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Amjad A, Ahmed S, Kabir M, Arif M, Alam T. A novel deep learning identifier for promoters and their strength using heterogeneous features. Methods 2024:S1046-2023(24)00185-3. [PMID: 39168294 DOI: 10.1016/j.ymeth.2024.08.005] [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: 06/25/2024] [Revised: 07/24/2024] [Accepted: 08/17/2024] [Indexed: 08/23/2024] Open
Abstract
Promoters, which are short (50-1500 base-pair) in DNA regions, have emerged to play a critical role in the regulation of gene transcription. Numerous dangerous diseases, likewise cancer, cardiovascular, and inflammatory bowel diseases, are caused by genetic variations in promoters. Consequently, the correct identification and characterization of promoters are significant for the discovery of drugs. However, experimental approaches to recognizing promoters and their strengths are challenging in terms of cost, time, and resources. Therefore, computational techniques are highly desirable for the correct characterization of promoters from unannotated genomic data. Here, we designed a powerful bi-layer deep-learning based predictor named "PROCABLES", which discriminates DNA samples as promoters in the first-phase and strong or weak promoters in the second-phase respectively. The proposed method utilizes five distinct features, such as word2vec, k-spaced nucleotide pairs, trinucleotide propensity-based features, trinucleotide composition, and electron-ion interaction pseudopotentials, to extract the hidden patterns from the DNA sequence. Afterwards, a stacked framework is formed by integrating a convolutional neural network (CNN) with bidirectional long-short-term memory (LSTM) using multi-view attributes to train the proposed model. The PROCABLES model achieved an accuracy of 0.971 and 0.920 and the MCC 0.940 and 0.840 for the first and second-layer using the ten-fold cross-validation test, respectively. The predicted results anticipate that the proposed PROCABLES protocol outperformed the advanced computational predictors targeting promoters and their types. In summary, this research will provide useful hints for the recognition of large-scale promoters in particular and other DNA problems in general.
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Affiliation(s)
- Aqsa Amjad
- School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan
| | - Saeed Ahmed
- School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan
| | - Muhammad Kabir
- School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan.
| | - Muhammad Arif
- College of Science and Engineering, Hamad Bin Khalifa University, Doha 34110, Qatar.
| | - Tanvir Alam
- College of Science and Engineering, Hamad Bin Khalifa University, Doha 34110, Qatar.
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2
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Androutsos L, Pallante L, Bompotas A, Stojceski F, Grasso G, Piga D, Di Benedetto G, Alexakos C, Kalogeras A, Theofilatos K, Deriu MA, Mavroudi S. Predicting multiple taste sensations with a multiobjective machine learning method. NPJ Sci Food 2024; 8:47. [PMID: 39054312 PMCID: PMC11272927 DOI: 10.1038/s41538-024-00287-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 07/05/2024] [Indexed: 07/27/2024] Open
Abstract
Taste perception plays a pivotal role in guiding nutrient intake and aiding in the avoidance of potentially harmful substances through five basic tastes - sweet, bitter, umami, salty, and sour. Taste perception originates from molecular interactions in the oral cavity between taste receptors and chemical tastants. Hence, the recognition of taste receptors and the subsequent perception of taste heavily rely on the physicochemical properties of food ingredients. In recent years, several advances have been made towards the development of machine learning-based algorithms to classify chemical compounds' tastes using their molecular structures. Despite the great efforts, there remains significant room for improvement in developing multi-class models to predict the entire spectrum of basic tastes. Here, we present a multi-class predictor aimed at distinguishing bitter, sweet, and umami, from other taste sensations. The development of a multi-class taste predictor paves the way for a comprehensive understanding of the chemical attributes associated with each fundamental taste. It also opens the potential for integration into the evolving realm of multi-sensory perception, which encompasses visual, tactile, and olfactory sensations to holistically characterize flavour perception. This concept holds promise for introducing innovative methodologies in the rational design of foods, including pre-determining specific tastes and engineering complementary diets to augment traditional pharmacological treatments.
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Affiliation(s)
| | - Lorenzo Pallante
- PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, 10129, Italy
| | - Agorakis Bompotas
- Industrial Systems Institute, Athena Research Center, 265 04, Patras, Greece
| | - Filip Stojceski
- Department of Innovative Technologies, Dalle Molle Institute for Artificial Intelligence, Lugano-Viganello, 6962, Switzerland
| | - Gianvito Grasso
- Department of Innovative Technologies, Dalle Molle Institute for Artificial Intelligence, Lugano-Viganello, 6962, Switzerland
| | - Dario Piga
- Department of Innovative Technologies, Dalle Molle Institute for Artificial Intelligence, Lugano-Viganello, 6962, Switzerland
| | | | - Christos Alexakos
- Industrial Systems Institute, Athena Research Center, 265 04, Patras, Greece
| | | | | | - Marco A Deriu
- PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, 10129, Italy
| | - Seferina Mavroudi
- InSyBio PC, Patras, 265 04, Greece
- Department of Nursing, University of Patras, 265 04, Patras, Greece
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3
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Banić M, Butorac K, Čuljak N, Butorac A, Novak J, Pavunc AL, Rušanac A, Stanečić Ž, Lovrić M, Šušković J, Kos B. An Integrated Comprehensive Peptidomics and In Silico Analysis of Bioactive Peptide-Rich Milk Fermented by Three Autochthonous Cocci Strains. Int J Mol Sci 2024; 25:2431. [PMID: 38397111 PMCID: PMC10888711 DOI: 10.3390/ijms25042431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 02/12/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024] Open
Abstract
Bioactive peptides (BPs) are molecules of paramount importance with great potential for the development of functional foods, nutraceuticals or therapeutics for the prevention or treatment of various diseases. A functional BP-rich dairy product was produced by lyophilisation of bovine milk fermented by the autochthonous strains Lactococcus lactis subsp. lactis ZGBP5-51, Enterococcus faecium ZGBP5-52 and Enterococcus faecalis ZGBP5-53 isolated from the same artisanal fresh cheese. The efficiency of the proteolytic system of the implemented strains in the production of BPs was confirmed by a combined high-throughput mass spectrometry (MS)-based peptidome profiling and an in silico approach. First, peptides released by microbial fermentation were identified via a non-targeted peptide analysis (NTA) comprising reversed-phase nano-liquid chromatography (RP nano-LC) coupled with matrix-assisted laser desorption/ionisation-time-of-flight/time-of-flight (MALDI-TOF/TOF) MS, and then quantified by targeted peptide analysis (TA) involving RP ultrahigh-performance LC (RP-UHPLC) coupled with triple-quadrupole MS (QQQ-MS). A combined database and literature search revealed that 10 of the 25 peptides identified in this work have bioactive properties described in the literature. Finally, by combining the output of MS-based peptidome profiling with in silico bioactivity prediction tools, three peptides (75QFLPYPYYAKPA86, 40VAPFPEVFGK49, 117ARHPHPHLSF126), whose bioactive properties have not been previously reported in the literature, were identified as potential BP candidates.
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Affiliation(s)
- Martina Banić
- Laboratory for Antibiotic, Enzyme, Probiotic and Starter Culture Technologies, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia; (M.B.); (K.B.); (N.Č.); (J.N.); (A.L.P.); (A.R.); (J.Š.)
| | - Katarina Butorac
- Laboratory for Antibiotic, Enzyme, Probiotic and Starter Culture Technologies, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia; (M.B.); (K.B.); (N.Č.); (J.N.); (A.L.P.); (A.R.); (J.Š.)
| | - Nina Čuljak
- Laboratory for Antibiotic, Enzyme, Probiotic and Starter Culture Technologies, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia; (M.B.); (K.B.); (N.Č.); (J.N.); (A.L.P.); (A.R.); (J.Š.)
| | - Ana Butorac
- BICRO Biocentre Ltd., Borongajska cesta 83H, 10000 Zagreb, Croatia; (A.B.); (Ž.S.); (M.L.)
| | - Jasna Novak
- Laboratory for Antibiotic, Enzyme, Probiotic and Starter Culture Technologies, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia; (M.B.); (K.B.); (N.Č.); (J.N.); (A.L.P.); (A.R.); (J.Š.)
| | - Andreja Leboš Pavunc
- Laboratory for Antibiotic, Enzyme, Probiotic and Starter Culture Technologies, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia; (M.B.); (K.B.); (N.Č.); (J.N.); (A.L.P.); (A.R.); (J.Š.)
| | - Anamarija Rušanac
- Laboratory for Antibiotic, Enzyme, Probiotic and Starter Culture Technologies, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia; (M.B.); (K.B.); (N.Č.); (J.N.); (A.L.P.); (A.R.); (J.Š.)
| | - Željka Stanečić
- BICRO Biocentre Ltd., Borongajska cesta 83H, 10000 Zagreb, Croatia; (A.B.); (Ž.S.); (M.L.)
| | - Marija Lovrić
- BICRO Biocentre Ltd., Borongajska cesta 83H, 10000 Zagreb, Croatia; (A.B.); (Ž.S.); (M.L.)
| | - Jagoda Šušković
- Laboratory for Antibiotic, Enzyme, Probiotic and Starter Culture Technologies, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia; (M.B.); (K.B.); (N.Č.); (J.N.); (A.L.P.); (A.R.); (J.Š.)
| | - Blaženka Kos
- Laboratory for Antibiotic, Enzyme, Probiotic and Starter Culture Technologies, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia; (M.B.); (K.B.); (N.Č.); (J.N.); (A.L.P.); (A.R.); (J.Š.)
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4
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Iwaniak A, Minkiewicz P, Darewicz M. Bioinformatics and bioactive peptides from foods: Do they work together? ADVANCES IN FOOD AND NUTRITION RESEARCH 2024; 108:35-111. [PMID: 38461003 DOI: 10.1016/bs.afnr.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2024]
Abstract
We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.
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Affiliation(s)
- Anna Iwaniak
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland.
| | - Piotr Minkiewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland
| | - Małgorzata Darewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland
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5
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Zou H. iDPPIV-SI: identifying dipeptidyl peptidase IV inhibitory peptides by using multiple sequence information. J Biomol Struct Dyn 2024; 42:2144-2152. [PMID: 37125813 DOI: 10.1080/07391102.2023.2203257] [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: 10/25/2022] [Accepted: 04/10/2023] [Indexed: 05/02/2023]
Abstract
Currently, diabetes has become a great threaten for people's health in the world. Recent study shows that dipeptidyl peptidase IV (DPP-IV) inhibitory peptides may be a potential pharmaceutical agent to treat diabetes. Thus, there is a need to discriminate DPP-IV inhibitory peptides from non-DPP-IV inhibitory peptides. To address this issue, a novel computational model called iDPPIV-SI was developed in this study. In the first, 50 different types of physicochemical (PC) properties were employed to denote the peptide sequences. Three different feature descriptors including the 1-order, 2-order correlation methods and discrete wavelet transform were applied to collect useful information from the PC matrix. Furthermore, the least absolute shrinkage and selection operator (LASSO) algorithm was employed to select these most discriminative features. All of these chosen features were fed into support vector machine (SVM) for identifying DPP-IV inhibitory peptides. The iDPPIV-SI achieved 91.26% and 98.12% classification accuracies on the training and independent dataset, respectively. There is a significantly improvement in the classification performance by the proposed method, as compared with the state-of-the-art predictors. The datasets and MATLAB codes (based on MATLAB2015b) used in current study are available at https://figshare.com/articles/online_resource/iDPPIV-SI/20085878.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Hongliang Zou
- School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang, China
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6
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Yu Y, Liu S, Zhang X, Yu W, Pei X, Liu L, Jin Y. Identification and prediction of milk-derived bitter taste peptides based on peptidomics technology and machine learning method. Food Chem 2024; 433:137288. [PMID: 37683467 DOI: 10.1016/j.foodchem.2023.137288] [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: 04/26/2023] [Revised: 07/19/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023]
Abstract
Bitter taste peptides (BPs) are vital for drug and nutrition research, but large-scale screening of them is still time-consuming and costly. This study developed a complete workflow for screening BPs based on peptidomics technology and machine learning method. Using an expanded dataset and a new combination of BPs' characteristic factors, a novel classification prediction model (CPM-BP) based on the Light Gradient Boosting Machine algorithm was constructed with an accuracy of 90.3 % for predicting BPs. Among 724 significantly different peptides between spoiled and fresh UHT milk, 180 potential BPs were predicted using CPM-BP and eleven of them were previously reported. One known BP (FALPQYLK) and three predicted potential BPs (FALPQYL, FFVAPFPEVFGKE, EMPFPKYP) were verified by determination of calcium mobilization of HEK293T cells expressing human bitter taste receptor T2R4 (hT2R4). Three potential BPs could activate the hT2R4 and are demonstrated to be BPs, which proved the effectiveness of CPM-BP.
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Affiliation(s)
- Yang Yu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China
| | - Shengchi Liu
- School of Information Science and Engineering, Dalian Polytechnic University, Dalian, Liaoning 116034, China
| | - Xinchen Zhang
- School of Information Science and Engineering, Dalian Polytechnic University, Dalian, Liaoning 116034, China
| | - Wenhao Yu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China
| | - Xiaoyan Pei
- Inner Mongolia Yili Industrial Group Co., Ltd., Hohhot, Inner Mongolia 010110, China
| | - Li Liu
- School of Information Science and Engineering, Dalian Polytechnic University, Dalian, Liaoning 116034, China.
| | - Yan Jin
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China.
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7
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Ayoub MA, Yap PG, Mudgil P, Khan FB, Anwar I, Muhammad K, Gan CY, Maqsood S. Invited review: Camel milk-derived bioactive peptides and diabetes-Molecular view and perspectives. J Dairy Sci 2024; 107:649-668. [PMID: 37709024 DOI: 10.3168/jds.2023-23733] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/20/2023] [Indexed: 09/16/2023]
Abstract
In dairy science, camel milk (CM) constitutes a center of interest for scientists due to its known beneficial effect on diabetes as demonstrated in many in vitro, in vivo, and clinical studies and trials. Overall, CM had positive effects on various parameters related to glucose transport and metabolism as well as the structural and functional properties of the pancreatic β-cells and insulin secretion. Thus, CM consumption may help manage diabetes; however, such a recommendation will become rationale and clinically conceivable only if the exact molecular mechanisms and pathways involved at the cellular levels are well understood. Moreover, the application of CM as an alternative antidiabetic tool may first require the identification of the exact bioactive molecules behind such antidiabetic properties. In this review, we describe the advances in our knowledge of the molecular mechanisms reported to be involved in the beneficial effects of CM in managing diabetes using different in vitro and in vivo models. This mainly includes the effects of CM on the different molecular pathways controlling (1) insulin receptor signaling and glucose uptake, (2) the pancreatic β-cell structure and function, and (3) the activity of key metabolic enzymes in glucose metabolism. Moreover, we described the current status of the identification of CM-derived bioactive peptides and their structure-activity relationship study and characterization in the context of molecular markers related to diabetes. Such an overview will not only enrich our scientific knowledge of the plausible mode of action of CM in diabetes but should ultimately rationalize the claim of the potential application of CM against diabetes. This will pave the way toward new directions and ideas for developing a new generation of antidiabetic products taking benefits from the chemical composition of CM.
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Affiliation(s)
- Mohammed Akli Ayoub
- Department of Biological Sciences, College of Medicine and Health Sciences, Khalifa University, 127788, Abu Dhabi, United Arab Emirates.
| | - Pei-Gee Yap
- Analytical Biochemistry Research Centre (ABrC), University Innovation Incubator (i2U) Building, SAINS@USM Campus, Universiti Sains Malaysia, Lebuh Bukit Jambul, 11900 Bayan Lepas, Penang, Malaysia
| | - Priti Mudgil
- Department of Food Science, College of Agriculture and Veterinary Medicine, United Arab Emirates University, 15551, Al Ain, United Arab Emirates
| | - Farheen Badrealam Khan
- Department of Biology, College of Science, United Arab Emirates University, 15551, Al Ain, United Arab Emirates
| | - Irfa Anwar
- Department of Biology, College of Science, United Arab Emirates University, 15551, Al Ain, United Arab Emirates
| | - Khalid Muhammad
- Department of Biology, College of Science, United Arab Emirates University, 15551, Al Ain, United Arab Emirates
| | - Chee-Yuen Gan
- Analytical Biochemistry Research Centre (ABrC), University Innovation Incubator (i2U) Building, SAINS@USM Campus, Universiti Sains Malaysia, Lebuh Bukit Jambul, 11900 Bayan Lepas, Penang, Malaysia
| | - Sajid Maqsood
- Department of Food Science, College of Agriculture and Veterinary Medicine, United Arab Emirates University, 15551, Al Ain, United Arab Emirates
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Du Z, Ding X, Hsu W, Munir A, Xu Y, Li Y. pLM4ACE: A protein language model based predictor for antihypertensive peptide screening. Food Chem 2024; 431:137162. [PMID: 37604011 DOI: 10.1016/j.foodchem.2023.137162] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/09/2023] [Accepted: 08/13/2023] [Indexed: 08/23/2023]
Abstract
Angiotensin-I converting enzyme (ACE) regulates the renin-angiotensin system and is a drug target in clinical treatment for hypertension. This study aims to develop a protein language model (pLM) with evolutionary scale modeling (ESM-2) embeddings that is trained on experimental data to screen peptides with strong ACE inhibitory activity. Twelve conventional peptide embedding approaches and five machine learning (ML) modeling methods were also tested for performance comparison. Among the 65 classifiers tested, logistic regression with ESM-2 embeddings showed the best performance, with balanced accuracy (BACC), Matthews correlation coefficient (MCC), and area under the curve of 0.883 ± 0.017, 0.77 ± 0.032, and 0.96 ± 0.009, respectively. Multilayer perceptron and support vector machine also exhibited great compatibility with ESM-2 embeddings. The ESM-2 embeddings showed superior performance in enhancing the prediction model compared to the 12 traditional embedding methods. A user-friendly webserver (https://sqzujiduce.us-east-1.awsapprunner.com) with the top three models is now freely available.
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Affiliation(s)
- Zhenjiao Du
- Department of Grain Science and Industry, Kansas State University, Manhattan, KS 66506, USA
| | - Xingjian Ding
- Department of Computer Science, Kansas State University, Manhattan, KS 66506, USA
| | - William Hsu
- Department of Computer Science, Kansas State University, Manhattan, KS 66506, USA
| | - Arslan Munir
- Department of Computer Science, Kansas State University, Manhattan, KS 66506, USA
| | - Yixiang Xu
- Healthy Processed Foods Research Unit, Western Regional Research Center, USDA-ARS, 800 Buchanan Street, Albany, CA 94710, USA
| | - Yonghui Li
- Department of Grain Science and Industry, Kansas State University, Manhattan, KS 66506, USA.
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Charoenkwan P, Kongsompong S, Schaduangrat N, Chumnanpuen P, Shoombuatong W. TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides. BMC Bioinformatics 2023; 24:356. [PMID: 37735626 PMCID: PMC10512532 DOI: 10.1186/s12859-023-05463-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 09/01/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Tyrosinase is an enzyme involved in melanin production in the skin. Several hyperpigmentation disorders involve the overproduction of melanin and instability of tyrosinase activity resulting in darker, discolored patches on the skin. Therefore, discovering tyrosinase inhibitory peptides (TIPs) is of great significance for basic research and clinical treatments. However, the identification of TIPs using experimental methods is generally cost-ineffective and time-consuming. RESULTS Herein, a stacked ensemble learning approach, called TIPred, is proposed for the accurate and quick identification of TIPs by using sequence information. TIPred explored a comprehensive set of various baseline models derived from well-known machine learning (ML) algorithms and heterogeneous feature encoding schemes from multiple perspectives, such as chemical structure properties, physicochemical properties, and composition information. Subsequently, 130 baseline models were trained and optimized to create new probabilistic features. Finally, the feature selection approach was utilized to determine the optimal feature vector for developing TIPred. Both tenfold cross-validation and independent test methods were employed to assess the predictive capability of TIPred by using the stacking strategy. Experimental results showed that TIPred significantly outperformed the state-of-the-art method in terms of the independent test, with an accuracy of 0.923, MCC of 0.757 and an AUC of 0.977. CONCLUSIONS The proposed TIPred approach could be a valuable tool for rapidly discovering novel TIPs and effectively identifying potential TIP candidates for follow-up experimental validation. Moreover, an online webserver of TIPred is publicly available at http://pmlabstack.pythonanywhere.com/TIPred .
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Sasikarn Kongsompong
- Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand
| | - Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Pramote Chumnanpuen
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand.
- Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok, 10900, Thailand.
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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10
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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.
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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
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11
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Rescifina A. Progress of the "Molecular Informatics" Section in 2022. Int J Mol Sci 2023; 24:ijms24119442. [PMID: 37298393 DOI: 10.3390/ijms24119442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023] Open
Abstract
This is the first Editorial of the "Molecular Informatics" Section (MIS) of the International Journal of Molecular Sciences (IJMS), which was created towards the end of 2018 (the first article was submitted on 27 September 2018) and has experienced significant growth from 2018 to now [...].
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Affiliation(s)
- Antonio Rescifina
- Department of Drug and Health Sciences, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
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12
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Zhang YF, Wang YH, Gu ZF, Pan XR, Li J, Ding H, Zhang Y, Deng KJ. Bitter-RF: A random forest machine model for recognizing bitter peptides. Front Med (Lausanne) 2023; 10:1052923. [PMID: 36778738 PMCID: PMC9909039 DOI: 10.3389/fmed.2023.1052923] [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: 09/24/2022] [Accepted: 01/05/2023] [Indexed: 01/27/2023] Open
Abstract
Introduction Bitter peptides are short peptides with potential medical applications. The huge potential behind its bitter taste remains to be tapped. To better explore the value of bitter peptides in practice, we need a more effective classification method for identifying bitter peptides. Methods In this study, we developed a Random forest (RF)-based model, called Bitter-RF, using sequence information of the bitter peptide. Bitter-RF covers more comprehensive and extensive information by integrating 10 features extracted from the bitter peptides and achieves better results than the latest generation model on independent validation set. Results The proposed model can improve the accurate classification of bitter peptides (AUROC = 0.98 on independent set test) and enrich the practical application of RF method in protein classification tasks which has not been used to build a prediction model for bitter peptides. Discussion We hope the Bitter-RF could provide more conveniences to scholars for bitter peptide research.
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Affiliation(s)
- Yu-Fei Zhang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yu-Hao Wang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhi-Feng Gu
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xian-Run Pan
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jian Li
- School of Basic Medical Sciences, Chengdu University, Chengdu, China
| | - Hui Ding
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China,*Correspondence: Hui Ding,
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China,Yang Zhang,
| | - Ke-Jun Deng
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China,Ke-Jun Deng,
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13
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Yu H, Luo X. IPPF-FE: an integrated peptide and protein function prediction framework based on fused features and ensemble models. Brief Bioinform 2023; 24:6834141. [PMID: 36403184 DOI: 10.1093/bib/bbac476] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/23/2022] [Accepted: 10/05/2022] [Indexed: 11/21/2022] Open
Abstract
The prediction of peptide and protein function is important for research and industrial applications, and many machine learning methods have been developed for this purpose. The existing models have encountered many challenges, including the lack of effective and comprehensive features and the limited applicability of each model. Here, we introduce an Integrated Peptide and Protein function prediction Framework based on Fused features and Ensemble models (IPPF-FE), which can accurately capture the relationship between features and labels. The results indicated that IPPF-FE outperformed existing state-of-the-art (SOTA) models on more than 8 different categories of peptide and protein tasks. In addition, t-distributed Stochastic Neighbour Embedding demonstrated the advantages of IPPF-FE. We anticipate that our method will become a versatile tool for peptide and protein prediction tasks and shed light on the future development of related models. The model is open source and available in the GitHub repository https://github.com/Luo-SynBioLab/IPPF-FE.
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Affiliation(s)
- Han Yu
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Xiaozhou Luo
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
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14
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Bioactive and Sensory Di- and Tripeptides Generated during Dry-Curing of Pork Meat. Int J Mol Sci 2023; 24:ijms24021574. [PMID: 36675084 PMCID: PMC9866438 DOI: 10.3390/ijms24021574] [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: 12/31/2022] [Revised: 01/09/2023] [Accepted: 01/10/2023] [Indexed: 01/15/2023] Open
Abstract
Dry-cured pork products, such as dry-cured ham, undergo an extensive proteolysis during manufacturing process which determines the organoleptic properties of the final product. As a result of endogenous pork muscle endo- and exopeptidases, many medium- and short-chain peptides are released from muscle proteins. Many of them have been isolated, identified, and characterized, and some peptides have been reported to exert relevant bioactivity with potential benefit for human health. However, little attention has been given to di- and tripeptides, which are far less known, although they have received increasing attention in recent years due to their high potential relevance in terms of bioactivity and role in taste development. This review gathers the current knowledge about di- and tripeptides, regarding their bioactivity and sensory properties and focusing on their generation during long-term processing such as dry-cured pork meats.
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15
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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.
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Affiliation(s)
- Jingna Yan
- College of Food Science, Southwest University, Chongqing, China
| | - Huarong Tong
- College of Food Science, Southwest University, Chongqing, China
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16
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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.
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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
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17
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IUP-BERT: Identification of Umami Peptides Based on BERT Features. Foods 2022; 11:foods11223742. [PMID: 36429332 PMCID: PMC9689418 DOI: 10.3390/foods11223742] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 11/14/2022] [Accepted: 11/16/2022] [Indexed: 11/23/2022] Open
Abstract
Umami is an important widely-used taste component of food seasoning. Umami peptides are specific structural peptides endowing foods with a favorable umami taste. Laboratory approaches used to identify umami peptides are time-consuming and labor-intensive, which are not feasible for rapid screening. Here, we developed a novel peptide sequence-based umami peptide predictor, namely iUP-BERT, which was based on the deep learning pretrained neural network feature extraction method. After optimization, a single deep representation learning feature encoding method (BERT: bidirectional encoder representations from transformer) in conjugation with the synthetic minority over-sampling technique (SMOTE) and support vector machine (SVM) methods was adopted for model creation to generate predicted probabilistic scores of potential umami peptides. Further extensive empirical experiments on cross-validation and an independent test showed that iUP-BERT outperformed the existing methods with improvements, highlighting its effectiveness and robustness. Finally, an open-access iUP-BERT web server was built. To our knowledge, this is the first efficient sequence-based umami predictor created based on a single deep-learning pretrained neural network feature extraction method. By predicting umami peptides, iUP-BERT can help in further research to improve the palatability of dietary supplements in the future.
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18
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Liu B, Li N, Chen F, Zhang J, Sun X, Xu L, Fang F. Review on the release mechanism and debittering technology of bitter peptides from protein hydrolysates. Compr Rev Food Sci Food Saf 2022; 21:5153-5170. [PMID: 36287032 DOI: 10.1111/1541-4337.13050] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 08/14/2022] [Accepted: 09/04/2022] [Indexed: 01/28/2023]
Abstract
Recent scientific evidence indicates that protein hydrolysates contain bioactive peptides that have potential benefits for human health. However, the bitter-tasting hydrophobic peptides in protein hydrolysates negatively affect the sensory quality of resulting products and limit their utilization in food and pharmaceutical industries. The approaches to reduce, mask, and remove bitter taste from protein hydrolysates have been extensively reported. This review paper focuses on the advances in the knowledge regarding the structure-bitterness relationship of peptides, the release mechanism of bitter peptides, and the debittering methods for protein hydrolysates. Bitter tastes generating with enzymatic hydrolysis of protein is influenced by the type, concentration, and bitter taste threshold of bitterness peptides. A "bell-shaped curve" is used to describe the relationship between the bitterness intensity of the hydrolysates and the degree of hydrolysis. The bitter receptor perceives bitter potencies of bitter peptides by the hydrophobicity recognition zone. The intensity of bitterness is influenced by hydrophobic and electronic properties of amino acids and the critical spatial structure of peptides. Compared to physicochemical debittering (i.e., selective separation, masking of bitter taste, encapsulation, Maillard reaction, and encapsulation) and other biological debittering (i.e., enzymatic hydrolysis, enzymatic deamidation, plastein reaction), enzymatic hydrolysis is a promising debittering approach as it combines protein hydrolyzation and debittering into a one-step process, but more work should be done to advance the knowledge on debittering mechanism of enzymatic hydrolysis and screening of suitable proteases. Further study can focus on combining physicochemical and biological approaches to achieve high debittering efficiency and produce high-quality products.
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Affiliation(s)
- Boye Liu
- College of Food Science and Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, Henan Province, 450001, People's Republic of China
| | - Nana Li
- College of Food Science and Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, Henan Province, 450001, People's Republic of China
| | - Fusheng Chen
- College of Food Science and Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, Henan Province, 450001, People's Republic of China
| | - Jingsi Zhang
- College of Food Science and Technology, Nanjing Agricultural University, 1 Weigang, Nanjing, Jiangsu Province, 210014, People's Republic of China
| | - Xiaorui Sun
- College of Food Science and Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, Henan Province, 450001, People's Republic of China
| | - Lei Xu
- Nestlé Product Technology Center, Nestlé Health Science, Bridgewater, NJ, 08807, USA
| | - Fang Fang
- Whistler Center for Carbohydrate Research and Department of Food Science, Purdue University, West Lafayette, IN, 47907, USA
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19
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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.
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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.
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20
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Identify Bitter Peptides by Using Deep Representation Learning Features. Int J Mol Sci 2022; 23:ijms23147877. [PMID: 35887225 PMCID: PMC9315524 DOI: 10.3390/ijms23147877] [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: 05/31/2022] [Revised: 07/01/2022] [Accepted: 07/14/2022] [Indexed: 02/04/2023] Open
Abstract
A bitter taste often identifies hazardous compounds and it is generally avoided by most animals and humans. Bitterness of hydrolyzed proteins is caused by the presence of bitter peptides. To improve palatability, bitter peptides need to be identified experimentally in a time-consuming and expensive process, before they can be removed or degraded. Here, we report the development of a machine learning prediction method, iBitter-DRLF, which is based on a deep learning pre-trained neural network feature extraction method. It uses three sequence embedding techniques, soft symmetric alignment (SSA), unified representation (UniRep), and bidirectional long short-term memory (BiLSTM). These were initially combined into various machine learning algorithms to build several models. After optimization, the combined features of UniRep and BiLSTM were finally selected, and the model was built in combination with a light gradient boosting machine (LGBM). The results showed that the use of deep representation learning greatly improves the ability of the model to identify bitter peptides, achieving accurate prediction based on peptide sequence data alone. By helping to identify bitter peptides, iBitter-DRLF can help research into improving the palatability of peptide therapeutics and dietary supplements in the future. A webserver is available, too.
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21
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Kang M, Oh JH. Editorial of Special Issue "Deep Learning and Machine Learning in Bioinformatics". Int J Mol Sci 2022; 23:ijms23126610. [PMID: 35743052 PMCID: PMC9224509 DOI: 10.3390/ijms23126610] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 06/10/2022] [Indexed: 02/04/2023] Open
Abstract
In recent years, deep learning has emerged as a highly active research field, achieving great success in various machine learning areas, including image processing, speech recognition, and natural language processing, and now rapidly becoming a dominant tool in biomedicine [...].
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Affiliation(s)
- Mingon Kang
- Department of Computer Science, University of Nevada, Las Vegas, NV 89154, USA;
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Correspondence:
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22
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Yu H, Wang X, Xie J, Ai L, Chen C, Tian H. Isolation and identification of bitter-tasting peptides in Shaoxing rice wine using ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry combined with taste orientation strategy. J Chromatogr A 2022; 1676:463193. [DOI: 10.1016/j.chroma.2022.463193] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 05/28/2022] [Accepted: 05/31/2022] [Indexed: 11/24/2022]
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23
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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.
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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
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24
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Charoenkwan P, Ahmed S, Nantasenamat C, Quinn JMW, Moni MA, Lio' P, Shoombuatong W. AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning. Sci Rep 2022; 12:7697. [PMID: 35546347 PMCID: PMC9095707 DOI: 10.1038/s41598-022-11897-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 05/03/2022] [Indexed: 12/13/2022] Open
Abstract
Amyloid proteins have the ability to form insoluble fibril aggregates that have important pathogenic effects in many tissues. Such amyloidoses are prominently associated with common diseases such as type 2 diabetes, Alzheimer's disease, and Parkinson's disease. There are many types of amyloid proteins, and some proteins that form amyloid aggregates when in a misfolded state. It is difficult to identify such amyloid proteins and their pathogenic properties, but a new and effective approach is by developing effective bioinformatics tools. While several machine learning (ML)-based models for in silico identification of amyloid proteins have been proposed, their predictive performance is limited. In this study, we present AMYPred-FRL, a novel meta-predictor that uses a feature representation learning approach to achieve more accurate amyloid protein identification. AMYPred-FRL combined six well-known ML algorithms (extremely randomized tree, extreme gradient boosting, k-nearest neighbor, logistic regression, random forest, and support vector machine) with ten different sequence-based feature descriptors to generate 60 probabilistic features (PFs), as opposed to state-of-the-art methods developed by a single feature-based approach. A logistic regression recursive feature elimination (LR-RFE) method was used to find the optimal m number of 60 PFs in order to improve the predictive performance. Finally, using the meta-predictor approach, the 20 selected PFs were fed into a logistic regression method to create the final hybrid model (AMYPred-FRL). Both cross-validation and independent tests showed that AMYPred-FRL achieved superior predictive performance than its constituent baseline models. In an extensive independent test, AMYPred-FRL outperformed the existing methods by 5.5% and 16.1%, respectively, with accuracy and MCC of 0.873 and 0.710. To expedite high-throughput prediction, a user-friendly web server of AMYPred-FRL is freely available at http://pmlabstack.pythonanywhere.com/AMYPred-FRL. It is anticipated that AMYPred-FRL will be a useful tool in helping researchers to identify new amyloid proteins.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Saeed Ahmed
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Julian M W Quinn
- Bone Biology Division, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW, 2010, Australia
| | - Mohammad Ali Moni
- Artificial Intelligence and Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Pietro Lio'
- Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD, UK
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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