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Prešern U, Goličnik M. Enzyme Databases in the Era of Omics and Artificial Intelligence. Int J Mol Sci 2023; 24:16918. [PMID: 38069254 PMCID: PMC10707154 DOI: 10.3390/ijms242316918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 11/24/2023] [Accepted: 11/26/2023] [Indexed: 12/18/2023] Open
Abstract
Enzyme research is important for the development of various scientific fields such as medicine and biotechnology. Enzyme databases facilitate this research by providing a wide range of information relevant to research planning and data analysis. Over the years, various databases that cover different aspects of enzyme biology (e.g., kinetic parameters, enzyme occurrence, and reaction mechanisms) have been developed. Most of the databases are curated manually, which improves reliability of the information; however, such curation cannot keep pace with the exponential growth in published data. Lack of data standardization is another obstacle for data extraction and analysis. Improving machine readability of databases is especially important in the light of recent advances in deep learning algorithms that require big training datasets. This review provides information regarding the current state of enzyme databases, especially in relation to the ever-increasing amount of generated research data and recent advancements in artificial intelligence algorithms. Furthermore, it describes several enzyme databases, providing the reader with necessary information for their use.
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Affiliation(s)
| | - Marko Goličnik
- Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia;
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Karp PD, Paley S, Krummenacker M, Kothari A, Wannemuehler MJ, Phillips GJ. Pathway Tools Management of Pathway/Genome Data for Microbial Communities. FRONTIERS IN BIOINFORMATICS 2022; 2:869150. [PMID: 36304298 PMCID: PMC9580912 DOI: 10.3389/fbinf.2022.869150] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 04/05/2022] [Indexed: 11/14/2022] Open
Abstract
The Pathway Tools (PTools) software provides a suite of capabilities for storing and analyzing integrated collections of genomic and metabolic information in the form of organism-specific Pathway/Genome Databases (PGDBs). A microbial community is represented in PTools by generating a PGDB from each metagenome-assembled genome (MAG). PTools computes a metabolic reconstruction for each organism, and predicts its operons. The properties of individual MAGs can be investigated using the many search and visualization operations within PTools. PTools also enables the user to investigate the properties of the microbial community by issuing searches across the full community, and by performing comparative operations across genome and pathway information. The software can generate a metabolic network diagram for the community, and it can overlay community omics datasets on that network diagram. PTools also provides a tool for searching for metabolic transformation routes across an organism community.
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Affiliation(s)
- Peter D. Karp
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA, United States,*Correspondence: Peter D. Karp,
| | - Suzanne Paley
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA, United States
| | - Markus Krummenacker
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA, United States
| | - Anamika Kothari
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA, United States
| | | | - Gregory J. Phillips
- Department of Veterinary Microbiology, Iowa State University, Ames, IA, United States
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Serral F, Pardo AM, Sosa E, Palomino MM, Nicolás MF, Turjanski AG, Ramos PIP, Fernández Do Porto D. Pathway Driven Target Selection in Klebsiella pneumoniae: Insights Into Carbapenem Exposure. Front Cell Infect Microbiol 2022; 12:773405. [PMID: 35174104 PMCID: PMC8841789 DOI: 10.3389/fcimb.2022.773405] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 01/07/2022] [Indexed: 12/13/2022] Open
Abstract
Carbapenem-resistant Klebsiella pneumoniae (CR-KP) represents an emerging threat to public health. CR-KP infections result in elevated morbidity and mortality. This fact, coupled with their global dissemination and increasingly limited number of therapeutic options, highlights the urgency of novel antimicrobials. Innovative strategies linking genome-wide interrogation with multi-layered metabolic data integration can accelerate the early steps of drug development, particularly target selection. Using the BioCyc ontology, we generated and manually refined a metabolic network for a CR-KP, K. pneumoniae Kp13. Converted into a reaction graph, we conducted topological-based analyses in this network to prioritize pathways exhibiting druggable features and fragile metabolic points likely exploitable to develop novel antimicrobials. Our results point to the aptness of previously recognized pathways, such as lipopolysaccharide and peptidoglycan synthesis, and casts light on the possibility of targeting less explored cellular functions. These functions include the production of lipoate, trehalose, glycine betaine, and flavin, as well as the salvaging of methionine. Energy metabolism pathways emerged as attractive targets in the context of carbapenem exposure, targeted either alone or in conjunction with current therapeutic options. These results prompt further experimental investigation aimed at controlling this highly relevant pathogen.
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Affiliation(s)
- Federico Serral
- Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (UBA), Buenos Aires, Argentina
| | - Agustin M. Pardo
- Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (UBA), Buenos Aires, Argentina
| | - Ezequiel Sosa
- Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina
| | - María Mercedes Palomino
- Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina
- Facultad de Ciencias Exactas y Naturales, Departamento de Química Biológica, Universidad de Buenos Aires, Cdad. Universitaria, Buenos Aires, Argentina
| | - Marisa F. Nicolás
- Laboratório de Bioinformática (LABINFO), Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil
| | - Adrian G. Turjanski
- Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina
- Facultad de Ciencias Exactas y Naturales, Departamento de Química Biológica, Universidad de Buenos Aires, Cdad. Universitaria, Buenos Aires, Argentina
| | - Pablo Ivan P. Ramos
- Centro de Integração de Dados e Conhecimentos para a Saúde (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz (Fiocruz - Bahia), Salvador, Brazil
- *Correspondence: Darío Fernández Do Porto, ; Pablo Ivan P. Ramos,
| | - Darío Fernández Do Porto
- Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (UBA), Buenos Aires, Argentina
- Facultad de Ciencias Exactas y Naturales, Departamento de Química Biológica, Universidad de Buenos Aires, Cdad. Universitaria, Buenos Aires, Argentina
- *Correspondence: Darío Fernández Do Porto, ; Pablo Ivan P. Ramos,
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Karp PD, Midford PE, Billington R, Kothari A, Krummenacker M, Latendresse M, Ong WK, Subhraveti P, Caspi R, Fulcher C, Keseler IM, Paley SM. Pathway Tools version 23.0 update: software for pathway/genome informatics and systems biology. Brief Bioinform 2019; 22:109-126. [PMID: 31813964 DOI: 10.1093/bib/bbz104] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 07/23/2019] [Accepted: 07/24/2019] [Indexed: 12/19/2022] Open
Abstract
MOTIVATION Biological systems function through dynamic interactions among genes and their products, regulatory circuits and metabolic networks. Our development of the Pathway Tools software was motivated by the need to construct biological knowledge resources that combine these many types of data, and that enable users to find and comprehend data of interest as quickly as possible through query and visualization tools. Further, we sought to support the development of metabolic flux models from pathway databases, and to use pathway information to leverage the interpretation of high-throughput data sets. RESULTS In the past 4 years we have enhanced the already extensive Pathway Tools software in several respects. It can now support metabolic-model execution through the Web, it provides a more accurate gap filler for metabolic models; it supports development of models for organism communities distributed across a spatial grid; and model results may be visualized graphically. Pathway Tools supports several new omics-data analysis tools including the Omics Dashboard, multi-pathway diagrams called pathway collages, a pathway-covering algorithm for metabolomics data analysis and an algorithm for generating mechanistic explanations of multi-omics data. We have also improved the core pathway/genome databases management capabilities of the software, providing new multi-organism search tools for organism communities, improved graphics rendering, faster performance and re-designed gene and metabolite pages. AVAILABILITY The software is free for academic use; a fee is required for commercial use. See http://pathwaytools.com. CONTACT pkarp@ai.sri.com. SUPPLEMENTARY INFORMATION Supplementary data are available at Briefings in Bioinformatics online.
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Affiliation(s)
- Peter D Karp
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
| | - Peter E Midford
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
| | - Richard Billington
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
| | - Anamika Kothari
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
| | | | - Mario Latendresse
- Artificial Intelligence Center, SRI International, Menlo Park, CA 94025, USA
| | - Wai Kit Ong
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
| | - Pallavi Subhraveti
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
| | - Ron Caspi
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
| | - Carol Fulcher
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
| | - Ingrid M Keseler
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
| | - Suzanne M Paley
- Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA
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