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Kleandrova VV, Cordeiro MNDS, Speck-Planche A. Optimizing drug discovery using multitasking models for quantitative structure-biological effect relationships: an update of the literature. Expert Opin Drug Discov 2023; 18:1231-1243. [PMID: 37639708 DOI: 10.1080/17460441.2023.2251385] [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: 06/08/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023]
Abstract
INTRODUCTION Drug discovery has provided modern societies with the means to fight against many diseases. In this sense, computational methods have been at the forefront, playing an important role in rationalizing the search for novel drugs. Yet, tackling phenomena such as the multi-genic nature of diseases and drug resistance are limitations of the current computational methods. Multi-tasking models for quantitative structure-biological effect relationships (mtk-QSBER) have emerged to overcome such limitations. AREAS COVERED The present review describes an update on the fundamentals and applications of the mtk-QSBER models as tools to accelerate multiple stages/substages of the drug discovery process. EXPERT OPINION Computational approaches are extremely important for the rationalization of the search for novel and efficacious therapeutic agents. However, they need to focus more on the multi-target drug discovery paradigm. In this sense, mtk-QSBER models are particularly suited for multi-target drug discovery, offering encouraging opportunities across multiple therapeutic areas and scientific disciplines associated with drug discovery.
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Affiliation(s)
- Valeria V Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Russian Biotechnological University, Moscow, Russian Federation
| | - M Natália D S Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Alejandro Speck-Planche
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
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Konstantopoulos G, Koumoulos EP, Charitidis CA. Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives. NANOMATERIALS 2022; 12:nano12152646. [PMID: 35957077 PMCID: PMC9370746 DOI: 10.3390/nano12152646] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 02/05/2023]
Abstract
Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised on effective resource management. The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge and data-driven design of a tailored behavioral profile of nanomaterials to serve the special needs of application environments. The previous knowledge of the physics and mathematical representation of material behaviors, as well as the utilization of already generated testing data, received specific attention by scientists. However, the exploration of available information is not always manageable, and machine intelligence can efficiently (computational resources, time) meet this challenge via high-throughput multidimensional search exploration capabilities. Moreover, the modelling of bio-chemical interactions with the environment and living organisms has been demonstrated to connect chemical structure with acute or tolerable effects upon exposure. Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making, in relation to increased impact in the field of advanced nanomaterials manufacturing and nanoinformatics, and to indicate the steps required to realize rapid, safe, and circular-by-design nanomaterials.
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Affiliation(s)
- Georgios Konstantopoulos
- RNANO Lab—Research Unit of Advanced, Composite, Nano Materials & Nanotechnology, School of Chemical Engineering, National Technical University of Athens, GR15773 Athens, Greece; (G.K.); (C.A.C.)
| | - Elias P. Koumoulos
- Innovation in Research & Engineering Solutions (IRES), Boulevard Edmond Machtens 79/22, 1080 Brussels, Belgium
- Correspondence:
| | - Costas A. Charitidis
- RNANO Lab—Research Unit of Advanced, Composite, Nano Materials & Nanotechnology, School of Chemical Engineering, National Technical University of Athens, GR15773 Athens, Greece; (G.K.); (C.A.C.)
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Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning. Int J Mol Sci 2021; 22:ijms222111519. [PMID: 34768951 PMCID: PMC8584266 DOI: 10.3390/ijms222111519] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/08/2021] [Accepted: 10/22/2021] [Indexed: 12/22/2022] Open
Abstract
The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Perturbation Theory Machine Learning (PTML) models were built to predict the probability of different pairs of drugs and nanoparticles creating DDNP complexes with anti-glioblastoma activity. PTML models use the perturbations of molecular descriptors of drugs and nanoparticles as inputs in experimental conditions. The raw dataset was obtained by mixing the nanoparticle experimental data with drug assays from the ChEMBL database. Ten types of machine learning methods have been tested. Only 41 features have been selected for 855,129 drug-nanoparticle complexes. The best model was obtained with the Bagging classifier, an ensemble meta-estimator based on 20 decision trees, with an area under the receiver operating characteristic curve (AUROC) of 0.96, and an accuracy of 87% (test subset). This model could be useful for the virtual screening of nanoparticle-drug complexes in glioblastoma. All the calculations can be reproduced with the datasets and python scripts, which are freely available as a GitHub repository from authors.
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He H, Yan S, Lyu D, Xu M, Ye R, Zheng P, Lu X, Wang L, Ren B. Deep Learning for Biospectroscopy and Biospectral Imaging: State-of-the-Art and Perspectives. Anal Chem 2021; 93:3653-3665. [PMID: 33599125 DOI: 10.1021/acs.analchem.0c04671] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
With the advances in instrumentation and sampling techniques, there is an explosive growth of data from molecular and cellular samples. The call to extract more information from the large data sets has greatly challenged the conventional chemometrics method. Deep learning, which utilizes very large data sets for finding hidden features therein and for making accurate predictions for a wide range of applications, has been applied in an unbelievable pace in biospectroscopy and biospectral imaging in the recent 3 years. In this Feature, we first introduce the background and basic knowledge of deep learning. We then focus on the emerging applications of deep learning in the data preprocessing, feature detection, and modeling of the biological samples for spectral analysis and spectroscopic imaging. Finally, we highlight the challenges and limitations in deep learning and the outlook for future directions.
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Affiliation(s)
- Hao He
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, China
| | - Sen Yan
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Danya Lyu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Mengxi Xu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Ruiqian Ye
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, China
| | - Peng Zheng
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, China
| | - Xinyu Lu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Lei Wang
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, China
| | - Bin Ren
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
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Urista DV, Carrué DB, Otero I, Arrasate S, Quevedo-Tumailli VF, Gestal M, González-Díaz H, Munteanu CR. Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models. BIOLOGY 2020; 9:biology9080198. [PMID: 32751710 PMCID: PMC7465777 DOI: 10.3390/biology9080198] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 07/22/2020] [Accepted: 07/27/2020] [Indexed: 12/13/2022]
Abstract
Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental resources and time by using a virtual screening for DDNPs. The raw data was obtained by the fusion of experimental data for nanoparticles with compound chemical assays from the ChEMBL database. The inputs for the eight Machine Learning classifiers were transformed features of drugs/compounds and nanoparticles as perturbations of molecular descriptors in specific experimental conditions (experiment-centered features). The resulting dataset contains 107 input features and 249,992 examples. The best classification model was provided by Random Forest, with 27 selected features of drugs/compounds and nanoparticles in all experimental conditions considered. The high performance of the model was demonstrated by the mean Area Under the Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 ± 0.000244 (10-fold cross-validation). The results demonstrated the power of information fusion of the experimental-centered features of drugs/compounds and nanoparticles for the prediction of nanoparticle–compound antimalarial activity. The scripts and dataset for this project are available in the open GitHub repository.
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Affiliation(s)
- Diana V. Urista
- Department of Organic Chemistry II, University of Basque Country (UPV/EHU), Sarriena w/n, 48940 Leioa, Spain; (D.V.U.); (S.A.); (H.G.-D.)
| | - Diego B. Carrué
- RNASA-IMEDIR, Computer Science Faculty, CITIC, University of A Coruna, Campus Elviña s/n, 15071 A Coruña, Spain; (D.B.C.); (I.O.); (V.F.Q.-T.); (M.G.)
| | - Iago Otero
- RNASA-IMEDIR, Computer Science Faculty, CITIC, University of A Coruna, Campus Elviña s/n, 15071 A Coruña, Spain; (D.B.C.); (I.O.); (V.F.Q.-T.); (M.G.)
| | - Sonia Arrasate
- Department of Organic Chemistry II, University of Basque Country (UPV/EHU), Sarriena w/n, 48940 Leioa, Spain; (D.V.U.); (S.A.); (H.G.-D.)
| | - Viviana F. Quevedo-Tumailli
- RNASA-IMEDIR, Computer Science Faculty, CITIC, University of A Coruna, Campus Elviña s/n, 15071 A Coruña, Spain; (D.B.C.); (I.O.); (V.F.Q.-T.); (M.G.)
- Universidad Estatal Amazónica UEA, Km. 2 1/2 vía Puyo a Tena (paso lateral), Puyo 160150, Pastaza, Ecuador
| | - Marcos Gestal
- RNASA-IMEDIR, Computer Science Faculty, CITIC, University of A Coruna, Campus Elviña s/n, 15071 A Coruña, Spain; (D.B.C.); (I.O.); (V.F.Q.-T.); (M.G.)
- Biomedical Research Institute of A Coruña (INIBIC), Hospital Teresa Herrera, Xubias de Arriba 84, 15006 A Coruña, Spain
| | - Humbert González-Díaz
- Department of Organic Chemistry II, University of Basque Country (UPV/EHU), Sarriena w/n, 48940 Leioa, Spain; (D.V.U.); (S.A.); (H.G.-D.)
- IKERBASQUE, Basque Foundation for Science, Alameda Urquijo 36, 48011 Bilbao, Spain
- Basque Centre for Biophysics CSIC-UPVEHU, University of Basque Country UPV/EHU, Barrio Sarriena, 48940 Leioa, Spain
| | - Cristian R. Munteanu
- RNASA-IMEDIR, Computer Science Faculty, CITIC, University of A Coruna, Campus Elviña s/n, 15071 A Coruña, Spain; (D.B.C.); (I.O.); (V.F.Q.-T.); (M.G.)
- Biomedical Research Institute of A Coruña (INIBIC), Hospital Teresa Herrera, Xubias de Arriba 84, 15006 A Coruña, Spain
- Correspondence:
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Santana R, Zuluaga R, Gañán P, Arrasate S, Onieva E, Montemore MM, González-Díaz H. PTML Model for Selection of Nanoparticles, Anticancer Drugs, and Vitamins in the Design of Drug-Vitamin Nanoparticle Release Systems for Cancer Cotherapy. Mol Pharm 2020; 17:2612-2627. [PMID: 32459098 DOI: 10.1021/acs.molpharmaceut.0c00308] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Nanosystems are gaining momentum in pharmaceutical sciences because of the wide variety of possibilities for designing these systems to have specific functions. Specifically, studies of new cancer cotherapy drug-vitamin release nanosystems (DVRNs) including anticancer compounds and vitamins or vitamin derivatives have revealed encouraging results. However, the number of possible combinations of design and synthesis conditions is remarkably high. In addition, a large number of anticancer and vitamin derivatives have been already assayed, but a notably less number of cases of DVRNs were assayed as a whole (with the anticancer compound and the vitamin linked to them). Our approach combines with the perturbation theory and machine learning (PTML) model to predict the probability of obtaining an interesting DVRN by changing the anticancer compound and/or the vitamin present in a DVRN that is already tested for other anticancer compounds or vitamins that have not been tested yet as part of a DVRN. In a previous work, we built a linear PTML model useful for the design of these nanosystems. In doing so, we used information fusion (IF) techniques to carry out data enrichment of DVRN data compiled from the literature with the data for preclinical assays of vitamins from the ChEMBL database. The design features of DVRNs and the assay conditions of nanoparticles (NPs) and vitamins were included as multiplicative PT operators (PTOs) to the system, which indicates the importance of these variables. However, the previous work omitted experiments with nonlinear ML techniques and different types of PTOs such as metric-based PTOs. More importantly, the previous work does not consider the structure of the anticancer drug to be included in the new DVRNs. In this work, we are going to accomplish three main objectives (tasks). In the first task, we found a new model, alternative to the one published before, for the rational design of DVRNs using metric-based PTOs. The most accurate PTML model was the artificial neural network model, which showed values of specificity, sensitivity, and accuracy in the range of 90-95% in training and external validation series for more than 130,000 cases (DVRNs vs ChEMBL assays). Furthermore, in the second task, we used IF techniques to carry out data enrichment of our previous data set. In doing so, we constructed a new working data set of >970,000 cases with the data of preclinical assays of DVRNs, vitamins, and anticancer compounds from the ChEMBL database. All these assays have multiple continuous variables or descriptors dk and categorical variables cj (conditions of the assays) for drugs (dack, cacj), vitamins (dvk, cvj), and NPs (dnk, cnj). These data include >20,000 potential anticancer compounds with >270 protein targets (cac1), >580 assay cell organisms (cac2), and so forth. Furthermore, we include >36,000 assay vitamin derivatives in >6200 types of cells (c2vit), >120 assay organisms (c3vit), >60 assay strains (c4vit), and so forth. The enriched data set also contains >20 types of DVRNs (c5n) with 9 NP core materials (c4n), 8 synthesis methods (c7n), and so forth. We expressed all this information with PTOs and developed a qualitatively new PTML model that incorporates information of the anticancer drugs. This new model presents 96-97% of accuracy for training and external validation subsets. In the last task, we carried out a comparative study of ML and/or PTML models published and described how the models we are presenting cover the gap of knowledge in terms of drug delivery. In conclusion, we present here for the first time a multipurpose PTML model that is able to select NPs, anticancer compounds, and vitamins and their conditions of assay for DVRN design.
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Affiliation(s)
- Ricardo Santana
- Department of Chemical and Biomolecular Engineering, Tulane University, 6823 St Charles Avenue, New Orleans, Louisiana 70118, United States.,University of Deusto, Avda. Universidades, 24, 48007 Bilbao, Spain.,Grupo de Investigación Sobre Nuevos Materiales, Facultad de Ingeniería Química, Universidad Pontificia Bolivariana, Circular 1 No. 70-01, 050031 Medellín, Colombia
| | - Robin Zuluaga
- Facultad de Ingeniería Agroindustrial, Universidad Pontificia Bolivariana, Circular 1 No. 70-01, 050031 Medellín, Colombia
| | - Piedad Gañán
- Grupo de Investigación Sobre Nuevos Materiales, Facultad de Ingeniería Química, Universidad Pontificia Bolivariana, Circular 1 No. 70-01, 050031 Medellín, Colombia
| | - Sonia Arrasate
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940 Leioa, Basque Country, Spain
| | - Enrique Onieva
- University of Deusto, Avda. Universidades, 24, 48007 Bilbao, Spain
| | - Matthew M Montemore
- Department of Chemical and Biomolecular Engineering, Tulane University, 6823 St Charles Avenue, New Orleans, Louisiana 70118, United States
| | - Humbert González-Díaz
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940 Leioa, Basque Country, Spain.,Basque Center for Biophysics, Spanish National Research Council (CSIC)-University of Basque Country UPV/EHU, 48940 Leioa, Basque Country, Spain.,Ikerbasque, Basque Foundation for Science, 48013 Bilbao, Basque Country, Spain
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Miyashiro D, Hamano R, Umemura K. A Review of Applications Using Mixed Materials of Cellulose, Nanocellulose and Carbon Nanotubes. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E186. [PMID: 31973149 PMCID: PMC7074973 DOI: 10.3390/nano10020186] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Revised: 01/14/2020] [Accepted: 01/16/2020] [Indexed: 02/06/2023]
Abstract
Carbon nanotubes (CNTs) have been extensively studied as one of the most interesting nanomaterials for over 25 years because they exhibit excellent mechanical, electrical, thermal, optical, and electrical properties. In the past decade, the number of publications and patents on cellulose and nanocellulose (NC) increased tenfold. Research on NC with excellent mechanical properties, flexibility, and transparency is accelerating due to the growing environmental problems surrounding us such as CO2 emissions, the accumulation of large amounts of plastic, and the depletion of energy resources such as oil. Research on mixed materials of cellulose, NC, and CNTs has been expanding because these materials exhibit various characteristics that can be controlled by varying the combination of cellulose, NC to CNTs while also being biodegradable and recyclable. An understanding of these mixed materials is required because these characteristics are diverse and are expected to solve various environmental problems. Thus far, many review papers on cellulose, NC or CNTs have been published. Although guidance for the suitable application of these mixed materials is necessary, there are few reviews summarizing them. Therefore, this review introduces the application and feature on mixed materials of cellulose, NC and CNTs.
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Affiliation(s)
- Daisuke Miyashiro
- Department of Physics, Tokyo University of Science, 1-3 Kagurazaka, Shinjuku-ku, Tokyo 162-8601, Japan; (R.H.); (K.U.)
- ESTECH CORP., 2-7-31 Fukuura, Kanazawa-ku, Yokohama 236-0004, Japan
| | - Ryo Hamano
- Department of Physics, Tokyo University of Science, 1-3 Kagurazaka, Shinjuku-ku, Tokyo 162-8601, Japan; (R.H.); (K.U.)
| | - Kazuo Umemura
- Department of Physics, Tokyo University of Science, 1-3 Kagurazaka, Shinjuku-ku, Tokyo 162-8601, Japan; (R.H.); (K.U.)
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Mikolajczyk A, Sizochenko N, Mulkiewicz E, Malankowska A, Rasulev B, Puzyn T. A chemoinformatics approach for the characterization of hybrid nanomaterials: safer and efficient design perspective. NANOSCALE 2019; 11:11808-11818. [PMID: 31184677 DOI: 10.1039/c9nr01162e] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
In this study, photocatalytic properties and in vitro cytotoxicity of 29 TiO2-based multi-component nanomaterials (i.e., hybrids of more than two composition types of nanoparticles) were evaluated using a combination of the experimental testing and supervised machine learning modeling. TiO2-based multi-component nanomaterials with metal clusters of silver, and their mixtures with gold, palladium, and platinum were successfully synthesized. Two activities, photocatalytic activity and cytotoxicity, were studied. A novel cheminformatic approach was developed and applied for the computational representation of the photocatalytic activity and cytotoxicity effect. In this approach, features of investigated TiO2-based hybrid nanomaterials were reflected by a series of novel additive descriptors for hybrid and hybrid nanostructures (denoted as "hybrid nanosctructure descriptors"). These descriptors are based on quantum chemical calculations and the Smoluchowski equation. The obtained experimental data and calculated hybrid-nanostructure descriptors were used to develop novel predictive Quantitative Structure-Activity Relationship computational models (called "nano-QSARmix"). The proposed modeling approach is an initial step in the understanding of the relationships between physicochemical properties of hybrid nanoparticles, their toxicity, and photochemical activity under UV-vis irradiation. Acquired knowledge supports the safe-by-design approaches relevant to the development of efficient hybrid nanomaterials with reduced hazardous effects.
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Affiliation(s)
- Alicja Mikolajczyk
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland.
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Liu Y, Munteanu CR, Kong Z, Ran T, Sahagún-Ruiz A, He Z, Zhou C, Tan Z. Identification of coenzyme-binding proteins with machine learning algorithms. Comput Biol Chem 2019; 79:185-192. [PMID: 30851647 DOI: 10.1016/j.compbiolchem.2019.01.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Revised: 09/11/2018] [Accepted: 01/25/2019] [Indexed: 01/12/2023]
Abstract
The coenzyme-binding proteins play a vital role in the cellular metabolism processes, such as fatty acid biosynthesis, enzyme and gene regulation, lipid synthesis, particular vesicular traffic, and β-oxidation donation of acyl-CoA esters. Based on the theory of Star Graph Topological Indices (SGTIs) of protein primary sequences, we proposed a method to develop a first classification model for predicting protein with coenzyme-binding properties. To simulate the properties of coenzyme-binding proteins, we created a dataset containing 2897 proteins, among 456 proteins functioned as coenzyme-binding activity. The SGTIs of peptide sequence were calculated with Sequence to Star Network (S2SNet) application. We used the SGTIs as inputs to several classification techniques with a machine learning software - Weka. A Random Forest classifier based on 3 features of the embedded and non-embedded graphs was identified as the best predictive model for coenzyme-binding proteins. This model developed was with the true positive (TP) rate of 91.7%, false positive (FP) rate of 7.6%, and Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.971. The prediction of new coenzyme-binding activity proteins using this model could be useful for further drug development or enzyme metabolism researches.
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Affiliation(s)
- Yong Liu
- Key Laboratory for Agro-Ecological Processes in Subtropical Region, National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, South Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, Institute of Subtropical Agriculture, The Chinese Academy of Sciences, Changsha, Hunan, 410125, PR China; Hunan Co-Innovation Center of Animal Production Safety, CICAPS, Changsha, Hunan, 410128, PR China
| | - Cristian R Munteanu
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruña, Spain; Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), A Coruña, 15006, Spain
| | - Zhiwei Kong
- Key Laboratory for Agro-Ecological Processes in Subtropical Region, National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, South Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, Institute of Subtropical Agriculture, The Chinese Academy of Sciences, Changsha, Hunan, 410125, PR China; University of the Chinese Academy of Sciences, Beijing, 100049, PR China
| | - Tao Ran
- Key Laboratory for Agro-Ecological Processes in Subtropical Region, National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, South Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, Institute of Subtropical Agriculture, The Chinese Academy of Sciences, Changsha, Hunan, 410125, PR China; Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, Lethbridge, Alberta, T1J 4B1, Canada
| | - Alfredo Sahagún-Ruiz
- Department of Microbiology and Immunology, Faculty of Veterinary Medicine and Animal Science, National Autonomous University of Mexico, Universidad 3000, Copilco Coyoacán, CP 04510, México D.F., Mexico
| | - Zhixiong He
- Key Laboratory for Agro-Ecological Processes in Subtropical Region, National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, South Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, Institute of Subtropical Agriculture, The Chinese Academy of Sciences, Changsha, Hunan, 410125, PR China; Hunan Co-Innovation Center of Animal Production Safety, CICAPS, Changsha, Hunan, 410128, PR China.
| | - Chuanshe Zhou
- Key Laboratory for Agro-Ecological Processes in Subtropical Region, National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, South Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, Institute of Subtropical Agriculture, The Chinese Academy of Sciences, Changsha, Hunan, 410125, PR China; Hunan Co-Innovation Center of Animal Production Safety, CICAPS, Changsha, Hunan, 410128, PR China
| | - Zhiliang Tan
- Key Laboratory for Agro-Ecological Processes in Subtropical Region, National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, South Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, Institute of Subtropical Agriculture, The Chinese Academy of Sciences, Changsha, Hunan, 410125, PR China; Hunan Co-Innovation Center of Animal Production Safety, CICAPS, Changsha, Hunan, 410128, PR China
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Zhang MC, Guo GC, Wang RZ, Cui YL, Feng XY, Wang BR. Coupling enhanced growth by nitrogen and hydrogen plasma of carbon nanotubes. CrystEngComm 2019. [DOI: 10.1039/c9ce00345b] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Coupling growth mechanism with N* and H*.
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Affiliation(s)
- Man-Chen Zhang
- College of Materials Science and Engineering
- Beijing University of Technology
- Beijing 100124
- China
- Key Laboratory of Advanced Functional Materials
| | - Gen-Cai Guo
- College of Materials Science and Engineering
- Beijing University of Technology
- Beijing 100124
- China
- Key Laboratory of Advanced Functional Materials
| | - Ru-Zhi Wang
- College of Materials Science and Engineering
- Beijing University of Technology
- Beijing 100124
- China
- Key Laboratory of Advanced Functional Materials
| | - Yan-Lei Cui
- College of Materials Science and Engineering
- Beijing University of Technology
- Beijing 100124
- China
- Key Laboratory of Advanced Functional Materials
| | - Xiao-Yu Feng
- College of Materials Science and Engineering
- Beijing University of Technology
- Beijing 100124
- China
- Key Laboratory of Advanced Functional Materials
| | - Bing-Rong Wang
- College of Materials Science and Engineering
- Beijing University of Technology
- Beijing 100124
- China
- Key Laboratory of Advanced Functional Materials
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González-Durruthy M, Manske Nunes S, Ventura-Lima J, Gelesky MA, González-Díaz H, Monserrat JM, Concu R, Cordeiro MND. MitoTarget Modeling Using ANN-Classification Models Based on Fractal SEM Nano-Descriptors: Carbon Nanotubes as Mitochondrial F0F1-ATPase Inhibitors. J Chem Inf Model 2018; 59:86-97. [DOI: 10.1021/acs.jcim.8b00631] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Michael González-Durruthy
- LAQV-REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, 4169-007, University of Porto, Porto, Portugal
| | - Silvana Manske Nunes
- Institute of Biological Sciences (ICB), Universidade Federal do Rio Grande -FURG, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
- ICB-FURG Post-Graduate Program in Physiological Sciences, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
| | - Juliane Ventura-Lima
- Institute of Biological Sciences (ICB), Universidade Federal do Rio Grande -FURG, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
- ICB-FURG Post-Graduate Program in Physiological Sciences, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
- National Institute of Carbon Nanomaterial Science and Technology, 30123970, Belo Horizonte, Minas Gerais, Brazil
- Nanotoxicology Network (MCTI/CNPq), 96270-900, Rio Grande, Rio Grande do Sul, Brazil
| | - Marcos A. Gelesky
- Post-Graduate Program in Technological and Environmental Chemistry, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
| | - Humberto González-Díaz
- Department of Organic Chemistry II, College of Science and Technology, University of the Basque Country UPV/EHU, 48940, Leioa, Bizkaia, Spain
- IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Bizkaia, Spain
| | - José M. Monserrat
- Institute of Biological Sciences (ICB), Universidade Federal do Rio Grande -FURG, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
- ICB-FURG Post-Graduate Program in Physiological Sciences, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
- National Institute of Carbon Nanomaterial Science and Technology, 30123970, Belo Horizonte, Minas Gerais, Brazil
- Nanotoxicology Network (MCTI/CNPq), 96270-900, Rio Grande, Rio Grande do Sul, Brazil
| | - Riccardo Concu
- LAQV-REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, 4169-007, University of Porto, Porto, Portugal
| | - M. Natália D.S. Cordeiro
- LAQV-REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, 4169-007, University of Porto, Porto, Portugal
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