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Karbhal R, Sawant S, Kulkarni-Kale U. iCEED: Integrated customized extraction of enzyme data. J Bioinform Comput Biol 2024; 22:2450005. [PMID: 38779780 DOI: 10.1142/s0219720024500057] [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] [Indexed: 05/25/2024]
Abstract
Enzymes catalyze diverse biochemical reactions and are building blocks of cellular and metabolic pathways. Data and metadata of enzymes are distributed across databases and are archived in various formats. The enzyme databases provide utilities for efficient searches and downloading enzyme records in batch mode but do not support organism-specific extraction of subsets of data. Users are required to write scripts for parsing entries for customized data extraction prior to downstream analysis. Integrated Customized Extraction of Enzyme Data (iCEED) has been developed to provide organism-specific customized data extraction utilities for seven commonly used enzyme databases and brings these resources under an integrated portal. iCEED provides dropdown menus and search boxes using typehead utility for submission of queries as well as enzyme class-based browsing utility. A utility to facilitate mapping and visualization of functionally important features on the three-dimensional (3D) structures of enzymes is integrated. The customized data extraction utilities provided in iCEED are expected to be useful for biochemists, biotechnologists, computational biologists, and life science researchers to build curated datasets of their choice through an easy to navigate web-based interface. The integrated feature visualization system is useful for a fine-grained understanding of the enzyme structure-function relationship. Desired subsets of data, extracted and curated using iCEED can be subsequently used for downstream processing, analyses, and knowledge discovery. iCEED can also be used for training and teaching purposes.
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Affiliation(s)
- Rajiv Karbhal
- Bioinformatics Centre, Savitribai Phule Pune University, Pune 411007, India
| | - Sangeeta Sawant
- Bioinformatics Centre, Savitribai Phule Pune University, Pune 411007, India
| | - Urmila Kulkarni-Kale
- Bioinformatics Centre, Savitribai Phule Pune University, Pune 411007, India
- Department of Natural Sciences and Environmental Health, University of Southeastern Norway, Gullbringvegen 36, 3800 Bø, Norway
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2
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Nursimulu N, Moses AM, Parkinson J. Architect: A tool for aiding the reconstruction of high-quality metabolic models through improved enzyme annotation. PLoS Comput Biol 2022; 18:e1010452. [PMID: 36074804 PMCID: PMC9488769 DOI: 10.1371/journal.pcbi.1010452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 09/20/2022] [Accepted: 07/29/2022] [Indexed: 11/19/2022] Open
Abstract
Constraint-based modeling is a powerful framework for studying cellular metabolism, with applications ranging from predicting growth rates and optimizing production of high value metabolites to identifying enzymes in pathogens that may be targeted for therapeutic interventions. Results from modeling experiments can be affected at least in part by the quality of the metabolic models used. Reconstructing a metabolic network manually can produce a high-quality metabolic model but is a time-consuming task. At the same time, current methods for automating the process typically transfer metabolic function based on sequence similarity, a process known to produce many false positives. We created Architect, a pipeline for automatic metabolic model reconstruction from protein sequences. First, it performs enzyme annotation through an ensemble approach, whereby a likelihood score is computed for an EC prediction based on predictions from existing tools; for this step, our method shows both increased precision and recall compared to individual tools. Next, Architect uses these annotations to construct a high-quality metabolic network which is then gap-filled based on likelihood scores from the ensemble approach. The resulting metabolic model is output in SBML format, suitable for constraints-based analyses. Through comparisons of enzyme annotations and curated metabolic models, we demonstrate improved performance of Architect over other state-of-the-art tools, notably with higher precision and recall on the eukaryote C. elegans and when compared to UniProt annotations in two bacterial species. Code for Architect is available at https://github.com/ParkinsonLab/Architect. For ease-of-use, Architect can be readily set up and utilized using its Docker image, maintained on Docker Hub.
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Affiliation(s)
- Nirvana Nursimulu
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Program in Molecular Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Alan M. Moses
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Department of Cell & Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - John Parkinson
- Program in Molecular Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada
- * E-mail:
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3
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Bhayani J, Iglesias MJ, Minen RI, Cereijo AE, Ballicora MA, Iglesias AA, Asencion Diez MD. Carbohydrate Metabolism in Bacteria: Alternative Specificities in ADP-Glucose Pyrophosphorylases Open Novel Metabolic Scenarios and Biotechnological Tools. Front Microbiol 2022; 13:867384. [PMID: 35572620 PMCID: PMC9093745 DOI: 10.3389/fmicb.2022.867384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 03/22/2022] [Indexed: 11/13/2022] Open
Abstract
We explored the ability of ADP-glucose pyrophosphorylase (ADP-Glc PPase) from different bacteria to use glucosamine (GlcN) metabolites as a substrate or allosteric effectors. The enzyme from the actinobacteria Kocuria rhizophila exhibited marked and distinctive sensitivity to allosteric activation by GlcN-6P when producing ADP-Glc from glucose-1-phosphate (Glc-1P) and ATP. This behavior is also seen in the enzyme from Rhodococcus spp., the only one known so far to portray this activation. GlcN-6P had a more modest effect on the enzyme from other Actinobacteria (Streptomyces coelicolor), Firmicutes (Ruminococcus albus), and Proteobacteria (Agrobacterium tumefaciens) groups. In addition, we studied the catalytic capacity of ADP-Glc PPases from the different sources using GlcN-1P as a substrate when assayed in the presence of their respective allosteric activators. In all cases, the catalytic efficiency of Glc-1P was 1-2 orders of magnitude higher than GlcN-1P, except for the unregulated heterotetrameric protein (GlgC/GgD) from Geobacillus stearothermophilus. The Glc-1P substrate preference is explained using a model of ADP-Glc PPase from A. tumefaciens based on the crystallographic structure of the enzyme from potato tuber. The substrate-binding domain localizes near the N-terminal of an α-helix, which has a partial positive charge, thus favoring the interaction with a hydroxyl rather than a charged primary amine group. Results support the scenario where the ability of ADP-Glc PPases to use GlcN-1P as an alternative occurred during evolution despite the enzyme being selected to use Glc-1P and ATP for α-glucans synthesis. As an associated consequence in such a process, certain bacteria could have improved their ability to metabolize GlcN. The work also provides insights in designing molecular tools for producing oligo and polysaccharides with amino moieties.
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Affiliation(s)
- Jaina Bhayani
- Department of Chemistry and Biochemistry, Loyola University Chicago, Chicago, IL, United States
| | - Maria Josefina Iglesias
- Facultad de Bioquímica y Ciencias Biológicas, Instituto de Agrobiotecnología del Litoral, Universidad Nacional del Litoral, Consejo Nacional de Investigaciones Científicas y Técnicas, Santa Fe, Argentina
| | - Romina I. Minen
- Facultad de Bioquímica y Ciencias Biológicas, Instituto de Agrobiotecnología del Litoral, Universidad Nacional del Litoral, Consejo Nacional de Investigaciones Científicas y Técnicas, Santa Fe, Argentina
| | - Antonela E. Cereijo
- Facultad de Bioquímica y Ciencias Biológicas, Instituto de Agrobiotecnología del Litoral, Universidad Nacional del Litoral, Consejo Nacional de Investigaciones Científicas y Técnicas, Santa Fe, Argentina
| | - Miguel A. Ballicora
- Department of Chemistry and Biochemistry, Loyola University Chicago, Chicago, IL, United States
| | - Alberto A. Iglesias
- Facultad de Bioquímica y Ciencias Biológicas, Instituto de Agrobiotecnología del Litoral, Universidad Nacional del Litoral, Consejo Nacional de Investigaciones Científicas y Técnicas, Santa Fe, Argentina
| | - Matias D. Asencion Diez
- Facultad de Bioquímica y Ciencias Biológicas, Instituto de Agrobiotecnología del Litoral, Universidad Nacional del Litoral, Consejo Nacional de Investigaciones Científicas y Técnicas, Santa Fe, Argentina
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4
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Feehan R, Montezano D, Slusky JSG. Machine learning for enzyme engineering, selection and design. Protein Eng Des Sel 2021; 34:gzab019. [PMID: 34296736 PMCID: PMC8299298 DOI: 10.1093/protein/gzab019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 06/18/2021] [Accepted: 06/23/2021] [Indexed: 11/15/2022] Open
Abstract
Machine learning is a useful computational tool for large and complex tasks such as those in the field of enzyme engineering, selection and design. In this review, we examine enzyme-related applications of machine learning. We start by comparing tools that can identify the function of an enzyme and the site responsible for that function. Then we detail methods for optimizing important experimental properties, such as the enzyme environment and enzyme reactants. We describe recent advances in enzyme systems design and enzyme design itself. Throughout we compare and contrast the data and algorithms used for these tasks to illustrate how the algorithms and data can be best used by future designers.
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Affiliation(s)
- Ryan Feehan
- Center for Computational Biology, The University of Kansas, 2030 Becker Dr., Lawrence, KS 66047-1620, USA
| | - Daniel Montezano
- Center for Computational Biology, The University of Kansas, 2030 Becker Dr., Lawrence, KS 66047-1620, USA
| | - Joanna S G Slusky
- Center for Computational Biology, The University of Kansas, 2030 Becker Dr., Lawrence, KS 66047-1620, USA
- Department of Molecular Biosciences, The University of Kansas, 1200 Sunnyside Ave. Lawrence, KS 66045-7600, USA
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5
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Mehta S, Crane M, Leith E, Batut B, Hiltemann S, Arntzen MØ, Kunath BJ, Pope PB, Delogu F, Sajulga R, Kumar P, Johnson JE, Griffin TJ, Jagtap PD. ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework. F1000Res 2021; 10:103. [PMID: 34484688 PMCID: PMC8383124 DOI: 10.12688/f1000research.28608.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/12/2021] [Indexed: 12/13/2022] Open
Abstract
The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.
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Affiliation(s)
- Subina Mehta
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Marie Crane
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Emma Leith
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Bérénice Batut
- Department of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, Freiburg, Germany
| | - Saskia Hiltemann
- Department of Pathology, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | | | | | | | - Ray Sajulga
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Praveen Kumar
- University of Minnesota, Twin Cities, MN, 55455, USA
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6
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Mehta S, Crane M, Leith E, Batut B, Hiltemann S, Arntzen MØ, Kunath BJ, Pope PB, Delogu F, Sajulga R, Kumar P, Johnson JE, Griffin TJ, Jagtap PD. ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework. F1000Res 2021; 10:103. [PMID: 34484688 PMCID: PMC8383124 DOI: 10.12688/f1000research.28608.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/03/2021] [Indexed: 12/13/2022] Open
Abstract
The Human Microbiome Project (HMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') in human health and disease. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). Conversely, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.
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Affiliation(s)
- Subina Mehta
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Marie Crane
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Emma Leith
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Bérénice Batut
- Department of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, Freiburg, Germany
| | - Saskia Hiltemann
- Department of Pathology, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | | | | | | | - Ray Sajulga
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Praveen Kumar
- University of Minnesota, Twin Cities, MN, 55455, USA
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7
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Conix S. Enzyme classification and the entanglement of values and epistemic standards. STUDIES IN HISTORY AND PHILOSOPHY OF SCIENCE 2020; 84:37-45. [PMID: 33218464 DOI: 10.1016/j.shpsa.2020.05.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 04/29/2020] [Accepted: 05/23/2020] [Indexed: 06/11/2023]
Abstract
This paper investigates the case of enzyme classification to evaluate different ideals for regulating values in science. I show that epistemic and non-epistemic considerations are inevitably and untraceably entangled in enzyme classification, and argue that this has significant implications for the two main kinds of views on values in science, namely, Epistemic Priority Views and Joint Satisfaction Views. More precisely, I argue that the case of enzyme classification poses a problem for the usability and descriptive accuracy of these two views. The paper ends by suggesting that these two views provide different but complementary perspectives, and that both are useful for evaluating values in science.
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Affiliation(s)
- Stijn Conix
- Centre for Logic and Philosophy of Science, Institute of Philosophy, KU Leuven, Vesaliusstraat 2, 3000, Leuven, Belgium.
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8
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Ann Benore M. What is in a name? (or a number?): The updated enzyme classifications. BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION : A BIMONTHLY PUBLICATION OF THE INTERNATIONAL UNION OF BIOCHEMISTRY AND MOLECULAR BIOLOGY 2019; 47:481-483. [PMID: 31063221 DOI: 10.1002/bmb.21251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Accepted: 04/07/2019] [Indexed: 06/09/2023]
Affiliation(s)
- Marilee Ann Benore
- Department of Natural Sciences, University of Michigan Dearborn, College of Arts Sciences and Letters, Dearborn, Michigan
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9
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Sivakumar TV, Bhaduri A, Duvvuru Muni RR, Park JH, Kim TY. SimCAL: a flexible tool to compute biochemical reaction similarity. BMC Bioinformatics 2018; 19:254. [PMID: 29969981 PMCID: PMC6029250 DOI: 10.1186/s12859-018-2248-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Accepted: 06/14/2018] [Indexed: 11/29/2022] Open
Abstract
Background Computation of reaction similarity is a pre-requisite for several bioinformatics applications including enzyme identification for specific biochemical reactions, enzyme classification and mining for specific inhibitors. Reaction similarity is often assessed at either two levels: (i) comparison across all the constituent substrates and products of a reaction, reaction level similarity, (ii) comparison at the transformation center with various degrees of neighborhood, transformation level similarity. Existing reaction similarity computation tools are designed for specific applications and use different features and similarity measures. A single system integrating these diverse features enables comparison of the impact of different molecular properties on similarity score computation. Results To address these requirements, we present SimCAL, an integrated system to calculate reaction similarity with novel features and capability to perform comparative assessment. SimCAL provides reaction similarity computation at both whole reaction level and transformation level. Novel physicochemical features such as stereochemistry, mass, volume and charge are included in computing reaction fingerprint. Users can choose from four different fingerprint types and nine molecular similarity measures. Further, a comparative assessment of these features is also enabled. The performance of SimCAL is assessed on 3,688,122 reaction pairs with Enzyme Commission (EC) number from MetaCyc and achieved an area under the curve (AUC) of > 0.9. In addition, SimCAL results showed strong correlation with state-of-the-art EC-BLAST and molecular signature based reaction similarity methods. Conclusions SimCAL is developed in java and is available as a standalone tool, with intuitive, user-friendly graphical interface and also as a console application. With its customizable feature selection and similarity calculations, it is expected to cater a wide audience interested in studying and analyzing biochemical reactions and metabolic networks. Electronic supplementary material The online version of this article (10.1186/s12859-018-2248-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - Anirban Bhaduri
- Bioinformatics Lab, Samsung Advanced Institute of Technology, Bangalore, 560037, India
| | | | - Jin Hwan Park
- Biomaterials Lab, Materials Center, Samsung Advanced Institute of Technology, Gyeonggi-do, 443803, South Korea
| | - Tae Yong Kim
- Biomaterials Lab, Materials Center, Samsung Advanced Institute of Technology, Gyeonggi-do, 443803, South Korea.
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10
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Cai Y, Yang H, Li W, Liu G, Lee PW, Tang Y. Multiclassification Prediction of Enzymatic Reactions for Oxidoreductases and Hydrolases Using Reaction Fingerprints and Machine Learning Methods. J Chem Inf Model 2018; 58:1169-1181. [PMID: 29733642 DOI: 10.1021/acs.jcim.7b00656] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Drug metabolism is a complex procedure in the human body, including a series of enzymatically catalyzed reactions. However, it is costly and time consuming to investigate drug metabolism experimentally; computational methods are hence developed to predict drug metabolism and have shown great advantages. As the first step, classification of metabolic reactions and enzymes is highly desirable for drug metabolism prediction. In this study, we developed multiclassification models for prediction of reaction types catalyzed by oxidoreductases and hydrolases, in which three reaction fingerprints were used to describe the reactions and seven machine learnings algorithms were employed for model building. Data retrieved from KEGG containing 1055 hydrolysis and 2510 redox reactions were used to build the models, respectively. The external validation data consisted of 213 hydrolysis and 512 redox reactions extracted from the Rhea database. The best models were built by neural network or logistic regression with a 2048-bit transformation reaction fingerprint. The predictive accuracies of the main class, subclass, and superclass classification models on external validation sets were all above 90%. This study will be very helpful for enzymatic reaction annotation and further study on metabolism prediction.
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Affiliation(s)
- Yingchun Cai
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China
| | - Hongbin Yang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China
| | - Philip W Lee
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China
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11
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Tyzack JD, Furnham N, Sillitoe I, Orengo CM, Thornton JM. Understanding enzyme function evolution from a computational perspective. Curr Opin Struct Biol 2017; 47:131-139. [PMID: 28892668 DOI: 10.1016/j.sbi.2017.08.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 07/08/2017] [Accepted: 08/13/2017] [Indexed: 10/18/2022]
Abstract
In this review, we will explore recent computational approaches to understand enzyme evolution from the perspective of protein structure, dynamics and promiscuity. We will present quantitative methods to measure the size of evolutionary steps within a structural domain, allowing the correlation between change in substrate and domain structure to be assessed, and giving insights into the evolvability of different domains in terms of the number, types and sizes of evolutionary steps observed. These approaches will help to understand the evolution of new catalytic and non-catalytic functionality in response to environmental demands, showing potential to guide de novoenzyme design and directed evolution experiments.
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Affiliation(s)
| | - Nicholas Furnham
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom
| | - Ian Sillitoe
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| | - Christine M Orengo
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, United Kingdom
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12
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Ebrecht AC, Solamen L, Hill BL, Iglesias AA, Olsen KW, Ballicora MA. Allosteric Control of Substrate Specificity of the Escherichia coli ADP-Glucose Pyrophosphorylase. Front Chem 2017; 5:41. [PMID: 28674689 PMCID: PMC5474683 DOI: 10.3389/fchem.2017.00041] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Accepted: 06/07/2017] [Indexed: 11/29/2022] Open
Abstract
The substrate specificity of enzymes is crucial to control the fate of metabolites to different pathways. However, there is growing evidence that many enzymes can catalyze alternative reactions. This promiscuous behavior has important implications in protein evolution and the acquisition of new functions. The question is how the undesirable outcomes of in vivo promiscuity can be prevented. ADP-glucose pyrophosphorylase from Escherichia coli is an example of an enzyme that needs to select the correct substrate from a broad spectrum of alternatives. This selection will guide the flow of carbohydrate metabolism toward the synthesis of reserve polysaccharides. Here, we show that the allosteric activator fructose-1,6-bisphosphate plays a role in such selection by increasing the catalytic efficiency of the enzyme toward the use of ATP rather than other nucleotides. In the presence of fructose-1,6-bisphosphate, the kcat/S0.5 for ATP was near ~600-fold higher that other nucleotides, whereas in the absence of activator was only ~3-fold higher. We propose that the allosteric regulation of certain enzymes is an evolutionary mechanism of adaptation for the selection of specific substrates.
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Affiliation(s)
- Ana C Ebrecht
- Department of Chemistry and Biochemistry, Loyola University ChicagoChicago, IL, United States.,Laboratorio de Enzimología Molecular, Instituto de Agrobiotecnología del Litoral (UNL-CONICET), CCT CONICETSanta Fe, Argentina
| | - Ligin Solamen
- Department of Chemistry and Biochemistry, Loyola University ChicagoChicago, IL, United States
| | - Benjamin L Hill
- Department of Chemistry and Biochemistry, Loyola University ChicagoChicago, IL, United States
| | - Alberto A Iglesias
- Laboratorio de Enzimología Molecular, Instituto de Agrobiotecnología del Litoral (UNL-CONICET), CCT CONICETSanta Fe, Argentina
| | - Kenneth W Olsen
- Department of Chemistry and Biochemistry, Loyola University ChicagoChicago, IL, United States
| | - Miguel A Ballicora
- Department of Chemistry and Biochemistry, Loyola University ChicagoChicago, IL, United States
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13
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Minkiewicz P, Darewicz M, Iwaniak A, Bucholska J, Starowicz P, Czyrko E. Internet Databases of the Properties, Enzymatic Reactions, and Metabolism of Small Molecules-Search Options and Applications in Food Science. Int J Mol Sci 2016; 17:ijms17122039. [PMID: 27929431 PMCID: PMC5187839 DOI: 10.3390/ijms17122039] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2016] [Revised: 11/17/2016] [Accepted: 11/29/2016] [Indexed: 01/02/2023] Open
Abstract
Internet databases of small molecules, their enzymatic reactions, and metabolism have emerged as useful tools in food science. Database searching is also introduced as part of chemistry or enzymology courses for food technology students. Such resources support the search for information about single compounds and facilitate the introduction of secondary analyses of large datasets. Information can be retrieved from databases by searching for the compound name or structure, annotating with the help of chemical codes or drawn using molecule editing software. Data mining options may be enhanced by navigating through a network of links and cross-links between databases. Exemplary databases reviewed in this article belong to two classes: tools concerning small molecules (including general and specialized databases annotating food components) and tools annotating enzymes and metabolism. Some problems associated with database application are also discussed. Data summarized in computer databases may be used for calculation of daily intake of bioactive compounds, prediction of metabolism of food components, and their biological activity as well as for prediction of interactions between food component and drugs.
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Affiliation(s)
- Piotr Minkiewicz
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Małgorzata Darewicz
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Anna Iwaniak
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Justyna Bucholska
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Piotr Starowicz
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Emilia Czyrko
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
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