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Peta VJ, Hartman T, Aryal S, Gurung BS, Singh R, Haas S, Bomgni A, Do T, Dhiman SS, Gadhamshetty V, Gnimpieba E. CREID - A ChemoReceptor-Effector Interaction Database. bioRxiv 2023:2023.05.04.539426. [PMID: 37461680 PMCID: PMC10349942 DOI: 10.1101/2023.05.04.539426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
The ChemoReceptor-Effector Interaction Database (CREID) is a collection of bacterial chemoreceptor and effector protein and interaction data to understand the process that chemoreceptors and effectors play in various environments. Our website includes terms associated with chemosensory pathways to educate users and those involved in collaborative research to help them understand this complex biological network. It includes 2,440 proteins involved in chemoreceptor and effector systems from 7 different bacterial families with 1,996 chemoeffector interactions. It is available at https://reactcreid.bicbioeng.org.
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Affiliation(s)
- Vincent J Peta
- Biomedical Engineering Department, University of South Dakota, Sioux Falls, SD 57107
| | - Timothy Hartman
- Biomedical Engineering Department, University of South Dakota, Sioux Falls, SD 57107
| | - Shiva Aryal
- Computer Science Department, University of South Dakota, Vermillion, SD 57069
| | | | - Ram Singh
- Chemical and Biological Engineering Department, South Dakota School of Mines and Technology, Rapid City, SD 57701
| | - Samuel Haas
- Biomedical Engineering Department, University of South Dakota, Sioux Falls, SD 57107
| | - Alain Bomgni
- Biomedical Engineering Department, University of South Dakota, Sioux Falls, SD 57107
| | - Tuyen Do
- Biomedical Engineering Department, University of South Dakota, Sioux Falls, SD 57107
| | - Saurabh S. Dhiman
- Chemical and Biological Engineering Department, South Dakota School of Mines and Technology, Rapid City, SD 57701
| | - Venkataramana Gadhamshetty
- Civil and Environmental Engineering Department, South Dakota School of Mines and Technology, Rapid City, SD 57701
| | - Etienne Gnimpieba
- Biomedical Engineering Department, University of South Dakota, Sioux Falls, SD 57107
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Raya D, Peta V, Bomgni A, Du Do T, Kalimuthu J, Salem DR, Gadhamshetty V, Gnimpieba EZ, Dhiman SS. Classification of bacterial nanowire proteins using Machine Learning and Feature Engineering model. bioRxiv 2023:2023.05.03.539336. [PMID: 37205598 PMCID: PMC10187271 DOI: 10.1101/2023.05.03.539336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Nanowires (NW) have been extensively studied for Shewanella spp. and Geobacter spp. and are mostly produced by Type IV pili or multiheme c-type cytochrome. Electron transfer via NW is the most studied mechanism in microbially induced corrosion, with recent interest in application in bioelectronics and biosensor. In this study, a machine learning (ML) based tool was developed to classify NW proteins. A manually curated 999 protein collection was developed as an NW protein dataset. Gene ontology analysis of the dataset revealed microbial NW is part of membranal proteins with metal ion binding motifs and plays a central role in electron transfer activity. Random Forest (RF), support vector machine (SVM), and extreme gradient boost (XGBoost) models were implemented in the prediction model and were observed to identify target proteins based on functional, structural, and physicochemical properties with 89.33%, 95.6%, and 99.99% accuracy. Dipetide amino acid composition, transition, and distribution protein features of NW are key important features aiding in the model's high performance.
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Saxena P, Rauniyar S, Thakur P, Singh RN, Bomgni A, Alaba MO, Tripathi AK, Gnimpieba EZ, Lushbough C, Sani RK. Integration of text mining and biological network analysis: Identification of essential genes in sulfate-reducing bacteria. Front Microbiol 2023; 14:1086021. [PMID: 37125195 PMCID: PMC10133479 DOI: 10.3389/fmicb.2023.1086021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 03/23/2023] [Indexed: 05/02/2023] Open
Abstract
The growth and survival of an organism in a particular environment is highly depends on the certain indispensable genes, termed as essential genes. Sulfate-reducing bacteria (SRB) are obligate anaerobes which thrives on sulfate reduction for its energy requirements. The present study used Oleidesulfovibrio alaskensis G20 (OA G20) as a model SRB to categorize the essential genes based on their key metabolic pathways. Herein, we reported a feedback loop framework for gene of interest discovery, from bio-problem to gene set of interest, leveraging expert annotation with computational prediction. Defined bio-problem was applied to retrieve the genes of SRB from literature databases (PubMed, and PubMed Central) and annotated them to the genome of OA G20. Retrieved gene list was further used to enrich protein-protein interaction and was corroborated to the pangenome analysis, to categorize the enriched gene sets and the respective pathways under essential and non-essential. Interestingly, the sat gene (dde_2265) from the sulfur metabolism was the bridging gene between all the enriched pathways. Gene clusters involved in essential pathways were linked with the genes from seleno-compound metabolism, amino acid metabolism, secondary metabolite synthesis, and cofactor biosynthesis. Furthermore, pangenome analysis demonstrated the gene distribution, where 69.83% of the 116 enriched genes were mapped under "persistent," inferring the essentiality of these genes. Likewise, 21.55% of the enriched genes, which involves specially the formate dehydrogenases and metallic hydrogenases, appeared under "shell." Our methodology suggested that semi-automated text mining and network analysis may play a crucial role in deciphering the previously unexplored genes and key mechanisms which can help to generate a baseline prior to perform any experimental studies.
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Affiliation(s)
- Priya Saxena
- Department of Chemical and Biological Engineering, South Dakota School of Mines and Technology, Rapid City, SD, United States
- Data Driven Material Discovery Center for Bioengineering Innovation, South Dakota School of Mines and Technology, Rapid City, SD, United States
| | - Shailabh Rauniyar
- Department of Chemical and Biological Engineering, South Dakota School of Mines and Technology, Rapid City, SD, United States
- 2-Dimensional Materials for Biofilm Engineering, Science and Technology, South Dakota School of Mines and Technology, Rapid City, SD, United States
| | - Payal Thakur
- Department of Chemical and Biological Engineering, South Dakota School of Mines and Technology, Rapid City, SD, United States
- Data Driven Material Discovery Center for Bioengineering Innovation, South Dakota School of Mines and Technology, Rapid City, SD, United States
| | - Ram Nageena Singh
- Department of Chemical and Biological Engineering, South Dakota School of Mines and Technology, Rapid City, SD, United States
- 2-Dimensional Materials for Biofilm Engineering, Science and Technology, South Dakota School of Mines and Technology, Rapid City, SD, United States
| | - Alain Bomgni
- Department of Biomedical Engineering, University of South Dakota, Sioux Falls, SD, United States
| | - Mathew O. Alaba
- Department of Biomedical Engineering, University of South Dakota, Sioux Falls, SD, United States
| | - Abhilash Kumar Tripathi
- Department of Chemical and Biological Engineering, South Dakota School of Mines and Technology, Rapid City, SD, United States
- 2-Dimensional Materials for Biofilm Engineering, Science and Technology, South Dakota School of Mines and Technology, Rapid City, SD, United States
| | - Etienne Z. Gnimpieba
- Department of Biomedical Engineering, University of South Dakota, Sioux Falls, SD, United States
- *Correspondence: Etienne Z. Gnimpieba,
| | - Carol Lushbough
- Department of Biomedical Engineering, University of South Dakota, Sioux Falls, SD, United States
| | - Rajesh Kumar Sani
- Department of Chemical and Biological Engineering, South Dakota School of Mines and Technology, Rapid City, SD, United States
- Data Driven Material Discovery Center for Bioengineering Innovation, South Dakota School of Mines and Technology, Rapid City, SD, United States
- 2-Dimensional Materials for Biofilm Engineering, Science and Technology, South Dakota School of Mines and Technology, Rapid City, SD, United States
- BuG ReMeDEE Consortium, South Dakota School of Mines and Technology, Rapid City, SD, United States
- Rajesh Kumar Sani,
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