymeric materials inspires an attempt to combine renewable biomass with upcycling to form a biorenewable closed system. It has been reported that 2,5-furandicarboxylic acid (FDCA) can be recovered for recycling when incorporated as monomers into photodegradable polymeric systems. Here, we develop a procedure to better understand the photodegradation reactions combining density functional theory (DFT) based time-dependent excited-state molecular dynamics (TDESMD) studies with machine learning-based quantitative structure-activity relationships (QSAR) methodology. This procedure allows for the unveiling of hidden structural features between active orbitals that affect the rate of photodegradation and is coined InfoTDESMD. Findings show that electrotopological features are influential factors affecting the rate of photodegradation in differing environments. Additionally, statistical validations and knowledge-based analysis of descriptors are conducted to further understand the structural features' influence on the rate of photodegradation of polymeric materials.
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Affiliation(s)
- Meade Erickson
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Gerardo Casañola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Yulun Han
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Dmitri Kilin
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58105, United States
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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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Fjodorova N, Novič M, Venko K, Rasulev B, Türker Saçan M, Tugcu G, Sağ Erdem S, Toropova AP, Toropov AA. Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives. Int J Mol Sci 2023; 24:14160. [PMID: 37762462 PMCID: PMC10531479 DOI: 10.3390/ijms241814160] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/05/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Fullerene derivatives (FDs) are widely used in nanomaterials production, the pharmaceutical industry and biomedicine. In the present study, we focused on the potential toxic effects of FDs on the aquatic environment. First, we analyzed the binding affinity of 169 FDs to 10 human proteins (1D6U, 1E3K, 1GOS, 1GS4, 1H82, 1OG5, 1UOM, 2F9Q, 2J0D, 3ERT) obtained from the Protein Data Bank (PDB) and showing high similarity to proteins from aquatic species. Then, the binding activity of 169 FDs to the enzyme acetylcholinesterase (AChE)-as a known target of toxins in fathead minnows and Daphnia magna, causing the inhibition of AChE-was analyzed. Finally, the structural aquatic toxicity alerts obtained from ToxAlert were used to confirm the possible mechanism of action. Machine learning and cheminformatics tools were used to analyze the data. Counter-propagation artificial neural network (CPANN) models were used to determine key binding properties of FDs to proteins associated with aquatic toxicity. Predicting the binding affinity of unknown FDs using quantitative structure-activity relationship (QSAR) models eliminates the need for complex and time-consuming calculations. The results of the study show which structural features of FDs have the greatest impact on aquatic organisms and help prioritize FDs and make manufacturing decisions.
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Affiliation(s)
- Natalja Fjodorova
- Laboratory for Chemoinformatics, Theory Department, National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, Slovenia; (M.N.); (K.V.)
| | - Marjana Novič
- Laboratory for Chemoinformatics, Theory Department, National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, Slovenia; (M.N.); (K.V.)
| | - Katja Venko
- Laboratory for Chemoinformatics, Theory Department, National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, Slovenia; (M.N.); (K.V.)
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, NDSU Dept 2510, P.O. Box 6050, Fargo, ND 58108, USA;
| | - Melek Türker Saçan
- Ecotoxicology and Chemometrics Lab, Institute of Environmental Sciences, Bogazici University, Hisar Campus, 34342 Istanbul, Turkey;
| | - Gulcin Tugcu
- Department of Toxicology, Faculty of Pharmacy, Yeditepe University, Atasehir, 34755 Istanbul, Turkey;
| | - Safiye Sağ Erdem
- Department of Chemistry, Marmara University, 34722 Istanbul, Turkey;
| | - Alla P. Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri, Via Mario Negri 2, 20156 Milano, Italy; (A.P.T.); (A.A.T.)
| | - Andrey A. Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri, Via Mario Negri 2, 20156 Milano, Italy; (A.P.T.); (A.A.T.)
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6
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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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7
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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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Tomic A, Kovacic M, Kusic H, Karamanis P, Rasulev B, Loncaric Bozic A. Structural Features Promoting Photocatalytic Degradation of Contaminants of Emerging Concern: Insights into Degradation Mechanism Employing QSA/PR Modeling. Molecules 2023; 28:molecules28062443. [PMID: 36985414 PMCID: PMC10057466 DOI: 10.3390/molecules28062443] [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] [Received: 02/17/2023] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 03/10/2023] Open
Abstract
Although heterogeneous photocatalysis has shown promising results in degradation of contaminants of emerging concern (CECs), the mechanistic implications related to structural diversity of chemicals, affecting oxidative (by HO•) or reductive (by O2•−) degradation pathways are still scarce. In this study, the degradation extents and rates of selected organics in the absence and presence of common scavengers for reactive oxygen species (ROS) generated during photocatalytic treatment were determined. The obtained values were then brought into correlation as K coefficients (MHO•/MO2•−), denoting the ratio of organics degraded by two occurring mechanisms: oxidation and reduction via HO• and O2•−. The compounds possessing K >> 1 favor oxidative degradation over HO•, and vice versa for reductive degradation (i.e., if K << 1 compounds undergo reductive reactions driven by O2•−). Such empirical values were brought into correlation with structural features of CECs, represented by molecular descriptors, employing a quantitative structure activity/property relationship (QSA/PR) modeling. The functional stability and predictive power of the resulting QSA/PR model was confirmed by internal and external cross-validation. The most influential descriptors were found to be the size of the molecule and presence/absence of particular molecular fragments such as C − O and C − Cl bonds; the latter favors HO•-driven reaction, while the former the reductive pathway. The developed QSA/PR models can be considered robust predictive tools for evaluating distribution between degradation mechanisms occurring in photocatalytic treatment.
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Affiliation(s)
- Antonija Tomic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev Trg 19, 10000 Zagreb, Croatia
| | - Marin Kovacic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev Trg 19, 10000 Zagreb, Croatia
| | - Hrvoje Kusic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev Trg 19, 10000 Zagreb, Croatia
- Department for Packaging, Recycling and Environmental Protection, University North, Trg dr. Žarka Dolinara 1, 48000 Koprivnica, Croatia
- Correspondence: ; Tel.: +385-1-4597-160
| | - Panaghiotis Karamanis
- E2S UPPA, CNRS, IPREM, Université de Pau et des Pays de l’Adour, Hélioparc Pau Pyrénées, 2 Rue de President Angot, 64053 Pau, France
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
| | - Ana Loncaric Bozic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev Trg 19, 10000 Zagreb, Croatia
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10
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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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11
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Diéguez-Santanaa K, Puris A, Rivera-Borroto OM, Casanola-Marting GM, Rasulev B, González-Díaz H. A Fuzzy System Classification Approach for QSAR Modeling of α-Amylase and α-Glucosidase Inhibitors. Curr Comput Aided Drug Des 2022; 18:469-479. [DOI: 10.2174/1573409918666220929124820] [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] [Received: 02/27/2022] [Revised: 07/07/2022] [Accepted: 08/09/2022] [Indexed: 11/22/2022]
Abstract
Introduction:
This report proposes the application of a new Machine Learning algorithm called Fuzzy Unordered Rules Induction Algorithm (FURIA)-C in the classification of drug-like compounds with antidiabetic inhibitory ability toward the main two pharmacological targets: α-amylase and α-glucosidase.
Methods:
The two obtained QSAR models were tested for classification capability, achieving satisfactory accuracy scores of 94.5% and 96.5%, respectively. Another important outcome was to achieve various α-amylase and α-glucosidase fuzzy rules with high Certainty Factor values. Fuzzy-Rules derived from the training series and active classification rules were interpreted. An important external validation step, comparing our method with those previously reported, was also included.
Results:
The Holm’s test comparison showed significant differences (p-value<0.05) between FURIA-C, Linear Discriminating Analysis (LDA), and Bayesian Networks, the former beating the two latter ones according to the relative ranking score of the Holm’s test.
Conclusion:
From these results, the FURIA-C algorithm could be used as a cutting-edge technique to predict (classify or screen) the α-amylase and α-glucosidase inhibitory activity of new compounds and hence speed up the discovery of new potent multi-target antidiabetic agents.
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Affiliation(s)
| | - Amilkar Puris
- Facultad de Ciencias de La Ingeniería, Universidad Técnica Estatal de Quevedo, Ecuador
| | - Oscar M. Rivera-Borroto
- Department of Mathematics, Houston Community College-West Loop Campus, Houston TX, 77081, USA
- Department of Mathematics, Lone Star College-CyFair Campus, Houston, TX, 77433, USA
- Departamento de Química Física Aplicada, Facultad de Ciencias,
Universidad Autonoma de Madrid, 28049 Madrid, Spain
| | - Gerardo M. Casanola-Marting
- Department of Coatings and Polymer Materials, North Dakota State University, Fargo, North Dakota, 58102, USA
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials,
North Dakota State University, Fargo, ND, 58102, USA
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, 48940, Leioa, Spain
- Basque Center for Biophysics CSIC-UPVEH, University of Basque Country UPV/EHU, 48940 Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
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12
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Usmanov D, Azamatov A, Baykuziyev T, Yusupova U, Rasulev B. Chemical constituents, anti-inflammatory and analgesic activities of iridoids preparation from Phlomoides labiosa bunge. Nat Prod Res 2022; 37:1709-1713. [PMID: 35879860 DOI: 10.1080/14786419.2022.2104274] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
This study reports the isolation of iridoids and cycloartane glycosides from the aerial parts of Phlomoides labiosa Bunge. Six compounds were isolated and the chemical structures were identified as phlorigidoside С (1), 8-O-acetylharpagide (2), shanzhiside methyl ester (3), cyclosiversioside A (4), cyclosiversioside E (5), and cyclosiversioside C (6). Compounds 4-6 are reported for the first time in this plant. In addition, anti-inflammatory and analgesic activities of iridoid fraction were studied. The sum of iridoids (SI) with intragastric administration is 5.2 and 52.5 times less toxic, than such market drugs as analgin and diclofenac sodium, respectively. In terms of the latitude of analgesic action (LD50/ED50), the SI exceeds analgin by 19.2 times and diclofenac sodium by 16 times. The anti-inflammatory and analgesic activities of the sum of iridoids were confirmed to be effective and nontoxic, and exceed known drugs diclofenac sodium and analgin (metamizole sodium).
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Affiliation(s)
- Durbek Usmanov
- Institute of the Chemistry of Plant Substances, Academy of Sciences, Tashkent, Uzbekistan.,Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, USA
| | - Aziz Azamatov
- Institute of the Chemistry of Plant Substances, Academy of Sciences, Tashkent, Uzbekistan
| | - Temur Baykuziyev
- Qatar University College of Medicine, Hamad Medical Corporation, Doha, Qatar
| | - Ugiloy Yusupova
- Institute of the Chemistry of Plant Substances, Academy of Sciences, Tashkent, Uzbekistan
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, USA
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13
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Diéguez-Santana K, Casañola-Martin GM, Torres R, Rasulev B, Green JR, González-Díaz H. Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds. Mol Pharm 2022; 19:2151-2163. [PMID: 35671399 PMCID: PMC9986951 DOI: 10.1021/acs.molpharmaceut.2c00029] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research.
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Affiliation(s)
- Karel Diéguez-Santana
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain.,Universidad Regional Amazónica IKIAM, Tena, Napo 150150, Ecuador
| | - Gerardo M Casañola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States.,Department of Systems and Computer Engineering, Carleton University, K1S5B6 Ottawa, Ontario, Canada
| | - Roldan Torres
- Universidad Regional Amazónica IKIAM, Tena, Napo 150150, Ecuador
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - James R Green
- Department of Systems and Computer Engineering, Carleton University, K1S5B6 Ottawa, Ontario, Canada
| | - Humbert González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain.,BIOFISIKA, Basque Center for Biophysics CSIC-UPVEH, 48940 Leioa, Spain.,IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
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14
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Erickson M, Han Y, Rasulev B, Kilin D. Molecular Dynamics Study of the Photodegradation of Polymeric Chains. J Phys Chem Lett 2022; 13:4374-4380. [PMID: 35544382 DOI: 10.1021/acs.jpclett.2c00802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The development of reusable polymeric materials inspires an attempt to combine renewable biomass with upcycling to form a biorenewable closed system. It has been reported that 2,5-furandicarboxylic acid (FDCA) can be recovered for recycling when incorporated as monomers into photodegradable polymeric systems. Here, we conduct density functional theory (DFT) studies with periodic boundary conditions on microscopic structures involved in the photodegradation of polymeric chains incorporating FDCA and 2-nitro-1,3-benzenedimethanol. The photodegradation process of polymeric chains is studied using time-dependent excited-state molecular dynamics (TDESMD) in vacuum and aqueous environments. Changes in the photophysical properties for reaction intermediates are characterized by ground-state observables. The distribution of reaction intermediates and products is obtained from TDESMD trajectories using cheminformatics techniques. Results show that a higher degree of polymeric chain degradation is achieved in the vacuum environment. Additionally, one finds that the FDCA molecule is recoverable in the aqueous environment, in qualitative agreement with experimental findings.
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Affiliation(s)
- Meade Erickson
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Yulun Han
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Dmitri Kilin
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58108, United States
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15
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Patnode K, Rasulev B, Voronov A. Synergistic Behavior of Plant Proteins and Biobased Latexes in Bioplastic Food Packaging Materials: Experimental and Machine Learning Study. ACS Appl Mater Interfaces 2022; 14:8384-8393. [PMID: 35119263 DOI: 10.1021/acsami.1c21650] [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] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Plant-based proteins are attractive components which may serve as sustainable alternatives to current petrochemical products. Both soy protein and major corn protein, zein, are of interest in food packaging applications due to their sustainability, biodegradation properties, and inherent physicochemical properties. This study discusses the development of bioplastic materials, where it explores the effects of combining zein, soy protein, and plasticizing latexes derived from plant oil-based monomers (POBMs) on properties of resulting bioplastic films. By looking for synergistic effects of soy protein's inherent film formation ability and zein's higher strength, we prepare strong yet flexible soy-zein films as materials, called proteoposites. Incorporation of natural additive POBM-latexes helps to plasticize and hydrophobize the bioplastic films and thus to improve mechanical and barrier properties. Variation of the POBM-latexes' particle size further aims to enhance the performance of resulting bioplastic films. As a result, modified soy-zein proteoposite films with improved moisture resistance, enhanced mechanical behavior, and greater barrier properties were developed. Machine learning-based computational models were utilized in order to find main structural factors affecting the bioplastic's properties and develop a quantitative structure-property relationship model between the physicochemical properties of the film components and the resulted bioplastics' properties and performance. The developed model effectively predicts experimental outcomes with >85% (R2: 0.85) accuracy. The newly synthesized proteoposites confirmed the machine learning model predictions. As a result, proteoposite films made of two plant proteins and modified with POBM-latexes can be considered as an attractive and viable replacement for petrochemical food packaging products.
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Affiliation(s)
- Kristen Patnode
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102-6050, United States
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102-6050, United States
| | - Andriy Voronov
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102-6050, United States
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16
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Varghese P. J G, David DA, Karuth A, Manamkeri Jafferali JF, P. M SB, George JJ, Rasulev B, Raghavan P. Experimental and Simulation Studies on Nonwoven Polypropylene-Nitrile Rubber Blend: Recycling of Medical Face Masks to an Engineering Product. ACS Omega 2022; 7:4791-4803. [PMID: 35187299 PMCID: PMC8851451 DOI: 10.1021/acsomega.1c04913] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 12/08/2021] [Indexed: 05/05/2023]
Abstract
The battle against the COVID-19 pandemic counters the waste management system, as billions of single-use face masks are used per day all over the world. Proper disposal of used face masks without jeopardizing the health and the environment is a challenge. Herein, a novel method for recycling of medical face masks has been studied. This method incorporates the nonwoven polypropylene (PP) fiber, which is taken off from the mask after disinfecting it, with acrylonitrile butadiene rubber (NBR) using maleic anhydride as the compatibilizer, which results in a PP-NBR blend with a high percentage economy. The PP-NBR blends show enhanced thermomechanical properties among which, 70 wt % PP content shows superior properties compared to other composites with 40, 50, and 60 wt % of PP. The fully Atomistic simulation of PP-NBR blend with compatibilizer shows an improved tensile and barrier properties, which is in good agreement with the experimental studies. The molecular dynamics simulation confirms that the compatibility between non-polar PP and polar NBR phases are vitally important for increasing the interfacial adhesion and impeding the phase separation.
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Affiliation(s)
- George Varghese P. J
- Department
of Metallurgical and Materials Engineering, Indian Institute of Technology Patna (IIT P), Patna 801106, Bihar, India
- Materials
Science and NanoEngineering Lab, Department of Polymer Science and
Rubber Technology, Cochin University of
Science and Technology (CUSAT), Kochi 682022, Kerala, India
| | - Deepthi Anna David
- Materials
Science and NanoEngineering Lab, Department of Polymer Science and
Rubber Technology, Cochin University of
Science and Technology (CUSAT), Kochi 682022, Kerala, India
- Department
of Applied Chemistry, Cochin University
of Science and Technology (CUSAT), Kochi 682022, Kerala, India
| | - Anas Karuth
- Department
of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Jabeen Fatima Manamkeri Jafferali
- Materials
Science and NanoEngineering Lab, Department of Polymer Science and
Rubber Technology, Cochin University of
Science and Technology (CUSAT), Kochi 682022, Kerala, India
| | - Sabura Begum P. M
- Department
of Applied Chemistry, Cochin University
of Science and Technology (CUSAT), Kochi 682022, Kerala, India
| | - Jinu Jacob George
- Materials
Science and NanoEngineering Lab, Department of Polymer Science and
Rubber Technology, Cochin University of
Science and Technology (CUSAT), Kochi 682022, Kerala, India
| | - Bakhtiyor Rasulev
- Department
of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Prasanth Raghavan
- Materials
Science and NanoEngineering Lab, Department of Polymer Science and
Rubber Technology, Cochin University of
Science and Technology (CUSAT), Kochi 682022, Kerala, India
- Department
of Materials Engineering and Convergence Technology, Gyeongsang National University, 501 Jinju-daero, Jinju 52828, Republic of Korea
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17
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Han Y, Iduoku K, Grant G, Rasulev B, Leontyev A, Hobbie EK, Tretiak S, Kilina SV, Kilin DS. Hot Carrier Dynamics at Ligated Silicon(111) Surfaces: A Computational Study. J Phys Chem Lett 2021; 12:7504-7511. [PMID: 34342460 DOI: 10.1021/acs.jpclett.1c02084] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
We provide a case-study for thermal grafting of benzenediazonium bromide onto a hydrogenated Si(111) surface using ab initio molecular dynamics (AIMD) calculations. A sequence of reaction steps is identified in the AIMD trajectory, including the loss of N2 from the diazonium salt, proton transfer from the surface to the bromide ion that eliminates HBr, and deposition of the phenyl group onto the surface. We next assess the influence of the phenyl groups on photophysics of hydrogen-terminated Si(111) slabs. The nonadiabatic couplings necessary for a description of the excited-state dynamics are calculated by combining ab initio electronic structures and reduced density matrix formalism with Redfield theory. The phenyl-terminated slab shows reduced nonradiative relaxation and recombination rates of hot charge carriers in comparison with the hydrogen-terminated slab. Altogether, our results provide atomistic insights revealing that (i) the diazonium salt thermally decomposes at the surface allowing the formation of covalently bonded phenyl group, and (ii) the coverage of phenyl groups on the surface slows down charge carrier cooling driven by electron-phonon interactions, which increases photoluminescence efficiency at the near-infrared spectral region.
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Affiliation(s)
- Yulun Han
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Kweeni Iduoku
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Gena Grant
- Turtle Mountain Community College, 10145 BIA Road 7, PO Box 340, Belcourt, North Dakota 58316, United States
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Alexey Leontyev
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Erik K Hobbie
- Department of Physics, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Sergei Tretiak
- Theoretical Division and Center for Integrated Nanotechnologies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Svetlana V Kilina
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Dmitri S Kilin
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58108, United States
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18
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Diéguez-Santana K, Casañola-Martin GM, Green JR, Rasulev B, González-Díaz H. Predicting Metabolic Reaction Networks with Perturbation-Theory Machine Learning (PTML) Models. Curr Top Med Chem 2021; 21:819-827. [PMID: 33797370 DOI: 10.2174/1568026621666210331161144] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 12/30/2020] [Accepted: 01/07/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Checking the connectivity (structure) of complex Metabolic Reaction Networks (MRNs) models proposed for new microorganisms with promising properties is an important goal for chemical biology. OBJECTIVE In principle, we can perform a hand-on checking (Manual Curation). However, this is a challenging task due to the high number of combinations of pairs of nodes (possible metabolic reactions). RESULTS The CPTML linear model obtained using the LDA algorithm is able to discriminate nodes (metabolites) with the correct assignation of reactions from incorrect nodes with values of accuracy, specificity, and sensitivity in the range of 85-100% in both training and external validation data series. METHODS In this work, we used Combinatorial Perturbation Theory and Machine Learning techniques to seek a CPTML model for MRNs >40 organisms compiled by Barabasis' group. First, we quantified the local structure of a very large set of nodes in each MRN using a new class of node index called Markov linear indices fk. Next, we calculated CPT operators for 150000 combinations of query and reference nodes of MRNs. Last, we used these CPT operators as inputs of different ML algorithms. CONCLUSION Meanwhile, PTML models based on Bayesian network, J48-Decision Tree and Random Forest algorithms were identified as the three best non-linear models with accuracy greater than 97.5%. The present work opens the door to the study of MRNs of multiple organisms using PTML models.
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Affiliation(s)
- Karel Diéguez-Santana
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, and Basque Center for Biophysics CSIC-UPV/EHU, Leioa 48940, Great Bilbao, Biscay, Basque Country, Spain
| | | | - James R Green
- Department of Systems and Computer Engineering, Carleton University, K1S 5B6, Ottawa, ON, Canada
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, United States
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, and Basque Center for Biophysics CSIC-UPV/EHU, Leioa 48940, Great Bilbao, Biscay, Basque Country, Spain
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19
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Karuth A, Alesadi A, Xia W, Rasulev B. Predicting glass transition of amorphous polymers by application of cheminformatics and molecular dynamics simulations. POLYMER 2021. [DOI: 10.1016/j.polymer.2021.123495] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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20
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Simsek T, Rasulev B, Mayer C, Simsek S. Preparation and Characterization of Inclusion Complexes of β-Cyclodextrin and Phenolics from Wheat Bran by Combination of Experimental and Computational Techniques. Molecules 2020; 25:E4275. [PMID: 32961927 PMCID: PMC7570723 DOI: 10.3390/molecules25184275] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/10/2020] [Accepted: 09/14/2020] [Indexed: 11/24/2022] Open
Abstract
Bitterness often associated with whole wheat products may be related to phenolics in the bran. Cyclodextrins (CDs) are known to form inclusion complexes. The objective was to form inclusion complexes between β-CD and wheat phenolics. Pure phenolic acids (trans-ferulic acid (FA), caffeic acid (CA), and p-coumaric acid (CO)) and phenolic acids from wheat bran were used to investigate complex formation potential. Complexes were characterized by spectroscopy techniques, and a computational and molecular modeling study was carried out. The relative amount of complex formation between β-CD and wheat bran extract was CA > CO > FA. The phenolic compounds formed inclusion complexes with β-CDs by non-covalent bonds. The quantum-mechanical calculations supported the experimental results. The most stable complex was CO/β-CD complex. The ΔH value for CO/β-CD complex was -11.72 kcal/mol and was about 3 kcal/mol more stable than the other complexes. The QSPR model showed good correlation between binding energy and 1H NMR shift for the H5 signal. This research shows that phenolics and β-CD inclusion complexes could be utilized to improve the perception of whole meal food products since inclusion complexes have the potential to mask the bitter flavor and enhance the stability of the phenolics in wheat bran.
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Affiliation(s)
- Tuba Simsek
- Department of Physical Chemistry, Duisburg-Essen University, Universitätsstr. 2, 45141 Essen, Germany;
- Department of Plant Sciences, North Dakota State University, Fargo, ND 58102, USA
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA;
| | - Christian Mayer
- Department of Physical Chemistry, Duisburg-Essen University, Universitätsstr. 2, 45141 Essen, Germany;
| | - Senay Simsek
- Department of Plant Sciences, North Dakota State University, Fargo, ND 58102, USA
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21
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Serdiuk V, Shogren KL, Kovalenko T, Rasulev B, Yaszemski M, Maran A, Voronov A. Detection of macromolecular inversion-induced structural changes in osteosarcoma cells by FTIR microspectroscopy. Anal Bioanal Chem 2020; 412:7253-7262. [PMID: 32879994 DOI: 10.1007/s00216-020-02858-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 07/01/2020] [Accepted: 08/03/2020] [Indexed: 11/29/2022]
Abstract
Fourier transform infrared (FTIR) microspectroscopy provides a biochemical fingerprint of the cells. In this study, chemical changes in 143B osteosarcoma cells were investigated using FTIR analysis of cancer cells after their treatment with polymeric invertible micellar assemblies (IMAs) and curcumin-loaded IMAs and compared with untreated osteosarcoma cells. A comprehensive principal component analysis (PCA) was applied to analyze the FTIR results and confirm noticeable changes in cell surface chemical structures in the fingerprint regions of 1480-900 cm-1. The performed clustering shows visible differences for all investigated groups of cancer cells. It is demonstrated that a combination of FTIR microspectroscopy with PCA can be an efficient approach in determining interactions of osteosarcoma cells and drug-loaded polymer micellar assemblies. Graphical abstract.
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Affiliation(s)
- Vitalii Serdiuk
- Department of Orthopedics, Mayo Clinic, Rochester, MN, 55905, USA.,Department of Coatings & Polymeric Materials, North Dakota State University, Fargo, ND, 58105, USA.,Department of Organic Chemistry, Lviv Polytechnic National University, Lviv, 79013, Ukraine
| | | | - Tetiana Kovalenko
- Department of Organic Chemistry, Lviv Polytechnic National University, Lviv, 79013, Ukraine
| | - Bakhtiyor Rasulev
- Department of Coatings & Polymeric Materials, North Dakota State University, Fargo, ND, 58105, USA
| | | | | | - Andriy Voronov
- Department of Coatings & Polymeric Materials, North Dakota State University, Fargo, ND, 58105, USA.
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22
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Erickson ME, Ngongang M, Rasulev B. A Refractive Index Study of a Diverse Set of Polymeric Materials by QSPR with Quantum-Chemical and Additive Descriptors. Molecules 2020; 25:molecules25173772. [PMID: 32825028 PMCID: PMC7503810 DOI: 10.3390/molecules25173772] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 08/10/2020] [Accepted: 08/14/2020] [Indexed: 11/23/2022] Open
Abstract
Predicting the activities and properties of materials via in silico methods has been shown to be a cost- and time-effective way of aiding chemists in synthesizing materials with desired properties. Refractive index (n) is one of the most important defining characteristics of an optical material. Presented in this work is a quantitative structure–property relationship (QSPR) model that was developed to predict the refractive index for a diverse set of polymers. A number of models were created, where a four-variable model showed the best predictive performance with R2 = 0.904 and Q2LOO = 0.897. The robustness and predictability of the best model was validated using the leave-one-out technique, external set and y-scrambling methods. The predictive ability of the model was confirmed with the external set, showing the R2ext = 0.880. For the refractive index, the ionization potential, polarizability, 2D and 3D geometrical descriptors were the most influential properties. The developed model was transparent and mechanistically explainable and can be used in the prediction of the refractive index for new and untested polymers.
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23
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Sigurnjak M, Ukić Š, Cvetnić M, Markić M, Novak Stankov M, Rasulev B, Kušić H, Lončarić Božić A, Rogošić M, Bolanča T. Combined toxicities of binary mixtures of alachlor, chlorfenvinphos, diuron and isoproturon. Chemosphere 2020; 240:124973. [PMID: 31726602 DOI: 10.1016/j.chemosphere.2019.124973] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 09/23/2019] [Accepted: 09/24/2019] [Indexed: 06/10/2023]
Abstract
Pesticides are the chemicals of increased concern regarding their adverse environmental effects. In particular, the reports on their joint toxicity effects are scarce in the literature. Therefore, this paper describes the experiments on toxicities of four pesticides: alachlor, chlorfenvinphos, diuron, and isoproturon, toward Vibrio fischeri. In particular, the joint toxicity effects for all possible binary combinations of the pesticides were analyzed. The analysis included the application of concentration addition and independent action models at two toxicity levels: EC10 and EC50. The analysis revealed additive behavior between all pesticide pairs. The only exception was isoproturon and chlorfenvinphos whose combination resulted in synergistic toxic activity. The original form of the logistic function was given preference over the linearized form in describing the response-dose relationships of investigated pesticides.
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Affiliation(s)
- M Sigurnjak
- University of Zagreb, Faculty of Chemical Engineering and Technology, Marulićev Trg 19, 10000, Zagreb, Croatia
| | - Š Ukić
- University of Zagreb, Faculty of Chemical Engineering and Technology, Marulićev Trg 19, 10000, Zagreb, Croatia.
| | - M Cvetnić
- University of Zagreb, Faculty of Chemical Engineering and Technology, Marulićev Trg 19, 10000, Zagreb, Croatia
| | - M Markić
- University of Zagreb, Faculty of Chemical Engineering and Technology, Marulićev Trg 19, 10000, Zagreb, Croatia
| | - M Novak Stankov
- University of Zagreb, Faculty of Chemical Engineering and Technology, Marulićev Trg 19, 10000, Zagreb, Croatia
| | - B Rasulev
- North Dakota State University, Department of Coatings and Polymeric Materials, Fargo, ND, 58102, USA
| | - H Kušić
- University of Zagreb, Faculty of Chemical Engineering and Technology, Marulićev Trg 19, 10000, Zagreb, Croatia
| | - A Lončarić Božić
- University of Zagreb, Faculty of Chemical Engineering and Technology, Marulićev Trg 19, 10000, Zagreb, Croatia
| | - M Rogošić
- University of Zagreb, Faculty of Chemical Engineering and Technology, Marulićev Trg 19, 10000, Zagreb, Croatia
| | - T Bolanča
- University of Zagreb, Faculty of Chemical Engineering and Technology, Marulićev Trg 19, 10000, Zagreb, Croatia
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24
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Fjodorova N, Novič M, Venko K, Rasulev B. A Comprehensive Cheminformatics Analysis of Structural Features Affecting the Binding Activity of Fullerene Derivatives. Nanomaterials (Basel) 2020; 10:E90. [PMID: 31906497 PMCID: PMC7023229 DOI: 10.3390/nano10010090] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 12/24/2019] [Accepted: 12/27/2019] [Indexed: 01/08/2023]
Abstract
Nanostructures like fullerene derivatives (FDs) belong to a new family of nano-sized organic compounds. Fullerenes have found a widespread application in material science, pharmaceutical, biomedical, and medical fields. This fact caused the importance of the study of pharmacological as well as toxicological properties of this relatively new family of chemicals. In this work, a large set of 169 FDs and their binding activity to 1117 proteins was investigated. The structure-based descriptors widely used in drug design (so-called drug-like descriptors) were applied to understand cheminformatics characteristics related to the binding activity of fullerene nanostructures. Investigation of applied descriptors demonstrated that polarizability, topological diameter, and rotatable bonds play the most significant role in the binding activity of FDs. Various cheminformatics methods, including the counter propagation artificial neural network (CPANN) and Kohonen network as visualization tool, were applied. The results of this study can be applied to compose the priority list for testing in risk assessment related to the toxicological properties of FDs. The pharmacologist can filter the data from the heat map to view all possible side effects for selected FDs.
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Affiliation(s)
- Natalja Fjodorova
- National Institute of Chemistry, SI-1000 Ljubljana, Slovenia; (M.N.); (K.V.)
| | - Marjana Novič
- National Institute of Chemistry, SI-1000 Ljubljana, Slovenia; (M.N.); (K.V.)
| | - Katja Venko
- National Institute of Chemistry, SI-1000 Ljubljana, Slovenia; (M.N.); (K.V.)
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA;
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25
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Ukić Š, Sigurnjak M, Cvetnić M, Markić M, Stankov MN, Rogošić M, Rasulev B, Lončarić Božić A, Kušić H, Bolanča T. Toxicity of pharmaceuticals in binary mixtures: Assessment by additive and non-additive toxicity models. Ecotoxicol Environ Saf 2019; 185:109696. [PMID: 31585393 DOI: 10.1016/j.ecoenv.2019.109696] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 09/16/2019] [Accepted: 09/17/2019] [Indexed: 06/10/2023]
Abstract
Current risk assessment in many countries, including European Union, is still placing focus on single substances rather than their mixtures, although mixtures are commonly found in the environment. To overcome this problem and gain new insights, six pharmaceuticals, namely: azithromycin (AZM), erythromycin (ERM), carbamazepine (CBA), oxytetracycline (OTC), dexamethasone (DXM), and diclofenac (DCF), were selected in order to analyze their combined toxicity in binary mixtures. Overall, 45 binary mixtures were analyzed. Single component toxicities were determined as well, for modelling purpose. Two most common mathematical models for the description of mixture toxicities were applied: concentration addition (CA) and independent action (IA) model. Comparison of the predicted and experimentally obtained toxicities provided information about the modes of toxicity action in the mixtures. OTC-DCF binary mixture indicated synergism with respect to additive behavior (CA model). All other binary combinations containing OTC or DCF were acting very similarly: the synergism with respect to additive behavior was observed for OTC-CBA and DCF-CBA combinations, while OTC-AZM, OTC-ERM, DCF-AZM and DCF-ERM exhibited antagonistic behavior with respect to CA model. All the remaining binary mixtures indicated additive behavior. The applicability of IA model as a proof of independent toxic action of the components was confirmed in cases of DCF-AZM, DCF-ERM, and OTC-AZM mixtures.
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Affiliation(s)
- Š Ukić
- University of Zagreb, Faculty of Chemical Engineering and Technology, Marulićev trg 19, 10000, Zagreb, Croatia.
| | - M Sigurnjak
- University of Zagreb, Faculty of Chemical Engineering and Technology, Marulićev trg 19, 10000, Zagreb, Croatia
| | - M Cvetnić
- University of Zagreb, Faculty of Chemical Engineering and Technology, Marulićev trg 19, 10000, Zagreb, Croatia
| | - M Markić
- University of Zagreb, Faculty of Chemical Engineering and Technology, Marulićev trg 19, 10000, Zagreb, Croatia
| | - M Novak Stankov
- University of Zagreb, Faculty of Chemical Engineering and Technology, Marulićev trg 19, 10000, Zagreb, Croatia
| | - M Rogošić
- University of Zagreb, Faculty of Chemical Engineering and Technology, Marulićev trg 19, 10000, Zagreb, Croatia
| | - B Rasulev
- North Dakota State University, Department of Coatings and Polymeric Materials, Fargo, ND, 58102, USA
| | - A Lončarić Božić
- University of Zagreb, Faculty of Chemical Engineering and Technology, Marulićev trg 19, 10000, Zagreb, Croatia
| | - H Kušić
- University of Zagreb, Faculty of Chemical Engineering and Technology, Marulićev trg 19, 10000, Zagreb, Croatia
| | - T Bolanča
- University of Zagreb, Faculty of Chemical Engineering and Technology, Marulićev trg 19, 10000, Zagreb, Croatia
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26
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Sizochenko N, Syzochenko M, Fjodorova N, Rasulev B, Leszczynski J. Evaluating genotoxicity of metal oxide nanoparticles: Application of advanced supervised and unsupervised machine learning techniques. Ecotoxicol Environ Saf 2019; 185:109733. [PMID: 31580980 DOI: 10.1016/j.ecoenv.2019.109733] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 09/21/2019] [Accepted: 09/24/2019] [Indexed: 06/10/2023]
Abstract
Presence of missing data points in datasets is among main challenges in handling the toxicological data for nanomaterials. As the processing of missing data is an important part of data analysis, we have introduced a read-across approach that uses a combination of supervised and unsupervised machine learning techniques to fill the missing values. A series of classification models (supervised learning) was developed to predict class label, and self-organizing map approach (unsupervised learning) was used to estimate relative distances between nanoparticles and refine results obtained during supervised learning. In this study, genotoxicity of 49 silicon and metal oxide nanoparticles in Ames and Comet tests. Collected literature data did not demonstrate significant variations related to the change of size including selected bulk materials. Genotoxicity-related features of nanomaterials were represented by ionic characteristics. General tendencies found in the current study were convincingly linked to known theories of genotoxic action at nano-level. Mechanisms of primary and secondary genotoxic effects were discussed in the context of developed models.
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Affiliation(s)
- Natalia Sizochenko
- Interdisciplinary Center for Nanotoxicity, Jackson State University, Jackson, MS, USA; Department of Computer Science, Dartmouth College, Hanover, 03755, NH, USA.
| | - Michael Syzochenko
- Interdisciplinary Center for Nanotoxicity, Jackson State University, Jackson, MS, USA; Department of Computer Science, Dartmouth College, Hanover, 03755, NH, USA.
| | - Natalja Fjodorova
- Department of Chemoinformatics, National Institute of Chemistry, Ljubljana, 1000, Slovenia.
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, 58108, ND, USA.
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Jackson State University, Jackson, MS, USA.
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27
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Usmanov D, Yusupova U, Syrov V, Ramazonov N, Rasulev B. Iridoid glucosides and triterpene acids from Phlomis linearifolia, growing in Uzbekistan and its hepatoprotective activity. Nat Prod Res 2019; 35:2449-2453. [PMID: 31646905 DOI: 10.1080/14786419.2019.1677650] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
A number of iridoids and triterpene acids, such as pulchelloside, sesamoside, shanshiside methyl ester, barlerin, gypsogenin acid and acetate of gypsogenic acid were isolated from the aerial part of Phlomis linearifolia and their structures were confirmed by NMR, mass and IR spectroscopy. In addition, the hepatoprotective potential of iridoid fraction from P. linearifolia aerial parts was tested against CCl4 induced fibrosis in rats. The iridoid fraction not only prevented the manifestation of the hepatotoxic effect of CCl4, but rather quickly eliminated the effects of developing intoxication. The hepatoprotective activity of the SI was confirmed to be effective and exceeds knows drug carsil.Supplemental data for this article can be accessed at https://doi.org/10.1080/14786419.2019.1677650.
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Affiliation(s)
- Durbek Usmanov
- Institute of the Chemistry of Plant Substances, Academy of Sciences , Tashkent , Uzbekistan
| | - Ugiloy Yusupova
- Institute of the Chemistry of Plant Substances, Academy of Sciences , Tashkent , Uzbekistan
| | - Vladimir Syrov
- Institute of the Chemistry of Plant Substances, Academy of Sciences , Tashkent , Uzbekistan
| | - Nurmurod Ramazonov
- Institute of the Chemistry of Plant Substances, Academy of Sciences , Tashkent , Uzbekistan
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University , Fargo , ND , USA
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28
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Mikolajczyk A, Sizochenko N, Mulkiewicz E, Malankowska A, Rasulev B, Puzyn T. A chemoinformatics approach for the characterization of hybrid nanomaterials: safer and efficient design perspective. Nanoscale 2019; 11:11808-11818. [PMID: 31184677 DOI: 10.1039/c9nr01162e] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
In this study, photocatalytic properties and in vitro cytotoxicity of 29 TiO2-based multi-component nanomaterials (i.e., hybrids of more than two composition types of nanoparticles) were evaluated using a combination of the experimental testing and supervised machine learning modeling. TiO2-based multi-component nanomaterials with metal clusters of silver, and their mixtures with gold, palladium, and platinum were successfully synthesized. Two activities, photocatalytic activity and cytotoxicity, were studied. A novel cheminformatic approach was developed and applied for the computational representation of the photocatalytic activity and cytotoxicity effect. In this approach, features of investigated TiO2-based hybrid nanomaterials were reflected by a series of novel additive descriptors for hybrid and hybrid nanostructures (denoted as "hybrid nanosctructure descriptors"). These descriptors are based on quantum chemical calculations and the Smoluchowski equation. The obtained experimental data and calculated hybrid-nanostructure descriptors were used to develop novel predictive Quantitative Structure-Activity Relationship computational models (called "nano-QSARmix"). The proposed modeling approach is an initial step in the understanding of the relationships between physicochemical properties of hybrid nanoparticles, their toxicity, and photochemical activity under UV-vis irradiation. Acquired knowledge supports the safe-by-design approaches relevant to the development of efficient hybrid nanomaterials with reduced hazardous effects.
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Affiliation(s)
- Alicja Mikolajczyk
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland.
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29
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Simsek T, Simsek S, Mayer C, Rasulev B. Combined computational and experimental study on the inclusion complexes of β-cyclodextrin with selected food phenolic compounds. Struct Chem 2019. [DOI: 10.1007/s11224-019-01347-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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30
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Diéguez-Santana K, Rivera-Borroto OM, Puris A, Pham-The H, Le-Thi-Thu H, Rasulev B, Casañola-Martin GM. Beyond model interpretability using LDA and decision trees for α-amylase and α-glucosidase inhibitor classification studies. Chem Biol Drug Des 2019; 94:1414-1421. [PMID: 30908888 DOI: 10.1111/cbdd.13518] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2018] [Revised: 02/17/2019] [Accepted: 03/03/2019] [Indexed: 12/17/2022]
Abstract
In this report are used two data sets involving the main antidiabetic enzyme targets α-amylase and α-glucosidase. The prediction of α-amylase and α-glucosidase inhibitory activity as antidiabetic is carried out using LDA and classification trees (CT). A large data set of 640 compounds for α-amylase and 1546 compounds in the case of α-glucosidase are selected to develop the tree model. In the case of CT-J48 have the better classification model performances for both targets with values above 80%-90% for the training and prediction sets, correspondingly. The best model shows an accuracy higher than 95% for training set; the model was also validated using 10-fold cross-validation procedure and through a test set achieving accuracy values of 85.32% and 86.80%, correspondingly. Additionally, the obtained model is compared with other approaches previously published in the international literature showing better results. Finally, we can say that the present results provided a double-target approach for increasing the estimation of antidiabetic chemicals identification aimed by double-way workflow in virtual screening pipelines.
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Affiliation(s)
| | - Oscar M Rivera-Borroto
- Departamento de Química Física Aplicada, Facultad de Ciencias, Universidad Autónoma de Madrid, Madrid, Spain
| | - Amilkar Puris
- Facultad de Ciencias de La Ingeniería, Universidad Técnica Estatal de Quevedo, Quevedo, Ecuador
| | | | - Huong Le-Thi-Thu
- School of Medicine and Pharmacy, Vietnam National University, Hanoi, Vietnam
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota
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31
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Petrosyan LS, Sizochenko N, Leszczynski J, Rasulev B. Modeling of Glass Transition Temperatures for Polymeric Coating Materials: Application of QSPR Mixture-based Approach. Mol Inform 2019; 38:e1800150. [PMID: 30945811 DOI: 10.1002/minf.201800150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 03/04/2019] [Indexed: 11/08/2022]
Abstract
Cross-linked block copolymers are structurally complex, and utilization of traditional methods of molecular representation in chemoinformatics is only of limited applicability. Therefore, we introduced new techniques of structural representation for block copolymers. We developed additive and combinatorial approaches that treat a copolymer as a mixture system. In this approach, DRAGON descriptors are concentration-weighted for all chemicals in the reaction mixture. As a proof of concept, we have studied glass transition temperatures of block copolymers of hydroxyalkyl- and dihydroxyalkyl carbamate terminated poly(dimethylsiloxane) oligomers with poly(-caprolactone) and developed four quantitative structure-property relationships (QSPR) models. The correlation coefficient (R2 ) for mentioned QSPR models ranges from 0.851 to 0.911 for the training set. In addition to the newly introduced technique we found that the octanol-water partition coefficient and 3D-MoRSE unweighted descriptors were the most important descriptors for the studied property. The results of the study demonstrated that all chemicals in reaction mixture influenced the glass transition temperatures.
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Affiliation(s)
- Lyudvig S Petrosyan
- Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA.,Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, 58105, USA
| | - Natalia Sizochenko
- Interdisciplinary Center for Nanotoxicity, Jackson State University, Jackson, MS, 39217, USA.,Department of Computer Science, Dartmouth College, Hanover, NH, 03755, USA
| | - Jerzy Leszczynski
- Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA.,Interdisciplinary Center for Nanotoxicity, Jackson State University, Jackson, MS, 39217, USA
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, 58105, USA
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32
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Pham-The H, Cabrera-Pérez MÁ, Nam NH, Castillo-Garit JA, Rasulev B, Le-Thi-Thu H, Casañola-Martin GM. In Silico Assessment of ADME Properties: Advances in Caco-2 Cell Monolayer Permeability Modeling. Curr Top Med Chem 2019; 18:2209-2229. [PMID: 30499410 DOI: 10.2174/1568026619666181130140350] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Revised: 10/16/2018] [Accepted: 11/19/2018] [Indexed: 11/22/2022]
Abstract
One of the main goals of in silico Caco-2 cell permeability models is to identify those drug substances with high intestinal absorption in human (HIA). For more than a decade, several in silico Caco-2 models have been made, applying a wide range of modeling techniques; nevertheless, their capacity for intestinal absorption extrapolation is still doubtful. There are three main problems related to the modest capacity of obtained models, including the existence of inter- and/or intra-laboratory variability of recollected data, the influence of the metabolism mechanism, and the inconsistent in vitro-in vivo correlation (IVIVC) of Caco-2 cell permeability. This review paper intends to sum up the recent advances and limitations of current modeling approaches, and revealed some possible solutions to improve the applicability of in silico Caco-2 permeability models for absorption property profiling, taking into account the above-mentioned issues.
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Affiliation(s)
- Hai Pham-The
- Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hanoi, Vietnam
| | - Miguel Á Cabrera-Pérez
- Unit of Modeling and Experimental Biopharmaceutics, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba.,Department of Engineering, Area of Pharmacy and Pharmaceutical Technology, Miguel Hernández University, 03550 Sant Juan d'Alacant, Alicante, Spain
| | - Nguyen-Hai Nam
- Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hanoi, Vietnam
| | - Juan A Castillo-Garit
- Unidad de Toxicologia Experimental, Universidad de Ciencias Medicas "Dr. Serafín Ruiz de Zarate Ruiz" de Villa Clara, Santa Clara, 50200, Villa Clara, Cuba
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymer Materials, North Dakota State University, Fargo, ND, 58102, United States
| | - Huong Le-Thi-Thu
- School of Medicine and Pharmacy, Vietnam National University, 144 Xuan Thuy, Hanoi, Vietnam
| | - Gerardo M Casañola-Martin
- Department of Coatings and Polymer Materials, North Dakota State University, Fargo, ND, 58102, United States
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33
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Cvetnic M, Juretic Perisic D, Kovacic M, Ukic S, Bolanca T, Rasulev B, Kusic H, Loncaric Bozic A. Toxicity of aromatic pollutants and photooxidative intermediates in water: A QSAR study. Ecotoxicol Environ Saf 2019; 169:918-927. [PMID: 30597792 DOI: 10.1016/j.ecoenv.2018.10.100] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2018] [Revised: 10/23/2018] [Accepted: 10/26/2018] [Indexed: 06/09/2023]
Abstract
Extensive commercial use of aromatic hydrocarbons results with significant amounts of these chemicals and related by-products in waters, causing a severe ecological and health threat, thus requiring an increased attention. This study was aimed at developing models for prediction of the initial toxicity of the aromatic water-pollutants (expressed as EC50 and TU0) as well as the toxicity of their intermediates at half-life of the parent pollutant (TU1/2). For that purpose, toxicity toward Vibrio fischery was determined for 36 single-benzene ring compounds (S-BRCs), diversified by the type, number and position of substituents. Quantitative structure-activity relationship (QSAR) methodology paired with genetic algorithm optimization tool and multiple linear regression was applied to obtain the models predicting the targeted toxicity, which are based on pure structural characteristics of the tested pollutants, avoiding thus additional experimentation. Upon derivation of the models and extensive analysis on training and test sets, 4-, 4- and 5-variable models (for EC50 and TU0, TU1/2, respectively) were selected as the most predictive possessing 0.839<R2< 0.901 and 0.789<Q2< 0.859. The analysis of the selected descriptors indicated three major structural characteristics influencing the toxicity: electronegativity, geometry and electrotopological states of the molecule. Degradation kinetics determining as well the pathways of intermediates formation, reflected over ionization potential, was found to be an important parameter determining the toxicity in half-life.
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Affiliation(s)
- Matija Cvetnic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev trg 19, Zagreb 10000, Croatia
| | - Daria Juretic Perisic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev trg 19, Zagreb 10000, Croatia
| | - Marin Kovacic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev trg 19, Zagreb 10000, Croatia
| | - Sime Ukic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev trg 19, Zagreb 10000, Croatia.
| | - Tomislav Bolanca
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev trg 19, Zagreb 10000, Croatia
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
| | - Hrvoje Kusic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev trg 19, Zagreb 10000, Croatia.
| | - Ana Loncaric Bozic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev trg 19, Zagreb 10000, Croatia
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34
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Cvetnić M, Novak Stankov M, Kovačić M, Ukić Š, Bolanča T, Kušić H, Rasulev B, Dionysiou DD, Lončarić Božić A. Key structural features promoting radical driven degradation of emerging contaminants in water. Environ Int 2019; 124:38-48. [PMID: 30639906 DOI: 10.1016/j.envint.2018.12.043] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 12/05/2018] [Accepted: 12/18/2018] [Indexed: 06/09/2023]
Abstract
Diverse contaminants of emerging concern (CECs) can be found in nowadays aquatic environment, possessing high potential to cause adverse ecological and human health effects. Due to their recalcitrance, conventional water treatment methods are shown to be inadequately effective. Thus, their upgrade by advanced oxidation processes, involving the generation of highly reactive species (HO and SO4-), is highly demanded. In order to assess the susceptibility of CECs by HO and SO4-, as well as to determine the corresponding reaction rate constants kHO and kSO4-, the complex experimental studies has to be maintained. The alternative is the application of modeling approaches which correlate structural characteristics with activities/properties of interest, i.e. quantitative structure activity/property relationship (QSAR/QSPR). In this study kHO and kSO4- of fifteen selected CECs were determined by competitive kinetics, and afterward used to elucidate key structural features promoting their degradation. In that purpose, QSPR models were constructed using multiple linear regression (MLR) combined with genetic algorithm (GA) approach. The models were submitted to the internal and external validation (using additional set of 17 CECs). Selected 3-variable models predicting kHO and kSO4- were characterized with high accuracy and predictivity (R2 = 0.876 and Q2 = 0.847 and R2 = 0.832 and Q2 = 0.778, respectively). Although selected models at the first sight include descriptors derived through complicated calculation procedures, their weighting schemes indicate on their relevance and transparency toward established reaction theories and differences regarding radical type.
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Affiliation(s)
- Matija Cvetnić
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulićev trg 19, 10000 Zagreb, Croatia
| | - Mirjana Novak Stankov
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulićev trg 19, 10000 Zagreb, Croatia
| | - Marin Kovačić
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulićev trg 19, 10000 Zagreb, Croatia
| | - Šime Ukić
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulićev trg 19, 10000 Zagreb, Croatia
| | - Tomislav Bolanča
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulićev trg 19, 10000 Zagreb, Croatia
| | - Hrvoje Kušić
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulićev trg 19, 10000 Zagreb, Croatia.
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
| | - Dionysios D Dionysiou
- Environmental Engineering and Science Program, University of Cincinnati, Cincinnati, OH 45221-0012, USA
| | - Ana Lončarić Božić
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulićev trg 19, 10000 Zagreb, Croatia
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35
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Chitemere RP, Stafslien S, Rasulev B, Webster DC, Quadir M. Soysome: A Surfactant-Free, Fully Biobased, Self-Assembled Platform for Nanoscale Drug Delivery Applications. ACS Appl Bio Mater 2018; 1:1830-1841. [DOI: 10.1021/acsabm.8b00317] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Ruvimbo P. Chitemere
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Shane Stafslien
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Dean C. Webster
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Mohiuddin Quadir
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58108, United States
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36
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Khan P, Rasulev B, Roy K. QSPR Modeling of the Refractive Index for Diverse Polymers Using 2D Descriptors. ACS Omega 2018; 3:13374-13386. [PMID: 31458051 PMCID: PMC6645227 DOI: 10.1021/acsomega.8b01834] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 09/28/2018] [Indexed: 06/10/2023]
Abstract
In the present work, predictive quantitative structure-property relationship models have been developed to predict refractive indices (RIs) of a set of 221 diverse organic polymers using theoretical two-dimensional descriptors generated on the basis of the structures of polymers' monomer units. Four models have been developed by applying partial least squares (PLS) regression with a different combination of six descriptors obtained via double cross-validation approaches. The predictive ability and robustness of the proposed models were checked using multiple validation strategies. Subsequently, the validated models were used for the generation of "intelligent" consensus models (http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) to improve the quality of predictions for the external data set. The selected consensus models were used for the prediction of refractive index values of various classes of polymers. The final selected model was used to predict the refractive index of four small virtual libraries of monomers recently reported. We also used a true external data set of 98 diverse monomer units with the experimental RI values of the corresponding polymers. The obtained models showed a good predictive ability as evidenced from a very good external predicted variance.
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Affiliation(s)
- Pathan
Mohsin Khan
- Department
of Pharmacoinformatics, National Institute
of Pharmaceutical Educational and Research (NIPER), Chunilal Bhawan, 168, Manikata Main Road, 700054 Kolkata, India
| | - Bakhtiyor Rasulev
- Department
of Coatings and Polymeric Materials, North
Dakota State University, Fargo, North Dakota 58108-6050, United States
| | - Kunal Roy
- Drug
Theoretics and Cheminformatics Laboratory, Division of Medicinal and
Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, 700032 Kolkata, India
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37
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Golbamaki A, Golbamaki N, Sizochenko N, Rasulev B, Leszczynski J, Benfenati E. Genotoxicity induced by metal oxide nanoparticles: a weight of evidence study and effect of particle surface and electronic properties. Nanotoxicology 2018; 12:1113-1129. [PMID: 29888633 DOI: 10.1080/17435390.2018.1478999] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
The genetic toxicology of nanomaterials is a crucial toxicology issue and one of the least investigated topics. Substantially, the genotoxicity of metal oxide nanomaterials' data is resulting from in vitro comet assay. Current contributions to the genotoxicity data assessed by the comet assay provide a case-by-case evaluation of different types of metal oxides. The existing inconsistency in the literature regarding the genotoxicity testing data requires intelligent assessment strategies, such as weight of evidence evaluation. Two main tasks were performed in the present study. First, the genotoxicity data from comet assay for 16 noncoated metal oxide nanomaterials with different core composition were collected. An evaluation criterion was applied to establish which of these individual lines of evidence were of sufficient quality and what weight could have been given to them in inferring genotoxic results. The collected data were surveyed on (1) minimum necessary characterization points for nanomaterials and (2) principals of correct comet assay testing for nanomaterials. Second, in this study the genotoxicity effect of metal oxide nanomaterials was investigated by quantitative nanostructure-activity relationship approach. A set of quantum-chemical descriptors was developed for all investigated metal oxide nanomaterials. A classification model based on decision tree was developed for the investigated dataset. Thus, three descriptors were identified as the most responsible factors for genotoxicity effect: heat of formation, molecular weight, and surface area of the oxide cluster based on the conductor-like screening model. Conclusively, the proposed genotoxicity assessment strategy is useful to prioritize the study of the nanomaterials for further risk assessment evaluations.
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Affiliation(s)
- Azadi Golbamaki
- a Department of Environmental Health Sciences , Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
| | - Nazanin Golbamaki
- a Department of Environmental Health Sciences , Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
| | - Natalia Sizochenko
- b Interdisciplinary Center for Nanotoxicity , Jackson State University , Jackson , MS , USA.,c Department of Computer Science , Dartmouth College, Sudikoff Lab , Hanover , NH , USA
| | - Bakhtiyor Rasulev
- b Interdisciplinary Center for Nanotoxicity , Jackson State University , Jackson , MS , USA.,d Department of Coatings and Polymeric Materials , North Dakota State University , Fargo , ND , USA
| | - Jerzy Leszczynski
- b Interdisciplinary Center for Nanotoxicity , Jackson State University , Jackson , MS , USA
| | - Emilio Benfenati
- a Department of Environmental Health Sciences , Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
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38
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Chen M, Jabeen F, Rasulev B, Ossowski M, Boudjouk P. A computational structure–property relationship study of glass transition temperatures for a diverse set of polymers. ACTA ACUST UNITED AC 2018. [DOI: 10.1002/polb.24602] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Min Chen
- Center for Computationally Assisted Science and TechnologyNorth Dakota State UniversityFargo North Dakota58102
- Department of Computer ScienceNorth Dakota State UniversityFargo North Dakota58102
| | - Farukh Jabeen
- Center for Computationally Assisted Science and TechnologyNorth Dakota State UniversityFargo North Dakota58102
| | - Bakhtiyor Rasulev
- Center for Computationally Assisted Science and TechnologyNorth Dakota State UniversityFargo North Dakota58102
- Department of Coatings and Polymeric MaterialsNorth Dakota State UniversityFargo North Dakota58102
| | - Martin Ossowski
- Center for Computationally Assisted Science and TechnologyNorth Dakota State UniversityFargo North Dakota58102
| | - Philip Boudjouk
- Department of Chemistry and BiochemistryNorth Dakota State UniversityFargo North Dakota58102
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39
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Sizochenko N, Mikolajczyk A, Jagiello K, Puzyn T, Leszczynski J, Rasulev B. How the toxicity of nanomaterials towards different species could be simultaneously evaluated: a novel multi-nano-read-across approach. Nanoscale 2018; 10:582-591. [PMID: 29168526 DOI: 10.1039/c7nr05618d] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Application of predictive modeling approaches can solve the problem of missing data. Numerous studies have investigated the effects of missing values on qualitative or quantitative modeling, but only a few studies have discussed it for the case of applications in nanotechnology-related data. The present study is aimed at the development of a multi-nano-read-across modeling technique that helps in predicting the toxicity of different species such as bacteria, algae, protozoa, and mammalian cell lines. Herein, the experimental toxicity of 184 metal and silica oxide (30 unique chemical types) nanoparticles from 15 datasets is analyzed. A hybrid quantitative multi-nano-read-across approach that combines interspecies correlation analysis and self-organizing map analysis is developed. In the first step, hidden patterns of toxicity among nanoparticles are identified using a combination of methods. Subsequently, the developed model based on categorization of the toxicity of the metal oxide nanoparticle outcomes is evaluated via the combination of supervised and unsupervised machine learning techniques to determine the underlying factors responsible for the toxicity.
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Affiliation(s)
- Natalia Sizochenko
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
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40
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González-Durruthy M, Monserrat JM, Rasulev B, Casañola-Martín GM, Barreiro Sorrivas JM, Paraíso-Medina S, Maojo V, González-Díaz H, Pazos A, Munteanu CR. Carbon Nanotubes' Effect on Mitochondrial Oxygen Flux Dynamics: Polarography Experimental Study and Machine Learning Models using Star Graph Trace Invariants of Raman Spectra. Nanomaterials (Basel) 2017; 7:nano7110386. [PMID: 29137126 PMCID: PMC5707603 DOI: 10.3390/nano7110386] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2017] [Revised: 11/06/2017] [Accepted: 11/08/2017] [Indexed: 11/16/2022]
Abstract
This study presents the impact of carbon nanotubes (CNTs) on mitochondrial oxygen mass flux (Jm) under three experimental conditions. New experimental results and a new methodology are reported for the first time and they are based on CNT Raman spectra star graph transform (spectral moments) and perturbation theory. The experimental measures of Jm showed that no tested CNT family can inhibit the oxygen consumption profiles of mitochondria. The best model for the prediction of Jm for other CNTs was provided by random forest using eight features, obtaining test R-squared (R2) of 0.863 and test root-mean-square error (RMSE) of 0.0461. The results demonstrate the capability of encoding CNT information into spectral moments of the Raman star graphs (SG) transform with a potential applicability as predictive tools in nanotechnology and material risk assessments.
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Affiliation(s)
- Michael González-Durruthy
- Institute of Biological Science (ICB), Federal University of Rio Grande, Rio Grande, RS 96270-900, Brazil.
| | - Jose M Monserrat
- Institute of Biological Science (ICB), Federal University of Rio Grande, Rio Grande, RS 96270-900, Brazil.
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University (NDSU), Fargo, ND 58102, USA.
| | | | - José María Barreiro Sorrivas
- Computer Science School (ETSIINF), Polytechnic University of Madrid (UPM), Calle de losCiruelos, Boadilla del Monte, 28660 Madrid, Spain.
| | - Sergio Paraíso-Medina
- Biomedical Informatics Group, Artificial Intelligence Department, Polytechnic University of Madrid, Calle de los Ciruelos, Boadilla del Monte, 28660 Madrid, Spain.
| | - Víctor Maojo
- Biomedical Informatics Group, Artificial Intelligence Department, Polytechnic University of Madrid, Calle de los Ciruelos, Boadilla del Monte, 28660 Madrid, Spain.
| | - Humberto González-Díaz
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940 Leioa, Biscay, Spain.
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain.
| | - Alejandro Pazos
- INIBIC Institute of Biomedical Research, CHUAC, UDC, 15006 Coruña, Spain.
- RNASA-IMEDIR, Computer Sciences Faculty, University of Coruña, 15071 Coruña, Spain.
| | - Cristian R Munteanu
- INIBIC Institute of Biomedical Research, CHUAC, UDC, 15006 Coruña, Spain.
- RNASA-IMEDIR, Computer Sciences Faculty, University of Coruña, 15071 Coruña, Spain.
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41
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Antypenko O, Kovalenko S, Rasulev B, Leszczynski J. Synthesis of 6-N-R-Tetrazolo[1,5-c]quinazolin-5(6H)-ones and Their Anticancer Activity. Acta Chim Slov 2017; 63:638-45. [PMID: 27640391 DOI: 10.17344/acsi.2016.2464] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Chemical compounds with tetrazole ring are very interesting systems that can be valuable in pharmaceutical and clinical applications, especially as anticancer agents. In this work, novel 6-N-R-tetrazolo[1,5-c]quinazolin-5(6H)-ones were synthesized. A large set of IR, LC-, EI-MS, 1H, 13C NMR and elemental analysis data were collected and evaluated for their structures and purity. Details of synthesis, namely the N-alkylation, are discussed, including reactions with secondary and tertiary amides. Four new synthesized compounds (2.7, 3.2, 5.2, 5.3) were tested in vitro for anticancer activity at 10 μM against 60 cell lines of nine different cancer types: leukemia, melanoma, lung, colon, CNS, ovarian, renal, prostate, and breast cancers. Further synthesis of substances within the series of substituted tetrazolo[1,5-c]quinazoline systems will be attempted to develop improved compounds with better anticancer activity.
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42
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Han Y, Meng Q, Rasulev B, May PS, Berry MT, Kilin DS. Photoinduced Charge Transfer versus Fragmentation Pathways in Lanthanum Cyclopentadienyl Complexes. J Chem Theory Comput 2017. [DOI: 10.1021/acs.jctc.7b00050] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Yulun Han
- Department
of Chemistry, University of South Dakota, Vermillion, South Dakota 57069, United States
- Department
of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Qingguo Meng
- Shenyang
Institute of Automation, Guangzhou, Chinese Academy of Sciences, Guangzhou 511458, China
| | - Bakhtiyor Rasulev
- Center
for Computationally Assisted Science and Technology, North Dakota State University, Fargo, North Dakota 58102, United States
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - P. Stanley May
- Department
of Chemistry, University of South Dakota, Vermillion, South Dakota 57069, United States
| | - Mary T. Berry
- Department
of Chemistry, University of South Dakota, Vermillion, South Dakota 57069, United States
| | - Dmitri S. Kilin
- Department
of Chemistry, University of South Dakota, Vermillion, South Dakota 57069, United States
- Department
of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58108, United States
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43
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Gooch A, Sizochenko N, Rasulev B, Gorb L, Leszczynski J. In vivo toxicity of nitroaromatics: A comprehensive quantitative structure-activity relationship study. Environ Toxicol Chem 2017; 36:2227-2233. [PMID: 28169452 DOI: 10.1002/etc.3761] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Revised: 11/01/2016] [Accepted: 02/06/2017] [Indexed: 06/06/2023]
Abstract
The toxicity data of 90 nitroaromatic compounds related to their 50% lethal dose concentration for rats (LD50) were analyzed to develop quantitative structure-activity relationship (QSAR) models. Quantum-chemically calculated descriptors together with molecular descriptors generated by DRAGON, PaDEL, and HiT-QSAR software were utilized to build QSAR models. Quality and validity of the models were determined by internal and external validation techniques. The results show that the toxicity of nitroaromatic compounds depends on various factors, such as the number of nitro-groups, the topological state, and the presence of certain structural fragments. The developed models based on the largest (to date) dataset of nitroaromatics in vivo toxicity showed a good predictive ability. The results provide important input that could be applied in a preliminary assessment of nitroaromatic compounds' toxicity to mammals. Environ Toxicol Chem 2017;36:2227-2233. © 2017 SETAC.
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Affiliation(s)
- Aminah Gooch
- Department of Chemistry and Biochemistry, Jackson State University, Jackson, Mississippi, USA
| | - Natalia Sizochenko
- Department of Chemistry and Biochemistry, Jackson State University, Jackson, Mississippi, USA
| | - Bakhtiyor Rasulev
- Department of Chemistry and Biochemistry, Jackson State University, Jackson, Mississippi, USA
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota, USA
| | - Leonid Gorb
- Department of Chemistry and Biochemistry, Jackson State University, Jackson, Mississippi, USA
- HX5, Vicksburg, Mississippi, USA
| | - Jerzy Leszczynski
- Department of Chemistry and Biochemistry, Jackson State University, Jackson, Mississippi, USA
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44
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Ahmed L, Rasulev B, Kar S, Krupa P, Mozolewska MA, Leszczynski J. Inhibitors or toxins? Large library target-specific screening of fullerene-based nanoparticles for drug design purpose. Nanoscale 2017; 9:10263-10276. [PMID: 28696446 DOI: 10.1039/c7nr00770a] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Fullerene-based nanoparticles have been the subject of vital interest due to their unique properties and potential application in many areas, including medicine. Here we explore their characteristics that could make them prospective leads for known disease-related proteins. High-throughput virtual screening supported by comprehensive multi-software protein-ligand docking simulation and cheminformatics approaches has been applied in investigation of interactions of 1117 proteins with a 169 fullerene nanoparticles decorated with different small molecules. Moreover, obtained docking results were confirmed by the series of unrestricted all-atom molecular dynamics (MD) simulations. Hydrophobicity of fullerene core along with hydrophilic interaction of side chains plays a key role in binding with the studied proteins. We identified a series of nanoparticles that can lead to development of robust drugs for target proteins and another series that can behave as a highly toxic agent. The structure-activity relationship analysis revealed two significant molecular properties responsible for the binding score values. The application of carefully selected computational techniques and described outcome of the study facilitate development of functional fullerene nanoparticles for drug-like and drug delivery applications.
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Affiliation(s)
- Lucky Ahmed
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, 1400 J.R. Lynch Street, P.O. Box 17910, Jackson, MS 39217, USA.
| | - Bakhtiyor Rasulev
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, 1400 J.R. Lynch Street, P.O. Box 17910, Jackson, MS 39217, USA. and Center for Computationally Assisted Science and Technology (CCAST), North Dakota State University, 1805 NDSU Research Park Dr, PO Box 6050, Fargo, ND 58108, USA and Department of Coatings and Polymer Materials, North Dakota State University, NDSU Dept. 2760, PO Box 6050, Fargo, ND 58108, USA
| | - Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, 1400 J.R. Lynch Street, P.O. Box 17910, Jackson, MS 39217, USA.
| | - Paweł Krupa
- Institute of Physics, Polish Academy of Sciences, Aleja Lotnikow 32/46, PL-02668 Warsaw, Poland
| | - Magdalena A Mozolewska
- Institute of Computer Science, Polish Academy of Sciences, ul. Jana Kazimierza 5, Warszaw, 01-248, Poland
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, 1400 J.R. Lynch Street, P.O. Box 17910, Jackson, MS 39217, USA.
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Han Y, Rasulev B, Kilin DS. Photofragmentation of Tetranitromethane: Spin-Unrestricted Time-Dependent Excited-State Molecular Dynamics. J Phys Chem Lett 2017; 8:3185-3192. [PMID: 28618779 DOI: 10.1021/acs.jpclett.7b01330] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In this study, the photofragmentation dynamics of tetranitromethane (TNM) is explored by a spin-unrestricted time-dependent excited-state molecular dynamics (u-TDESMD) algorithm based on Rabi oscillations and principles similar to trajectory surface hopping, with a midintensity field approximation. The leading order process is represented by the molecule undergoing cyclic excitations and de-excitations. During excitation cycles, the nuclear kinetic energy is accumulated to overcome the dissociation barriers in the reactant and a sequence of intermediates. The dissociation pathway includes the ejection of NO2 groups followed by the formation of NO and CO. The simulated mass spectra at the ab initio level, based on the bond length in possible fragments, are extracted from simulation trajectories. The recently developed methodology has the potential to model and monitor photoreactions with open-shell intermediates and radicals.
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Affiliation(s)
- Yulun Han
- Department of Chemistry, University of South Dakota , Vermillion, South Dakota 57069, United States
- Department of Chemistry and Biochemistry, North Dakota State University , Fargo, North Dakota 58108, United States
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University , Fargo, North Dakota 58102, United States
| | - Dmitri S Kilin
- Department of Chemistry, University of South Dakota , Vermillion, South Dakota 57069, United States
- Department of Chemistry and Biochemistry, North Dakota State University , Fargo, North Dakota 58108, United States
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46
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Fjodorova N, Novic M, Gajewicz A, Rasulev B. The way to cover prediction for cytotoxicity for all existing nano-sized metal oxides by using neural network method. Nanotoxicology 2017; 11:475-483. [PMID: 28330416 DOI: 10.1080/17435390.2017.1310949] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The regulatory agencies should fulfil the data gap in toxicity for new chemicals including nano-sized compounds, like metal oxides nanoparticles (MeOx NPs) according to the registration, evaluation, authorisation and restriction of chemicals (REACH) legislation policy. This study demonstrates the perspective capability of neural network models for prediction of cytotoxicity of MeOx NPs to bacteria Escherichia coli (E. coli) for the widest range of metal oxides extracted from Periodic table. The counter propagation artificial neural network (CP ANN) models for prediction of cytotoxicity of MeOx NPs for data sets of 17, 36 and 72 metal oxides were employed in the study. The cytotoxicity of studied metal oxide NPs was correlated with (i) χ-metal electronegativity (EN) by Pauling scale and composition of metal oxides characterised by (ii) number of metal atoms in oxide, (iii) number of oxygen atoms in oxide and (iv) charge of metal cation in oxide. The paper describes the models in context of five OECD principles of validation models accepted for regulatory use. The recommendations were done for the minimal number of cytotoxicity tests needs for evaluation of the large set of MeOx with different oxidation states. The methodology is expected to be useful for potential hazard assessment of MeOx NPs and prioritisation for further testing and risk assessment.
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Affiliation(s)
- Natalja Fjodorova
- a Department of Chemoinformatics , National Institute of Chemistry , Ljubljana , Slovenia
| | - Marjana Novic
- a Department of Chemoinformatics , National Institute of Chemistry , Ljubljana , Slovenia
| | - Agnieszka Gajewicz
- b Laboratory of Environmental Chemometrics, Faculty of Chemistry , University of Gdansk , Gdańsk , Poland
| | - Bakhtiyor Rasulev
- c Department of Coatings and Polymeric Materials , North Dakota State University , Fargo , ND , USA
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47
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Rasulev B, Jabeen F, Stafslien S, Chisholm BJ, Bahr J, Ossowski M, Boudjouk P. Polymer Coating Materials and Their Fouling Release Activity: A Cheminformatics Approach to Predict Properties. ACS Appl Mater Interfaces 2017; 9:1781-1792. [PMID: 27982587 DOI: 10.1021/acsami.6b12766] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
A novel cheminformatics-based approach has been employed to investigate a set of polymer coating materials designed to mitigate the accumulation of marine biofouling on surfaces immersed in the sea. Specifically, a set of 27 nontoxic, amphiphilic polysiloxane-based polymer coatings was synthesized using a combinatorial, high-throughput approach and characterized for fouling-release (FR) activity toward a number of relevant marine fouling organisms, including bacteria, microalgae, and adult barnacles. In order to model these complex systems adequately, a new computational technique was used in which all investigated polymer-based coating materials were considered as mixture systems comprising several compositional variables at a range of concentrations. By applying a combination of methodologies for mixture systems and a quantitative structure-activity relationship approach (QSAR), seven unique QSAR models were developed that were able to successfully predict the desired FR properties. Furthermore, the developed models identified several significant descriptors responsible for FR activity of investigated polymer-based coating materials, with correlation coefficients ranging from rtest2 = 0.63 to 0.94. The computational models derived from this study may serve as a powerful set of tools to predict optimal combinations of source components to produce amphiphilic polysiloxane-based coating systems with effective, broad-spectrum FR properties.
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Affiliation(s)
- Bakhtiyor Rasulev
- Center for Computationally Assisted Science and Technology, North Dakota State University , Fargo, North Dakota, United States
- Department of Coatings and Polymeric Materials, North Dakota State University , Fargo, North Dakota, United States
| | - Farukh Jabeen
- Center for Computationally Assisted Science and Technology, North Dakota State University , Fargo, North Dakota, United States
| | - Shane Stafslien
- Research and Creative Activities, North Dakota State University , Fargo, North Dakota, United States
| | - Bret J Chisholm
- Department of Coatings and Polymeric Materials, North Dakota State University , Fargo, North Dakota, United States
| | - James Bahr
- Research and Creative Activities, North Dakota State University , Fargo, North Dakota, United States
| | - Martin Ossowski
- Center for Computationally Assisted Science and Technology, North Dakota State University , Fargo, North Dakota, United States
| | - Philip Boudjouk
- Center for Computationally Assisted Science and Technology, North Dakota State University , Fargo, North Dakota, United States
- Department of Chemistry and Biochemistry, North Dakota State University , Fargo, North Dakota, United States
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Petrosyan LS, Kar S, Leszczynski J, Rasulev B. Exploring Simple, Interpretable, and Predictive QSPR Model of Fullerene C60 Solubility in Organic Solvents. ACTA ACUST UNITED AC 2017. [DOI: 10.4018/jnn.2017010103] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Buckminsterfullerene (C60) and its derivatives have currently been used as promising nanomaterial for diagnostic and therapeutic agents. They are applied in pharmaceutical industry due to their nanostructure characteristics, stability and hydrophobic character. Due to its sparingly soluble nature, the solubility of C60 has been of enormous attention among carbon nanostructure investigators owing to its fundamental importance and practical interest in nanotechnology and medical industry. In order to study the diverse role of C60 and its derivatives the dependence of fullerene's solubility on molecular structure of the solvent must be understood. Current study was dedicated to the exploration of the solubility of fullerene C60 in 156 organic solvents using simple, interpretable and predictive 1D and 2D descriptors employing quantitative structure-property relationship (QSPR) technique. The authors employed genetic algorithm followed by multiple linear regression analysis (GA-MLR) to generate the correlation models. The best performance is accomplished by the four-variable MLR model with internal and external prediction coefficient of Q2 = 0.86 and R2pred = 0.89. The study identified vital properties and structural fragments, particularly valuable for guiding future synthetic as well as prediction efforts. The model generated with the highest number of organic solvents to date with simple descriptors can be reproduced in no time to predict the solubility of C60 in any new or existing organic solvents. This approach can be used as an efficient predictor for fullerenes' solubility in various organic solvents.
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Affiliation(s)
| | - Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS, USA
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS, USA
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, USA
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Watkins M, Sizochenko N, Rasulev B, Leszczynski J. Estimation of melting points of large set of persistent organic pollutants utilizing QSPR approach. J Mol Model 2016; 22:55. [PMID: 26874948 DOI: 10.1007/s00894-016-2917-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2015] [Accepted: 01/18/2016] [Indexed: 11/28/2022]
Abstract
The presence of polyhalogenated persistent organic pollutants (POPs), such as Cl/Br-substituted benzenes, biphenyls, diphenyl ethers, and naphthalenes has been identified in all environmental compartments. The exposure to these compounds can pose potential risk not only for ecological systems, but also for human health. Therefore, efficient tools for comprehensive environmental risk assessment for POPs are required. Among the factors vital for environmental transport and fate processes is melting point of a compound. In this study, we estimated the melting points of a large group (1419 compounds) of chloro- and bromo- derivatives of dibenzo-p-dioxins, dibenzofurans, biphenyls, naphthalenes, diphenylethers, and benzenes by utilizing quantitative structure-property relationship (QSPR) techniques. The compounds were classified by applying structure-based clustering methods followed by GA-PLS modeling. In addition, random forest method has been applied to develop more general models. Factors responsible for melting point behavior and predictive ability of each method were discussed.
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Affiliation(s)
- Marquita Watkins
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, P.O. Box: 17910, Jackson, MS, USA
| | - Natalia Sizochenko
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, P.O. Box: 17910, Jackson, MS, USA
| | - Bakhtiyor Rasulev
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, P.O. Box: 17910, Jackson, MS, USA.,Center for Computationally Assisted Science and Technology, North Dakota State University, Fargo, ND, USA
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, P.O. Box: 17910, Jackson, MS, USA.
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Jagiello K, Grzonkowska M, Swirog M, Ahmed L, Rasulev B, Avramopoulos A, Papadopoulos MG, Leszczynski J, Puzyn T. Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives. J Nanopart Res 2016; 18:256. [PMID: 27642255 PMCID: PMC5003910 DOI: 10.1007/s11051-016-3564-1] [Citation(s) in RCA: 24] [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] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Accepted: 08/18/2016] [Indexed: 05/19/2023]
Abstract
In this contribution, the advantages and limitations of two computational techniques that can be used for the investigation of nanoparticles activity and toxicity: classic nano-QSAR (Quantitative Structure-Activity Relationships employed for nanomaterials) and 3D nano-QSAR (three-dimensional Quantitative Structure-Activity Relationships, such us Comparative Molecular Field Analysis, CoMFA/Comparative Molecular Similarity Indices Analysis, CoMSIA analysis employed for nanomaterials) have been briefly summarized. Both approaches were compared according to the selected criteria, including: efficiency, type of experimental data, class of nanomaterials, time required for calculations and computational cost, difficulties in the interpretation. Taking into account the advantages and limitations of each method, we provide the recommendations for nano-QSAR modellers and QSAR model users to be able to determine a proper and efficient methodology to investigate biological activity of nanoparticles in order to describe the underlying interactions in the most reliable and useful manner.
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Affiliation(s)
- Karolina Jagiello
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, Institute for Environmental and Human Health Protection, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Monika Grzonkowska
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, Institute for Environmental and Human Health Protection, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Marta Swirog
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, Institute for Environmental and Human Health Protection, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Lucky Ahmed
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 JR Lynch Street, Jackson, MS 39217-0510 USA
| | - Bakhtiyor Rasulev
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 JR Lynch Street, Jackson, MS 39217-0510 USA
- Center for Computationally Assisted Science and Technology, North Dakota State University, 1805 NDSU Research Park Drive, Post Office Box 6050, Fargo, ND 58108 USA
| | - Aggelos Avramopoulos
- Institute of Biology, Pharmaceutical Chemistry and Biotechnology, National Hellenic Research Foundation, 48 Vas. Constantinou Ave., 11635 Athens, Greece
| | - Manthos G. Papadopoulos
- Institute of Biology, Pharmaceutical Chemistry and Biotechnology, National Hellenic Research Foundation, 48 Vas. Constantinou Ave., 11635 Athens, Greece
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 JR Lynch Street, Jackson, MS 39217-0510 USA
| | - Tomasz Puzyn
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, Institute for Environmental and Human Health Protection, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
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