1
|
Chachlioutaki K, Iordanopoulou A, Katsamenis OL, Tsitsos A, Koltsakidis S, Anastasiadou P, Andreadis D, Economou V, Ritzoulis C, Tzetzis D, Bouropoulos N, Xenikakis I, Fatouros D. Tailored Sticky Solutions: 3D-Printed Miconazole Buccal Films for Pediatric Oral Candidiasis. AAPS PharmSciTech 2024; 25:190. [PMID: 39164432 DOI: 10.1208/s12249-024-02908-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2024] [Accepted: 07/30/2024] [Indexed: 08/22/2024] Open
Abstract
In this research, 3D-printed antifungal buccal films (BFs) were manufactured as a potential alternative to commercially available antifungal oral gels addressing key considerations such as ease of manufacturing, convenience of administration, enhanced drug efficacy and suitability of paediatric patients. The fabrication process involved the use of a semi-solid extrusion method to create BFs from zein-Poly-Vinyl-Pyrrolidone (zein-PVP) polymer blend, which served as a carrier for drug (miconazole) and taste enhancers. After manufacturing, it was determined that the disintegration time for all films was less than 10 min. However, these films are designed to adhere to buccal tissue, ensuring sustained drug release. Approximately 80% of the miconazole was released gradually over 2 h from the zein/PVP matrix of the 3D printed films. Moreover, a detailed physicochemical characterization including spectroscopic and thermal methods was conducted to assess solid state and thermal stability of film constituents. Mucoadhesive properties and mechanical evaluation were also studied, while permeability studies revealed the extent to which film-loaded miconazole permeates through buccal tissue compared to commercially available oral gel formulation. Histological evaluation of the treated tissues was followed. Furthermore, in vitro antifungal activity was assessed for the developed films and the commercial oral gel. Finally, films underwent a two-month drug stability test to ascertain the suitability of the BFs for clinical application. The results demonstrate that 3D-printed films are a promising alternative for local administration of miconazole in the oral cavity.
Collapse
Affiliation(s)
- Konstantina Chachlioutaki
- Department of Pharmacy Division of Pharmaceutical Technology, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Thessaloniki, Greece
| | - Anastasia Iordanopoulou
- Department of Pharmacy Division of Pharmaceutical Technology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Orestis L Katsamenis
- x μ-VIS X-Ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK
- Institute for Life Sciences, University of Southampton, Southampton, SO17 1BJ, UK
| | - Anestis Tsitsos
- Laboratory of Animal Food Products Hygiene - Veterinary Public Health, School of Veterinary Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
| | - Savvas Koltsakidis
- Digital Manufacturing and Materials Characterization Laboratory, School of Science and Technology, International Hellenic University, 14km Thessaloniki-N. Moudania, 57001, Thermi, Greece
| | - Pinelopi Anastasiadou
- Department of Oral Medicine/Pathology, School of Dentistry, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
| | - Dimitrios Andreadis
- Department of Oral Medicine/Pathology, School of Dentistry, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
| | - Vangelis Economou
- Laboratory of Animal Food Products Hygiene - Veterinary Public Health, School of Veterinary Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
| | - Christos Ritzoulis
- Department of Food Science and Technology, International Hellenic University, Sindos Campus, 57400, Thessaloniki, Greece
| | - Dimitrios Tzetzis
- Digital Manufacturing and Materials Characterization Laboratory, School of Science and Technology, International Hellenic University, 14km Thessaloniki-N. Moudania, 57001, Thermi, Greece
| | - Nikolaos Bouropoulos
- Department of Materials Science, University of Patras, Rio, 26504, Patras, Greece
- Foundation for Research and Technology Hellas, Institute of Chemical Engineering and High Temperature Chemical Processes, 26504, Patras, Greece
| | - Iakovos Xenikakis
- Department of Pharmacy Division of Pharmaceutical Technology, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | - Dimitrios Fatouros
- Department of Pharmacy Division of Pharmaceutical Technology, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Thessaloniki, Greece
| |
Collapse
|
2
|
Duo L, Liu Y, Ren J, Tang B, Hirst JD. Artificial intelligence for small molecule anticancer drug discovery. Expert Opin Drug Discov 2024; 19:933-948. [PMID: 39074493 DOI: 10.1080/17460441.2024.2367014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 06/07/2024] [Indexed: 07/31/2024]
Abstract
INTRODUCTION The transition from conventional cytotoxic chemotherapy to targeted cancer therapy with small-molecule anticancer drugs has enhanced treatment outcomes. This approach, which now dominates cancer treatment, has its advantages. Despite the regulatory approval of several targeted molecules for clinical use, challenges such as low response rates and drug resistance still persist. Conventional drug discovery methods are costly and time-consuming, necessitating more efficient approaches. The rise of artificial intelligence (AI) and access to large-scale datasets have revolutionized the field of small-molecule cancer drug discovery. Machine learning (ML), particularly deep learning (DL) techniques, enables the rapid identification and development of novel anticancer agents by analyzing vast amounts of genomic, proteomic, and imaging data to uncover hidden patterns and relationships. AREA COVERED In this review, the authors explore the important landmarks in the history of AI-driven drug discovery. They also highlight various applications in small-molecule cancer drug discovery, outline the challenges faced, and provide insights for future research. EXPERT OPINION The advent of big data has allowed AI to penetrate and enable innovations in almost every stage of medicine discovery, transforming the landscape of oncology research through the development of state-of-the-art algorithms and models. Despite challenges in data quality, model interpretability, and technical limitations, advancements promise breakthroughs in personalized and precision oncology, revolutionizing future cancer management.
Collapse
Affiliation(s)
- Lihui Duo
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Yu Liu
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Jianfeng Ren
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Bencan Tang
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Jonathan D Hirst
- School of Chemistry, University of Nottingham University Park, Nottingham, UK
| |
Collapse
|
3
|
Ziaikin E, Tello E, Peterson DG, Niv MY. BitterMasS: Predicting Bitterness from Mass Spectra. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:10537-10547. [PMID: 38685906 PMCID: PMC11082931 DOI: 10.1021/acs.jafc.3c09767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 04/18/2024] [Accepted: 04/18/2024] [Indexed: 05/02/2024]
Abstract
Bitter compounds are common in nature and among drugs. Previously, machine learning tools were developed to predict bitterness from the chemical structure. However, known structures are estimated to represent only 5-10% of the metabolome, and the rest remain unassigned or "dark". We present BitterMasS, a Random Forest classifier that was trained on 5414 experimental mass spectra of bitter and nonbitter compounds, achieving precision = 0.83 and recall = 0.90 for an internal test set. Next, the model was tested against spectra newly extracted from the literature 106 bitter and nonbitter compounds and for additional spectra measured for 26 compounds. For these external test cases, BitterMasS exhibited 67% precision and 93% recall for the first and 58% accuracy and 99% recall for the second. The spectrum-bitterness prediction strategy was more effective than the spectrum-structure-bitterness prediction strategy and covered more compounds. These encouraging results suggest that BitterMasS can be used to predict bitter compounds in the metabolome without the need for structural assignment of individual molecules. This may enable identification of bitter compounds from metabolomics analyses, for comparing potential bitterness levels obtained by different treatments of samples and for monitoring bitterness changes overtime.
Collapse
Affiliation(s)
- Evgenii Ziaikin
- Food
Science and Nutrition, The Robert H. Smith Faculty of Agriculture,
Food and Environment, The Institute of Biochemistry, Food and Nutrition, The Hebrew University of Jerusalem, 76100 Rehovot, Israel
| | - Edisson Tello
- Department
of Food Science and Technology, College of Food, Agriculture, and
Environmental Sciences, The Ohio State University, Columbus, Ohio 43210, United States
| | - Devin G. Peterson
- Department
of Food Science and Technology, College of Food, Agriculture, and
Environmental Sciences, The Ohio State University, Columbus, Ohio 43210, United States
| | - Masha Y. Niv
- Food
Science and Nutrition, The Robert H. Smith Faculty of Agriculture,
Food and Environment, The Institute of Biochemistry, Food and Nutrition, The Hebrew University of Jerusalem, 76100 Rehovot, Israel
| |
Collapse
|
4
|
Dutta P, Jain D, Gupta R, Rai B. Classification of tastants: A deep learning based approach. Mol Inform 2023; 42:e202300146. [PMID: 37885360 DOI: 10.1002/minf.202300146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 09/26/2023] [Accepted: 10/26/2023] [Indexed: 10/28/2023]
Abstract
Predicting the taste of molecules is of critical importance in the food and beverages, flavor, and pharmaceutical industries for the design and screening of new tastants. In this work, we have built deep learning models to classify sweet, bitter, and umami molecules- the three basic tastes whose sensation is mediated by G protein-coupled receptors. An extensive dataset containing 1466 bitter, 1764 sweet, and 238 umami tastants was curated from existing literature. We analyzed the chemical characteristics of the molecules, with special focus on the presence of different functional groups. A deep neural network model based on molecular descriptors and a graph neural network model were trained for taste prediction. The class imbalance due to fewer umami molecules was tackled using special sampling techniques. Both models show comparable performance during evaluation, but the graph-based model can learn task-specific representations from the molecular structure without requiring handcrafted features. We further explain the deep neural network predictions using Shapley additive explanations. Finally, we demonstrated the applicability of the models by screening bitter, sweet, and umami molecules from a large food database. This study develops an in-silico approach to classify molecules based on their taste by leveraging the recent progress in deep learning, which can serve as a powerful tool for tastant design.
Collapse
Affiliation(s)
- Prantar Dutta
- Physical Sciences Research Area, Tata Research Development and Design Centre, TCS Research, 54-B, Hadapsar Industrial Estate, Pune, 411013, India
| | - Deepak Jain
- Physical Sciences Research Area, Tata Research Development and Design Centre, TCS Research, 54-B, Hadapsar Industrial Estate, Pune, 411013, India
| | - Rakesh Gupta
- Physical Sciences Research Area, Tata Research Development and Design Centre, TCS Research, 54-B, Hadapsar Industrial Estate, Pune, 411013, India
| | - Beena Rai
- Physical Sciences Research Area, Tata Research Development and Design Centre, TCS Research, 54-B, Hadapsar Industrial Estate, Pune, 411013, India
| |
Collapse
|
5
|
Rojas C, Ballabio D, Consonni V, Suárez-Estrella D, Todeschini R. Classification-based machine learning approaches to predict the taste of molecules: A review. Food Res Int 2023; 171:113036. [PMID: 37330849 DOI: 10.1016/j.foodres.2023.113036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 05/02/2023] [Accepted: 05/22/2023] [Indexed: 06/19/2023]
Abstract
The capacity to discriminate safe from dangerous compounds has played an important role in the evolution of species, including human beings. Highly evolved senses such as taste receptors allow humans to navigate and survive in the environment through information that arrives to the brain through electrical pulses. Specifically, taste receptors provide multiple bits of information about the substances that are introduced orally. These substances could be pleasant or not according to the taste responses that they trigger. Tastes have been classified into basic (sweet, bitter, umami, sour and salty) or non-basic (astringent, chilling, cooling, heating, pungent), while some compounds are considered as multitastes, taste modifiers or tasteless. Classification-based machine learning approaches are useful tools to develop predictive mathematical relationships in such a way as to predict the taste class of new molecules based on their chemical structure. This work reviews the history of multicriteria quantitative structure-taste relationship modelling, starting from the first ligand-based (LB) classifier proposed in 1980 by Lemont B. Kier and concluding with the most recent studies published in 2022.
Collapse
Affiliation(s)
- Cristian Rojas
- Grupo de Investigación en Quimiometría y QSAR, Facultad de Ciencia y Tecnología, Universidad del Azuay, Av. 24 de Mayo 7-77 y Hernán Malo, Cuenca 010107, Ecuador.
| | - Davide Ballabio
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, P.za della Scienza 1-20126, Milano, Italy
| | - Viviana Consonni
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, P.za della Scienza 1-20126, Milano, Italy
| | - Diego Suárez-Estrella
- Grupo de Investigación en Quimiometría y QSAR, Facultad de Ciencia y Tecnología, Universidad del Azuay, Av. 24 de Mayo 7-77 y Hernán Malo, Cuenca 010107, Ecuador
| | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, P.za della Scienza 1-20126, Milano, Italy
| |
Collapse
|
6
|
Yoo O, von Ungern-Sternberg BS, Lim LY. Paediatric Medicinal Formulation Development: Utilising Human Taste Panels and Incorporating Their Data into Machine Learning Training. Pharmaceutics 2023; 15:2112. [PMID: 37631326 PMCID: PMC10459634 DOI: 10.3390/pharmaceutics15082112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 08/04/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023] Open
Abstract
This review paper explores the role of human taste panels and artificial neural networks (ANNs) in taste-masking paediatric drug formulations. Given the ethical, practical, and regulatory challenges of employing children, young adults (18-40) can serve as suitable substitutes due to the similarity in their taste sensitivity. Taste panellists need not be experts in sensory evaluation so long as a reference product is used during evaluation; however, they should be screened for bitterness taste detection thresholds. For a more robust evaluation during the developmental phase, considerations of a scoring system and the calculation of an acceptance value may be beneficial in determining the likelihood of recommending a formulation for further development. On the technological front, artificial neural networks (ANNs) can be exploited in taste-masking optimisation of medicinal formulations as they can model complex relationships between variables and enable predictions not possible previously to optimise product profiles. Machine learning classifiers may therefore tackle the challenge of predicting the bitterness intensity of paediatric formulations. While advancements have been made, further work is needed to identify effective taste-masking techniques for specific drug molecules. Continuous refinement of machine learning algorithms, using human panellist acceptability scores, can aid in enhancing paediatric formulation development and overcoming taste-masking challenges.
Collapse
Affiliation(s)
- Okhee Yoo
- Pharmacy, School of Allied Health, University of Western Australia, Perth, WA 6009, Australia;
| | - Britta S. von Ungern-Sternberg
- Perioperative Medicine Team, Perioperative Care Program, Telethon Kids Institute, Perth, WA 6009, Australia;
- Department of Anaesthesia and Pain Medicine, Perth Children’s Hospital, Perth, WA 6009, Australia
- Division of Emergency Medicine, Anaesthesia and Pain Medicine, The University of Western Australia, Perth, WA 6009, Australia
| | - Lee Yong Lim
- Pharmacy, School of Allied Health, University of Western Australia, Perth, WA 6009, Australia;
| |
Collapse
|
7
|
Margulis E, Lang T, Tromelin A, Ziaikin E, Behrens M, Niv MY. Bitter Odorants and Odorous Bitters: Toxicity and Human TAS2R Targets. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023. [PMID: 37263600 DOI: 10.1021/acs.jafc.3c00592] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Flavor is perceived through the olfactory, taste, and trigeminal systems, mediated by designated GPCRs and channels. Signal integration occurs mainly in the brain, but some cross-reactivities occur at the receptor level. Here, we predict potential bitterness and taste receptors targets for thousands of odorants. BitterPredict and BitterIntense classifiers suggest that 3-9% of flavor and food odorants have bitter taste, but almost none are intensely bitter. About 14% of bitter molecules are expected to have an odor. Bitterness is more common for unpleasant smells such as fishy, amine, and ammoniacal, while non-bitter odorants often have pleasant smells. Experimental toxicity values suggest that fishy ammoniac smells are more toxic than pleasant smells, regardless of bitterness. TAS2R14 is predicted as the main bitter receptor for odorants, confirmed by in vitro profiling of 10 odorants. The activity of bitter odorants may have implications for physiology due to ectopic expression of taste and smell receptors.
Collapse
Affiliation(s)
- Eitan Margulis
- Food Science and Nutrition, The Robert H Smith Faculty of Agriculture, Food and Environment, The Institute of Biochemistry, Food and Nutrition, The Hebrew University of Jerusalem, 76100 Rehovot, Israel
| | - Tatjana Lang
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Anne Tromelin
- Centre des Sciences du Goût et de l'Alimentation, CNRS, INRAE, Institut Agro, Université de Bourgogne Franche-Comté, F-21000 Dijon, France
| | - Evgenii Ziaikin
- Food Science and Nutrition, The Robert H Smith Faculty of Agriculture, Food and Environment, The Institute of Biochemistry, Food and Nutrition, The Hebrew University of Jerusalem, 76100 Rehovot, Israel
| | - Maik Behrens
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Masha Y Niv
- Food Science and Nutrition, The Robert H Smith Faculty of Agriculture, Food and Environment, The Institute of Biochemistry, Food and Nutrition, The Hebrew University of Jerusalem, 76100 Rehovot, Israel
| |
Collapse
|
8
|
Kou X, Shi P, Gao C, Ma P, Xing H, Ke Q, Zhang D. Data-Driven Elucidation of Flavor Chemistry. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:6789-6802. [PMID: 37102791 PMCID: PMC10176570 DOI: 10.1021/acs.jafc.3c00909] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Flavor molecules are commonly used in the food industry to enhance product quality and consumer experiences but are associated with potential human health risks, highlighting the need for safer alternatives. To address these health-associated challenges and promote reasonable application, several databases for flavor molecules have been constructed. However, no existing studies have comprehensively summarized these data resources according to quality, focused fields, and potential gaps. Here, we systematically summarized 25 flavor molecule databases published within the last 20 years and revealed that data inaccessibility, untimely updates, and nonstandard flavor descriptions are the main limitations of current studies. We examined the development of computational approaches (e.g., machine learning and molecular simulation) for the identification of novel flavor molecules and discussed their major challenges regarding throughput, model interpretability, and the lack of gold-standard data sets for equitable model evaluation. Additionally, we discussed future strategies for the mining and designing of novel flavor molecules based on multi-omics and artificial intelligence to provide a new foundation for flavor science research.
Collapse
Affiliation(s)
- Xingran Kou
- Collaborative Innovation Center of Fragrance Flavour and Cosmetics, School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai 201418, China
| | - Peiqin Shi
- Collaborative Innovation Center of Fragrance Flavour and Cosmetics, School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai 201418, China
| | - Chukun Gao
- Laboratory for Physical Chemistry, ETH Zürich, 8093 Zürich, Switzerland
| | - Peihua Ma
- Department of Nutrition and Food Science, University of Maryland, College Park, Maryland 20742, United States
| | - Huadong Xing
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qinfei Ke
- Collaborative Innovation Center of Fragrance Flavour and Cosmetics, School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai 201418, China
| | - Dachuan Zhang
- National Centre of Competence in Research (NCCR) Catalysis, Institute of Environmental Engineering, ETH Zürich, 8093 Zürich, Switzerland
| |
Collapse
|
9
|
Fierro F, Peri L, Hübner H, Tabor-Schkade A, Waterloo L, Löber S, Pfeiffer T, Weikert D, Dingjan T, Margulis E, Gmeiner P, Niv MY. Inhibiting a promiscuous GPCR: iterative discovery of bitter taste receptor ligands. Cell Mol Life Sci 2023; 80:114. [PMID: 37012410 PMCID: PMC11072104 DOI: 10.1007/s00018-023-04765-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 03/09/2023] [Accepted: 03/21/2023] [Indexed: 04/05/2023]
Abstract
The human GPCR family comprises circa 800 members, activated by hundreds of thousands of compounds. Bitter taste receptors, TAS2Rs, constitute a large and distinct subfamily, expressed orally and extra-orally and involved in physiological and pathological conditions. TAS2R14 is the most promiscuous member, with over 150 agonists and 3 antagonists known prior to this study. Due to the scarcity of inhibitors and to the importance of chemical probes for exploring TAS2R14 functions, we aimed to discover new ligands for this receptor, with emphasis on antagonists. To cope with the lack of experimental structure of the receptor, we used a mixed experimental/computational methodology which iteratively improved the performance of the predicted structure. The increasing number of active compounds, obtained here through experimental screening of FDA-approved drug library, and through chemically synthesized flufenamic acid derivatives, enabled the refinement of the binding pocket, which in turn improved the structure-based virtual screening reliability. This mixed approach led to the identification of 10 new antagonists and 200 new agonists of TAS2R14, illustrating the untapped potential of rigorous medicinal chemistry for TAS2Rs. 9% of the ~ 1800 pharmaceutical drugs here tested activate TAS2R14, nine of them at sub-micromolar concentrations. The iterative framework suggested residues involved in the activation process, is suitable for expanding bitter and bitter-masking chemical space, and is applicable to other promiscuous GPCRs lacking experimental structures.
Collapse
Affiliation(s)
- Fabrizio Fierro
- The Institute of Biochemistry, Food Science and Nutrition, Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Lior Peri
- The Institute of Biochemistry, Food Science and Nutrition, Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Harald Hübner
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nikolaus-Fiebiger-Str. 10, 91058, Erlangen, Germany
| | - Alina Tabor-Schkade
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nikolaus-Fiebiger-Str. 10, 91058, Erlangen, Germany
| | - Lukas Waterloo
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nikolaus-Fiebiger-Str. 10, 91058, Erlangen, Germany
| | - Stefan Löber
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nikolaus-Fiebiger-Str. 10, 91058, Erlangen, Germany
| | - Tara Pfeiffer
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nikolaus-Fiebiger-Str. 10, 91058, Erlangen, Germany
| | - Dorothee Weikert
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nikolaus-Fiebiger-Str. 10, 91058, Erlangen, Germany
| | - Tamir Dingjan
- The Institute of Biochemistry, Food Science and Nutrition, Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Eitan Margulis
- The Institute of Biochemistry, Food Science and Nutrition, Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Peter Gmeiner
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nikolaus-Fiebiger-Str. 10, 91058, Erlangen, Germany.
| | - Masha Y Niv
- The Institute of Biochemistry, Food Science and Nutrition, Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel.
| |
Collapse
|
10
|
Waterloo L, Hübner H, Fierro F, Pfeiffer T, Brox R, Löber S, Weikert D, Niv MY, Gmeiner P. Discovery of 2-Aminopyrimidines as Potent Agonists for the Bitter Taste Receptor TAS2R14. J Med Chem 2023; 66:3499-3521. [PMID: 36847646 DOI: 10.1021/acs.jmedchem.2c01997] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
The bitter taste receptor TAS2R14 is a G protein-coupled receptor that is found on the tongue as well as in the human airway smooth muscle and other extraoral tissues. Because its activation causes bronchodilatation, TAS2R14 is a potential target for the treatment of asthma or chronic obstructive pulmonary disease. Structural variations of flufenamic acid, a nonsteroidal anti-inflammatory drug, led us to 2-aminopyridines showing considerable efficacy and potency in an IP1accumulation assay. In combination with an exchange of the carboxylic moiety by a tetrazole unit, a set of promising new TAS2R14 agonists was developed. The most potent ligand 28.1 (EC50 = 72 nM) revealed a six-fold higher potency than flufenamic acid and a maximum efficacy of 129%. Besides its unprecedented TAS2R14 activation, 28.1 revealed marked selectivity over a panel of 24 non-bitter taste human G protein-coupled receptors.
Collapse
Affiliation(s)
- Lukas Waterloo
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nikolaus-Fiebiger-Str. 10, Erlangen 91058, Germany
| | - Harald Hübner
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nikolaus-Fiebiger-Str. 10, Erlangen 91058, Germany
| | - Fabrizio Fierro
- The Robert H. Smith Faculty of Agriculture, Food and Environment, Institute of Biochemistry, Food Science and Nutrition, The Hebrew University, Rehovot 7610001, Israel
| | - Tara Pfeiffer
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nikolaus-Fiebiger-Str. 10, Erlangen 91058, Germany
| | - Regine Brox
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nikolaus-Fiebiger-Str. 10, Erlangen 91058, Germany
| | - Stefan Löber
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nikolaus-Fiebiger-Str. 10, Erlangen 91058, Germany
| | - Dorothee Weikert
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nikolaus-Fiebiger-Str. 10, Erlangen 91058, Germany
| | - Masha Y Niv
- The Robert H. Smith Faculty of Agriculture, Food and Environment, Institute of Biochemistry, Food Science and Nutrition, The Hebrew University, Rehovot 7610001, Israel
| | - Peter Gmeiner
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nikolaus-Fiebiger-Str. 10, Erlangen 91058, Germany
| |
Collapse
|
11
|
Design of Novel Phosphatidylinositol 3-Kinase Inhibitors for Non-Hodgkin's Lymphoma: Molecular Docking, Molecular Dynamics, and Density Functional Theory Studies on Gold Nanoparticles. Molecules 2023; 28:molecules28052289. [PMID: 36903539 PMCID: PMC10005307 DOI: 10.3390/molecules28052289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 02/25/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Abstract
Non-Hodgkin's lymphomas are a diverse collection of lymphoproliferative cancers that are much less predictable than Hodgkin's lymphomas with a far greater tendency to metastasize to extranodal sites. A quarter of non-Hodgkin's lymphoma cases develop at extranodal sites and the majority of them involve nodal and extranodal sites. The most common subtypes include follicular lymphoma, chronic/small lymphocytic leukaemia, mantel cell lymphoma, and marginal zone lymphoma. Umbralisib is one of the latest PI3Kδ inhibitors in clinical trials for several hematologic cancer indications. In this study, new umbralisib analogues were designed and docked to the active site of PI3Kδ, the main target of the phosphoinositol-3-kinase/Akt/mammalian target of the rapamycin pathway (PI3K/AKT/mTOR). This study resulted in eleven candidates, with strong binding to PI3Kδ with a docking score between -7.66 and -8.42 Kcal/mol. The docking analysis of ligand-receptor interactions between umbralisib analogues bound to PI3K showed that their interactions were mainly controlled by hydrophobic interactions and, to a lesser extent, by hydrogen bonding. In addition, the MM-GBSA binding free energy was calculated. Analogue 306 showed the highest free energy of binding with -52.22 Kcal/mol. To identify the structural changes and the complexes' stability of proposed ligands, molecular dynamic simulation was used. Based on this research finding, the best-designed analogue, analogue 306, formed a stable ligand-protein complex. In addition, pharmacokinetics and toxicity analysis using the QikProp tool demonstrated that analogue 306 had good absorption, distribution, metabolism, and excretion properties. Additionally, it has a promising predicted profile in immune toxicity, carcinogenicity, and cytotoxicity. In addition, analogue 306 had stable interactions with gold nanoparticles that have been studied using density functional theory calculations. The best interaction with gold was observed at the oxygen atom number 5 with -29.42 Kcal/mol. Further in vitro and in vivo investigations are recommended to be carried out to verify the anticancer activity of this analogue.
Collapse
|
12
|
Yan J, Tong H. An overview of bitter compounds in foodstuffs: Classifications, evaluation methods for sensory contribution, separation and identification techniques, and mechanism of bitter taste transduction. Compr Rev Food Sci Food Saf 2023; 22:187-232. [PMID: 36382875 DOI: 10.1111/1541-4337.13067] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 08/24/2022] [Accepted: 10/11/2022] [Indexed: 11/17/2022]
Abstract
The bitter taste is generally considered an undesirable sensory attribute. However, bitter-tasting compounds can significantly affect the overall flavor of many foods and beverages and endow them with various beneficial effects on human health. To better understand the relationship between chemical structure and bitterness, this paper has summarized the bitter compounds in foodstuffs and classified them based on the basic skeletons. Only those bitter compounds that are confirmed by human sensory evaluation have been included in this paper. To develop food products that satisfy consumer preferences, correctly ranking the key bitter compounds in foodstuffs according to their contributions to the overall bitterness intensity is the precondition. Generally, three methods were applied to screen out the key bitter compounds in foods and beverages and evaluate their sensory contributions, including dose-over-threshold factors, taste dilution analysis, and spectrum descriptive analysis method. This paper has discussed in detail the mechanisms and applications of these three methods. Typical procedures for separating and identifying the main bitter compounds in foodstuffs have also been summarized. Additionally, the activation of human bitter taste receptors (TAS2Rs) and the mechanisms of bitter taste transduction are outlined. Ultimately, a conclusion has been drawn to highlight the current problems and propose potential directions for further research.
Collapse
Affiliation(s)
- Jingna Yan
- College of Food Science, Southwest University, Chongqing, China
| | - Huarong Tong
- College of Food Science, Southwest University, Chongqing, China
| |
Collapse
|
13
|
Omoboyowa DA, Kareem JA, Saibu OA, Bodun DS, Ajayi TM, Oyeneyin OE. Identification of Phyto-Compounds from Ilex kudingcha as Inhibitors of Sterol-14α-Demethylase Protease: A Computational Approach Against Chagas Disease. CHEMISTRY AFRICA 2022. [DOI: 10.1007/s42250-022-00565-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
|
14
|
Pallante L, Korfiati A, Androutsos L, Stojceski F, Bompotas A, Giannikos I, Raftopoulos C, Malavolta M, Grasso G, Mavroudi S, Kalogeras A, Martos V, Amoroso D, Piga D, Theofilatos K, Deriu MA. Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach. Sci Rep 2022; 12:21735. [PMID: 36526644 PMCID: PMC9758219 DOI: 10.1038/s41598-022-25935-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022] Open
Abstract
The umami taste is one of the five basic taste modalities normally linked to the protein content in food. The implementation of fast and cost-effective tools for the prediction of the umami taste of a molecule remains extremely interesting to understand the molecular basis of this taste and to effectively rationalise the production and consumption of specific foods and ingredients. However, the only examples of umami predictors available in the literature rely on the amino acid sequence of the analysed peptides, limiting the applicability of the models. In the present study, we developed a novel ML-based algorithm, named VirtuousUmami, able to predict the umami taste of a query compound starting from its SMILES representation, thus opening up the possibility of potentially using such a model on any database through a standard and more general molecular description. Herein, we have tested our model on five databases related to foods or natural compounds. The proposed tool will pave the way toward the rationalisation of the molecular features underlying the umami taste and toward the design of specific peptide-inspired compounds with specific taste properties.
Collapse
Affiliation(s)
- Lorenzo Pallante
- grid.4800.c0000 0004 1937 0343Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129 Torino, Italy
| | | | | | - Filip Stojceski
- Department of Innovative Technologies, Dalle Molle Institute for Artificial Intelligence, 6962 Lugano-Viganello, Switzerland
| | - Agorakis Bompotas
- grid.435019.a0000 0004 0394 1287Industrial Systems Institute, Athena Research Center, 265 04 Patras, Greece
| | - Ioannis Giannikos
- grid.435019.a0000 0004 0394 1287Industrial Systems Institute, Athena Research Center, 265 04 Patras, Greece
| | - Christos Raftopoulos
- grid.435019.a0000 0004 0394 1287Industrial Systems Institute, Athena Research Center, 265 04 Patras, Greece
| | - Marta Malavolta
- grid.8954.00000 0001 0721 6013Faculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia
| | - Gianvito Grasso
- Department of Innovative Technologies, Dalle Molle Institute for Artificial Intelligence, 6962 Lugano-Viganello, Switzerland
| | - Seferina Mavroudi
- InSyBio PC, 265 04 Patras, Greece ,grid.11047.330000 0004 0576 5395Department of Nursing, University of Patras, 265 04 Patras, Greece
| | - Athanasios Kalogeras
- grid.435019.a0000 0004 0394 1287Industrial Systems Institute, Athena Research Center, 265 04 Patras, Greece
| | - Vanessa Martos
- grid.4489.10000000121678994Department of Plant Physiology, Institute of Biotechnology, University of Granada, 18011 Granada, Spain
| | | | - Dario Piga
- Department of Innovative Technologies, Dalle Molle Institute for Artificial Intelligence, 6962 Lugano-Viganello, Switzerland
| | | | - Marco A. Deriu
- grid.4800.c0000 0004 1937 0343Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129 Torino, Italy
| |
Collapse
|
15
|
De León G, Fröhlich E, Fink E, Di Pizio A, Salar-Behzadi S. Premexotac: Machine learning bitterants predictor for advancing pharmaceutical development. Int J Pharm 2022; 628:122263. [DOI: 10.1016/j.ijpharm.2022.122263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/27/2022] [Accepted: 09/29/2022] [Indexed: 10/31/2022]
|
16
|
Wooding SP, Ramirez VA. Global population genetics and diversity in the TAS2R bitter taste receptor family. Front Genet 2022; 13:952299. [PMID: 36303543 PMCID: PMC9592824 DOI: 10.3389/fgene.2022.952299] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 08/25/2022] [Indexed: 12/03/2022] Open
Abstract
Bitter taste receptors (TAS2Rs) are noted for their role in perception, and mounting evidence suggests that they mediate responses to compounds entering airways, gut, and other tissues. The importance of these roles suggests that TAS2Rs have been under pressure from natural selection. To determine the extent of variation in TAS2Rs on a global scale and its implications for human evolution and behavior, we analyzed patterns of diversity in the complete 25 gene repertoire of human TAS2Rs in ∼2,500 subjects representing worldwide populations. Across the TAS2R family as a whole, we observed 721 single nucleotide polymorphisms (SNPs) including 494 nonsynonymous SNPs along with 40 indels and gained and lost start and stop codons. In addition, computational predictions identified 169 variants particularly likely to affect receptor function, making them candidate sources of phenotypic variation. Diversity levels ranged widely among loci, with the number of segregating sites ranging from 17 to 41 with a mean of 32 among genes and per nucleotide heterozygosity (π) ranging from 0.02% to 0.36% with a mean of 0.12%. FST ranged from 0.01 to 0.26 with a mean of 0.13, pointing to modest differentiation among populations. Comparisons of observed π and FST values with their genome wide distributions revealed that most fell between the 5th and 95th percentiles and were thus consistent with expectations. Further, tests for natural selection using Tajima’s D statistic revealed only two loci departing from expectations given D’s genome wide distribution. These patterns are consistent with an overall relaxation of selective pressure on TAS2Rs in the course of recent human evolution.
Collapse
Affiliation(s)
- Stephen P. Wooding
- Department of Anthropology, University of California, Merced, Merced, CA, United States
- *Correspondence: Stephen P. Wooding,
| | - Vicente A. Ramirez
- Department of Public Health, University of California, Merced, Merced, CA, United States
| |
Collapse
|
17
|
Dubovski N, Fierro F, Margulis E, Ben Shoshan-Galeczki Y, Peri L, Niv MY. Taste GPCRs and their ligands. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2022; 193:177-193. [PMID: 36357077 DOI: 10.1016/bs.pmbts.2022.06.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Taste GPCRs are expressed in taste buds on the tongue and play a key role in food choice and consumption. They are also expressed extra-orally, with various physiological roles that are currently under study. Unraveling the roles of these receptors relies on the knowledge of their ligands. Combining sensory, cell-based and computational approaches enabled the discovery of numerous agonists and several antagonists. Here we provide a short overview of taste receptor families, main recent methods for ligands discovery, and current sources of information about known ligands. The future directions that are likely to impact the taste GPCR field include focus on ligand interactions with naturally occurring polymorphisms, as well as harnessing the power of CryoEM and of multiple signaling readout techniques.
Collapse
Affiliation(s)
- Nitzan Dubovski
- The Institute of Biochemistry, Food Science and Nutrition, Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Fabrizio Fierro
- The Institute of Biochemistry, Food Science and Nutrition, Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Eitan Margulis
- The Institute of Biochemistry, Food Science and Nutrition, Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Yaron Ben Shoshan-Galeczki
- The Institute of Biochemistry, Food Science and Nutrition, Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Lior Peri
- The Institute of Biochemistry, Food Science and Nutrition, Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Masha Y Niv
- The Institute of Biochemistry, Food Science and Nutrition, Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel.
| |
Collapse
|
18
|
Ahmed S, Ali MC, Ruma RA, Mahmud S, Paul GK, Saleh MA, Alshahrani MM, Obaidullah AJ, Biswas SK, Rahman MM, Rahman MM, Islam MR. Molecular Docking and Dynamics Simulation of Natural Compounds from Betel Leaves ( Piper betle L.) for Investigating the Potential Inhibition of Alpha-Amylase and Alpha-Glucosidase of Type 2 Diabetes. Molecules 2022; 27:molecules27144526. [PMID: 35889399 PMCID: PMC9316265 DOI: 10.3390/molecules27144526] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 07/06/2022] [Accepted: 07/07/2022] [Indexed: 11/16/2022] Open
Abstract
Piper betle L. is widely distributed and commonly used medicinally important herb. It can also be used as a medication for type 2 diabetes patients. In this study, compounds of P. betle were screened to investigate the inhibitory action of alpha-amylase and alpha-glucosidase against type 2 diabetes through molecular docking, molecular dynamics simulation, and ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis. The molecule apigenin-7-O-glucoside showed the highest binding affinity among 123 (one hundred twenty-three) tested compounds. This compound simultaneously bound with the two-target proteins alpha-amylase and alpha-glucosidase, with high molecular mechanics-generalized born surface area (MM/GBSA) values (ΔG Bind = -45.02 kcal mol-1 for alpha-amylase and -38.288 for alpha-glucosidase) compared with control inhibitor acarbose, which had binding affinities of -36.796 kcal mol-1 for alpha-amylase and -29.622 kcal mol-1 for alpha-glucosidase. The apigenin-7-O-glucoside was revealed to be the most stable molecule with the highest binding free energy through molecular dynamics simulation, indicating that it could compete with the inhibitors' native ligand. Based on ADMET analysis, this phytochemical exhibited a wide range of physicochemical, pharmacokinetic, and drug-like qualities and had no significant side effects, making them prospective drug candidates for type 2 diabetes. Additional in vitro, in vivo, and clinical investigations are needed to determine the precise efficacy of drugs.
Collapse
Affiliation(s)
- Sabbir Ahmed
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Islamic University, Kushtia 7003, Bangladesh; (S.A.); (M.C.A.); (R.A.R.); (S.K.B.); (M.M.R.); (M.R.I.)
| | - Md Chayan Ali
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Islamic University, Kushtia 7003, Bangladesh; (S.A.); (M.C.A.); (R.A.R.); (S.K.B.); (M.M.R.); (M.R.I.)
| | - Rumana Akter Ruma
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Islamic University, Kushtia 7003, Bangladesh; (S.A.); (M.C.A.); (R.A.R.); (S.K.B.); (M.M.R.); (M.R.I.)
| | - Shafi Mahmud
- Division of Genome Sciences and Cancer, The John Curtin School of Medical Research and The Shine-Dalgarno Centre for RNA Innovation, The Australian National University, Canberra, ACT 2601, Australia;
| | - Gobindo Kumar Paul
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh; (G.K.P.); (M.A.S.)
| | - Md Abu Saleh
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh; (G.K.P.); (M.A.S.)
| | - Mohammed Merae Alshahrani
- Department of Clinical Laboratory Sciences, Faculty of Applied Medical Sciences, Najran University, Najran 61441, Saudi Arabia;
| | - Ahmad J. Obaidullah
- Drug Exploration and Development Chair (DEDC), Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia;
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
| | - Sudhangshu Kumar Biswas
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Islamic University, Kushtia 7003, Bangladesh; (S.A.); (M.C.A.); (R.A.R.); (S.K.B.); (M.M.R.); (M.R.I.)
| | - Md Mafizur Rahman
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Islamic University, Kushtia 7003, Bangladesh; (S.A.); (M.C.A.); (R.A.R.); (S.K.B.); (M.M.R.); (M.R.I.)
- Correspondence:
| | - Md Mizanur Rahman
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Islamic University, Kushtia 7003, Bangladesh; (S.A.); (M.C.A.); (R.A.R.); (S.K.B.); (M.M.R.); (M.R.I.)
| | - Md Rezuanul Islam
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Islamic University, Kushtia 7003, Bangladesh; (S.A.); (M.C.A.); (R.A.R.); (S.K.B.); (M.M.R.); (M.R.I.)
| |
Collapse
|
19
|
Margulis E, Slavutsky Y, Lang T, Behrens M, Benjamini Y, Niv MY. BitterMatch: recommendation systems for matching molecules with bitter taste receptors. J Cheminform 2022; 14:45. [PMID: 35799226 PMCID: PMC9261901 DOI: 10.1186/s13321-022-00612-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 05/14/2022] [Indexed: 11/10/2022] Open
Abstract
Bitterness is an aversive cue elicited by thousands of chemically diverse compounds. Bitter taste may prevent consumption of foods and jeopardize drug compliance. The G protein-coupled receptors for bitter taste, TAS2Rs, have species-dependent number of subtypes and varying expression levels in extraoral tissues. Molecular recognition by TAS2R subtypes is physiologically important, and presents a challenging case study for ligand-receptor matchmaking. Inspired by hybrid recommendation systems, we developed a new set of similarity features, and created the BitterMatch algorithm that predicts associations of ligands to receptors with ~ 80% precision at ~ 50% recall. Associations for several compounds were tested in-vitro, resulting in 80% precision and 42% recall. The encouraging performance was achieved by including receptor properties and integrating experimentally determined ligand-receptor associations with chemical ligand-to-ligand similarities. BitterMatch can predict off-targets for bitter drugs, identify novel ligands and guide flavor design. The novel features capture information regarding the molecules and their receptors, which could inform various chemoinformatic tasks. Inclusion of neighbor-informed similarities improves as experimental data mounts, and provides a generalizable framework for molecule-biotarget matching.
Collapse
Affiliation(s)
- Eitan Margulis
- The Institute of Biochemistry, Food Science and Nutrition, The Robert H Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Yuli Slavutsky
- Department of Statistics and Data Science, Faculty of Social Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Tatjana Lang
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, Freising, Germany
| | - Maik Behrens
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, Freising, Germany
| | - Yuval Benjamini
- Department of Statistics and Data Science, Faculty of Social Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Masha Y Niv
- The Institute of Biochemistry, Food Science and Nutrition, The Robert H Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel.
| |
Collapse
|
20
|
Malavolta M, Pallante L, Mavkov B, Stojceski F, Grasso G, Korfiati A, Mavroudi S, Kalogeras A, Alexakos C, Martos V, Amoroso D, Di Benedetto G, Piga D, Theofilatos K, Deriu MA. A survey on computational taste predictors. Eur Food Res Technol 2022; 248:2215-2235. [PMID: 35637881 PMCID: PMC9134981 DOI: 10.1007/s00217-022-04044-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/29/2022] [Accepted: 04/30/2022] [Indexed: 11/29/2022]
Abstract
Taste is a sensory modality crucial for nutrition and survival, since it allows the discrimination between healthy foods and toxic substances thanks to five tastes, i.e., sweet, bitter, umami, salty, and sour, associated with distinct nutritional or physiological needs. Today, taste prediction plays a key role in several fields, e.g., medical, industrial, or pharmaceutical, but the complexity of the taste perception process, its multidisciplinary nature, and the high number of potentially relevant players and features at the basis of the taste sensation make taste prediction a very complex task. In this context, the emerging capabilities of machine learning have provided fruitful insights in this field of research, allowing to consider and integrate a very large number of variables and identifying hidden correlations underlying the perception of a particular taste. This review aims at summarizing the latest advances in taste prediction, analyzing available food-related databases and taste prediction tools developed in recent years. Supplementary Information The online version contains supplementary material available at 10.1007/s00217-022-04044-5.
Collapse
Affiliation(s)
- Marta Malavolta
- PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Lorenzo Pallante
- PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
| | - Bojan Mavkov
- GIPSA-lab, F-38000, Université Grenoble Alpes, Grenoble, France
| | - Filip Stojceski
- Dalle Molle Institute for Artificial Intelligence (IDSIA-USI/SUPSI), Lugano-Viganello, Switzerland
| | - Gianvito Grasso
- Dalle Molle Institute for Artificial Intelligence (IDSIA-USI/SUPSI), Lugano-Viganello, Switzerland
| | | | - Seferina Mavroudi
- InSyBio PC, Patras, Greece
- Department of Nursing, School of Rehabilitation Sciences, University of Patras, Patras, Greece
| | | | - Christos Alexakos
- Athena Research Center, Industrial Systems Institute, Patras, Greece
| | - Vanessa Martos
- Department of Plant Physiology, Institute of Biotechnology, University of Granada, Granada, Spain
| | - Daria Amoroso
- Enginlife Engineering Solutions, Turin, Italy
- 7hc srl, Rome, Italy
| | | | - Dario Piga
- Dalle Molle Institute for Artificial Intelligence (IDSIA-USI/SUPSI), Lugano-Viganello, Switzerland
| | | | - Marco Agostino Deriu
- PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
| |
Collapse
|
21
|
Wang W, Cui Z, Ning M, Zhou T, Liu Y. In-silico investigation of umami peptides with receptor T1R1/T1R3 for the discovering potential targets: A combined modeling approach. Biomaterials 2021; 281:121338. [PMID: 34998173 DOI: 10.1016/j.biomaterials.2021.121338] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 12/05/2021] [Accepted: 12/24/2021] [Indexed: 12/22/2022]
Abstract
Umami, providing amino acids/peptides for animal growth, represents one of the major attractive taste modalities. The biochemical and umami properties of peptide are both important for scientific research and food industry. In this study, we did the sequence analysis of 205 umami peptides with 2-18 amino acids, sought the active sites of umami peptides by quantum chemical simulations and investigated their recognition residues with receptor T1R1/T1R3 by molecular docking. The results showed the peptides with 2-3 amino acids accounting for 44% of the total umami peptides. Residues D and E are the key active sites no matter where they are in the peptides (N-terminal, C-terminal or middle), when umami peptides contain D/E residues. N69, D147, R151, A170, S172, S276 and R277 residues in T1R1 receptor were deemed to be the key residues binding umami peptides. Finally, a powerful decision rule for umami peptides was proposed to predict potential umami peptides, which was convenient and efficient.
Collapse
Affiliation(s)
- Wenli Wang
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Zhiyong Cui
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Menghua Ning
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Tianxing Zhou
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yuan Liu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, 200240, China.
| |
Collapse
|
22
|
Bayer S, Mayer AI, Borgonovo G, Morini G, Di Pizio A, Bassoli A. Chemoinformatics View on Bitter Taste Receptor Agonists in Food. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:13916-13924. [PMID: 34762411 PMCID: PMC8630789 DOI: 10.1021/acs.jafc.1c05057] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Food compounds with a bitter taste have a role in human health, both for their capability to influence food choice and preferences and for their possible systemic effect due to the modulation of extra-oral bitter taste receptors (TAS2Rs). Investigating the interaction of bitter food compounds with TAS2Rs is a key step to unravel their complex effects on health and to pave the way to rationally design new additives for food formulation or drugs. Here, we propose a collection of food bitter compounds, for which in vitro activity data against TAS2Rs are available. The patterns of TAS2R subtype-specific agonists were analyzed using scaffold decomposition and chemical space analysis, providing a detailed characterization of the associations between food bitter tastants and TAS2Rs.
Collapse
Affiliation(s)
- Sebastian Bayer
- Leibniz
Institute for Food Systems Biology at the Technical University of
Munich, Lise-Meitner Str. 34, D-85354 Freising, Germany
- Faculty
of Life Sciences, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria
| | - Ariane Isabell Mayer
- Department
of Food, Environmental and Nutritional Sciences-DeFENS, University of Milan, via Celoria 2, 20147 Milano, Italy
| | - Gigliola Borgonovo
- Department
of Food, Environmental and Nutritional Sciences-DeFENS, University of Milan, via Celoria 2, 20147 Milano, Italy
| | - Gabriella Morini
- University
of Gastronomic Sciences, piazza Vittorio Emanuele 9, 12042 Pollenzo, (Bra, CN), Italy
| | - Antonella Di Pizio
- Leibniz
Institute for Food Systems Biology at the Technical University of
Munich, Lise-Meitner Str. 34, D-85354 Freising, Germany
- . Phone: +49(0)8161716516
| | - Angela Bassoli
- Department
of Food, Environmental and Nutritional Sciences-DeFENS, University of Milan, via Celoria 2, 20147 Milano, Italy
- . Phone: +39(0)250316815
| |
Collapse
|
23
|
Simões S, Almeida AJ, Marto J. Palatability of pediatric formulations: do rats predict aversiveness? Drug Dev Ind Pharm 2021; 47:1121-1126. [PMID: 34545750 DOI: 10.1080/03639045.2021.1984519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
BACKGROUND The brief-access taste aversion (BATA) model has been used as an alternative taste assessment tool to human taste panels and became an important element of pharmaceutical drug development, especially regarding pediatric patient's compliance. This model has been validated, demonstrating a concentration-dependent sensitivity to drug aversiveness, as well as the capacity to evaluate the taste-masking effects of cyclodextrins. In the BATA model, samples are presented randomly to rodents in numerous sipper tubes and a lickometer is used for the electronic record of licks in a sophisticated approach. OBJECTIVES The aim of this study was to test possible drug taste-masking strategies. Additionally, we have used an alternative approach to measure the animal lick number in the presence of different compounds, non-simultaneously. RESULTS In the present work we show for the first time the licking profile of different compounds during the time course of the experiment, with each animal being exposed to only one bottle of testing product. To validate the experiments, quinine hydrochloride dihydrate (QHD) was used as a bitter reference compound. CONCLUSION The results obtained using this simple approach showed that aversiveness is dependent on the assay duration, and that it is possible to predict the aversiveness just by measuring the mass of the tested substance consumption. Moreover, some taste-masking strategies, such as those used in pediatric formulations and corresponding to the addition of sweeteners or flavors, cannot be predicted from rodents BATA model.
Collapse
Affiliation(s)
- Sandra Simões
- Faculty of Pharmacy, Research Institute for Medicines (iMed.ULisboa), Universidade de Lisboa, Lisboa, Portugal
| | - Antonio J Almeida
- Faculty of Pharmacy, Research Institute for Medicines (iMed.ULisboa), Universidade de Lisboa, Lisboa, Portugal
| | - Joana Marto
- Faculty of Pharmacy, Research Institute for Medicines (iMed.ULisboa), Universidade de Lisboa, Lisboa, Portugal
| |
Collapse
|
24
|
Dhanaraj P, Muthiah I, Rozbu MR, Nuzhat S, Paulraj MS. Computational Studies on T2Rs Agonist-Based Anti-COVID-19 Drug Design. Front Mol Biosci 2021; 8:637124. [PMID: 34485378 PMCID: PMC8416165 DOI: 10.3389/fmolb.2021.637124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 07/02/2021] [Indexed: 11/13/2022] Open
Abstract
The expeditious and world pandemic viral disease of new coronavirus (SARS-CoV-2) has formed a prompt urgency to discover auspicious target-based ligand for the treatment of COVID-19. Symptoms of novel coronavirus disease (COVID-19) typically include dry cough, fever, and shortness of breath. Recent studies on many COVID-19 patients in Italy and the United Kingdom found increasing anosmia and ageusia among the COVID-19-infected patients. SARS-CoV-2 possibly infects neurons in the nasal passage and disrupts the senses of smell and taste, like other coronaviruses, such as SARS-CoV and MERS-CoV that could target the central nervous system. Developing a drug based on the T2Rs might be of better understanding and worth finding better molecules to act against COVID-19. In this research, we have taken a taste receptor agonist molecule to find a better core molecule that may act as the best resource to design a drug or corresponding derivatives. Based on the computational docking studies, the antibiotic tobramycin showed the best interaction against 6LU7 COVID-19 main protease. Aromatic carbonyl functional groups of the molecule established intermolecular hydrogen bonding interaction with GLN189 amino acid and it showed the two strongest carbonyl interactions with receptor protein resulting in a glide score of -11.159. To conclude, depending on the molecular recognition of the GPCR proteins, the agonist molecule can be recognized to represent the cell secondary mechanism; thus, it provides enough confidence to design a suitable molecule based on the tobramycin drug.
Collapse
Affiliation(s)
- Premnath Dhanaraj
- Department of Biotechnology, School of Agriculture and Biosciences, Karunya Institute of Technology and Science (Deemed to be University), Coimbatore, India
| | - Indiraleka Muthiah
- Department of Biotechnology, Mepco Schlenk Engineering College, Sivakasi, India
| | - Mahtabin Rodela Rozbu
- Department of Science and Math Program, Asian University for Women, Chittagong, Bangladesh
| | - Samiha Nuzhat
- Department of Science and Math Program, Asian University for Women, Chittagong, Bangladesh
| | | |
Collapse
|
25
|
Alfonso-Prieto M. Bitter Taste and Olfactory Receptors: Beyond Chemical Sensing in the Tongue and the Nose. J Membr Biol 2021; 254:343-352. [PMID: 34173018 PMCID: PMC8231087 DOI: 10.1007/s00232-021-00182-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 04/29/2021] [Indexed: 11/24/2022]
Abstract
Abstract The Up-and-Coming-Scientist section of the current issue of the Journal of Membrane Biology features the invited essay by Dr. Mercedes Alfonso-Prieto, Assistant Professor at the Forschungszentrum Jülich (FZJ), Germany, and the Heinrich-Heine University Düsseldorf, Vogt Institute for Brain Research.
Dr. Alfonso-Prieto completed her doctoral degree in chemistry at the Barcelona Science Park, Spain, in 2009, pursued post-doctoral research in computational molecular sciences at Temple University, USA, and then, as a Marie Curie post-doctoral fellow at the University of Barcelona, worked on computations of enzyme reactions and modeling of photoswitchable ligands targeting neuronal receptors. In 2016, she joined the Institute for Advanced Science and the Institute for Computational Biomedicine at the FZJ, where she pursues research on modeling and simulation of chemical senses.
The invited essay by Dr. Alfonso-Prieto discusses state-of-the-art modeling of molecular receptors involved in chemical sensing – the senses of taste and smell. These receptors, and computational methods to study them, are the focus of Dr. Alfonso-Prieto’s research. Recently, Dr. Alfonso-Prieto and colleagues have presented a new methodology to predict ligand binding poses for GPCRs, and extensive computations that deciphered the ligand selectivity determinants of bitter taste receptors. These developments inform our current understanding of how taste occurs at the molecular level. Graphic Abstract ![]()
Collapse
Affiliation(s)
- Mercedes Alfonso-Prieto
- Institute for Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Computational Biomedicine, Forschungszentrum Jülich GmbH, Jülich, Germany. .,Medical Faculty, Cécile and Oskar Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
| |
Collapse
|
26
|
Bitter taste in silico: A review on virtual ligand screening and characterization methods for TAS2R-bitterant interactions. Int J Pharm 2021; 600:120486. [PMID: 33744445 DOI: 10.1016/j.ijpharm.2021.120486] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 02/21/2021] [Accepted: 03/09/2021] [Indexed: 11/21/2022]
Abstract
The growing pharmaceutical interest in the human bitter taste receptors (hTAS2Rs) has two dimensions; i) evaluation of the bitterness of active pharmaceutical compounds, in order to develop strategies for improving patients' adherence to medication, and ii) application of ligands for extra-cellular hTAS2Rs for potential preventive therapeutic achievements. The result is an increasing demand on robust tools for bitterness assessment and screening the receptor-ligand affinity. In silico tools are useful for aiding experimental-screening, as well as to elucide ligand-receptor interactions. In this review, the ligand-based and structure-based approaches are described as the two main in silico tools for bitter taste analysis. The strengths and weaknesses of each approach are discussed. Both approaches provide key tools for understanding and exploiting bitter taste for human health applications.
Collapse
|